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  <front>
    <journal-meta>
      <journal-id journal-id-type="publisher-id">jbm</journal-id>
      <journal-title-group>
        <journal-title>Journal of Biosciences and Medicines</journal-title>
      </journal-title-group>
      <issn pub-type="epub">2327-509X</issn>
      <issn pub-type="ppub">2327-5081</issn>
      <publisher>
        <publisher-name>Scientific Research Publishing</publisher-name>
      </publisher>
    </journal-meta>
    <article-meta>
      <article-id pub-id-type="doi">10.4236/jbm.2026.146011</article-id>
      <article-id pub-id-type="publisher-id">jbm-151869</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>Advances in Ultrastructural Pathology for Renal Biopsy Diagnosis</article-title>
      </title-group>
      <contrib-group>
        <contrib contrib-type="author">
          <name name-style="western">
            <surname>Liu</surname>
            <given-names>Yunyun</given-names>
          </name>
          <xref ref-type="aff" rid="aff1">1</xref>
        </contrib>
        <contrib contrib-type="author" corresp="yes">
          <contrib-id contrib-id-type="orcid">0009-0009-8260-9437</contrib-id>
          <name name-style="western">
            <surname>Wang</surname>
            <given-names>Chen</given-names>
          </name>
          <xref ref-type="aff" rid="aff1">1</xref>
        </contrib>
      </contrib-group>
      <aff id="aff1"><label>1</label> The Second Clinical Medical College of Shanxi Medical University, Taiyuan, China </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>02</day>
        <month>06</month>
        <year>2026</year>
      </pub-date>
      <pub-date pub-type="collection">
        <month>06</month>
        <year>2026</year>
      </pub-date>
      <volume>14</volume>
      <issue>06</issue>
      <fpage>172</fpage>
      <lpage>181</lpage>
      <history>
        <date date-type="received">
          <day>23</day>
          <month>04</month>
          <year>2026</year>
        </date>
        <date date-type="accepted">
          <day>12</day>
          <month>06</month>
          <year>2026</year>
        </date>
        <date date-type="published">
          <day>15</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/jbm.2026.146011">https://doi.org/10.4236/jbm.2026.146011</self-uri>
      <abstract>
        <p>Renal biopsy remains the cornerstone of precise diagnosis and classification in nephrology. Ultrastructural pathology, centered on electron microscopy (EM), provides indispensable diagnostic value in immune-complex-mediated nephropathies, hereditary kidney diseases, and transplant-related injuries due to its nanometer-scale resolution. However, traditional transmission electron microscopy (TEM) is hindered by labor-intensive sample preparation, long turnaround times, limited field of view, and an inability to assess functional status, which limits its utility in the era of rapid, standardized, and quantitative precision medicine. Recently, emerging imaging technologies—including low-vacuum scanning electron microscopy (LVSEM), multiphoton microscopy (MPM), ultrasound localization microscopy (sULM), and structured illumination microscopy (SIM)—have expanded the scope of ultrastructural pathology from static two-dimensional morphological observation to three-dimensional structural reconstruction and real-time functional imaging. Concurrently, the integration of digital pathology and artificial intelligence (AI) has enabled automated recognition, quantitative analysis, and disease classification, significantly enhancing diagnostic efficiency and consistency. This article systematically reviews the traditional diagnostic value of ultrastructural pathology in renal biopsies, summarizes the breakthroughs and advantages of novel imaging techniques, discusses the progress of AI and digital pathology in ultrastructural analysis, and provides a forward-looking perspective on the development of multimodal, intelligent diagnostic systems to facilitate the modernization of renal pathology.</p>
      </abstract>
      <kwd-group kwd-group-type="author-generated" xml:lang="en">
        <kwd>Ultrastructural Pathology</kwd>
        <kwd>Renal Biopsy</kwd>
        <kwd>Electron Microscopy</kwd>
        <kwd>Artificial Intelligence</kwd>
        <kwd>Precision Diagnosis</kwd>
        <kwd>Digital Pathology</kwd>
      </kwd-group>
    </article-meta>
  </front>
  <body>
    <sec id="sec1">
      <title>1. Introduction</title>
      <p>Kidney diseases are highly heterogeneous. While clinical manifestations may be similar, pathological mechanisms, treatment responses, and prognoses differ significantly. Renal biopsy pathological diagnosis has become the cornerstone for clinical classification, guiding treatment, and determining prognosis. Among the three major technical systems—light microscopy, immunofluorescence/immunohistochemistry, and electron microscopy—ultrastructural pathology, centered on electron microscopy, can reveal nanoscale fine structural changes in the glomerular basement membrane, foot processes, mesangial matrix, electron-dense deposits (EDD), and organelles. It serves as the “gold standard” for the definitive diagnosis of many kidney diseases. In certain diseases, the absence of electron microscopy examination can directly lead to misdiagnosis or missed diagnosis, resulting in severe clinical consequences [<xref ref-type="bibr" rid="B1">1</xref>].</p>
      <p>Since the application of electron microscopy to renal pathology in the mid-20<sup>th</sup> century, it has continuously played a core role in identifying the location of electron-dense deposits, assessing basement membrane lesions, differentiating podocyte injury, and evaluating ultrastructural changes in transplant kidneys. However, with the development of precision medicine, the clinical demand for renal biopsy has expanded from purely morphological diagnosis to a comprehensive assessment encompassing disease activity, functional status, molecular mechanisms, and prognostic risk. Traditional transmission electron microscopy (TEM) suffers from complex procedures, long turnaround times, limited throughput, and heavy reliance on pathologist experience, presenting significant shortcomings in standardization, quantification, and rapid diagnosis.</p>
      <p>In recent years, the rapid advancement of microscopic imaging technology, digital pathology, and artificial intelligence has brought revolutionary changes to ultrastructural pathology [<xref ref-type="bibr" rid="B2">2</xref>]. Low-vacuum scanning electron microscopy (LVSEM) simplifies sample preparation and shortens diagnostic cycles; multiphoton microscopy (MPM) and super-resolution ultrasound localization microscopy (sULM) enable structural and functional imaging of living kidneys; structured illumination microscopy (SIM) breaks the optical diffraction limit, allowing quantitative analysis of nanoscale ultrastructure; and deep learning models have demonstrated high accuracy in tasks such as identifying electron-dense deposits, assessing foot process effacement, and classifying nephropathies. Ultrastructural pathology is evolving from a traditional morphological tool into a multimodal, intelligent, and digital comprehensive diagnostic system. This article focuses on the evolution of ultrastructural pathology applications in renal biopsy, systematically reviewing its traditional value, technological innovations, integration with artificial intelligence, and future development trends.</p>
    </sec>
    <sec id="sec2">
      <title>2. Materials and Methods</title>
      <p><bold>Methods:</bold> A systematic search was conducted in the PubMed database using the following search strategy:</p>
      <p>(ultrapatho*[Title/Abstract] OR microscopy[Title]) AND (kidne*[Title] OR nephrid*[Title] OR renal*[Title])</p>
      <p><bold>Database and Time Range:</bold>The search was performed in PubMed, covering the period from January 1, 2016, to January 1, 2026 (the past decade).</p>
      <p><bold>Inclusion and Exclusion Criteria:</bold></p>
      <p>Inclusion criteria: 1) Studies involving the application of ultrastructural pathology or microscopic imaging techniques in the diagnosis of kidney diseases; 2) Article types including original research, reviews, systematic reviews, or guidelines; 3) Articles written in English.</p>
      <p>Exclusion criteria: 1) Conference abstracts, case reports, or dissertations; 2) Duplicate publications or articles for which full text was unavailable.</p>
      <p><bold>Study Screening P</bold><bold>roc</bold><bold>ess:</bold></p>
      <p>A total of 162 articles were identified in the initial search. After abstract screening, 72 articles were excluded; after full-text screening, 61 articles were excluded. Ultimately, 29 articles were included. The screening was conducted independently by the researcher.</p>
    </sec>
    <sec id="sec3">
      <title>3. The Traditional Role and Core Value of Ultrastructural Pathology in Renal Biopsy Diagnosis</title>
      <p>The core advantage of ultrastructural pathology lies in its nanoscale spatial resolution, enabling the detection of subtle structural changes invisible to light microscopy, thereby facilitating disease diagnosis, differential diagnosis, and prognostic assessment.</p>
      <sec id="sec3dot1">
        <title>3.1. Key Diagnostic Basis for Immune-Mediated Glomerular Diseases</title>
