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  <front>
    <journal-meta>
      <journal-id journal-id-type="publisher-id">abb</journal-id>
      <journal-title-group>
        <journal-title>Advances in Bioscience and Biotechnology</journal-title>
      </journal-title-group>
      <issn pub-type="epub">2156-8502</issn>
      <issn pub-type="ppub">2156-8456</issn>
      <publisher>
        <publisher-name>Scientific Research Publishing</publisher-name>
      </publisher>
    </journal-meta>
    <article-meta>
      <article-id pub-id-type="doi">10.4236/abb.2026.176017</article-id>
      <article-id pub-id-type="publisher-id">abb-152252</article-id>
      <article-categories>
        <subj-group>
          <subject>Article</subject>
        </subj-group>
        <subj-group>
          <subject>Biomedical</subject>
          <subject>Life Sciences</subject>
        </subj-group>
      </article-categories>
      <title-group>
        <article-title>Nutraceutical Potential and Bioactivities of Wild Trametes polyzona (Pers.) Justo</article-title>
      </title-group>
      <contrib-group>
        <contrib contrib-type="author">
          <name name-style="western">
            <surname>Adongbede</surname>
            <given-names>Erute Magdalene</given-names>
          </name>
          <xref ref-type="aff" rid="aff1">1</xref>
          <xref ref-type="aff" rid="aff2">2</xref>
        </contrib>
        <contrib contrib-type="author">
          <name name-style="western">
            <surname>Khatiwada</surname>
            <given-names>Janak Raj</given-names>
          </name>
          <xref ref-type="aff" rid="aff1">1</xref>
        </contrib>
        <contrib contrib-type="author">
          <name name-style="western">
            <surname>Williams</surname>
            <given-names>Leonard Lamont</given-names>
          </name>
          <xref ref-type="aff" rid="aff1">1</xref>
        </contrib>
      </contrib-group>
      <aff id="aff1"><label>1</label> Centre for Excellence in Post-Harvest Technologies, North Carolina Agricultural &amp; Technical State University, Kannapolis, NC, USA </aff>
      <aff id="aff2"><label>2</label> Department of Botany, University of Lagos, Lagos, Nigeria </aff>
      <author-notes>
        <fn fn-type="conflict" id="fn-conflict">
          <p>The authors declare no conflicts of interest regarding the publication of this paper.</p>
        </fn>
      </author-notes>
      <pub-date pub-type="epub">
        <day>10</day>
        <month>06</month>
        <year>2026</year>
      </pub-date>
      <pub-date pub-type="collection">
        <month>06</month>
        <year>2026</year>
      </pub-date>
      <volume>17</volume>
      <issue>06</issue>
      <fpage>245</fpage>
      <lpage>274</lpage>
      <history>
        <date date-type="received">
          <day>05</day>
          <month>03</month>
          <year>2026</year>
        </date>
        <date date-type="accepted">
          <day>27</day>
          <month>06</month>
          <year>2026</year>
        </date>
        <date date-type="published">
          <day>30</day>
          <month>06</month>
          <year>2026</year>
        </date>
      </history>
      <permissions>
        <copyright-statement>© 2026 by the authors and Scientific Research Publishing Inc.</copyright-statement>
        <copyright-year>2026</copyright-year>
        <license license-type="open-access">
          <license-p> This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( <ext-link ext-link-type="uri" xlink:href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</ext-link> ). </license-p>
        </license>
      </permissions>
      <self-uri content-type="doi" xlink:href="https://doi.org/10.4236/abb.2026.176017">https://doi.org/10.4236/abb.2026.176017</self-uri>
      <abstract>
        <p>Therapeutic resistance, systemic toxicity, and limited tumor selectivity remain major challenges in cancer and antimicrobial treatment strategies, highlighting the need for multifunctional bioactive agents like fungi. Medicinal mushrooms of the genus <italic>Trametes</italic> are recognized sources of structurally diverse compounds, however, the nutraceutical potential of wild <italic>Trametes</italic><italic>polyzona</italic> remains insufficiently characterized. This study evaluated the antioxidant, antibacterial, and anticancer activities of chemically distinct fractions derived from wild <italic>T.</italic><italic>polyzona</italic>. The mushroom species was identified using both morphological features and phylogenetic analysis. Methanol, hot water, and ethanol extractions yielded polyphenols, polysaccharides, and oligosaccharides (high- and low-molecular-weight carbohydrate rich fractions), respectively; their concentrations were subsequently measured. Antioxidant activity was assessed using DPPH and ABTS assays (EC<sub>50</sub>), antibacterial activity against <italic>Escherichia</italic><italic>coli</italic> and <italic>Staphylococcus</italic><italic>aureus</italic> (IC<sub>50</sub>), and cytotoxicity was evaluated in HepG2 (liver), HCT116 (colon), and MDA-MB-231 (triple negative breast) cancer cells, with NCTC 929 fibroblasts as a non-malignant reference with MTT Assay. Selectivity indices (SI) were calculated. All fractions demonstrated significant concentration-dependent activity (p &lt; 0.0001). Oligosaccharides exhibited superior radical scavenging and antibacterial potency, achieving 60% - 90% inhibition, with greater activity against <italic>S.</italic><italic>aureus</italic>. Polysaccharides demonstrated the most pronounced antiproliferative activity against HepG2 cells, with an IC<sub>50</sub> value of 60.10 μg/ml and a selectivity index (SI) of 2.33, indicating preferential cytotoxicity. In contrast, polyphenols exhibited greater potency toward colon cancer. Notably, both polyphenol and polysaccharide fractions induced moderate selective growth inhibition in the chemo-resistant triple-negative breast cancer cell line MDA-MB-231 (SI = 1.53 - 1.64). In contrast, conventional chemotherapeutics exhibited lower IC<sub>50</sub> values but selectivity indices below unity under the same experimental conditions. Collectively, these findings establish wild <italic>T.</italic><italic>polyzona</italic> as a multifunctional bioactive resource with differentiated functional properties driven by their composition and, as such, have promising nutraceutical relevance that supports further structural elucidation of bioactive compounds and mechanistic investigations.</p>
      </abstract>
      <kwd-group kwd-group-type="author-generated" xml:lang="en">
        <kwd>Biological Macromolecules</kwd>
        <kwd>Selectivity Index</kwd>
        <kwd>Multidrug Resistant Bacteria</kwd>
        <kwd>Medicinal Polypore Fungi</kwd>
        <kwd>Low Molecular Weight Compounds</kwd>
      </kwd-group>
    </article-meta>
  </front>
  <body>
    <sec id="sec1">
      <title>1. Introduction</title>
      <p>Cancer progression, antimicrobial resistance, and oxidative stress-associated disorders remain major global health challenges. Despite advances in chemotherapeutic and antimicrobial interventions, therapeutic resistance, systemic toxicity, and limited tumour selectivity continue to compromise clinical outcomes [<xref ref-type="bibr" rid="B1">1</xref>]. The increasing prevalence of multidrug-resistant pathogens and treatment-associated adverse effects underscores the need for multifunctional bioactive agents with improved safety and selectivity profiles [<xref ref-type="bibr" rid="B2">2</xref>]. Natural products, particularly those derived from medicinal fungi, have re-emerged as promising complementary resources for nutraceutical and therapeutic development [<xref ref-type="bibr" rid="B3">3</xref>]. Traditional chemotherapeutic agents including platinum-based drugs and anthracyclines affect both healthy and malignant cells, which often leads to harmful side effects [<xref ref-type="bibr" rid="B1">1</xref>]. These challenges have driven ongoing research into bioactive compounds that can more precisely target tumours while minimizing harm to normal tissues.</p>
      <p>Medicinal mushrooms are rich in structurally diverse metabolites, including polysaccharides, phenolic compounds, terpenoids, and low-molecular-weight carbohydrates [<xref ref-type="bibr" rid="B4">4</xref>][<xref ref-type="bibr" rid="B5">5</xref>]. Fungal polysaccharides, especially <italic>β</italic>-glucans, have been widely reported to exhibit antioxidant, antimicrobial, and antiproliferative properties [<xref ref-type="bibr" rid="B6">6</xref>][<xref ref-type="bibr" rid="B7">7</xref>]. Phenolic constituents contribute additional redox-modulating capacity, while smaller carbohydrate fractions may demonstrate enhanced bioavailability and membrane interaction potential [<xref ref-type="bibr" rid="B3">3</xref>].</p>
      <p>Mushroom <italic>β</italic>-glucans modulate oxidative stress, promote cancer cell death without harming healthy tissue, and enhance immune responses via activation of Dectin-1 and Toll-like receptors, increasing cytokine production and tumour elimination [<xref ref-type="bibr" rid="B6">6</xref>][<xref ref-type="bibr" rid="B8">8</xref>]. Meanwhile, mushroom-derived oligosaccharides, short-chain carbohydrates found in glycoproteins and glycolipids, show strong antibacterial, anti-inflammatory, and anticancer effects [<xref ref-type="bibr" rid="B9">9</xref>][<xref ref-type="bibr" rid="B10">10</xref>]. The integration of such chemically distinct fractions within a single organism offers the possibility of complementary biological effects, supporting their application in functional food and nutraceutical formulations.</p>
      <p>Species within the genus <italic>Trametes</italic> (family Polyporaceae) have attracted significant attention due to their diversity of secondary metabolites, including polyphenols, polysaccharides (specifically PSK and related <italic>β</italic>-glucans), terpenoids, and glycoproteins, which exhibit antioxidant, antimicrobial, immunomodulatory, and anticancer activities [<xref ref-type="bibr" rid="B11">11</xref>][<xref ref-type="bibr" rid="B12">12</xref>]. Noteworthy species comprise <italic>T.</italic><italic>versicolor</italic>, <italic>T.</italic><italic>hirsute</italic>, <italic>T.</italic><italic>pubescens</italic>, and <italic>T.</italic><italic>ochracea</italic>. In tropical African regions such as Nigeria and Benin, species including <italic>T.</italic><italic>cingulata</italic>, <italic>T.</italic><italic>elegans</italic>, <italic>T.</italic><italic>polyzona</italic>, <italic>T.</italic><italic>sanguinea</italic>, and <italic>T.</italic><italic>socotrana</italic> are found, though their diversity and evolutionary relationships are not fully resolved [<xref ref-type="bibr" rid="B13">13</xref>]. While <italic>Trametes</italic><italic>versicolor</italic> has been extensively studied, other wild species within the genus remain comparatively underexplored. Extracts from <italic>Trametes</italic><italic>versicolor</italic>, for example, have demonstrated immunomodulatory and anticancer properties, leading to clinical applications of polysaccharide-based preparations [<xref ref-type="bibr" rid="B11">11</xref>][<xref ref-type="bibr" rid="B14">14</xref>]. For example, previous research has demonstrated that <italic>Trametes</italic><italic>versicolor</italic> polysaccharides reduced tumour growth in mice by boosting cytotoxic T cells and inducing apoptosis through EGFR and mitochondrial pathways [<xref ref-type="bibr" rid="B15">15</xref>]. Additionally, polyphenols from <italic>Trametes</italic> species exhibit antioxidant and antimicrobial effects by scavenging free radicals, donating electrons, and damaging microbial membranes [<xref ref-type="bibr" rid="B5">5</xref>].</p>
      <p>The present study aimed to evaluate the multifunctional bioactivities of different extracts of <italic>Trametes</italic><italic>polyzona</italic> by combining dose-response analysis with selectivity assessment. Various fractions of <italic>T.</italic><italic>polyzona</italic> extracts were examined for antioxidant and antibacterial properties, as well as cytotoxic effects on HepG2, HCT116, and MDA-MB-231 cancer cell lines, using NCTC clone 929 fibroblast cells as non-malignant controls.</p>
    </sec>
    <sec id="sec2">
      <title>2. Materials and Methods</title>
      <sec id="sec2dot1">
        <title>2.1. Materials and Reagents</title>
        <p>Unless otherwise specified, all chemicals and reagents used were of analytical grade and purchased from Sigma-Aldrich (St. Louis, MO, USA). This included methanol, ethanol, acetone, sulfuric acid, phenol, Folin-Ciocalteu reagent, 2,2-diphenyl-1-picrylhydrazyl (DPPH), 2,2'-azino-bis (3-ethylbeothiazoline-6-sulfonic acid) (ABTS) salts, ferric chloride, potassium ferricyanide, trichloroacetic acid, and phosphate-buffered saline (PBS). Cell culture media and supplements were sourced from Gibco (Thermo Fisher Scientific, USA). Doxorubicin (#44593) and Cisplatin (C2210000), both from Sigma-Aldrich, were dissolved in DMSO and DMF, respectively. Drug-resistant bacterial strains <italic>Escherichia</italic><italic>coli</italic> (Migula) Castellani and Chalmers ATCC BAA-2452 and methicillin-resistant <italic>Staphylococcus</italic><italic>aureus</italic> Rosenbach ATCC BAA-1695 (MRSA) were obtained from the American Type Culture Collection (ATCC, Manassas, VA, USA). The human cancer cell lines HCT116 (colorectal carcinoma), HepG2 (hepatocellular carcinoma), and MDA-MB-231 (triple-negative breast adenocarcinoma), as well as the normal NCTC Clone 929 fibroblast cell line, were also acquired from ATCC. </p>
      </sec>
      <sec id="sec2dot2">
        <title>2.2. Collection and Identification of Wild Polypore Mushroom</title>
        <p>Fresh fruit bodies of <italic>Trametes</italic><italic>polyzona</italic> were collected from decaying logs of <italic>Terminalia</italic><italic>catappa</italic> L. in the University of Lagos Staff Quarters (High Rise), at GPS coordinates 006˚30'67'' N, 003˚23'75''E, during May and June (<xref ref-type="fig" rid="fig1">Figure 1</xref>). </p>
        <fig id="fig1">
          <label>Figure 1</label>
          <graphic xlink:href="https://html.scirp.org/file/7302256-rId17.jpeg?20260630110416" />
        </fig>
        <p><bold>Figure 1.</bold> Fruiting body of wild <italic>Trametes</italic><italic>polyzona</italic> Pers. Justo.</p>
        <p>The collected specimens were divided into two groups: one was preserved for identification and storage at the University of Lagos Herbarium, while the other was designated for bioassays. Samples intended for curation were dried using a food-grade dehydrator at 40˚C until fully desiccated (no further weight loss), then placed in large, sealed Ziploc bags and stored at −80<sup>°</sup>C for two weeks. </p>
        <p>Identification of specimens was based on morphological characteristics such as pore surface features, pileus and stipe dimensions, and spore print, complemented by microscopic analysis following the identification manual (Largent <italic>et</italic><italic>al.</italic>, 1988) [<xref ref-type="bibr" rid="B16">16</xref>]. The herbarium voucher is recorded as LUH8957 (Collection Number: ULHR/M010) and deposited at the University of Lagos Herbarium. Additional characterization was performed using DNA barcoding and phylogenetic analysis. </p>
        <p>2.2.1. DNA Extraction</p>
        <p>DNA extraction from fungal tissues was performed utilizing the Norgen Fungi/Yeast DNA Extraction Kit in accordance with the manufacturer’s guidelines. In summary, 100 mg of fungal tissue was flash-frozen in liquid nitrogen and finely ground to powder. Extraction proceeded with the addition of 500 µl lysis buffer supplemented with 1 µl RNAase A, followed by incubation at 65˚C for 10 minutes. Genomic DNA was subsequently isolated through binding to filter columns, centrifugation at approximately 14,000 rpm for 2 minutes, and sequential washing with provided wash buffers. The quality and concentration of the extracted DNA were assessed using a Nano-Drop spectrophotometer.</p>
        <p>2.2.2. PCR Amplification of the ITS Region</p>
        <p>The rDNA of presumptive <italic>Trametes</italic><italic>polyzona</italic> was amplified with ITS1 (Forward) and ITS4 (Reverse) primers. Each 20 µl PCR reaction contained 10 µl Platinum Direct PCR Universal Master Mix, 1 µl each primer, 4 µl Platinum GC Enhancer, 5 µl nuclease-free water, and 1 µl DNA template. Thermal cycling included activation at 94˚C for 2 min; 35 cycles of denaturation at 94˚C for 30 s, annealing at 60˚C for 1 min, extension at 68˚C for 1 min; then a final hold at 4˚C. Amplified products were confirmed on 1% agarose gel, purified with the GeneJet kit, and sequenced by Eton Bioscience using the same primers and an Applied Biosystem Analyzer.</p>
        <p>2.2.3. Phylogenetic Analysis</p>
        <p>Sequences were visually optimized and selectively trimmed with Geneious Prime 2025. Both newly generated sequences from this study and additional ones obtained from GenBank were aligned using CLUSTAL W; after visual optimization, ambiguous sections and sequences were removed, resulting in a final dataset of nineteen species. A phylogenetic tree was then constructed using the PHYLIP Neighbour-Joining method, incorporating both these nineteen species and the sequence generated in current study. Statistical support, based on 500 bootstrap replications, is indicated on the phylogenetic tree, providing evidence for inferences and verification of identifications. The corresponding sequences have been submitted to GenBank with accession number OK324051 (<xref ref-type="fig" rid="fig2">Figure 2</xref>).</p>
        <fig id="fig2">
          <label>Figure 2</label>
          <graphic xlink:href="https://html.scirp.org/file/7302256-rId19.jpeg?20260630110417" />
        </fig>
        <p><bold>Figure 2.</bold> Phylogenetic tree showing the position of test <italic>Trametes</italic><italic>polyzona</italic> with existing GenBank ITS sequences.</p>
      </sec>
      <sec id="sec2dot3">
        <title>2.3. Preparation of Extracts</title>
        <p>2.3.1. Preparation of Oligosaccharide (Low-Molecular-Weight-Rich Carbohydrate) Fraction</p>
        <p>Fresh fruiting bodies were cleaned, cut in pieces, kept in the −80˚C overnight and freeze-dried, then pulverized into a fine powder using a laboratory grinder. The pulverized tissues were stored in airtight containers at 4˚C until extraction. Samples (10 g) were extracted with 50% ethanol while stirring on a hot plate at 80˚C following modification of methods by Ekvall <italic>et</italic><italic>al.</italic> (2007) [<xref ref-type="bibr" rid="B17">17</xref>] in 500 ml Erlenmeyer flasks with trehalose as internal standard for oligosaccharides. Large debris and cellular materials were removed by centrifuging at 3000 g for 10 minutes. The high molecular weight polysaccharides and proteins (DP &gt; 20) in the supernatant were removed by precipitation with 80% ethanol overnight at 4˚C. The low molecular weight compounds were further separated from the precipitated compounds by centrifuging at 0˚C and 3000 g for 15 minutes. The supernatant was evaporated to dryness in a rotary evaporator at 40˚C and resuspended in sterile deionized water to make a working solution of 100 mg/ml. The solution was filter sterilized first with 0.45 µm and then 0.22 µm micropore filter. </p>
        <p>2.3.2. Extraction of Polysaccharide (High-Molecular-Weight-Rich Carbohydrate) Fraction</p>
        <p>Sterile deionized water was utilized to extract 10 g of lyophilized and pulverized <italic>T.</italic><italic>polyzona</italic> tissues in a shaking water bath at 80˚C for 2 hours, following a modified protocol based on Pan <italic>et</italic><italic>al.</italic> (2015) [<xref ref-type="bibr" rid="B18">18</xref>]. The resulting supernatants were collected and concentrated under reduced pressure. An 80% ethanol solution was prepared using a 1:4 dilution ratio, and precipitation was performed at 4˚C overnight. Crude polysaccharide fractions were obtained by centrifugation at 6000 rpm for 30 minutes. The resultant polysaccharide pellet was washed, dissolved in distilled water, and subjected to deproteinization via the Sevag method. Thereafter, the sample was dialyzed (dialysis bag MWCO-10 Da) against distilled water, frozen at −80˚C, and lyophilized to produce a powdered extract, which was subsequently weighed to determine yield. Expected co-extractives are low molecular weight phenolic compounds.</p>
        <p>2.3.3. Extraction Polyphenol Fraction</p>
        <p>Polyphenols were extracted using modification of methods by Tepsongkroh <italic>et</italic><italic>al.</italic> (2019) [<xref ref-type="bibr" rid="B19">19</xref>] with methanol. Methanol typically elutes high concentrations gallic acid, chlorogenic acid, protocatechuic acid and catechin from mushroom tissues. Ten grams of <italic>pulverized</italic><italic>T.</italic><italic>polyzona</italic> tissues were extracted with 200 ml of 0.1% HCl in methanol with stirring at 150 rpm for 1 hour at room temperature. Extraction was repeated three times with the same volume of methanol, and the supernatants were collected each time after centrifuging at 5000 × g for 30 minutes. The combined filtrate was concentrated after filtering with 0.45 µm filter using a rotary evaporator. The concentrate was lyophilized and weighed to determine yield.</p>
      </sec>
      <sec id="sec2dot4">
        <title>2.4. Myco-Chemical Analysis</title>
        <p>2.4.1. Total Phenolic Content (Gallic Acid Equivalent)</p>
        <p>Total phenolic content (TPC) was determined using the Folin-Ciocalteu method, with results expressed as mg gallic acid equivalents (GAE) per gram of extract. The BQC Polyphenol Quantification Assay Kit (BQC-KB03006-100, BQC Redox Technologies, USA) was used for the assay, following manufacturer’s protocol. The working solution was diluted 1:10 with double distilled water, and gallic acid standards were prepared at concentrations of 0, 2.5, 5, 10, 25, and 50 µg GAE/ml by mixing standard with varying amounts of reagent C for the standard curve. Twenty microliters of each sample (25, 50, 75, and 100 mg/ml) and standards were placed in a 96-well plate. Then, 100 µl R.A (supplied by manufacturer) was added to triplicate wells, followed by the working solution and 80 µl reagent B. Absorbance was measured at 700 nm at room temperature using a microplate reader. A standard curve was established using the average values of the concentrations evaluated. The concentrations of the samples were determined based on this curve following the manual’s formula, and results were modified to account for each sample’s dilution factor.</p>
        <disp-formula id="FD1">
          <mml:math display="inline">
            <mml:mrow>
              <mml:mtext>Gallic Acid Equivalent</mml:mtext>
              <mml:mrow>
                <mml:mo>(</mml:mo>
                <mml:mrow>
                  <mml:mtext>GAE</mml:mtext>
                </mml:mrow>
                <mml:mo>)</mml:mo>
              </mml:mrow>
              <mml:mrow>
                <mml:mo>(</mml:mo>
                <mml:mrow>
                  <mml:mrow>
                    <mml:mrow>
                      <mml:mtext>μg GA</mml:mtext>
                    </mml:mrow>
                    <mml:mo>/</mml:mo>
                    <mml:mrow>
                      <mml:mtext>ml</mml:mtext>
                    </mml:mrow>
                  </mml:mrow>
                </mml:mrow>
                <mml:mo>)</mml:mo>
              </mml:mrow>
              <mml:mo>=</mml:mo>
              <mml:mfrac>
                <mml:mrow>
                  <mml:msub>
                    <mml:mi>A</mml:mi>
                    <mml:mi>s</mml:mi>
                  </mml:msub>
                  <mml:mo>−</mml:mo>
                  <mml:mi>I</mml:mi>
                  <mml:mi>n</mml:mi>
                  <mml:mi>t</mml:mi>
                  <mml:mi>e</mml:mi>
                  <mml:mi>r</mml:mi>
                  <mml:mi>c</mml:mi>
                  <mml:mi>e</mml:mi>
                  <mml:mi>p</mml:mi>
                  <mml:mi>t</mml:mi>
                </mml:mrow>
                <mml:mrow>
                  <mml:mi>S</mml:mi>
                  <mml:mi>l</mml:mi>
                  <mml:mi>o</mml:mi>
                  <mml:mi>p</mml:mi>
                  <mml:mi>e</mml:mi>
                </mml:mrow>
              </mml:mfrac>
            </mml:mrow>
          </mml:math>
        </disp-formula>
        <p>2.4.2. Total Carbohydrate Content (Glucose Equivalent)</p>
        <p>Total carbohydrate content in polysaccharides and oligosaccharide was quantified with the phenol-sulfuric acid method using the CheKineTM Micro Total Polysaccharide Assay Kit (MBS9719594), following manufacturer instructions. A glucose stock (10 mg/ml) was serially diluted to prepare standards. Standards and samples were mixed with assay reagents, incubated at 90˚C for 20 minutes, cooled, then transferred to a 96-well plate. Samples were appropriately diluted, and results were calculated using the respective dilution factors.</p>
      </sec>
      <sec id="sec2dot5">
        <title>2.5. Antioxidant Activity</title>
        <p>Antioxidant activity was assessed by DPPH, and ABTS assays. Extracts (6.25 - 37.5 µg/mL) were evaluated, and absorbance was measured at appropriate wavelengths. </p>
        <p>2.5.1. DPPH Radical Scavenging Assay</p>
        <p>The free radical scavenging activities of the polyphenol, polysaccharide, and oligosaccharide extracts were assessed employing a modified 2,2-diphenyl-1-picrylhydrazyl (DPPH) assay. In brief, freshly prepared DPPH solution (0.1 mM) in methanol was combined with varying concentrations of each extract. The reaction mixtures were incubated in the dark at ambient temperature for 30 minutes. Absorbance readings were obtained at 517 nm using a UV-visible spectrophotometer. Radical scavenging activity was expressed as percentage inhibition relative to the control, calculated by the following equation:</p>
        <disp-formula id="FD2">
          <mml:math display="inline">
            <mml:mrow>
              <mml:mtext>DPPH scavenging activity</mml:mtext>
              <mml:mrow>
                <mml:mo>(</mml:mo>
                <mml:mtext>%</mml:mtext>
                <mml:mo>)</mml:mo>
              </mml:mrow>
              <mml:mo>=</mml:mo>
              <mml:mfrac>
                <mml:mrow>
                  <mml:msub>
                    <mml:mi>A</mml:mi>
                    <mml:mn>0</mml:mn>
                  </mml:msub>
                  <mml:mo>−</mml:mo>
                  <mml:msub>
                    <mml:mi>A</mml:mi>
                    <mml:mi>s</mml:mi>
                  </mml:msub>
                </mml:mrow>
                <mml:mrow>
                  <mml:msub>
                    <mml:mi>A</mml:mi>
                    <mml:mn>0</mml:mn>
                  </mml:msub>
                </mml:mrow>
              </mml:mfrac>
              <mml:mo>×</mml:mo>
              <mml:mtext>100</mml:mtext>
            </mml:mrow>
          </mml:math>
        </disp-formula>
        <p>where <italic>A</italic><sub>0</sub> is the absorbance of the control and <inline-formula><mml:math display="inline"><mml:mrow><mml:msub><mml:mi> A </mml:mi><mml:mi> s </mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> is the absorbance in the presence of the extract.</p>
        <p>2.5.2. ABTS Radical Cation Scavenging Assay</p>
        <p>The antioxidant capacity of all test extracts was also evaluated utilizing 2,2'-azinobis-(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS<sup>+</sup>) radical cation decolorization assay. For this assay, the ABTS<sup>•</sup><sup>+</sup> radical cation was produced by reacting a 7 mM ABTS solution with 2.45 mM potassium persulfate, followed by incubation in the dark at ambient temperature for 12 - 16 hours prior to use. The resulting ABTS<sup>•+</sup> solution was then diluted with phosphate-buffered saline to achieve an absorbance of 0.70 ± 0.02 at 734 nm. Subsequently, aliquots of each extract at varying concentrations were introduced to the diluted ABTS<sup>•+</sup> solution and incubated at room temperature for six minutes. Absorbance measurements were recorded at 734 nm using a spectrophotometer. The percentage of ABTS radical scavenging activity was calculated according to the following equation:</p>