        <p>The core pathological feature of immune complex-mediated glomerular diseases is the abnormal deposition of electron-dense deposits (EDD) in different regions of the glomerulus, and their distribution pattern is a key marker for disease classification [<xref ref-type="bibr" rid="B3">3</xref>]. Electron microscopy can clearly distinguish deposition patterns in subepithelial, subendothelial, mesangial, and intramembranous locations, providing decisive evidence for disease diagnosis [<xref ref-type="bibr" rid="B4">4</xref>]. Acute post-streptococcal glomerulonephritis is characterized by pathognomonic subepithelial “hump-shaped” electron-dense deposits [<xref ref-type="bibr" rid="B5">5</xref>]. Active lupus nephritis often shows massive subendothelial electron-dense deposits, which may be accompanied by tubuloreticular inclusions, significant for assessing disease activity [<xref ref-type="bibr" rid="B6">6</xref>]. IgA nephropathy is diagnosed based on predominantly mesangial electron-dense deposits; electron microscopy can simultaneously assess the extent and degree of foot process effacement. Foot process effacement is clearly associated with the clinical prognosis of IgA nephropathy. Extensive foot process effacement (&gt;50% of capillary loops) is independently associated with more severe proteinuria and faster decline in renal function [<xref ref-type="bibr" rid="B7">7</xref>]. Membranous nephropathy is characterized by typical subepithelial spike-like deposits; electron microscopy is an important supplement for distinguishing primary from secondary membranous lesions. By evaluating the ultrastructural distribution pattern of deposits and, in antigen-negative cases, assessing GBM structural changes and deposit morphology, electron microscopy can provide crucial clues for differential diagnosis [<xref ref-type="bibr" rid="B8">8</xref>], thereby avoiding the simplistic treatment of MN as a single disease and guiding the development of more targeted therapeutic strategies.</p>
      </sec>
      <sec id="sec3dot2">
        <title>3.2. The “Gold Standard” for Hereditary Kidney Diseases</title>
        <p>Hereditary nephropathies primarily involve structural abnormalities of the basement membrane or podocyte cytoskeleton. Light microscopy often lacks specific findings, making electron microscopy the only means to provide definitive diagnostic evidence. Alport syndrome is characterized by typical irregular thickening, lamellation, splitting, and basket-weave changes of the glomerular basement membrane [<xref ref-type="bibr" rid="B9">9</xref>][<xref ref-type="bibr" rid="B10">10</xref>], with basement membrane thickness significantly deviating from the normal range, allowing precise differentiation from the diffuse thinning of the basement membrane seen in thin basement membrane nephropathy (TBMD). Such diseases are difficult to diagnose solely by light microscopy and immunofluorescence; ultrastructural changes are the most important morphological evidence prior to genetic testing.</p>
      </sec>
      <sec id="sec3dot3">
        <title>3.3. Essential Tool for Transplant Renal Pathology</title>
        <p>The causes of transplant kidney injury are complex and diverse, primarily including antibody-mediated rejection (ABMR), cellular rejection, recurrent glomerular disease, and drug toxicity. Recurrence of glomerulonephritis after transplantation is a significant clinical challenge leading to graft loss [<xref ref-type="bibr" rid="B11">11</xref>]. For example, the recurrence of focal segmental glomerulosclerosis (FSGS) is closely related to various circulating factors and molecular biomarkers. Membranous nephropathy (MN) can either recur or de novo occur after transplantation, and both conditions are often accompanied by a similar risk of antibody-mediated rejection. In such cases, electron microscopy (EM) can accurately determine the nature of the lesion by observing the degree of foot process effacement, the location of electron-dense deposits, and ultrastructural changes in the basement membrane [<xref ref-type="bibr" rid="B12">12</xref>]-[<xref ref-type="bibr" rid="B14">14</xref>], providing key evidence for adjusting clinical treatment strategies. Transplant glomerulopathy is primarily caused by chronic antibody-mediated rejection [<xref ref-type="bibr" rid="B15">15</xref>]. Electron microscopy can clearly reveal characteristic ultrastructural changes that are difficult to discern under light microscopy, such as double contours of the basement membrane, basement membrane duplication, and widening of the subendothelial space.</p>
      </sec>
    </sec>
    <sec id="sec4">
      <title>4. Technical Limitations of Traditional Ultrastructural Pathology</title>
      <p>Despite its clear diagnostic value, traditional TEM faces multiple bottlenecks in modern clinical application. The maintenance cost of electron microscopy equipment is high [<xref ref-type="bibr" rid="B16">16</xref>], and it requires a specialized laboratory environment. Furthermore, traditional electron microscopy faces increasingly severe challenges in data management. Currently, the data volume generated from a single EM experiment has escalated from the gigabyte (GB) level to the terabyte (TB) or even petabyte (PB) level. Traditional local storage and manual management methods struggle to cope with the archiving, retrieval, and long-term preservation of such massive datasets [<xref ref-type="bibr" rid="B17">17</xref>]. The observation field of TEM is extremely small (typically only at the micrometer level). If the section happens to miss the lesional area, it can easily lead to missed diagnosis or misdiagnosis. TEM observes two-dimensional ultrathin sections and cannot fully represent the complex microvascular network within the glomerulus, the three-dimensional morphology of foot processes, or the spatial distribution of deposits, limiting the in-depth understanding of pathological mechanisms. These issues collectively constrain the widespread application of ultrastructural pathology in the era of precision medicine and have also driven a new wave of technological innovation.</p>
    </sec>
    <sec id="sec5">
      <title>5. Technological Innovations: From Morphological Observation to Functional Imaging</title>
      <p>Ultrastructural pathology, centered on electron microscopy, studies nanoscale structural changes in cells, tissues, and the extracellular matrix. Its traditional scope was limited to two-dimensional static morphological observation of *<italic>ex vivo</italic>* biopsy tissue. In recent years, the emergence of various novel imaging technologies has expanded the boundaries of ultrastructural pathology research from *<italic>ex vivo</italic>* tissue to *<italic>in vivo</italic>* organs, from static structure to dynamic function, and from two-dimensional sections to three-dimensional space. Based on their application scenarios, these new technologies can be categorized into the following three groups.</p>
      <sec id="sec5dot1">
        <title>
          5.1. Biopsy Tissue-Related Technologies: Enhancing Diagnostic Efficiency and Dimensionality of *
          <italic>Ex Vivo</italic>
          * Samples**
        </title>
        <p>These technologies are directly applied to the ultrastructural analysis of *<italic>ex vivo</italic>* renal biopsy tissue, aiming to simplify sample preparation, shorten diagnostic turnaround time, or provide three-dimensional structural information unattainable by traditional TEM.</p>
        <p>5.1.1. Low-Vacuum Scanning Electron Microscopy (LVSEM)</p>
        <p>LVSEM allows direct observation of resin-embedded blocks under low-vacuum conditions [<xref ref-type="bibr" rid="B18">18</xref>], eliminating the need for ultrathin sectioning. This significantly simplifies sample preparation and markedly shortens diagnostic time, making it more suitable for rapid clinical diagnosis. Studies have shown that LVSEM can clearly visualize glomerular basement membrane duplication, lamellation, and mesangial changes. It demonstrates high sensitivity for detecting early ultrastructural changes associated with antibody-mediated rejection, offering the potential for rapid early warning of subclinical transplant injury [<xref ref-type="bibr" rid="B19">19</xref>] and showing good potential for clinical translation.</p>
        <p>5.1.2. Structured Illumination Microscopy (SIM)</p>
        <p>SIM, a super-resolution optical microscopy technique, overcomes the diffraction limit, enabling nanoscale structural observation. Its application in renal pathology has extended beyond single disease types. Liu<italic>et al.</italic> [<xref ref-type="bibr" rid="B20">20</xref>] applied dual-color fluorescence SIM to renal mass biopsies. By performing rapid, non-destructive optical sectioning imaging of fresh biopsy tissue while maintaining tissue integrity, they achieved high sensitivity and specificity in diagnosing renal tumors, providing a new clinical translation pathway for real-time pathological assessment of renal masses. Furthermore, SIM has demonstrated unique advantages in evaluating foot process effacement, measuring basement membrane thickness, and quantifying autophagosomes in non-neoplastic renal diseases [<xref ref-type="bibr" rid="B21">21</xref>][<xref ref-type="bibr" rid="B22">22</xref>], indicating its potential as a versatile analytical tool for renal ultrastructural pathology.</p>
      </sec>
      <sec id="sec5dot2">
        <title>
          5.2.