        <disp-formula id="FD3">
          <mml:math display="inline">
            <mml:mrow>
              <mml:mtext>ABTS scavenging activity</mml:mtext>
              <mml:mrow>
                <mml:mo>(</mml:mo>
                <mml:mi>%</mml:mi>
                <mml:mo>)</mml:mo>
              </mml:mrow>
              <mml:mo>=</mml:mo>
              <mml:mfrac>
                <mml:mrow>
                  <mml:msub>
                    <mml:mi>A</mml:mi>
                    <mml:mn>0</mml:mn>
                  </mml:msub>
                  <mml:mo>−</mml:mo>
                  <mml:msub>
                    <mml:mi>A</mml:mi>
                    <mml:mi>s</mml:mi>
                  </mml:msub>
                </mml:mrow>
                <mml:mrow>
                  <mml:msub>
                    <mml:mi>A</mml:mi>
                    <mml:mn>0</mml:mn>
                  </mml:msub>
                </mml:mrow>
              </mml:mfrac>
              <mml:mo>×</mml:mo>
              <mml:mtext>100</mml:mtext>
            </mml:mrow>
          </mml:math>
        </disp-formula>
        <p>where <italic>A</italic><sub>0</sub> represents the absorbance of the ABTS<sup>•+</sup> control and <inline-formula><mml:math display="inline"><mml:mrow><mml:msub><mml:mi> A </mml:mi><mml:mi> s </mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> represents the absorbance in the presence of the extract.</p>
        <p>2.5.3. Half-Maximal Effective Concentration (EC<sub>50</sub>)</p>
        <p>Half-maximal effective concentration (EC<sub>50</sub>) values for antioxidant activities were obtained from dose-response curves generated from ABTS and DPPH radical scavenging assays through nonlinear regression analysis using the GraphPad Prism Software Version 10.6.1.</p>
      </sec>
      <sec id="sec2dot6">
        <title>2.6. Antibacterial Assay</title>
        <p>Bacterial cultures were propagated to the logarithmic growth phase, and antimicrobial activity was evaluated via broth microdilution in accordance with CLSI guidelines. The antibacterial properties of polyphenol, polysaccharide and oligosaccharide compounds were assessed against drug-resistant <italic>Escherichia</italic><italic>coli</italic> ATCC BAA-2452 and methicillin-resistant <italic>Staphylococcus</italic><italic>aureus</italic> ATCC BAA-1695 using concentration-dependent inhibition assays (6.25, 12.50, 25.00, and 37.50 μg/ml). Standardized bacterial suspensions, 0.5 McFarland; 1.5 × 10<sup>8</sup> CFU/ml was the final inocula used in Mueller-Hinton broth, which was exposed to test compounds in 96-well plates at concentrations of 6.25, 12.50, 25.00 and 37.5 μg/ml in three different treatments wells as replicates of each final concentration. Antibiotics, ciprofloxacin and ceftazidime were used as positive antibiotic controls, while wells without treatment served as negative controls. Following incubation at 37 ˚C for 16 - 24 hours, bacterial growth was determined by measuring OD₆₀₀, and percentage inhibition was calculated relative to the optical densities from control groups. All experiments were performed in triplicate, and results are presented as mean ± SD. </p>
        <p>Half-maximal inhibitory concentration (IC<sub>50</sub>) values calculated based on viability readout at OD<sub>600</sub>, at different concentrations, were used for statistical analysis (non-linear regression analysis) to draw conclusions on the antibacterial effects of the different extracts and standard antibiotics tested.</p>
        <p>Half-maximal inhibitory concentration (IC<sub>50</sub>) values for antibacterial activities were acquired from dose-response curves constructed from the responses (viability readouts from microplate) (percentage inhibitions at different concentrations of extracts) via nonlinear regression analysis. Percentage inhibition was plotted against the logarithm of compound concentrations, and IC<sub>50</sub> values were calculated using a nonlinear regression (curve fit) model in GraphPad Prism (version 10.6.1, GraphPad Software, USA). Each IC<sub>50</sub> value is reported as the mean of three independent experiments, with each experiment conducted in triplicate.</p>
      </sec>
      <sec id="sec2dot7">
        <title>2.7. Cell Culture and Anticancer Activity Assay</title>
        <p>The HCT116 (human colon adenocarcinoma), HepG2 (hepatocellular carcinoma), and MDA-MB-231 (triple-negative breast adenocarcinoma) cancer cell lines, as well as the non-cancerous mouse fibroblast cell line NCTC clone 929, were procured from the American Type Culture Collection (ATCC). Cell cultures were maintained in either DMEM or EMEM media supplemented with 10% foetal bovine serum and 1% penicillin-streptomycin, incubated at 37 ˚C in a humidified environment with 5% CO<sub>2</sub>. Sub-culturing was performed once cultures reached 80% - 90% confluence.</p>
        <p>For antiproliferative assays, cells were seeded in 96-well plates at a density of 5 × 10<sup>3</sup> cells per well and permitted to adhere for 24 hours. Polysaccharide and polyphenol extracts were administered at final concentrations of 6.25, 12.50, 25, and 37.50 µg/ml. Cisplatin and doxorubicin (dissolved in PBS), used as reference anticancer agents, were applied at final well concentrations 2.5, 5.0, 7.5 and 10 µg/ml with each well replicated thrice. Untreated cells served as negative controls. All treatments and controls were cultured as monolayers under identical incubation conditions before treatments were applied.</p>
        <p>Following 48 hours of treatment, cell viability was assessed using the Cell Proliferation Kit (MTT) (Roche Diagnostics GmbH, Mannheim, Germany), following manufacturer’s instructions. Absorbance measurements were taken at 570 nm, and antiproliferative activity was expressed as percentage inhibition relative to untreated controls. IC<sub>50</sub> values were subsequently determined. Experiments were conducted in triplicate within each 96-well plate, with results reported as mean ± SD.</p>
        <p>To determine anticancer half maximal inhibitory concentrations (IC50) values, dose-response curves were generated from microplate-derived cell viability data. Percentage inhibition was plotted against log concentrations of the extracts, and a nonlinear regression curve-fit model was applied in GraphPad Prism (version 10.6.1). The reported values are the mean results from three independent experiments, with each run performed in triplicate.</p>
        <p>Selectivity Index (SI) Calculation</p>
        <p>The selectivity of polyphenol, polysaccharide, and oligosaccharide extracts for cancer versus non-malignant cells was assessed using the murine fibroblast line NCTC 929 (L929), cultured alongside cancer lines and treated with the same extract concentrations (6.25 - 37.50 µg/mL). After 48 hours, cell viability was measured with the anticancer assay, and IC<sub>50</sub> values were determined by nonlinear regression.</p>
        <p>The Selectivity Index (SI) was calculated according to the following equation:</p>
        <disp-formula id="FD4">
          <mml:math display="inline">
            <mml:mrow>
              <mml:mtext>SI</mml:mtext>
              <mml:mo>=</mml:mo>
              <mml:mfrac>
                <mml:mrow>
                  <mml:msub>
                    <mml:mrow>
                      <mml:mtext>IC</mml:mtext>
                    </mml:mrow>
                    <mml:mrow>
                      <mml:mtext>50</mml:mtext>
                    </mml:mrow>
                  </mml:msub>
                  <mml:mrow>
                    <mml:mo>(</mml:mo>
                    <mml:mrow>
                      <mml:mtext>NCTC 929 cells</mml:mtext>
                    </mml:mrow>
                    <mml:mo>)</mml:mo>
                  </mml:mrow>
                </mml:mrow>
                <mml:mrow>
                  <mml:msub>
                    <mml:mrow>
                      <mml:mtext>IC</mml:mtext>
                    </mml:mrow>
                    <mml:mrow>
                      <mml:mtext>50</mml:mtext>
                    </mml:mrow>
                  </mml:msub>
                  <mml:mrow>
                    <mml:mo>(</mml:mo>
                    <mml:mrow>
                      <mml:mtext>Cancer cells</mml:mtext>
                    </mml:mrow>
                    <mml:mo>)</mml:mo>
                  </mml:mrow>
                </mml:mrow>
              </mml:mfrac>
            </mml:mrow>
          </mml:math>
        </disp-formula>
        <p>An SI value ≥ 2 was considered indicative of selective antiproliferative activity toward cancer cells relative to non-malignant cells. SI values are reported using the means from three independent experiments performed in triplicate at a confidence interval of 95%.</p>
      </sec>
      <sec id="sec2dot8">
        <title>2.8. Statistical Analysis</title>
        <p>Experiments were performed in triplicate; data are shown as mean ± SD. Statistical analysis used GraphPad Prism 10.6.1, with differences among groups assessed via two-way ANOVA and Tukey’s post hoc test. EC<sub>50</sub> and IC<sub>50</sub> values were derived from non-linear regression curves. Significance was set at *p &lt; 0.5, **p &lt; 0.01, ***p &lt; 0.0001.</p>
      </sec>
    </sec>
    <sec id="sec3">
      <title>3. Results and Discussion</title>
      <p>The study found that polyphenol, polysaccharide, and oligosaccharide extracts from wild <italic>Trametes</italic><italic>polyzona</italic> exhibited antioxidant and antibacterial effects, with potency influenced by molecular structure. The extract of polyphenols and polysaccharides demonstrated a selective effect on specific types of cancer cells.</p>
      <sec id="sec3dot1">
        <title>3.1. Extraction Yield and Chemical Composition</title>
        <p>There were no significant differences in the yield of the different fractions; oligosaccharides (13.39%), polysaccharides (14.64%), and polyphenols (10.50%) from the lyophilized pulverized tissues of <italic>Trametes</italic><italic>polyzona</italic> p = 0.0801 (<xref ref-type="fig" rid="fig3">Figure 3</xref>, <bold>Table S1</bold>).</p>
        <p>Methanol was chosen to recover phenolic compounds because it is known for dissolving secondary metabolites of low to medium polarity, such as phenolic acids and flavonoid-like substances often found in mushrooms and <italic>Trametes</italic> species. The methanolic fraction was therefore classified as a polyphenol-rich extract, based on its solvent properties and its measurable total phenolic content. The extraction and purification processes from lyophilized and pulverized tissues of <italic>T.</italic><italic>polyzona</italic> with water and 50% ethanol produced chemically distinct fractions: the polysaccharide extract contained high-molecular-weight carbohydrate polymers, while the oligosaccharide fraction consisted of low-molecular-weight carbohydrate rich compounds [<xref ref-type="bibr" rid="B17">17</xref>]. </p>
        <p>Yield varied slightly depending on the extracting solvent, as <italic>T.</italic><italic>polyzona</italic> tissues produced different polyphenol, polysaccharide, and oligosaccharide fractions that reflected solvent polarity and macromolecular solubility [<xref ref-type="bibr" rid="B17">17</xref>].</p>
        <fig id="fig3">
          <label>Figure 3</label>
          <graphic xlink:href="https://html.scirp.org/file/7302256-rId32.jpeg?20260630110421" />
        </fig>
        <p>Data are expressed as mean ± SD (n = 3). One-way with Brown-Forsythe test showed no significant difference in yield among extract, p = 0.0801 vs other fractions<bold>.</bold></p>
        <p><bold>Figure 3.</bold> Extraction yield and chemical composition of <italic>Trametes</italic><italic>polyzona</italic> fractions.</p>
        <p>Myco-chemical analysis revealed that the polyphenolic fraction contained a total phenolic content (TPC) of 289.31 mg GAE/g DW, while the polysaccharide and oligosaccharide fractions had total carbohydrate contents (TCC) of 1082.40 mg GE/g DW and 1170.40 mg GE/g DW, respectively (<xref ref-type="fig" rid="fig3">Figure 3</xref>). These values highlight clear compositional differences between the fractions and validate the extraction method used<italic>.</italic></p>
        <p>This distinction forms the basis for the bioactivity profiles seen in later assays and strengthens the case for utilizing specific solvents and extraction techniques in nutraceutical development [<xref ref-type="bibr" rid="B20">20</xref>]. Findings indicate that <italic>T.</italic><italic>polyzona</italic> tissues are mainly composed of carbohydrates; however, their phenolic content is highly concentrated, making them a notable source of bioactive substances (<xref ref-type="fig" rid="fig3">Figure 3</xref>) [<xref ref-type="bibr" rid="B10">10</xref>].</p>
      </sec>
      <sec id="sec3dot2">
        <title>
          3.2. Antioxidant Activity of
          <italic>Trametes</italic>
          <italic>polyzona</italic>
          Extracts
        </title>
        <p>All test extracts demonstrated concentration-dependent radical scavenging activity as assessed by both DPPH and ABTS assays (<xref ref-type="fig" rid="fig4">Figure 4</xref>). The oligosaccharide fraction, characterized by low-molecular-weight rich carbohydrate fraction, showed significantly greater antioxidant efficacy at the 25 and 37.5 μg/ml concentrations (p &lt; 0.0001), according to Tukey’s test (<bold>Table S2</bold>).</p>