          <italic>In Vivo</italic>
          * and Non-Invasive Adjunctive Technologies: Enabling Structural and Functional Imaging of Living Kidneys**
        </title>
        <p>These technologies require minimal or no invasive procedures, allowing real-time observation of kidney structure and function *<italic>in vivo</italic>*, extending ultrastructural pathology from *<italic>ex vivo</italic>* diagnosis to *<italic>in vivo</italic>* functional assessment.</p>
        <p>5.2.1. Multiphoton Microscopy (MPM)</p>
        <p>MPM offers advantages such as deep tissue imaging, low phototoxicity, and the ability for long-term *<italic>in vivo</italic>* observation. It enables real-time visualization of tubular blood flow, organelle dynamics, immune cell migration, reactive oxygen species (ROS) generation, and mitochondrial functional changes in living kidneys. This provides novel tools for elucidating the pathophysiological processes of diabetic nephropathy, ischemia-reperfusion injury, hypertensive nephropathy, and renal inflammation [<xref ref-type="bibr" rid="B23">23</xref>]. Compared to traditional static electron microscopy, MPM truly achieves simultaneous assessment of structure and function, propelling renal pathology from “morphological diagnosis” towards “mechanistic analysis.”</p>
        <p>5.2.2. Super-Resolution Ultrasound Localization Microscopy (sULM)</p>
        <p>sULM overcomes the resolution limitations of traditional ultrasound, enabling non-invasive visualization of microvasculature. It can precisely quantify renal microvascular hemodynamics, representing a breakthrough technology for early non-invasive diagnosis of kidney diseases. In early diabetic kidney disease (DKD), glomerular microvascular injury is the core initiating factor. Traditional ultrasound cannot detect subtle blood flow abnormalities, whereas sULM can clearly visualize glomerular microvascular distribution, blood flow velocity, and perfusion, capturing the characteristics of early DKD microvascular lesions and providing a new avenue for non-invasive, early, and precise diagnosis [<xref ref-type="bibr" rid="B24">24</xref>][<xref ref-type="bibr" rid="B25">25</xref>]. Furthermore, sULM can directly visualize glomerular structures in both native and transplanted kidneys in humans without invasive procedures, holding broad application prospects for non-invasive monitoring of transplant kidneys and screening high-risk populations, extending ultrastructural pathology from invasive biopsy to non-invasive screening [<xref ref-type="bibr" rid="B26">26</xref>].</p>
      </sec>
      <sec id="sec5dot3">
        <title>5.3. Virtual Digital Microscopy (VM): The Infrastructure for Digital Workflow</title>
        <p>Virtual microscopy (VM) technology itself is not a novel microscopic imaging technique. Instead, it involves the whole-slide digital scanning of traditional pathology slides (including light microscopy, immunofluorescence, and electron microscopy slides) to generate high-resolution digital images that can be viewed, annotated, and shared on computers. The rationale for including it within the framework of ultrastructural pathology technological innovation is as follows: 1) **Technological Bridge: **VM serves as the bridge connecting ultrastructural pathology images with AI analysis, teleconsultation, and multi-center collaboration. Without the digital foundation provided by VM, AI models cannot obtain training data, and remote pathological evaluation cannot be realized. 2) **Process Restructuring: **VM transforms the workflow of ultrastructural pathology from a linear model of “sectioning → microscopy → reporting” to a parallel model of “sectioning → digitization → AI-assisted analysis → remote review → reporting,” significantly improving diagnostic efficiency and accessibility. 3) **Prerequisite for Standardization: **VM enables the storage, retrieval, and quantitative analysis of ultrastructural pathology images, serving as the foundational infrastructure for advancing ultrastructural pathology from empirical interpretation towards standardized, quantitative, and traceable diagnosis.</p>
        <p>Therefore, although VM does not directly enhance imaging resolution or functional imaging capabilities, as the core supporting technology of digital pathology, it is an indispensable component of the ultrastructural pathology technology system. Through digital slides, experts can perform remote pathological evaluation and achieve rapid sharing of donor kidney biopsy results, breaking down geographical barriers and significantly optimizing the efficiency of donor kidney assessment for transplantation, multi-center pathological review, and consultation for difficult cases [<xref ref-type="bibr" rid="B27">27</xref>], driving the transformation of the renal pathology workflow towards digitization and networking.</p>
      </sec>
    </sec>
    <sec id="sec6">
      <title>6. Deep Integration of Digital Pathology and Artificial Intelligence</title>
      <p>AI and deep learning provide critical support for the standardization, quantification, and automation of ultrastructural pathology, reshaping diagnostic paradigms.</p>
      <p>Automated Identification and Pattern Recognition: Deep learning models can automatically identify EDD in TEM images [<xref ref-type="bibr" rid="B28">28</xref>], determine their distribution, and assist in the rapid classification of immune-complex-mediated nephropathies, thereby reducing human error. Intelligent Disease Classification: Models such as MedKidneyEM-v1 have demonstrated high accuracy in automatically classifying amyloidosis, diabetic nephropathy, membranous nephropathy, and TBMD [<xref ref-type="bibr" rid="B29">29</xref>], serving as reliable diagnostic tools.</p>
      <p>Despite these achievements, clinical translation faces challenges. AI model training is highly dependent on high-quality, expert-annotated datasets, which are currently scarce. Furthermore, data heterogeneity—arising from different equipment, staining protocols, and section thicknesses—limits model generalizability. Finally, the “black-box” nature of deep learning models limits clinical trust, necessitating the development of explainable AI.</p>
    </sec>
    <sec id="sec7">
      <title>7. Clinical Translation and Future Perspectives</title>
      <p>Ultrastructural pathology remains indispensable, and technological innovation is revitalizing the field. Future trends include: 1) Multimodal Imaging Fusion: Integrating LVSEM, SIM, sULM, and MPM to achieve comprehensive “structure + function + molecular + non-invasive” assessment; 2) Full-Process Automation: Streamlining preparation, scanning, AI screening, and remote consultation to compress diagnostic cycles to within 24 hours; 3) Integrated Multi-omics: Correlating EM morphological data with genomic, transcriptomic, and proteomic data to build precise molecular classification systems.; 4) Intelligent Standardization: Using digital pathology and AI to unify quantitative metrics, reducing subjective variability.</p>
      <p>Overall, ultrastructural pathology is gradually advancing from traditional electron microscopybased morphological diagnosis toward a new era of multimodality, intelligence, digitalization, and functionalization, and will play an increasingly critical role in the precise diagnosis and treatment system of kidney diseases.</p>
    </sec>
    <sec id="sec8">
      <title>8. Conclusion</title>