        <p>Two-way ANOVA showed no significant interaction between extract concentration and type for DPPH radical scavenging activity (F(6, 24) = 0.03871, p = 0.9997), indicating a consistent effect of concentration across all extracts. Both factors had significant main effects (p &lt; 0.0001), accounting for 85.63% (concentration) and 11.74% (extract type) of variation (<bold>Table S2</bold>). This suggests antioxidant activity increases predictably with concentration regardless of extract type, without synergistic or antagonistic effects. A similar pattern was found for ABTS scavenging activity (<bold>Table S3</bold>).</p>
        <p>The oligosaccharide fraction recorded the lowest EC<sub>50</sub> values 12.69 (µg/ml) [11.00 to 14.47], reflecting superior antioxidant potency, followed by polyphenols and polysaccharides (<bold>Table 1</bold>). </p>
        <fig id="fig4">
          <label>Figure 4</label>
          <graphic xlink:href="https://html.scirp.org/file/7302256-rId33.jpeg?20260630110422" />
        </fig>
        <p>Data are expressed as mean ± SD (n = 3). Two-way ANOVA revealed significant main effects of concentration and extract type (p &lt; 0.00001) with no significant interaction. ****p &lt; 0.0001 vs untreated control.</p>
        <p><bold>Figure 4.</bold> Antioxidant activity of oligosaccharide, polysaccharide, and polyphenol fractions from <italic>Trametes</italic><italic>polyzona</italic>.</p>
        <p><bold>Table 1.</bold> Half-maximal effective concentration (EC<sub>50</sub>) values of <italic>Trametes</italic><italic>polyzona</italic> fractions for DPPH and ABTS radical scavenging activity.</p>
        <table-wrap id="tbl1">
          <label>Table 1</label>
          <table>
            <tbody>
              <tr>
                <td>Extract Type</td>
                <td>DPPH RADICAL EC50 [95% CI] (µg/ml)</td>
                <td>ABTS RADICAL EC50 [95% CI] (µg/ml)</td>
              </tr>
              <tr>
                <td>Oligosaccharide</td>
                <td>12.69 [11.00 to 14.47]</td>
                <td>18.31 [16.40 to 20.44]</td>
              </tr>
              <tr>
                <td>Polyphenol</td>
                <td>17.23 [15.11 to 19.60]</td>
                <td>30.09 [27.71 to 33.05]</td>
              </tr>
              <tr>
                <td>Polysaccharide</td>
                <td>24.33 [22.37 to 26.59]</td>
                <td>34.83 [32.49 to 37.77]</td>
              </tr>
            </tbody>
          </table>
        </table-wrap>
        <p>EC<sub>50</sub> values (µg/mL) were calculated from nonlinear regression analysis of dose-response curves and represent the concentration required to achieve 50% radical scavenging activity. Data are presented as mean [95% confidence interval] (n = 3). Lower EC<sub>50</sub> values indicate greater antioxidant potency. </p>
        <p>The consistent antioxidant superiority of the oligosaccharide fraction may relate to increased solubility, accessible hydroxyl groups, or higher reducing sugar content [<xref ref-type="bibr" rid="B9">9</xref>]. These findings suggest functional differences among mushroom-derived fractions, highlighting their potential as natural antioxidants [<xref ref-type="bibr" rid="B10">10</xref>]. The enhanced radical scavenging capacity of oligosaccharides may be attributed to lower molecular weight carbohydrates facilitating hydrogen donation and free radical neutralization [<xref ref-type="bibr" rid="B10">10</xref>][<xref ref-type="bibr" rid="B21">21</xref>]. Polysaccharide-mediated antioxidant effects may additionally involve <italic>β</italic>-glucan-associated redox modulation [<xref ref-type="bibr" rid="B6">6</xref>]. Collectively, these findings highlight the antioxidant competence of carbohydrate-enriched fractions and position <italic>T.</italic><italic>polyzona</italic> as a promising source of functional nutraceutical components [<xref ref-type="bibr" rid="B22">22</xref>]. The polyphenol extracts also showed strong antioxidant effects, consistent with phenolic mechanisms (<bold>Table 1</bold>). Notably, ABTS assays generally yield higher antioxidant values than DPPH for pigmented and hydrophilic compounds, aligning with previous studies and making ABTS the preferred method [<xref ref-type="bibr" rid="B23">23</xref>][<xref ref-type="bibr" rid="B24">24</xref>].</p>
      </sec>
      <sec id="sec3dot3">
        <title>
          3.3. Antibacterial Activity of Polyphenol, Polysaccharide and Oligosaccharide Compounds from
          <italic>Trametes</italic>
          <italic>polyzona</italic>
        </title>
        <p>All tested fractions exhibited antibacterial activity against <italic>Escherichia</italic><italic>coli</italic> and <italic>Staphylococcus</italic><italic>aureus</italic>, with inhibition rates ranging from 60% to 90% (<xref ref-type="fig" rid="fig5">Figure 5</xref>).</p>
        <fig id="fig5">
          <label>Figure 5</label>
          <graphic xlink:href="https://html.scirp.org/file/7302256-rId34.jpeg?20260630110422" />
        </fig>
        <p>Percentage inhibition was determined at 6.25 - 37.50 µg/ml. Data are mean ± SD (n = 3). Two-way ANOVA followed by Tukey’s post hoc test was used to assess statistical significance. *p &lt; 0.05, ****p &lt; 0.0001 vs untreated control.</p>
        <p><bold>Figure 5.</bold> Antibacterial activity of oligosaccharide, polysaccharide, and polyphenol fractions of <italic>T.</italic><italic>polyzona</italic> against <italic>E.</italic><italic>coli</italic><italic>and</italic><italic>S.</italic><italic>aureus</italic></p>
        <p>There was significant dose-dependent inhibition against both <italic>Escherichia</italic><italic>coli</italic> and <italic>Staphylococcus</italic><italic>aureus,</italic> and the different extract types also varied significantly in their activity (p &lt; 0.0001) (<xref ref-type="fig" rid="fig5">Figure 5</xref>; <bold>Table S4</bold>). All extracts demonstrated greater efficacy compared to ceftazidime but are less active than ciprofloxacin, with lower molecular weight carbohydrates being associated with higher bioactivity (<xref ref-type="fig" rid="fig5">Figure 5</xref>) [<xref ref-type="bibr" rid="B21">21</xref>][<xref ref-type="bibr" rid="B25">25</xref>].</p>
        <p>Oligosaccharides consistently exhibited the lowest IC<sub>50</sub> values (15.90 µg/ml for <italic>E.</italic><italic>coli</italic> and 11.56 µg/ml for <italic>S.</italic><italic>aureus</italic>) (<bold>Table 2</bold>), outperforming the polysaccharide and polyphenol extract and matching standard antibiotics. Ceftazidime was effective against <italic>E.</italic><italic>coli</italic> (IC<sub>50</sub>: 11.41 µg/ml) but less so against <italic>S.</italic><italic>aureus</italic> (IC<sub>50</sub>: 55.57 µg/ml) (<bold>Table 2</bold>). </p>
        <p>Although conventional antibiotics exhibited lower IC<sub>50</sub> values, the substantial inhibitory capacity of <italic>T.</italic><italic>polyzona</italic> fractions supports their potential as complementary antimicrobial nutraceutical agents (<bold>Table 2</bold>). All extracts demonstrate greater efficacy compared to ceftazidime but are less active than ciprofloxacin, with lower molecular weight carbohydrates being associated with higher bioactivity (<xref ref-type="fig" rid="fig5">Figure 5</xref>) [<xref ref-type="bibr" rid="B21">21</xref>][<xref ref-type="bibr" rid="B25">25</xref>].</p>
        <p>Research suggests that the stronger inhibitory effect of polyphenols on Gram-positive bacteria is due to increased membrane permeability and direct interactions with bioactive molecules [<xref ref-type="bibr" rid="B26">26</xref>]. Previous studies have shown that the thick peptidoglycan layer of Gram-positive bacteria allows for enhanced interaction with carbohydrate-rich fractions, while the outer membrane of Gram-negative organisms provides an additional permeability barrier [<xref ref-type="bibr" rid="B21">21</xref>]. Additionally, the antibacterial activity of carbohydrate-enriched fractions has been linked to disruptions in membrane integrity, interference with cell wall biosynthesis, or modulation of osmotic balance [<xref ref-type="bibr" rid="B27">27</xref>]. </p>
        <p><bold>Table 2.</bold> Half-maximal inhibitory concentration (IC<sub>50</sub>) values of <italic>Trametes</italic><italic>polyzona</italic> fractions against <italic>Escherichia</italic><italic>coli</italic> and <italic>Staphylococcus</italic><italic>aureus</italic>.</p>
        <table-wrap id="tbl2">
          <label>Table 2</label>
          <table>
            <tbody>
              <tr>
                <td>Extracts and Reference Antibiotics</td>
                <td>
                  <italic>E.</italic>
                  <italic>coli</italic>
                  <bold>(</bold>
                  IC
                  <sub>50</sub>
                  µg/ml) [95% CI]
                </td>
                <td>
                  <italic>S.</italic>
                  <italic>aureus</italic>
                  (IC
                  <sub>50</sub>
                  µg/ml) [95% CI]
                </td>
              </tr>
              <tr>
                <td>Polyphenol</td>
                <td>26.26 [23.91 to 29.10]</td>
                <td>20.38 [17.38 to 24.11]</td>
              </tr>
              <tr>
                <td>Polysaccharide</td>
                <td>21.65 [18.92 - 24.99]</td>
                <td>16.30 [13.55 to 19.49]</td>
              </tr>
              <tr>
                <td>Oligosaccharide</td>
                <td>15.90 [14.48 to 17.42]</td>
                <td>11.56 [9.23 to 13.97]</td>
              </tr>
              <tr>
                <td>Ciprofloxacin</td>
                <td>13.41 [11.73 to 14.24]</td>
                <td>9.12 [7.12 to 11.04]</td>
              </tr>
              <tr>
                <td>Ceftazidime</td>
                <td>11.41 [9.19 to 13.69]</td>
                <td>55.57 [45.93 to 76.38]</td>
              </tr>
            </tbody>
          </table>
        </table-wrap>
        <p>IC<sub>50</sub> values (µg/mL) were determined from nonlinear regression analysis and represent the concentration required to inhibit 50% of bacterial growth. Data are presented as mean [95% confidence interval] (n = 3) (<bold>Table S5</bold>). Lower IC<sub>50</sub> values indicate greater antibacterial activity.</p>
        <p>The oligosaccharide fraction of this polypore demonstrated strong antibacterial activity, potentially, by disrupting membranes and causing oxidative stress; it showed promise of being bacteriostatic at low doses and bactericidal at higher concentrations (<xref ref-type="fig" rid="fig5">Figure 5</xref>). This finding aligns with earlier research demonstrating increased activity against <italic>E.</italic><italic>coli</italic>. For example, cranberry oligosaccharides have been observed to inhibit the formation of <italic>E.</italic><italic>coli</italic> biofilms, underscoring the significance of molecule size [<xref ref-type="bibr" rid="B28">28</xref>].</p>
        <p>While reports also suggest that modifying polysaccharides can boost their antibacterial activity by increasing charge density or adding functional groups [<xref ref-type="bibr" rid="B20">20</xref>][<xref ref-type="bibr" rid="B29">29</xref>], the current study aligns with previous research indicating that chitosan oligosaccharides are most effective against bacteria due to their enhanced solubility and interaction with bacterial envelopes, outperforming high-molecular-weight polysaccharides [<xref ref-type="bibr" rid="B21">21</xref>][<xref ref-type="bibr" rid="B25">25</xref>].</p>
        <p>Polyphenols showed moderate antimicrobial activity that depended on their molecular structure and target, with weaker effects likely due to differences in extract composition (<xref ref-type="fig" rid="fig5">Figure 5</xref>) [<xref ref-type="bibr" rid="B26">26</xref>]. Studies consistently indicate <italic>S.</italic><italic>aureus</italic> is more susceptible than <italic>E.</italic><italic>coli</italic> as the latter’s outer membrane limits agent entry, while <italic>S.</italic><italic>aureus</italic> allows easier penetration [<xref ref-type="bibr" rid="B27">27</xref>][<xref ref-type="bibr" rid="B30">30</xref>].</p>
      </sec>
      <sec id="sec3dot4">
        <title>3.4. Anticancer Activity</title>
        <p>Polyphenol and polysaccharide (high-molecular-weight carbohydrate rich fraction) from <italic>T.</italic><italic>polyzona</italic> demonstrated significant, concentration-dependent inhibition of HepG2, HCT116, and MDA-MB-231 cell proliferation as evidenced by cytotoxic evaluation (p &lt; 0.0001) (<xref ref-type="fig" rid="fig6">Figure 6</xref>, <bold>Table S6</bold>).</p>
        <fig id="fig6">
          <label>Figure 6</label>
          <graphic xlink:href="https://html.scirp.org/file/7302256-rId35.jpeg?20260630110423" />
        </fig>
        <p>Cells were treated with 25 - 100 µg/mL of extracts for 48 h. Data represent mean ± SD (n = 3 independent experiments). Two-way ANOVA followed by Tukey’s post hoc test was used for statistical significance. *p &lt; 0.05, **p &lt; 0.01, ***p &lt; 0.001 vs untreated control.</p>
        <p><bold>Figure 6.</bold> Comparative cytotoxic effects of polyphenol and polysaccharide fractions from <italic>Trametes</italic><italic>polyzona</italic> on human cancer cell lines.</p>
        <p>Both fractions reduced viability of HepG2, HCT116, and MDA-MB-231 cancer cells in a concentration-dependent manner (<xref ref-type="fig" rid="fig6">Figure 6</xref>). Two-way ANOVA confirmed significant effects for extract type, concentration, and their interaction (p &lt; 0.0001) (<bold>Table S6</bold>).</p>
        <p>The polysaccharide fraction induced a marked reduction in HepG2 cell proliferation (by 70% - 75%) at concentrations of 75 and 100 μg/ml (<xref ref-type="fig" rid="fig6">Figure 6</xref>). In comparison, the polyphenol fraction significantly reduced HCT116 cell viability by 80% at 100 μg/mL (<xref ref-type="fig" rid="fig6">Figure 6</xref>). Meanwhile, MDA-MB-231 cells exhibited relatively lower sensitivity to both fractions. It is noteworthy that the standard chemotherapeutic agents, cisplatin and doxorubicin, demonstrated greater potency (<xref ref-type="fig" rid="fig7">Figure 7</xref>; <bold>Table S8</bold>).</p>
        <p>The polysaccharide fraction demonstrated the greatest potency against HepG2 hepatocellular carcinoma cells (IC<sub>50</sub> = 60.10 μg/mL) and achieved selectivity index of 2.33 relative to non-malignant NCTC 929 fibroblasts, suggesting preferential cytotoxicity toward malignant cells (<bold>Table 3</bold> and <bold>Table 4</bold>). </p>
        <fig id="fig7">
          <label>Figure 7</label>
          <graphic xlink:href="https://html.scirp.org/file/7302256-rId36.jpeg?20260630110423" />
        </fig>
        <p>Cells were treated with 2.5 - 10 µg/mL of Control Drugs for 48 h. Data represent mean ± SD (n = 3 independent experiments). Two-way ANOVA followed by Tukey’s post hoc test was used for statistical significance. *p &lt; 0.05, **p &lt; 0.01, ***p &lt; 0.001, ****p &lt; 0.0001 vs untreated control.</p>
        <p><bold>Figure 7.</bold> Effect of reference anticancer drugs on viability of malignant and non-malignant cell types. </p>
        <p><bold>Table 3.</bold> Half-maximal inhibitory concentration (IC<sub>50</sub>) values of <italic>Trametes</italic><italic>polyzona</italic> fractions and reference drugs against human cancer cell lines.</p>
        <table-wrap id="tbl3">
          <label>Table 3</label>
          <table>
            <tbody>
              <tr>
                <td>Extracts andControl Drugs</td>
                <td>HepG2</td>
                <td>HCT116</td>
                <td>MDA-MB 231</td>
                <td>NCTC929</td>
              </tr>
              <tr>
                <td>Polyphenol</td>
                <td>73.05 [67.41 - 79.40]</td>
                <td>58.22 [53.88 - 62.72]</td>
                <td>76.74 [72.51 - 81.53]</td>
                <td>117.50 [112.70 - 123.00]</td>
              </tr>
              <tr>
                <td>Polysaccharide</td>
                <td>60.10 [55.45 - 64.94]</td>
                <td>74.39 [70.92 - 78.18]</td>
                <td>85.42 [79.75 - 92.62]</td>
                <td>140.10 [133.1 - 148.60]</td>
              </tr>
              <tr>
                <td>Cisplatin</td>
                <td>7.14 [6.84 - 7.47]</td>
                <td>8.87 [8.35 - 9.54]</td>
                <td>6.42 [5.77 - 7.19]</td>
                <td>4.76 [4.20 - 5.33]</td>
              </tr>
              <tr>
                <td>Doxorubicin</td>
                <td>5.25 [4.74 - 5.77]</td>
                <td>4.94 [4.39 - 5.51]</td>
                <td>12.86 [12.24 - 13.61]</td>
                <td>4.60 [3.98 - 5.23]</td>
              </tr>
            </tbody>
          </table>
        </table-wrap>
        <p>IC<sub>50</sub> values (µg/mL) were calculated from nonlinear regression analysis of dose-response curves and represent the concentration required to inhibit 50% of cell viability. Data are presented as mean [95% CI] (n = 3) (<bold>Table S</bold><bold>9</bold>). Cisplatin and doxorubicin were included as reference chemotherapeutics. Lower IC<sub>50</sub> values indicate greater cytotoxic potency.</p>
        <p><bold>Table 4.</bold> Selectivity indices (SI) of <italic>T</italic>. <italic>polyzona</italic> fractions and reference compounds against human cancer cell lines relative to NCTC 929 fibroblast cells.</p>
        <table-wrap id="tbl4">
          <label>Table 4</label>
          <table>
            <tbody>
              <tr>
                <td rowspan="2">Extracts and Control Drugs</td>
                <td colspan="3">Selectivity Indices of Test Cancer Cell Lines</td>
              </tr>
              <tr>
                <td>HepG2 (Liver)</td>
                <td>HCT116 (Colon)</td>
                <td>MDA-MB231 (Breast)</td>
              </tr>
              <tr>
                <td>Polyphenol</td>
                <td>1.61</td>
                <td>2.02</td>
                <td>1.53</td>
              </tr>
              <tr>
                <td>Polysaccharide</td>
                <td>2.33</td>
                <td>1.88</td>
                <td>1.64</td>
              </tr>
              <tr>
                <td>Cisplatin</td>
                <td>0.67</td>
                <td>0.54</td>
                <td>
                  <bold>0.74</bold>
                </td>
              </tr>
              <tr>
                <td>Doxorubicin</td>
                <td>0.88</td>
                <td>
                  <bold>0.93</bold>
                </td>
                <td>0.36</td>
              </tr>
            </tbody>
          </table>
        </table-wrap>
        <p>SI was calculated as the ratio of IC<sub>50</sub> in NCTC 929 mouse fibroblasts to IC<sub>50</sub> in each human cancer cell line (HepG2, HCT116, MDA-MB-231). SI values &gt; 1 indicate preferential cytotoxicity toward cancer cells relative to the non-malignant fibroblast reference, whereas SI &lt;1 indicates greater toxicity toward NCTC 929 cells. </p>
        <p>The selective inhibitory effects observed for the polysaccharide derived from the test fungus may be attributed to its <italic>β</italic>-glucan-containing polymers. These polymers are recognized for their influence on oxidative stress pathways, modulation of mitochondrial membrane potential, initiation of caspase-dependent apoptotic mechanisms, direct suppression of cancer cell proliferation, and regulation of immune responses [<xref ref-type="bibr" rid="B31">31</xref>]. HepG2 cells may be uniquely sensitive to the polysaccharide fraction from <italic>T.</italic><italic>polyzona</italic> due to their redox imbalance and metabolic weakness typical of hepatocellular carcinoma [<xref ref-type="bibr" rid="B32">32</xref>]. Mitochondrial dysfunction, ROS-mediated signalling, or cell cycle arrest could also contribute to the antiproliferative effects seen in this study, as noted in prior research [<xref ref-type="bibr" rid="B14">14</xref>]. </p>
        <p>Studies show that mushroom polysaccharides like <italic>β</italic>-glucans fight cancer by causing cell cycle arrest, altering the Bcl-2/Bax ratio, activating caspase-3, boosting immune cells, increasing cytokine levels (such as IL-2 and TNF-<italic>α</italic>), and blocking tumour angiogenesis [<xref ref-type="bibr" rid="B4">4</xref>]. Although NCTC 929 represents a murine fibroblast reference, the observed selectivity observed here for liver cancer cells to <italic>T.</italic><italic>polyzona</italic> polysaccharide provides preliminary evidence of tumour-targeted activity.</p>
        <p>The fungus’s polyphenol fraction on the other hand, demonstrated notable efficacy against HCT116 colorectal carcinoma cells (IC<sub>50</sub>-58.22 µg/ml and SI = 2.02) (<bold>Table 3</bold> and <bold>Table 4</bold>), consistent with prior studies suggesting that phenolic compounds extracted from mushrooms promote apoptosis in colon cancer models via mitochondrial depolarization and enhanced reactive oxygen species production [<xref ref-type="bibr" rid="B33">33</xref>]-[<xref ref-type="bibr" rid="B35">35</xref>]. </p>
        <p>Other studies record that polyphenols of polypore mushroom origin leverage this vulnerability by inducing disruptions in mitochondrial membrane potential, triggering intrinsic apoptotic pathways through redox sensitive mechanisms, inhibiting NF-κB and PI3K/Akt signalling, thereby demonstrating notable anticancer properties [<xref ref-type="bibr" rid="B8">8</xref>][<xref ref-type="bibr" rid="B32">32</xref>][<xref ref-type="bibr" rid="B36">36</xref>]. These mechanisms are well-documented in edible and medicinal mushrooms such as <italic>Agaricus</italic>, <italic>Pleurotus</italic>, <italic>Ganoderma</italic>, and <italic>Lentinus</italic> species [<xref ref-type="bibr" rid="B32">32</xref>][<xref ref-type="bibr" rid="B37">37</xref>].</p>
        <p>Both polyphenol and polysaccharide fractions showed concentration-dependent inhibition of MDA-MB-231 cell viability. Nonlinear regression analysis determined IC<sub>50</sub> values of 76.74 µg/mL (95% CI: 72.51 - 81.53) for the polyphenol fraction and 85.42 µg/mL (95% CI: 79.75 - 92.62) for the polysaccharide fraction, with overlapping confidence intervals indicating similar cytotoxic potency in this triple-negative breast cancer (TNBC) model (<bold>Table S7</bold>). Selectivity indices were 1.53 for polyphenols and 1.64 for polysaccharides, demonstrating that both fractions preferentially target TNBC cells over non-malignant references (<bold>Table 4</bold>). These moderate SI values are noteworthy considering MDA-MB-231 cells’ aggressive, chemo-resistant phenotype due to the absence of ER (Estrogen Receptor), PR (Progesterone Receptor), and HER2 (Human Epidermal Growth Factor Receptor 2) receptors. Lacking these markers, triple-negative breast cancer cells like MDA-MB-231 often show resistance to therapy and poor outcomes [<xref ref-type="bibr" rid="B38">38</xref>]. </p>
        <p>The reference anticancer agents utilized in this study were Cisplatin, a platinum-based compound known to induce DNA alkylation, and doxorubicin, an anthracycline that causes DNA damage and inhibits topoisomerase activity; both are widely employed for the assessment of new adjuvant therapies [<xref ref-type="bibr" rid="B39">39</xref>]. Cisplatin and doxorubicin, established chemotherapeutics, demonstrated potent, dose-dependent inhibition of HepG2, HCT116, and MDA-MB-231 cancer cell lines (IC<sub>50</sub>: cisplatin 5.6 - 6.9 µg/mL; doxorubicin 3.1 - 3.8 µg/mL) (<bold>Table 3</bold>). Their cytotoxicity toward non-malignant fibroblasts was like that observed in cancer cells (SI: 0.36 - 0.88), reflecting limited selectivity (<bold>Table 4</bold>). Both drugs exhibited maximal efficacy against HepG2 and HCT116, whereas MDA-MB-231 cells displayed greater resistance (<bold>Table 3</bold> and <bold>Table 4</bold>). </p>
        <p>While these anti-cancer agents are potent, they lack the ability to differentiate between healthy and tumour cells. In contrast, natural extracts such as polysaccharide fractions offer greater therapeutic promise because they inhibit tumour cells more selectively (<bold>Table 4</bold>). Despite the superior efficacy of cisplatin and doxorubicin, their pronounced cytotoxic effects on NCTC929 cells also highlight well-established dose-limiting toxicities, such as nephrotoxicity associated with cisplatin and cardiotoxicity related to doxorubicin [<xref ref-type="bibr" rid="B1">1</xref>]. So, although less potent overall, the fungal fractions retained measurable tumour selectivity, which is especially important for nutraceutical development given the need for safety and selectivity (<bold>Table 4</bold>) [<xref ref-type="bibr" rid="B38">38</xref>][<xref ref-type="bibr" rid="B39">39</xref>].</p>
        <p>The antioxidant activity observed in this study and previous studies may contribute to the antiproliferative effects of the extracts (polyphenols and polysaccharide), as modulation of cellular redox balance is a key mechanism influencing cancer cell survival and apoptosis [<xref ref-type="bibr" rid="B35">35</xref>][<xref ref-type="bibr" rid="B40">40</xref>][<xref ref-type="bibr" rid="B41">41</xref>]. However, the differences in anticancer activity among the fractions suggest that additional mechanisms beyond antioxidant capacity are involved, particularly for polysaccharide-rich fractions [<xref ref-type="bibr" rid="B15">15</xref>][<xref ref-type="bibr" rid="B42">42</xref>]. The study supports the role of <italic>Trametes</italic>-derived extracts as promising adjunctive or chemo-sensitizing agents, highlighting their ability to target cancer cells while minimizing toxicity to healthy cells [<xref ref-type="bibr" rid="B40">40</xref>]. The ~10 - 15-fold lower potency of mushroom extracts is consistent with previous reports positioning natural compounds as adjunctive or chemo-sensitizing agents rather than standalone replacements [<xref ref-type="bibr" rid="B39">39</xref>]. </p>
      </sec>
    </sec>
    <sec id="sec4">
      <title>4. Conclusion</title>
      <p>This study identifies that wild <italic>Trametes</italic><italic>polyzona</italic> is a source of chemically distinct bioactive compounds with differentiated and complimentary biological activities and therefore as a promising source of bioactive compounds for nutraceutical applications. The selective antioxidant, antibacterial and anticancer activities observed, particularly the moderate but targeted effects against chemo-resistant MDA_MB 231 cells, highlight its potential as a multifunctional nutraceutical resource. Oligosaccharide fractions showed notable antioxidant and antibacterial effects linked to unique membrane interactions [<xref ref-type="bibr" rid="B10">10</xref>], while polysaccharides selectively inhibited hepatocellular carcinoma cell growth potentially via <italic>β</italic>-glucan-mediated immunomodulation and redox regulation [<xref ref-type="bibr" rid="B6">6</xref>]. The polyphenols selectively targeted colon cancer cells, and both extracts showed moderate selectivity for triple negative cancer cells. Selectivity toward cancer cells over non-malignant fibroblasts indicates potential tumour-targeting specificity, though further testing with human cells is needed. Although standard chemotherapeutics were stronger, <italic>T.</italic><italic>polyzona</italic> fractions exhibited greater tumour specificity under the tested conditions. Future research will investigate molecular weight profiles, structural characteristics, mechanisms of action, and conduct in vivo experiments to assess the therapeutic potential of <italic>T.</italic><italic>polyzona</italic> extract fractions.</p>
    </sec>
    <sec id="sec5">
      <title>Authors’ Contributions</title>
      <p>Conceptualization/Experimental design and investigation: EM Adongbede, LL William, Original Draft Preparation/Formal Analysis: EM Adongbede, J Khatiwada: Collection of specimens/Methodology: EM Adongbede; Data Analysis and Supervision: EM Adongbede, LL Williams; Reviewing and Editing: LL William and J Khatiwada</p>
      <p>All authors have reviewed and provided their consent for the publication of this manuscript.</p>
    </sec>
    <sec id="sec6">
      <title>Funding</title>