      <p>Ultrastructural pathology is a core technology in renal biopsy diagnosis, particularly for immune-complex-mediated, hereditary, and transplant-related diseases. While traditional TEM faces limitations, novel technologies like LVSEM, MPM, sULM, and SIM are driving the field toward rapid, three-dimensional, and functional imaging. The deep integration of digital pathology and AI is enabling automated recognition and intelligent classification. Moving forward, the synergy of multimodal imaging and AI will render ultrastructural pathology more efficient, precise, and standardized, providing robust support for early diagnosis, precise classification, and personalized treatment, thereby ushering renal pathology into a new era of precision medicine.</p>
    </sec>
  </body>
  <back>
    <ref-list>
      <title>References</title>
      <ref id="B1">
        <label>1.</label>
        <citation-alternatives>
          <mixed-citation publication-type="other">Yamashita, M., Lin, M.Y., Hou, J., Ren, K.Y.M. and Haas, M. (2021) The Continuing Need for Electron Microscopy in Examination of Medical Renal Biopsies: Examples in Practice. <italic>Glomerular</italic><italic>Diseases</italic>, 1, 145-159. https://doi.org/10.1159/000516831 <pub-id pub-id-type="doi">10.1159/000516831</pub-id><pub-id pub-id-type="pmid">36751496</pub-id><ext-link ext-link-type="uri" xlink:href="https://doi.org/10.1159/000516831">https://doi.org/10.1159/000516831</ext-link></mixed-citation>
          <element-citation publication-type="other">
            <person-group person-group-type="author">
              <string-name>Yamashita, M.</string-name>
              <string-name>Lin, M.Y.</string-name>
              <string-name>Hou, J.</string-name>
              <string-name>Ren, K.Y.M.</string-name>
              <string-name>Haas, M.</string-name>
            </person-group>
            <year>2021</year>
            <article-title>The Continuing Need for Electron Microscopy in Examination of Medical Renal Biopsies: Examples in Practice</article-title>
            <source>Glomerular Diseases</source>
            <volume>1</volume>
            <pub-id pub-id-type="doi">10.1159/000516831</pub-id>
            <pub-id pub-id-type="pmid">36751496</pub-id>
          </element-citation>
        </citation-alternatives>
      </ref>
      <ref id="B2">
        <label>2.</label>
        <citation-alternatives>
          <mixed-citation publication-type="journal">Barisoni, L., Gimpel, C., Kain, R., Laurinavicius, A., Bueno, G., Zeng, C., <italic>et al</italic>. (2017) Digital Pathology Imaging as a Novel Platform for Standardization and Globalization of Quantitative Nephropathology. <italic>Clinical</italic><italic>Kidney</italic><italic>Journal</italic>, 10, 176-187. https://doi.org/10.1093/ckj/sfw129 <pub-id pub-id-type="doi">10.1093/ckj/sfw129</pub-id><pub-id pub-id-type="pmid">28584625</pub-id><ext-link ext-link-type="uri" xlink:href="https://doi.org/10.1093/ckj/sfw129">https://doi.org/10.1093/ckj/sfw129</ext-link></mixed-citation>
          <element-citation publication-type="journal">
            <person-group person-group-type="author">
              <string-name>Barisoni, L.</string-name>
              <string-name>Gimpel, C.</string-name>
              <string-name>Kain, R.</string-name>
              <string-name>Laurinavicius, A.</string-name>
              <string-name>Bueno, G.</string-name>
              <string-name>Zeng, C.</string-name>
            </person-group>
            <year>2017</year>
            <article-title>Digital Pathology Imaging as a Novel Platform for Standardization and Globalization of Quantitative Nephropathology</article-title>
            <source>Clinical Kidney Journal</source>
            <volume>10</volume>
            <pub-id pub-id-type="doi">10.1093/ckj/sfw129</pub-id>
            <pub-id pub-id-type="pmid">28584625</pub-id>
          </element-citation>
        </citation-alternatives>
      </ref>
      <ref id="B3">
        <label>3.</label>
        <citation-alternatives>
          <mixed-citation publication-type="other">Howell, D.N. and Herrera, G.A. (2020) Electron Microscopy in Renal Pathology: Overall Applications and Guidelines for Tissue, Collection, Preparation, and Stains. <italic>Ultrastructural</italic><italic>Pathology</italic>, 45, 1-18. https://doi.org/10.1080/01913123.2020.1854407 <pub-id pub-id-type="doi">10.1080/01913123.2020.1854407</pub-id><pub-id pub-id-type="pmid">33320036</pub-id><ext-link ext-link-type="uri" xlink:href="https://doi.org/10.1080/01913123.2020.1854407">https://doi.org/10.1080/01913123.2020.1854407</ext-link></mixed-citation>
          <element-citation publication-type="other">
            <person-group person-group-type="author">
              <string-name>Howell, D.N.</string-name>
              <string-name>Herrera, G.A.</string-name>
              <string-name>Tissue, C</string-name>
            </person-group>
            <year>2020</year>
            <article-title>Electron Microscopy in Renal Pathology: Overall Applications and Guidelines for Tissue, Collection, Preparation, and Stains</article-title>
            <source>Ultrastructural Pathology</source>
            <volume>45</volume>
            <pub-id pub-id-type="doi">10.1080/01913123.2020.1854407</pub-id>
            <pub-id pub-id-type="pmid">33320036</pub-id>
          </element-citation>
        </citation-alternatives>
      </ref>
      <ref id="B4">
        <label>4.</label>
        <citation-alternatives>
          <mixed-citation publication-type="other">Qasim, H., Hayajneh, Z., Khattab, K., Leoni, M.L.G. and Varrassi, G. (2025) Electron Microscopy in Renal Biopsy Interpretation: When and Why It Still Matters. <italic>Cureus</italic>, 17, e75000. https://doi.org/10.7759/cureus.96311 <pub-id pub-id-type="doi">10.7759/cureus.96311</pub-id><ext-link ext-link-type="uri" xlink:href="https://doi.org/10.7759/cureus.96311">https://doi.org/10.7759/cureus.96311</ext-link></mixed-citation>
          <element-citation publication-type="other">
            <person-group person-group-type="author">
              <string-name>Qasim, H.</string-name>
              <string-name>Hayajneh, Z.</string-name>
              <string-name>Khattab, K.</string-name>
              <string-name>Leoni, M.L.G.</string-name>
              <string-name>Varrassi, G.</string-name>
            </person-group>
            <year>2025</year>
            <article-title>Electron Microscopy in Renal Biopsy Interpretation: When and Why It Still Matters</article-title>
            <source>Cureus</source>
            <volume>17</volume>
            <pub-id pub-id-type="doi">10.7759/cureus.96311</pub-id>
          </element-citation>
        </citation-alternatives>
      </ref>
      <ref id="B5">
        <label>5.</label>
        <citation-alternatives>
          <mixed-citation publication-type="other">Munif, M.R., Hart, R.A., Rafeek, R.A.M., Mallawaarachchi, A.C., Anderson, L., McMillan, D.J., <italic>et al</italic>. (2024) Mechanisms That Potentially Contribute to the Development of Post-Streptococcal Glomerulonephritis. <italic>Pathogens</italic><italic>and</italic><italic>Disease</italic>, 82, ftae005. https://doi.org/10.1093/femspd/ftae024 <pub-id pub-id-type="doi">10.1093/femspd/ftae024</pub-id><pub-id pub-id-type="pmid">39341789</pub-id><ext-link ext-link-type="uri" xlink:href="https://doi.org/10.1093/femspd/ftae024">https://doi.org/10.1093/femspd/ftae024</ext-link></mixed-citation>
          <element-citation publication-type="other">
            <person-group person-group-type="author">
              <string-name>Munif, M.R.</string-name>
              <string-name>Hart, R.A.</string-name>
              <string-name>Rafeek, R.A.M.</string-name>
              <string-name>Mallawaarachchi, A.C.</string-name>
              <string-name>Anderson, L.</string-name>
              <string-name>McMillan, D.J.</string-name>
            </person-group>
            <year>2024</year>
            <article-title>Mechanisms That Potentially Contribute to the Development of Post-Streptococcal Glomerulonephritis</article-title>