      <p>This research was not funded by any public, commercial, or non-profit agencies. Support for the study was provided through the facilities and resources of North Carolina Agricultural &amp; Technical State University in Greensboro, NC, USA, and the University of Lagos in Lagos, Nigeria.</p>
    </sec>
    <sec id="sec7">
      <title>Data Availability Statement</title>
      <p>Data are presented in the current manuscript.</p>
    </sec>
    <sec id="sec8">
      <title>Acknowledgements</title>
      <p>The authors thank the staff of the University of Lagos Herbarium for their help during identification and curation of the herbarium voucher specimens of the test mushrooms.</p>
    </sec>
    <sec id="sec9">
      <title>Supplementary Data Files</title>
      <p><bold>Table S1.</bold> One-way ANOVA summary of yield of polysaccharides, oligosaccharides and polyphenol extract from <italic>Trametes</italic><italic>polyzona</italic>.</p>
      <table-wrap id="tbl5">
        <label>Table 5</label>
        <table>
          <tbody>
            <tr>
              <td>Table analyzed</td>
              <td colspan="5">Percentage yield</td>
            </tr>
            <tr>
              <td colspan="6">
                <bold>Ordinary</bold>
                <bold>one-way</bold>
                <bold>ANOVA</bold>
              </td>
            </tr>
            <tr>
              <td>Data sets analyzed</td>
              <td colspan="5">A-C</td>
            </tr>
            <tr>
              <td>Distribution assumption</td>
              <td colspan="5">Normal (Gaussian)</td>
            </tr>
            <tr>
              <td colspan="6">
                <bold>ANOVA</bold>
                <bold>summary</bold>
              </td>
            </tr>
            <tr>
              <td>F</td>
              <td colspan="5">3.959</td>
            </tr>
            <tr>
              <td>p value</td>
              <td colspan="5">0.0801</td>
            </tr>
            <tr>
              <td>p value summary</td>
              <td colspan="5">ns</td>
            </tr>
            <tr>
              <td>Significant diff. among means (p &lt; 0.05)</td>
              <td colspan="5">No</td>
            </tr>
            <tr>
              <td>R squared</td>
              <td colspan="5">0.5689</td>
            </tr>
            <tr>
              <td colspan="6">
                <bold>Brown-Forsythe</bold>
                <bold>test</bold>
              </td>
            </tr>
            <tr>
              <td>F (DFn, DFd)</td>
              <td colspan="5">0.1814 (2, 6)</td>
            </tr>
            <tr>
              <td>p value</td>
              <td colspan="5">0.8385</td>
            </tr>
            <tr>
              <td>p value summary</td>
              <td colspan="5">ns</td>
            </tr>
            <tr>
              <td>Are SDs significantly different (p &lt; 0.05)</td>
              <td colspan="5">No</td>
            </tr>
            <tr>
              <td>ANOVA table</td>
              <td>SS</td>
              <td>DF</td>
              <td>MS</td>
              <td>F (DFn, DFd)</td>
              <td>
                <italic>P</italic>
                value
              </td>
            </tr>
            <tr>
              <td>Treatment (between columns)</td>
              <td>26.74</td>
              <td>2</td>
              <td>13.37</td>
              <td>F (2, 6) = 3.959</td>
              <td>P=0.0801</td>
            </tr>
            <tr>
              <td>Residual (within columns)</td>
              <td>20.26</td>
              <td>6</td>
              <td>3.377</td>
              <td>
              </td>
              <td>
              </td>
            </tr>
            <tr>
              <td>Total</td>
              <td>47.00</td>
              <td>8</td>
              <td>
              </td>
              <td>
              </td>
              <td>
              </td>
            </tr>
            <tr>
              <td colspan="6">
                <bold>Data</bold>
                <bold>summary</bold>
              </td>
            </tr>
            <tr>
              <td>Number of treatments (columns)</td>
              <td colspan="5">3</td>
            </tr>
            <tr>
              <td>Number of values (total)</td>
              <td colspan="5">9</td>
            </tr>
          </tbody>
        </table>
      </table-wrap>
      <p><bold>Table S2.</bold> Antioxidant activity (DPPH radical reduction).</p>
      <table-wrap id="tbl6">
        <label>Table 6</label>
        <table>
          <tbody>
            <tr>
              <td>Table Analysed</td>
              <td colspan="5">DPPH ANTIOXIDANT Data 6</td>
            </tr>
            <tr>
              <td colspan="6">Ordinary Two-Way ANOVA</td>
            </tr>
            <tr>
              <td>Alpha</td>
              <td colspan="5">0.05</td>
            </tr>
            <tr>
              <td>Source of Variation</td>
              <td>% of total variation</td>
              <td>p value</td>
              <td>p value summary</td>
              <td colspan="2">Significant?</td>
            </tr>
            <tr>
              <td>Interaction</td>
              <td>0.02516</td>
              <td>0.9997</td>
              <td>ns</td>
              <td>No</td>
              <td>
              </td>
            </tr>
            <tr>
              <td>Row Factor</td>
              <td>85.63</td>
              <td>&lt;0.0001</td>
              <td>****</td>
              <td>Yes</td>
              <td>
              </td>
            </tr>
            <tr>
              <td>Column Factor</td>
              <td>11.74</td>
              <td>&lt;0.0001</td>
              <td>****</td>
              <td>Yes</td>
              <td>
              </td>
            </tr>
            <tr>
              <td>ANOVA Table</td>
              <td>SS</td>
              <td>DF</td>
              <td>MS</td>
              <td>F (DFn, DFd)</td>
              <td>p value</td>
            </tr>
            <tr>
              <td>Interaction</td>
              <td>3.958</td>
              <td>6</td>
              <td>0.6597</td>
              <td>F (6, 24) = 0.03871</td>
              <td>p = 0.9997</td>
            </tr>
            <tr>
              <td>Row Factor</td>
              <td>13472</td>
              <td>3</td>
              <td>4491</td>
              <td>F (3, 24) = 263.6</td>
              <td>p &lt; 0.0001</td>
            </tr>
            <tr>
              <td>Column Factor</td>
              <td>1847</td>
              <td>2</td>
              <td>923.6</td>
              <td>F (2, 24) = 54.21</td>
              <td>p &lt; 0.0001</td>
            </tr>
            <tr>
              <td>Residual</td>
              <td>408.9</td>
              <td>24</td>
              <td>17.04</td>
              <td>
              </td>
              <td>
              </td>
            </tr>
            <tr>
              <td colspan="6">
                <bold>Data</bold>
                <bold>Summary</bold>
              </td>
            </tr>
            <tr>
              <td colspan="6">Number of columns (Column Factor): 3</td>
            </tr>
            <tr>
              <td colspan="6">Number of rows (Row Factor): 4</td>
            </tr>
            <tr>
              <td>Number of Values</td>
              <td colspan="5">36</td>
            </tr>
          </tbody>
        </table>
      </table-wrap>
      <p><bold>Table S3.</bold> Antioxidant activity (ABTS radical reduction).</p>
      <table-wrap id="tbl7">
        <label>Table 7</label>
        <table>
          <tbody>
            <tr>
              <td>Table Analyzed</td>
              <td colspan="5">ABTS Data 13</td>
            </tr>
            <tr>
              <td colspan="6">Ordinary Two-way ANOVA</td>
            </tr>
            <tr>
              <td>Alpha</td>
              <td colspan="5">0.05</td>
            </tr>
            <tr>
              <td>Source of Variation</td>
              <td>% of total variation</td>
              <td>p value</td>
              <td>
                <italic>P</italic>
                value summary
              </td>
              <td>Significant?</td>
              <td>
              </td>
            </tr>
            <tr>
              <td>Interaction</td>
              <td>0.06551</td>
              <td>0.9819</td>
              <td>ns</td>
              <td>No</td>
              <td>
              </td>
            </tr>
            <tr>
              <td>Row Factor</td>
              <td>82.16</td>
              <td>&lt;0.0001</td>
              <td>****</td>
              <td>Yes</td>
              <td>
              </td>
            </tr>
            <tr>
              <td>Column Factor</td>
              <td>16.25</td>
              <td>&lt;0.0001</td>
              <td>****</td>
              <td>Yes</td>
              <td>
              </td>
            </tr>
            <tr>
              <td>ANOVA Table</td>
              <td>SS</td>
              <td>DF</td>
              <td>MS</td>
              <td>F (DFn, DFd)</td>
              <td>p value</td>
            </tr>
            <tr>
              <td>Interaction</td>
              <td>9.485</td>
              <td>6</td>
              <td>1.581</td>
              <td>F (6, 24) = 0.1718</td>
              <td>p = 0.9819</td>
            </tr>
            <tr>
              <td>Row Factor</td>
              <td>11896</td>
              <td>3</td>
              <td>3965</td>
              <td>F (3, 24) = 430.9</td>
              <td>p &lt; 0.0001</td>
            </tr>
            <tr>
              <td>Column Factor</td>
              <td>2352</td>
              <td>2</td>
              <td>1176</td>
              <td>F (2, 24) = 127.8</td>
              <td>p &lt; 0.0001</td>
            </tr>
            <tr>
              <td>Residual</td>
              <td>220.9</td>
              <td>24</td>
              <td>9.203</td>
              <td>
              </td>
              <td>
              </td>
            </tr>
            <tr>
              <td colspan="6">
                <bold>Data</bold>
                <bold>Summary</bold>
              </td>
            </tr>
            <tr>
              <td colspan="2">Number of columns (Column Factor)</td>
              <td colspan="4">3</td>
            </tr>
            <tr>
              <td colspan="2">Number of rows (Row Factor)</td>
              <td colspan="4">4</td>
            </tr>
            <tr>
              <td colspan="2">Number of values</td>
              <td colspan="4">36</td>
            </tr>
          </tbody>
        </table>
      </table-wrap>
      <p><bold>Table S4.</bold> Two-way ANOVA of antibacterial activity of <italic>T.</italic><italic>polyzona</italic> extracts.</p>
      <table-wrap id="tbl8">
        <label>Table 8</label>
        <table>
          <tbody>
            <tr>
              <td>Table Analyzed</td>
              <td colspan="5">Antibacterial Activity Data 11</td>
            </tr>
            <tr>
              <td>Two-way ANOVA</td>
              <td colspan="5">Ordinary</td>
            </tr>
            <tr>
              <td>Alpha</td>
              <td colspan="5">0.05</td>
            </tr>
            <tr>
              <td>Source of Variation</td>
              <td>% of total variation</td>
              <td>p value</td>
              <td>p value summary</td>
              <td>Significant?</td>
              <td>
              </td>
            </tr>
            <tr>
              <td>Interaction</td>
              <td>2.991</td>
              <td>0.0377</td>
              <td>**</td>
              <td>Yes</td>
              <td>
              </td>
            </tr>
            <tr>
              <td>Row Factor</td>
              <td>64.36</td>
              <td>&lt;0.0001</td>
              <td>****</td>
              <td>Yes</td>
              <td>
              </td>
            </tr>
            <tr>
              <td>Column Factor</td>
              <td>29.01</td>
              <td>&lt;0.0001</td>
              <td>****</td>
              <td>Yes</td>
              <td>
              </td>
            </tr>
            <tr>
              <td>ANOVA Table</td>
              <td>SS</td>
              <td>DF</td>
              <td>MS</td>
              <td>F (DFn, DFd)</td>
              <td>p value</td>
            </tr>
            <tr>
              <td>Interaction</td>
              <td>1823</td>
              <td>27</td>
              <td>67.51</td>
              <td>F (27, 80) = 2.437</td>
              <td>p = 0.0012</td>
            </tr>
            <tr>
              <td>Row Factor</td>
              <td>39228</td>
              <td>3</td>
              <td>13076</td>
              <td>F (3, 80) = 472.0</td>
              <td>p &lt; 0.0001</td>
            </tr>
            <tr>
              <td>Column Factor</td>
              <td>17685</td>
              <td>9</td>
              <td>1965</td>
              <td>F (9, 80) = 70.93</td>
              <td>p &lt; 0.0001</td>
            </tr>
            <tr>
              <td>Residual</td>
              <td>2216</td>
              <td>80</td>
              <td>27.70</td>
              <td>
              </td>
              <td>
              </td>
            </tr>
            <tr>
              <td colspan="6">
                <bold>Data</bold>
                <bold>Summary</bold>
              </td>
            </tr>
            <tr>
              <td colspan="2">Number of columns (Column Factor)</td>
              <td>10</td>
              <td>
              </td>
              <td>
              </td>
              <td>
              </td>
            </tr>
            <tr>
              <td colspan="2">Number of rows (Row Factor)</td>
              <td>4</td>
              <td>
              </td>
              <td>
              </td>
              <td>
              </td>
            </tr>
            <tr>
              <td colspan="2">Number of values</td>
              <td>120</td>
              <td>
              </td>