            <source>Pathogens and Disease</source>
            <volume>82</volume>
            <pub-id pub-id-type="doi">10.1093/femspd/ftae024</pub-id>
            <pub-id pub-id-type="pmid">39341789</pub-id>
          </element-citation>
        </citation-alternatives>
      </ref>
      <ref id="B6">
        <label>6.</label>
        <citation-alternatives>
          <mixed-citation publication-type="other">Herrera, G.A. (1999) The Value of Electron Microscopy in the Diagnosis and Clinical Management of Lupus Nephritis. <italic>Ultrastructural</italic><italic>Pathology</italic>, 23, 63-77. https://doi.org/10.1080/019131299281725 <pub-id pub-id-type="doi">10.1080/019131299281725</pub-id><pub-id pub-id-type="pmid">10369101</pub-id><ext-link ext-link-type="uri" xlink:href="https://doi.org/10.1080/019131299281725">https://doi.org/10.1080/019131299281725</ext-link></mixed-citation>
          <element-citation publication-type="other">
            <person-group person-group-type="author">
              <string-name>Herrera, G.A.</string-name>
            </person-group>
            <year>1999</year>
            <article-title>The Value of Electron Microscopy in the Diagnosis and Clinical Management of Lupus Nephritis</article-title>
            <source>Ultrastructural Pathology</source>
            <volume>23</volume>
            <pub-id pub-id-type="doi">10.1080/019131299281725</pub-id>
            <pub-id pub-id-type="pmid">10369101</pub-id>
          </element-citation>
        </citation-alternatives>
      </ref>
      <ref id="B7">
        <label>7.</label>
        <citation-alternatives>
          <mixed-citation publication-type="journal">Terinte-Balcan, G., Stancu, S., Zugravu, A., Capusa, C., Radu, A., Mircescu, G., <italic>et al</italic>. (2023) Prognostic Role of Glomerular Electron Microscopy Lesions in IgA Nephropathy: “The Devil Is in the Details”. <italic>Journal</italic><italic>of</italic><italic>Nephrology</italic>, 36, 2233-2243. https://doi.org/10.1007/s40620-023-01744-3 <pub-id pub-id-type="doi">10.1007/s40620-023-01744-3</pub-id><pub-id pub-id-type="pmid">37632668</pub-id><ext-link ext-link-type="uri" xlink:href="https://doi.org/10.1007/s40620-023-01744-3">https://doi.org/10.1007/s40620-023-01744-3</ext-link></mixed-citation>
          <element-citation publication-type="journal">
            <person-group person-group-type="author">
              <string-name>Terinte-Balcan, G.</string-name>
              <string-name>Stancu, S.</string-name>
              <string-name>Zugravu, A.</string-name>
              <string-name>Capusa, C.</string-name>
              <string-name>Radu, A.</string-name>
              <string-name>Mircescu, G.</string-name>
            </person-group>
            <year>2023</year>
            <article-title>Prognostic Role of Glomerular Electron Microscopy Lesions in IgA Nephropathy: “The Devil Is in the Details”</article-title>
            <source>Journal of Nephrology</source>
            <volume>36</volume>
            <pub-id pub-id-type="doi">10.1007/s40620-023-01744-3</pub-id>
            <pub-id pub-id-type="pmid">37632668</pub-id>
          </element-citation>
        </citation-alternatives>
      </ref>
      <ref id="B8">
        <label>8.</label>
        <citation-alternatives>
          <mixed-citation publication-type="journal">Sethi, S. (2021) Membranous Nephropathy: A Single Disease or a Pattern of Injury Resulting from Different Diseases. <italic>Clinical</italic><italic>Kidney</italic><italic>Journal</italic>, 14, 2166-2169. https://doi.org/10.1093/ckj/sfab069 <pub-id pub-id-type="doi">10.1093/ckj/sfab069</pub-id><pub-id pub-id-type="pmid">34603694</pub-id><ext-link ext-link-type="uri" xlink:href="https://doi.org/10.1093/ckj/sfab069">https://doi.org/10.1093/ckj/sfab069</ext-link></mixed-citation>
          <element-citation publication-type="journal">
            <person-group person-group-type="author">
              <string-name>Sethi, S.</string-name>
            </person-group>
            <year>2021</year>
            <article-title>Membranous Nephropathy: A Single Disease or a Pattern of Injury Resulting from Different Diseases</article-title>
            <source>Clinical Kidney Journal</source>
            <volume>14</volume>
            <pub-id pub-id-type="doi">10.1093/ckj/sfab069</pub-id>
            <pub-id pub-id-type="pmid">34603694</pub-id>
          </element-citation>
        </citation-alternatives>
      </ref>
      <ref id="B9">
        <label>9.</label>
        <citation-alternatives>
          <mixed-citation publication-type="other">Rumpelt, H.J. (1987) Alport’s Syndrome: Specificity and Pathogenesis of Glomerular Basement Membrane Alterations. <italic>Pediatric</italic><italic>Nephrology</italic>, 1, 422-427. https://doi.org/10.1007/bf00849248 <pub-id pub-id-type="doi">10.1007/bf00849248</pub-id><pub-id pub-id-type="pmid">3153312</pub-id><ext-link ext-link-type="uri" xlink:href="https://doi.org/10.1007/bf00849248">https://doi.org/10.1007/bf00849248</ext-link></mixed-citation>
          <element-citation publication-type="other">
            <person-group person-group-type="author">
              <string-name>Rumpelt, H.J.</string-name>
            </person-group>
            <year>1987</year>
            <article-title>Alport’s Syndrome: Specificity and Pathogenesis of Glomerular Basement Membrane Alterations</article-title>
            <source>Pediatric Nephrology</source>
            <volume>1</volume>
            <pub-id pub-id-type="doi">10.1007/bf00849248</pub-id>
            <pub-id pub-id-type="pmid">3153312</pub-id>
          </element-citation>
        </citation-alternatives>
      </ref>
      <ref id="B10">
        <label>10.</label>
        <citation-alternatives>
          <mixed-citation publication-type="journal">Mabillard, H., Ryan, R., Tzoumas, N., Gear, S. and Sayer, J.A. (2024) Explaining Alport Syndrome—Lessons from the Adult Nephrology Clinic. <italic>Journal</italic><italic>of</italic><italic>Rare</italic><italic>Diseases</italic>, 3, Article No. 14. https://doi.org/10.1007/s44162-024-00036-z <pub-id pub-id-type="doi">10.1007/s44162-024-00036-z</pub-id><pub-id pub-id-type="pmid">38745975</pub-id><ext-link ext-link-type="uri" xlink:href="https://doi.org/10.1007/s44162-024-00036-z">https://doi.org/10.1007/s44162-024-00036-z</ext-link></mixed-citation>
          <element-citation publication-type="journal">
            <person-group person-group-type="author">
              <string-name>Mabillard, H.</string-name>
              <string-name>Ryan, R.</string-name>
              <string-name>Tzoumas, N.</string-name>
              <string-name>Gear, S.</string-name>
              <string-name>Sayer, J.A.</string-name>
            </person-group>
            <year>2024</year>
            <article-title>Explaining Alport Syndrome—Lessons from the Adult Nephrology Clinic</article-title>
            <source>Journal of Rare Diseases</source>
            <volume>3</volume>
            <elocation-id>No</elocation-id>
            <pub-id pub-id-type="doi">10.1007/s44162-024-00036-z</pub-id>
            <pub-id pub-id-type="pmid">38745975</pub-id>
          </element-citation>
        </citation-alternatives>
      </ref>
      <ref id="B11">
        <label>11.</label>
        <citation-alternatives>
          <mixed-citation publication-type="other">Lazarou, C., Moysidou, E., Christodoulou, M., Stai, S., Lioulios, G., Kasimatis, E., <italic>et al</italic>. (2025) Protocol Biopsies in Kidney Transplant Recipients: Current Practice after Much Discussion. <italic>Biomedicines</italic>, 13, Article No. 1660. https://doi.org/10.3390/biomedicines13071660 <pub-id pub-id-type="doi">10.3390/biomedicines13071660</pub-id><pub-id pub-id-type="pmid">40722731</pub-id><ext-link ext-link-type="uri" xlink:href="https://doi.org/10.3390/biomedicines13071660">https://doi.org/10.3390/biomedicines13071660</ext-link></mixed-citation>
          <element-citation publication-type="other">
            <person-group person-group-type="author">
              <string-name>Lazarou, C.</string-name>
              <string-name>Moysidou, E.</string-name>
              <string-name>Christodoulou, M.</string-name>
              <string-name>Stai, S.</string-name>
              <string-name>Lioulios, G.</string-name>
              <string-name>Kasimatis, E.</string-name>
            </person-group>
            <year>2025</year>
            <article-title>Protocol Biopsies in Kidney Transplant Recipients: Current Practice after Much Discussion</article-title>
            <source>Biomedicines</source>
            <volume>13</volume>
            <elocation-id>No</elocation-id>
            <pub-id pub-id-type="doi">10.3390/biomedicines13071660</pub-id>
            <pub-id pub-id-type="pmid">40722731</pub-id>
          </element-citation>
        </citation-alternatives>
      </ref>
      <ref id="B12">
        <label>12.</label>
        <citation-alternatives>
          <mixed-citation publication-type="journal">Infante, B., Rossini, M., Leo, S., Troise, D., Netti, G.S., Ranieri, E., <italic>et al</italic>. (2020) Recurrent Glomerulonephritis after Renal Transplantation: The Clinical Problem. <italic>Inter</italic><italic>national</italic><italic>Journal</italic><italic>of</italic><italic>Molecular</italic><italic>Sciences</italic>, 21, Article No. 5954. https://doi.org/10.3390/ijms21175954 <pub-id pub-id-type="doi">10.3390/ijms21175954</pub-id><pub-id pub-id-type="pmid">32824988</pub-id><ext-link ext-link-type="uri" xlink:href="https://doi.org/10.3390/ijms21175954">https://doi.org/10.3390/ijms21175954</ext-link></mixed-citation>
          <element-citation publication-type="journal">
            <person-group person-group-type="author">
              <string-name>Infante, B.</string-name>
              <string-name>Rossini, M.</string-name>
              <string-name>Leo, S.</string-name>
              <string-name>Troise, D.</string-name>
              <string-name>Netti, G.S.</string-name>
              <string-name>Ranieri, E.</string-name>
            </person-group>
            <year>2020</year>
            <article-title>Recurrent Glomerulonephritis after Renal Transplantation: The Clinical Problem</article-title>
            <source>International Journal of Molecular Sciences</source>
            <volume>21</volume>
            <elocation-id>No</elocation-id>
            <pub-id pub-id-type="doi">10.3390/ijms21175954</pub-id>
            <pub-id pub-id-type="pmid">32824988</pub-id>
          </element-citation>
        </citation-alternatives>
      </ref>
      <ref id="B13">
        <label>13.</label>
        <citation-alternatives>
          <mixed-citation publication-type="other">Shan, S., Li, X., Zhang, Y., Luo, Z. and Sun, Q. (2025) Post-Transplant Recurrence of Focal Segmental Glomerulosclerosis: Circulating Factors, Molecular Biomarkers, and Treatments. <italic>Kidney</italic><italic>Diseases</italic>, 11, 560-583. https://doi.org/10.1159/000547336 <pub-id pub-id-type="doi">10.1159/000547336</pub-id><ext-link ext-link-type="uri" xlink:href="https://doi.org/10.1159/000547336">https://doi.org/10.1159/000547336</ext-link></mixed-citation>
          <element-citation publication-type="other">
            <person-group person-group-type="author">
              <string-name>Shan, S.</string-name>
              <string-name>Li, X.</string-name>
              <string-name>Zhang, Y.</string-name>
              <string-name>Luo, Z.</string-name>
              <string-name>Sun, Q.</string-name>
              <string-name>Factors, M</string-name>
            </person-group>
            <year>2025</year>
            <article-title>Post-Transplant Recurrence of Focal Segmental Glomerulosclerosis: Circulating Factors, Molecular Biomarkers, and Treatments</article-title>
            <source>Kidney Diseases</source>
            <volume>11</volume>
            <pub-id pub-id-type="doi">10.1159/000547336</pub-id>
          </element-citation>
        </citation-alternatives>
      </ref>
      <ref id="B14">
        <label>14.</label>
        <citation-alternatives>
          <mixed-citation publication-type="journal">Khorsandi, N., Han, H.S., Rajalingam, R., Shoji, J. and Urisman, A. (2024) De Novo and Recurrent Post-Transplant Membranous Nephropathy Cases Show Similar Rates of Concurrent Antibody-Mediated Rejection. <italic>Frontiers</italic><italic>in</italic><italic>Nephrology</italic>, 4, Article ID: 1438065. https://doi.org/10.3389/fneph.2024.1438065 <pub-id pub-id-type="doi">10.3389/fneph.2024.1438065</pub-id><pub-id pub-id-type="pmid">39290350</pub-id><ext-link ext-link-type="uri" xlink:href="https://doi.org/10.3389/fneph.2024.1438065">https://doi.org/10.3389/fneph.2024.1438065</ext-link></mixed-citation>
          <element-citation publication-type="journal">
            <person-group person-group-type="author">
              <string-name>Khorsandi, N.</string-name>
              <string-name>Han, H.S.</string-name>
              <string-name>Rajalingam, R.</string-name>
              <string-name>Shoji, J.</string-name>
              <string-name>Urisman, A.</string-name>
            </person-group>
            <year>2024</year>
            <article-title>De Novo and Recurrent Post-Transplant Membranous Nephropathy Cases Show Similar Rates of Concurrent Antibody-Mediated Rejection</article-title>
            <source>Frontiers in Nephrology</source>
            <volume>4</volume>
            <fpage>143806</fpage>
            <elocation-id>ID</elocation-id>
            <pub-id pub-id-type="doi">10.3389/fneph.2024.1438065</pub-id>
            <pub-id pub-id-type="pmid">39290350</pub-id>
          </element-citation>
        </citation-alternatives>
      </ref>
      <ref id="B15">
        <label>15.</label>
        <citation-alternatives>
          <mixed-citation publication-type="other">Haas, M. (2011) Transplant Glomerulopathy: It’s Not Always about Chronic Rejection. <italic>Kidney</italic><italic>Inter</italic><italic>national</italic>, 80, 801-803. https://doi.org/10.1038/ki.2011.192 <pub-id pub-id-type="doi">10.1038/ki.2011.192</pub-id><pub-id pub-id-type="pmid">21960169</pub-id><ext-link ext-link-type="uri" xlink:href="https://doi.org/10.1038/ki.2011.192">https://doi.org/10.1038/ki.2011.192</ext-link></mixed-citation>
          <element-citation publication-type="other">
            <person-group person-group-type="author">
              <string-name>Haas, M.</string-name>
            </person-group>
            <year>2011</year>
            <article-title>Transplant Glomerulopathy: It’s Not Always about Chronic Rejection</article-title>
            <source>Kidney International</source>
            <volume>80</volume>
            <pub-id pub-id-type="doi">10.1038/ki.2011.192</pub-id>
            <pub-id pub-id-type="pmid">21960169</pub-id>
          </element-citation>
        </citation-alternatives>
      </ref>
      <ref id="B16">
        <label>16.</label>
        <citation-alternatives>
          <mixed-citation publication-type="other">Winey, M., Meehl, J.B., O’Toole, E.T. and Giddings, T.H. (2014) Conventional Transmission Electron Microscopy. <italic>Molecular</italic><italic>Biology</italic><italic>of</italic><italic>the</italic><italic>Cell</italic>, 25, 319-323. https://doi.org/10.1091/mbc.e12-12-0863 <pub-id pub-id-type="doi">10.1091/mbc.e12-12-0863</pub-id><pub-id pub-id-type="pmid">24482357</pub-id><ext-link ext-link-type="uri" xlink:href="https://doi.org/10.1091/mbc.e12-12-0863">https://doi.org/10.1091/mbc.e12-12-0863</ext-link></mixed-citation>