              <td>
              </td>
              <td>
              </td>
            </tr>
          </tbody>
        </table>
      </table-wrap>
      <p><bold>Table S5.</bold> Non-linear regression analysis of dose response of <italic>T.</italic><italic>polyzon</italic><italic>a</italic> extracts and reference antibiotics—half maximal inhibitory concentrations (IC<sub>50</sub>) against test bacteria.</p>
      <table-wrap id="tbl9">
        <label>Table 9</label>
        <table>
          <tbody>
            <tr>
              <td colspan="11">
                <bold>Inhibitor</bold>
                <bold>vs.</bold>
                <bold>normalized</bold>
                <bold>response</bold>
                —
                <bold>Variable</bold>
                <bold>slope</bold>
              </td>
            </tr>
            <tr>
              <td colspan="11">
                <bold>Best-fit</bold>
                <bold>values</bold>
              </td>
            </tr>
            <tr>
              <td>
              </td>
              <td colspan="5">
                <italic>E.</italic>
                <italic>coli</italic>
              </td>
              <td colspan="5">
                <italic>S</italic>
                <italic>aureus</italic>
              </td>
            </tr>
            <tr>
              <td>
              </td>
              <td>Polyphenol</td>
              <td>Polysaccharide</td>
              <td>Oligosaccharide</td>
              <td>Ciprofloxacin</td>
              <td>Ceftazidime</td>
              <td>Polyphenol</td>
              <td>Polysaccharide</td>
              <td>Oligosaccharide</td>
              <td>Ciprofloxacin</td>
              <td>Ceftazidime</td>
            </tr>
            <tr>
              <td>
                <bold>IC</bold>
                <bold>
                  <sub>50</sub>
                </bold>
              </td>
              <td>26.26</td>
              <td>21.65</td>
              <td>15.90</td>
              <td>13.41</td>
              <td>11.41</td>
              <td>20.38</td>
              <td>16.30</td>
              <td>11.56</td>
              <td>9.117</td>
              <td>55.67</td>
            </tr>
            <tr>
              <td>HillSlope</td>
              <td>1.345</td>
              <td>1.140</td>
              <td>1.111</td>
              <td>1.832</td>
              <td>1.119</td>
              <td>1.145</td>
              <td>1.112</td>
              <td>1.118</td>
              <td>1.163</td>
              <td>1.538</td>
            </tr>
            <tr>
              <td>
                logIC
                <sub>50</sub>
              </td>
              <td>1.419</td>
              <td>1.335</td>
              <td>1.201</td>
              <td>1.128</td>
              <td>1.057</td>
              <td>1.309</td>
              <td>1.212</td>
              <td>1.063</td>
              <td>0.9598</td>
              <td>1.746</td>
            </tr>
            <tr>
              <td colspan="11">
                <bold>95%</bold>
                <bold>CI</bold>
                <bold>(profile</bold>
                <bold>likelihood)</bold>
              </td>
            </tr>
            <tr>
              <td>
                IC
                <sub>50</sub>
              </td>
              <td>23.91to 29.10</td>
              <td>18.92to 24.99</td>
              <td>14.48to 17.42</td>
              <td>11.73to 15.24</td>
              <td>9.190to 13.69</td>
              <td>17.38to 24.11</td>
              <td>13.55to 19.49</td>
              <td>9.225to 13.97</td>
              <td>7.121to 11.04</td>
              <td>45.93to 76.38</td>
            </tr>
            <tr>
              <td>HillSlope</td>
              <td>1.126to 1.590</td>
              <td>0.8924to 1.416</td>
              <td>0.9539to 1.277</td>
              <td>1.457to 2.289</td>
              <td>0.8217to 1.451</td>
              <td>0.8592to 1.469</td>
              <td>0.8137to 1.448</td>
              <td>0.8081to 1.464</td>
              <td>0.8491to 1.517</td>
              <td>1.101to 2.133</td>
            </tr>
            <tr>
              <td>
                logIC
                <sub>50</sub>
              </td>
              <td>1.379to 1.464</td>
              <td>1.277to 1.398</td>
              <td>1.161to 1.241</td>
              <td>1.069to 1.183</td>
              <td>0.9633to 1.136</td>
              <td>1.240to 1.382</td>
              <td>1.132to 1.290</td>
              <td>0.9650to 1.145</td>
              <td>0.8525to 1.043</td>
              <td>1.662to 1.883</td>
            </tr>
            <tr>
              <td colspan="11">
                <bold>Goodness</bold>
                <bold>of</bold>
                <bold>Fit</bold>
              </td>
            </tr>
            <tr>
              <td>Degrees of Freedom</td>
              <td>10</td>
              <td>10</td>
              <td>10</td>
              <td>10</td>
              <td>10</td>
              <td>10</td>
              <td>10</td>
              <td>10</td>
              <td>10</td>
              <td>10</td>
            </tr>
            <tr>
              <td>R squared</td>
              <td>0.9648</td>
              <td>0.9310</td>
              <td>0.9690</td>
              <td>0.9509</td>
              <td>0.8924</td>
              <td>0.9084</td>
              <td>0.8921</td>
              <td>0.8839</td>
              <td>0.8900</td>
              <td>0.9111</td>
            </tr>
            <tr>
              <td>Sum of Squares</td>
              <td>158.0</td>
              <td>270.3</td>
              <td>120.4</td>
              <td>433.3</td>
              <td>448.6</td>
              <td>382.5</td>
              <td>455.8</td>
              <td>489.1</td>
              <td>454.3</td>
              <td>192.4</td>
            </tr>
            <tr>
              <td>Sy.x</td>
              <td>3.975</td>
              <td>5.199</td>
              <td>3.470</td>
              <td>6.583</td>
              <td>6.698</td>
              <td>6.185</td>
              <td>6.751</td>
              <td>6.994</td>
              <td>6.740</td>
              <td>4.386</td>
            </tr>
            <tr>
              <td colspan="11">
                <bold>Constraints</bold>
              </td>
            </tr>
            <tr>
              <td>
                IC
                <sub>50</sub>
              </td>
              <td>
                IC
                <sub>50</sub>
                &gt; 0
              </td>
              <td>
                IC
                <sub>50</sub>
                &gt; 0
              </td>
              <td>
                IC
                <sub>50</sub>
                &gt; 0
              </td>
              <td>
                IC
                <sub>50</sub>
                &gt; 0
              </td>
              <td>
                IC
                <sub>50</sub>
                &gt;0
              </td>
              <td>
                IC
                <sub>50</sub>
                &gt; 0
              </td>
              <td>
                IC
                <sub>50</sub>
                &gt; 0
              </td>
              <td>
                IC
                <sub>50</sub>
                &gt; 0
              </td>
              <td>
                IC
                <sub>50</sub>
                &gt; 0
              </td>
              <td>
                IC
                <sub>50</sub>
                &gt; 0
              </td>
            </tr>
            <tr>
              <td colspan="11">
                <bold>Number</bold>
                <bold>of</bold>
                <bold>points</bold>
              </td>
            </tr>
            <tr>
              <td># of X values</td>
              <td>12</td>
              <td>12</td>
              <td>12</td>
              <td>12</td>
              <td>12</td>
              <td>12</td>
              <td>12</td>
              <td>12</td>
              <td>12</td>
              <td>12</td>
            </tr>
            <tr>
              <td># of Y values analyzed</td>
              <td>12</td>
              <td>12</td>
              <td>12</td>
              <td>12</td>
              <td>12</td>
              <td>12</td>
              <td>12</td>
              <td>12</td>
              <td>12</td>
              <td>12</td>
            </tr>
          </tbody>
        </table>
      </table-wrap>
      <p><bold>Table S6.</bold> Two-way ANOVA of anticancer activity of <italic>T.</italic><italic>polyzona</italic> extracts.</p>
      <table-wrap id="tbl10">
        <label>Table 10</label>
        <table>
          <tbody>
            <tr>
              <td>Table Analyzed</td>
              <td colspan="5">Anticancer with control cell Data 20</td>
            </tr>
            <tr>
              <td>Two-way ANOVA</td>
              <td colspan="5">Ordinary</td>
            </tr>
            <tr>
              <td>Alpha</td>
              <td colspan="5">0.05</td>
            </tr>
            <tr>
              <td>Source of Variation</td>
              <td>% of total variation</td>
              <td>p value</td>
              <td>p value summary</td>
              <td>Significant?</td>
              <td>
              </td>
            </tr>
            <tr>
              <td>Interaction</td>
              <td>3.391</td>
              <td>&lt;0.0001</td>
              <td>***</td>
              <td>Yes</td>
              <td>
              </td>
            </tr>
            <tr>
              <td>Row Factor</td>
              <td>79.41</td>
              <td>&lt;0.0001</td>
              <td>****</td>
              <td>Yes</td>
              <td>
              </td>
            </tr>
            <tr>
              <td>Column Factor</td>
              <td>15.96</td>
              <td>&lt;0.0001</td>
              <td>****</td>
              <td>Yes</td>
              <td>
              </td>
            </tr>
            <tr>
              <td>ANOVA table</td>
              <td>SS</td>
              <td>DF</td>
              <td>MS</td>
              <td>F (DFn, DFd)</td>
              <td>p value</td>
            </tr>
            <tr>
              <td>Interaction</td>
              <td>1345</td>
              <td>21</td>
              <td>64.04</td>
              <td>F (21, 64) = 8.334</td>
              <td>p &lt; 0.0001</td>
            </tr>
            <tr>
              <td>Row Factor</td>
              <td>31495</td>
              <td>3</td>
              <td>10498</td>
              <td>F (3, 64) = 1366</td>
              <td>p &lt; 0.0001</td>
            </tr>
            <tr>
              <td>Column Factor</td>
              <td>6330</td>
              <td>7</td>
              <td>904.3</td>
              <td>F (7, 64) = 117.7</td>
              <td>p &lt; 0.0001</td>
            </tr>
            <tr>
              <td>Residual</td>
              <td>491.8</td>
              <td>64</td>
              <td>7.684</td>
              <td>
              </td>
              <td>
              </td>
            </tr>
            <tr>
              <td colspan="6">
                <bold>Data</bold>
                <bold>summary</bold>
              </td>
            </tr>
            <tr>
              <td colspan="2">Number of columns (Column Factor)</td>
              <td>8</td>
              <td>
              </td>
              <td>
              </td>
              <td>
              </td>
            </tr>
            <tr>
              <td colspan="2">Number of rows (Row Factor)</td>
              <td>4</td>
              <td>
              </td>
              <td>
              </td>
              <td>
              </td>
            </tr>
            <tr>
              <td colspan="2">Number of values</td>
              <td>96</td>
              <td>
              </td>
              <td>
              </td>
              <td>
              </td>
            </tr>
          </tbody>
        </table>
      </table-wrap>
      <p><bold>Table S7.</bold> Non-linear regression analysis of dose response of <italic>T.</italic><italic>polyzona</italic> polyphenol and polysaccharides and reference cancer drugs (half maximal inhibitory concentration (IC<sub>50</sub>)).</p>
      <table-wrap id="tbl11">
        <label>Table 11</label>
        <table>
          <tbody>
            <tr>
              <td colspan="9">[Inhibitor] vs. normalized response—Variable slope</td>
            </tr>
            <tr>
              <td rowspan="2">Best-fit values</td>
              <td colspan="2">HepG2</td>
              <td colspan="2">HCT 116</td>
              <td colspan="2">MDA-MB 231</td>
              <td colspan="2">NCTC 929</td>
            </tr>
            <tr>
              <td>Polyphenol</td>
              <td>Polysaccharide.</td>
              <td>Polyphenol</td>
              <td>Polysaccharide</td>
              <td>Polyphenol</td>
              <td>Polysaccharide</td>
              <td>Polysaccharide</td>
              <td>Polyphenol</td>
            </tr>
            <tr>
              <td>
                IC
                <sub>50</sub>
              </td>
              <td>73.05</td>
              <td>60.10</td>
              <td>58.22</td>
              <td>74.39</td>
              <td>76.74</td>
              <td>85.42</td>
              <td>140.1</td>
              <td>117.5</td>
            </tr>
            <tr>
              <td>HillSlope</td>
              <td>2.086</td>
              <td>1.821</td>
              <td>1.744</td>
              <td>1.951</td>
              <td>2.097</td>
              <td>2.069</td>
              <td>1.333</td>
              <td>1.400</td>
            </tr>
            <tr>
              <td>
                logIC
                <sub>50</sub>
              </td>
              <td>1.864</td>
              <td>1.779</td>
              <td>1.765</td>
              <td>1.872</td>
              <td>1.885</td>
              <td>1.932</td>