          <element-citation publication-type="other">
            <person-group person-group-type="author">
              <string-name>Winey, M.</string-name>
              <string-name>Meehl, J.B.</string-name>
              <string-name>Toole, E.T.</string-name>
              <string-name>Giddings, T.H.</string-name>
            </person-group>
            <year>2014</year>
            <article-title>Conventional Transmission Electron Microscopy</article-title>
            <source>Molecular Biology of the Cell</source>
            <volume>25</volume>
            <pub-id pub-id-type="doi">10.1091/mbc.e12-12-0863</pub-id>
            <pub-id pub-id-type="pmid">24482357</pub-id>
          </element-citation>
        </citation-alternatives>
      </ref>
      <ref id="B17">
        <label>17.</label>
        <citation-alternatives>
          <mixed-citation publication-type="other">Poger, D., Yen, L. and Braet, F. (2023) Big Data in Contemporary Electron Microscopy: Challenges and Opportunities in Data Transfer, Compute and Management. <italic>Histochemistry</italic><italic>and</italic><italic>Cell</italic><italic>Biology</italic>, 160, 169-192. https://doi.org/10.1007/s00418-023-02191-8 <pub-id pub-id-type="doi">10.1007/s00418-023-02191-8</pub-id><pub-id pub-id-type="pmid">37052655</pub-id><ext-link ext-link-type="uri" xlink:href="https://doi.org/10.1007/s00418-023-02191-8">https://doi.org/10.1007/s00418-023-02191-8</ext-link></mixed-citation>
          <element-citation publication-type="other">
            <person-group person-group-type="author">
              <string-name>Poger, D.</string-name>
              <string-name>Yen, L.</string-name>
              <string-name>Braet, F.</string-name>
              <string-name>Transfer, C</string-name>
            </person-group>
            <year>2023</year>
            <article-title>Big Data in Contemporary Electron Microscopy: Challenges and Opportunities in Data Transfer, Compute and Management</article-title>
            <source>Histochemistry and Cell Biology</source>
            <volume>160</volume>
            <pub-id pub-id-type="doi">10.1007/s00418-023-02191-8</pub-id>
            <pub-id pub-id-type="pmid">37052655</pub-id>
          </element-citation>
        </citation-alternatives>
      </ref>
      <ref id="B18">
        <label>18.</label>
        <citation-alternatives>
          <mixed-citation publication-type="other">Tojo, A., Abe, M. and Matsuyama, K. (2023) Direct Observation of Epoxy Resin Blocks for Renal Biopsy by Low-Vacuum Scanning Electron Microscopy. <italic>Medical</italic><italic>Molecular</italic><italic>Morphology</italic>, 56, 206-216. https://doi.org/10.1007/s00795-023-00356-x <pub-id pub-id-type="doi">10.1007/s00795-023-00356-x</pub-id><pub-id pub-id-type="pmid">37165248</pub-id><ext-link ext-link-type="uri" xlink:href="https://doi.org/10.1007/s00795-023-00356-x">https://doi.org/10.1007/s00795-023-00356-x</ext-link></mixed-citation>
          <element-citation publication-type="other">
            <person-group person-group-type="author">
              <string-name>Tojo, A.</string-name>
              <string-name>Abe, M.</string-name>
              <string-name>Matsuyama, K.</string-name>
            </person-group>
            <year>2023</year>
            <article-title>Direct Observation of Epoxy Resin Blocks for Renal Biopsy by Low-Vacuum Scanning Electron Microscopy</article-title>
            <source>Medical Molecular Morphology</source>
            <volume>56</volume>
            <pub-id pub-id-type="doi">10.1007/s00795-023-00356-x</pub-id>
            <pub-id pub-id-type="pmid">37165248</pub-id>
          </element-citation>
        </citation-alternatives>
      </ref>
      <ref id="B19">
        <label>19.</label>
        <citation-alternatives>
          <mixed-citation publication-type="other">Yokoyama, H., Okada, S., Yamada, Y., Kitamoto, K., Inaga, S., Nakane, H., <italic>et al</italic>. (2020) Low-Vacuum Scanning Electron Microscopy May Allow Early Diagnosis of Human Renal Transplant Antibody-Mediated Rejection. <italic>Biomedical</italic><italic>Research</italic>, 41, 81-90. https://doi.org/10.2220/biomedres.41.81 <pub-id pub-id-type="doi">10.2220/biomedres.41.81</pub-id><pub-id pub-id-type="pmid">32307401</pub-id><ext-link ext-link-type="uri" xlink:href="https://doi.org/10.2220/biomedres.41.81">https://doi.org/10.2220/biomedres.41.81</ext-link></mixed-citation>
          <element-citation publication-type="other">
            <person-group person-group-type="author">
              <string-name>Yokoyama, H.</string-name>
              <string-name>Okada, S.</string-name>
              <string-name>Yamada, Y.</string-name>
              <string-name>Kitamoto, K.</string-name>
              <string-name>Inaga, S.</string-name>
              <string-name>Nakane, H.</string-name>
            </person-group>
            <year>2020</year>
            <article-title>Low-Vacuum Scanning Electron Microscopy May Allow Early Diagnosis of Human Renal Transplant Antibody-Mediated Rejection</article-title>
            <source>Biomedical Research</source>
            <volume>41</volume>
            <pub-id pub-id-type="doi">10.2220/biomedres.41.81</pub-id>
            <pub-id pub-id-type="pmid">32307401</pub-id>
          </element-citation>
        </citation-alternatives>
      </ref>
      <ref id="B20">
        <label>20.</label>
        <citation-alternatives>
          <mixed-citation publication-type="other">Liu, J., Wang, M., Tulman, D., Mandava, S.H., Elfer, K.N., Gabrielson, A., <italic>et al</italic>. (2016) Nondestructive Diagnosis of Kidney Cancer on 18-Gauge Core Needle Renal Biopsy Using Dual-Color Fluorescence Structured Illumination Microscopy. <italic>Urology</italic>, 98, 195-199. https://doi.org/10.1016/j.urology.2016.08.036 <pub-id pub-id-type="doi">10.1016/j.urology.2016.08.036</pub-id><pub-id pub-id-type="pmid">27597632</pub-id><ext-link ext-link-type="uri" xlink:href="https://doi.org/10.1016/j.urology.2016.08.036">https://doi.org/10.1016/j.urology.2016.08.036</ext-link></mixed-citation>
          <element-citation publication-type="other">
            <person-group person-group-type="author">
              <string-name>Liu, J.</string-name>
              <string-name>Wang, M.</string-name>
              <string-name>Tulman, D.</string-name>
              <string-name>Mandava, S.H.</string-name>
              <string-name>Elfer, K.N.</string-name>
              <string-name>Gabrielson, A.</string-name>
            </person-group>
            <year>2016</year>
            <article-title>Nondestructive Diagnosis of Kidney Cancer on 18-Gauge Core Needle Renal Biopsy Using Dual-Color Fluorescence Structured Illumination Microscopy</article-title>
            <source>Urology</source>
            <volume>98</volume>
            <pub-id pub-id-type="doi">10.1016/j.urology.2016.08.036</pub-id>
            <pub-id pub-id-type="pmid">27597632</pub-id>
          </element-citation>
        </citation-alternatives>
      </ref>
      <ref id="B21">
        <label>21.</label>
        <citation-alternatives>
          <mixed-citation publication-type="other">Matsumoto, A., Matsui, I., Katsuma, Y., Yasuda, S., Shimada, K., Namba-Hamano, T., <italic>et al</italic>. (2021) Quantitative Analyses of Foot Processes, Mitochondria, and Basement Membranes by Structured Illumination. <italic>Kidney</italic><italic>Inter</italic><italic>national</italic><italic>Reports</italic>, 6, 1923-1938. https://doi.org/10.1016/j.ekir.2021.04.021 <pub-id pub-id-type="doi">10.1016/j.ekir.2021.04.021</pub-id><pub-id pub-id-type="pmid">34307987</pub-id><ext-link ext-link-type="uri" xlink:href="https://doi.org/10.1016/j.ekir.2021.04.021">https://doi.org/10.1016/j.ekir.2021.04.021</ext-link></mixed-citation>