              <td>2.147</td>
              <td>2.070</td>
            </tr>
            <tr>
              <td colspan="9">95% CI (profile likelihood)</td>
            </tr>
            <tr>
              <td>
                IC
                <sub>50</sub>
              </td>
              <td>67.41 to 79.40</td>
              <td>55.45 to 64.94</td>
              <td>53.88 to 62.72</td>
              <td>70.92 to 78.18</td>
              <td>72.51 to 81.53</td>
              <td>79.75 to 92.62</td>
              <td>133.1 to 148.6</td>
              <td>112.7 to 123.0</td>
            </tr>
            <tr>
              <td>HillSlope</td>
              <td>1.615 to 2.680</td>
              <td>1.491 to 2.207</td>
              <td>1.449 to 2.079</td>
              <td>1.696 to 2.237</td>
              <td>1.756 to 2.491</td>
              <td>1.663 to 2.552</td>
              <td>1.235 to 1.437</td>
              <td>1.300 to 1.505</td>
            </tr>
            <tr>
              <td>
                logIC
                <sub>50</sub>
              </td>
              <td>1.829 to 1.900</td>
              <td>1.744 to 1.813</td>
              <td>1.731 to 1.797</td>
              <td>1.851 to 1.893</td>
              <td>1.860 to 1.911</td>
              <td>1.902 to 1.967</td>
              <td>2.124 to 2.172</td>
              <td>2.052 to 2.090</td>
            </tr>
            <tr>
              <td colspan="9">Goodness of Fit</td>
            </tr>
            <tr>
              <td>Degrees of Freedom</td>
              <td>10</td>
              <td>10</td>
              <td>10</td>
              <td>10</td>
              <td>10</td>
              <td>10</td>
              <td>10</td>
              <td>10</td>
            </tr>
            <tr>
              <td>R squared</td>
              <td>0.9469</td>
              <td>0.9588</td>
              <td>0.9627</td>
              <td>0.9813</td>
              <td>0.9725</td>
              <td>0.9593</td>
              <td>0.9930</td>
              <td>0.9936</td>
            </tr>
            <tr>
              <td>Sum of Squares</td>
              <td>282.3</td>
              <td>214.8</td>
              <td>184.8</td>
              <td>89.74</td>
              <td>141.4</td>
              <td>183.8</td>
              <td>10.26</td>
              <td>12.39</td>
            </tr>
            <tr>
              <td>Sy.x</td>
              <td>5.313</td>
              <td>4.634</td>
              <td>4.299</td>
              <td>2.996</td>
              <td>3.760</td>
              <td>4.287</td>
              <td>1.013</td>
              <td>1.113</td>
            </tr>
            <tr>
              <td colspan="9">Constraints</td>
            </tr>
            <tr>
              <td>
                IC
                <sub>50</sub>
              </td>
              <td>
                IC
                <sub>50</sub>
                &gt; 0
              </td>
              <td>
                IC
                <sub>50</sub>
                &gt; 0
              </td>
              <td>
                IC
                <sub>50</sub>
                &gt; 0
              </td>
              <td>
                IC
                <sub>50</sub>
                &gt; 0
              </td>
              <td>
                IC
                <sub>50</sub>
                &gt; 0
              </td>
              <td>
                IC
                <sub>50</sub>
                &gt; 0
              </td>
              <td>
                IC
                <sub>50</sub>
                &gt; 0
              </td>
              <td>
                IC
                <sub>50</sub>
                &gt; 0
              </td>
            </tr>
            <tr>
              <td colspan="9">Number of points</td>
            </tr>
            <tr>
              <td># of X values</td>
              <td>12</td>
              <td>12</td>
              <td>12</td>
              <td>12</td>
              <td>12</td>
              <td>12</td>
              <td>12</td>
              <td>12</td>
            </tr>
            <tr>
              <td># Y values analyzed</td>
              <td>12</td>
              <td>12</td>
              <td>12</td>
              <td>12</td>
              <td>12</td>
              <td>12</td>
              <td>12</td>
              <td>12</td>
            </tr>
          </tbody>
        </table>
      </table-wrap>
      <p><bold>Table S8.</bold> Two-way ANOVA anticancer activity-control drugs.</p>
      <table-wrap id="tbl12">
        <label>Table 12</label>
        <table>
          <tbody>
            <tr>
              <td>Table Analyzed</td>
              <td colspan="5">Anticancer with Cisplatin and Doxorubicin F. Data 19</td>
            </tr>
            <tr>
              <td colspan="6">Ordinary Two-way ANOVA</td>
            </tr>
            <tr>
              <td>Alpha</td>
              <td colspan="5">0.05</td>
            </tr>
            <tr>
              <td>Source of Variation</td>
              <td>% of total variation</td>
              <td>p value</td>
              <td>p value summary</td>
              <td>Significant?</td>
              <td>
              </td>
            </tr>
            <tr>
              <td>Interaction</td>
              <td>5.553</td>
              <td>&lt;0.0001</td>
              <td>***</td>
              <td>Yes</td>
              <td>
              </td>
            </tr>
            <tr>
              <td>Row Factor</td>
              <td>69.84</td>
              <td>&lt;0.0001</td>
              <td>****</td>
              <td>Yes</td>
              <td>
              </td>
            </tr>
            <tr>
              <td>Column Factor</td>
              <td>24.06</td>
              <td>&lt;0.0001</td>
              <td>****</td>
              <td>Yes</td>
              <td>
              </td>
            </tr>
            <tr>
              <td>ANOVA table</td>
              <td>SS</td>
              <td>DF</td>
              <td>MS</td>
              <td>F (DFn, DFd)</td>
              <td>p value</td>
            </tr>
            <tr>
              <td>Interaction</td>
              <td>3220</td>
              <td>21</td>
              <td>153.3</td>
              <td>F (21, 64) = 30.74</td>
              <td>p &lt; 0.0001</td>
            </tr>
            <tr>
              <td>Row Factor</td>
              <td>40,502</td>
              <td>3</td>
              <td>13501</td>
              <td>F (3, 64) = 2708</td>
              <td>p &lt; 0.0001</td>
            </tr>
            <tr>
              <td>Column Factor</td>
              <td>13,950</td>
              <td>7</td>
              <td>1993</td>
              <td>F (7, 64) = 399.8</td>
              <td>p &lt; 0.0001</td>
            </tr>
            <tr>
              <td>Residual</td>
              <td>319.0</td>
              <td>64</td>
              <td>4.985</td>
              <td>
              </td>
              <td>
              </td>
            </tr>
            <tr>
              <td colspan="6">
                <bold>Data</bold>
                <bold>summary</bold>
              </td>
            </tr>
            <tr>
              <td colspan="2">Number of columns (Column Factor)</td>
              <td colspan="4">8</td>
            </tr>
            <tr>
              <td colspan="2">Number of rows (Row Factor)</td>
              <td colspan="4">4</td>
            </tr>
            <tr>
              <td colspan="2">Number of values</td>
              <td colspan="4">96</td>
            </tr>
          </tbody>
        </table>
      </table-wrap>
      <p><bold>Table S9.</bold> Half maximal inhibitory concentration (IC<sub>50</sub>)-control drugs against cancer cell Lines and non-malignant cell line.</p>
      <table-wrap id="tbl13">
        <label>Table 13</label>
        <table>
          <tbody>
            <tr>
              <td colspan="9">[Inhibitor] vs. normalized response—Variable slope</td>
            </tr>
            <tr>
              <td rowspan="2">Best-fit values</td>
              <td colspan="2">HepG2</td>
              <td colspan="2">HCT116</td>
              <td colspan="2">MDA-MB231</td>
              <td colspan="2">NCTC 929</td>
            </tr>
            <tr>
              <td>Cisplatin</td>
              <td>Doxorubicin</td>
              <td>Cisplatin</td>
              <td>Doxorubicin</td>
              <td>Cisplatin</td>
              <td>Doxorubicin</td>
              <td>Cisplatin</td>
              <td>Doxorubicin</td>
            </tr>
            <tr>
              <td>
                IC
                <sub>50</sub>
              </td>
              <td>7.143</td>
              <td>5.249</td>
              <td>8.873</td>
              <td>4.943</td>
              <td>6.416</td>
              <td>12.86</td>
              <td>4.760</td>
              <td>4.599</td>
            </tr>
            <tr>
              <td>HillSlope</td>
              <td>1.756</td>
              <td>2.141</td>
              <td>1.619</td>
              <td>2.263</td>
              <td>1.126</td>
              <td>1.486</td>
              <td>2.323</td>
              <td>2.444</td>
            </tr>
            <tr>
              <td>
                logIC
                <sub>50</sub>
              </td>
              <td>0.8539</td>
              <td>0.7201</td>
              <td>0.9481</td>
              <td>0.6940</td>
              <td>0.8073</td>
              <td>1.109</td>
              <td>0.6776</td>
              <td>0.6626</td>
            </tr>
            <tr>
              <td colspan="9">95% CI (profile likelihood)</td>
            </tr>
            <tr>
              <td>
                IC
                <sub>50</sub>
              </td>
              <td>6.837 to 7.471</td>
              <td>4.737 to 5.767</td>
              <td>8.346 to 9.537</td>
              <td>4.386 to 5.505</td>
              <td>5.766 to 7.185</td>
              <td>12.24 to 13.61</td>
              <td>4.202 to 5.326</td>
              <td>3.978 to 5.233</td>
            </tr>
            <tr>
              <td>HillSlope</td>
              <td>1.561 to 1.970</td>
              <td>1.691 to 2.703</td>
              <td>1.376 to 1.891</td>
              <td>1.742 to 2.947</td>
              <td>0.8751 to 1.398</td>
              <td>1.364 to 1.615</td>
              <td>1.783 to 3.040</td>
              <td>1.811 to 3.343</td>
            </tr>
            <tr>
              <td>
                logIC
                <sub>50</sub>
              </td>
              <td>0.8349 to 0.8734</td>
              <td>0.6755 to 0.7609</td>
              <td>0.9215 to 0.9794</td>
              <td>0.6420 to 0.7408</td>
              <td>0.7609 to 0.8564</td>
              <td>1.088 to 1.134</td>
              <td>0.6234 to 0.7264</td>
              <td>0.5996 to 0.7187</td>
            </tr>
            <tr>
              <td colspan="9">Goodness of Fit</td>
            </tr>
            <tr>
              <td>Degrees of Freedom</td>
              <td>10</td>
              <td>10</td>
              <td>10</td>
              <td>10</td>
              <td>10</td>
              <td>10</td>
              <td>10</td>
              <td>10</td>
            </tr>
            <tr>
              <td>R squared</td>
              <td>0.9851</td>
              <td>0.9488</td>
              <td>0.9726</td>
              <td>0.9393</td>
              <td>0.9223</td>
              <td>0.9920</td>
              <td>0.9381</td>
              <td>0.9247</td>
            </tr>
            <tr>
              <td>Sum of Squares</td>
              <td>64.28</td>
              <td>364.0</td>
              <td>85.79</td>
              <td>481.1</td>
              <td>186.4</td>
              <td>13.99</td>
              <td>514.8</td>
              <td>684.8</td>
            </tr>
            <tr>
              <td>Sy.x</td>
              <td>2.535</td>
              <td>6.033</td>
              <td>2.929</td>
              <td>6.936</td>
              <td>4.317</td>
              <td>1.183</td>
              <td>7.175</td>
              <td>8.275</td>
            </tr>
            <tr>
              <td colspan="9">Constraints</td>
            </tr>
            <tr>
              <td>
                IC
                <sub>50</sub>
              </td>
              <td>
                IC
                <sub>50</sub>
                &gt; 0
              </td>
              <td>
                IC
                <sub>50</sub>
                &gt; 0
              </td>
              <td>
                IC
                <sub>50</sub>
                &gt; 0
              </td>
              <td>
                IC
                <sub>50</sub>
                &gt; 0
              </td>
              <td>
                IC
                <sub>50</sub>
                &gt; 0
              </td>
              <td>
                IC
                <sub>50</sub>
                &gt; 0
              </td>
              <td>
                IC
                <sub>50</sub>
                &gt; 0
              </td>
              <td>
                IC
                <sub>50</sub>
                &gt; 0
              </td>
            </tr>
            <tr>
              <td colspan="9">Number of points</td>
            </tr>
            <tr>
              <td># of X values</td>
              <td>12</td>
              <td>12</td>
              <td>12</td>
              <td>12</td>
              <td>12</td>
              <td>12</td>
              <td>12</td>
              <td>12</td>
            </tr>
            <tr>
              <td># Y values analyzed</td>
              <td>12</td>
              <td>12</td>
              <td>12</td>
              <td>12</td>
              <td>12</td>
              <td>12</td>
              <td>12</td>
              <td>12</td>
            </tr>
          </tbody>
        </table>
      </table-wrap>
    </sec>
  </body>
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