          <element-citation publication-type="other">
            <person-group person-group-type="author">
              <string-name>Matsumoto, A.</string-name>
              <string-name>Matsui, I.</string-name>
              <string-name>Katsuma, Y.</string-name>
              <string-name>Yasuda, S.</string-name>
              <string-name>Shimada, K.</string-name>
              <string-name>Namba-Hamano, T.</string-name>
              <string-name>Processes, M</string-name>
            </person-group>
            <year>2021</year>
            <article-title>Quantitative Analyses of Foot Processes, Mitochondria, and Basement Membranes by Structured Illumination</article-title>
            <source>Kidney International Reports</source>
            <volume>6</volume>
            <pub-id pub-id-type="doi">10.1016/j.ekir.2021.04.021</pub-id>
            <pub-id pub-id-type="pmid">34307987</pub-id>
          </element-citation>
        </citation-alternatives>
      </ref>
      <ref id="B22">
        <label>22.</label>
        <citation-alternatives>
          <mixed-citation publication-type="other">Ranjit, S., Lanzanò, L., Libby, A.E., Gratton, E. and Levi, M. (2021) Advances in Fluorescence Microscopy Techniques to Study Kidney Function. <italic>Nature</italic><italic>Reviews</italic><italic>Nephrology</italic>, 17, 128-144. https://doi.org/10.1038/s41581-020-00337-8 <pub-id pub-id-type="doi">10.1038/s41581-020-00337-8</pub-id><pub-id pub-id-type="pmid">32948857</pub-id><ext-link ext-link-type="uri" xlink:href="https://doi.org/10.1038/s41581-020-00337-8">https://doi.org/10.1038/s41581-020-00337-8</ext-link></mixed-citation>
          <element-citation publication-type="other">
            <person-group person-group-type="author">
              <string-name>Ranjit, S.</string-name>
              <string-name>Libby, A.E.</string-name>
              <string-name>Gratton, E.</string-name>
              <string-name>Levi, M.</string-name>
            </person-group>
            <year>2021</year>
            <article-title>Advances in Fluorescence Microscopy Techniques to Study Kidney Function</article-title>
            <source>Nature Reviews Nephrology</source>
            <volume>17</volume>
            <pub-id pub-id-type="doi">10.1038/s41581-020-00337-8</pub-id>
            <pub-id pub-id-type="pmid">32948857</pub-id>
          </element-citation>
        </citation-alternatives>
      </ref>
      <ref id="B23">
        <label>23.</label>
        <citation-alternatives>
          <mixed-citation publication-type="other">Peti-Peterdi, J., Kidokoro, K. and Riquier-Brison, A. (2015) Novel <italic>in Vivo</italic> Techniques to Visualize Kidney Anatomy and Function. <italic>Kidney</italic><italic>Inter</italic><italic>national</italic>, 88, 44-51. https://doi.org/10.1038/ki.2015.65 <pub-id pub-id-type="doi">10.1038/ki.2015.65</pub-id><pub-id pub-id-type="pmid">25738253</pub-id><ext-link ext-link-type="uri" xlink:href="https://doi.org/10.1038/ki.2015.65">https://doi.org/10.1038/ki.2015.65</ext-link></mixed-citation>
          <element-citation publication-type="other">
            <person-group person-group-type="author">
              <string-name>Peti-Peterdi, J.</string-name>
              <string-name>Kidokoro, K.</string-name>
              <string-name>Riquier-Brison, A.</string-name>
            </person-group>
            <year>2015</year>
            <article-title>Novel in Vivo Techniques to Visualize Kidney Anatomy and Function</article-title>
            <source>Kidney International</source>
            <volume>88</volume>
            <pub-id pub-id-type="doi">10.1038/ki.2015.65</pub-id>
            <pub-id pub-id-type="pmid">25738253</pub-id>
          </element-citation>
        </citation-alternatives>
      </ref>
      <ref id="B24">
        <label>24.</label>
        <citation-alternatives>
          <mixed-citation publication-type="journal">Zhang, H., Huang, L., Yang, Y., Qiu, L., He, Q., Liu, J., <italic>et al</italic>. (2023) Evaluation of Early Diabetic Kidney Disease Using Ultrasound Localization Microscopy: A Feasibility Study. <italic>Journal</italic><italic>of</italic><italic>Ultrasound</italic><italic>in</italic><italic>Medicine</italic>, 42, 2277-2292. https://doi.org/10.1002/jum.16249 <pub-id pub-id-type="doi">10.1002/jum.16249</pub-id><pub-id pub-id-type="pmid">37146242</pub-id><ext-link ext-link-type="uri" xlink:href="https://doi.org/10.1002/jum.16249">https://doi.org/10.1002/jum.16249</ext-link></mixed-citation>
          <element-citation publication-type="journal">
            <person-group person-group-type="author">
              <string-name>Zhang, H.</string-name>
              <string-name>Huang, L.</string-name>
              <string-name>Yang, Y.</string-name>
              <string-name>Qiu, L.</string-name>
              <string-name>He, Q.</string-name>
              <string-name>Liu, J.</string-name>
            </person-group>
            <year>2023</year>
            <article-title>Evaluation of Early Diabetic Kidney Disease Using Ultrasound Localization Microscopy: A Feasibility Study</article-title>
            <source>Journal of Ultrasound in Medicine</source>
            <volume>42</volume>
            <pub-id pub-id-type="doi">10.1002/jum.16249</pub-id>
            <pub-id pub-id-type="pmid">37146242</pub-id>
          </element-citation>
        </citation-alternatives>
      </ref>
      <ref id="B25">
        <label>25.</label>
        <citation-alternatives>
          <mixed-citation publication-type="other">Foiret, J., Zhang, H., Ilovitsh, T., Mahakian, L., Tam, S. and Ferrara, K.W. (2017) Ultrasound Localization Microscopy to Image and Assess Microvasculature in a Rat Kidney. <italic>Scientific</italic><italic>Reports</italic>, 7, Article No. 13662. https://doi.org/10.1038/s41598-017-13676-7 <pub-id pub-id-type="doi">10.1038/s41598-017-13676-7</pub-id><pub-id pub-id-type="pmid">29057881</pub-id><ext-link ext-link-type="uri" xlink:href="https://doi.org/10.1038/s41598-017-13676-7">https://doi.org/10.1038/s41598-017-13676-7</ext-link></mixed-citation>
          <element-citation publication-type="other">
            <person-group person-group-type="author">
              <string-name>Foiret, J.</string-name>
              <string-name>Zhang, H.</string-name>
              <string-name>Ilovitsh, T.</string-name>
              <string-name>Mahakian, L.</string-name>
              <string-name>Tam, S.</string-name>
              <string-name>Ferrara, K.W.</string-name>
            </person-group>
            <year>2017</year>
            <article-title>Ultrasound Localization Microscopy to Image and Assess Microvasculature in a Rat Kidney</article-title>
            <source>Scientific Reports</source>
            <volume>7</volume>
            <elocation-id>No</elocation-id>
            <pub-id pub-id-type="doi">10.1038/s41598-017-13676-7</pub-id>
            <pub-id pub-id-type="pmid">29057881</pub-id>
          </element-citation>
        </citation-alternatives>
      </ref>
      <ref id="B26">
        <label>26.</label>
        <citation-alternatives>
          <mixed-citation publication-type="other">Bodard, S., Denis, L., Chabouh, G., Battaglia, J., Anglicheau, D., Hélénon, O., <italic>et al</italic>. (2024) Visualization of Renal Glomeruli in Human Native Kidneys with Sensing Ultrasound Localization Microscopy. <italic>Investigative</italic><italic>Radiology</italic>, 59, 561-568. https://doi.org/10.1097/rli.0000000000001061 <pub-id pub-id-type="doi">10.1097/rli.0000000000001061</pub-id><pub-id pub-id-type="pmid">38214557</pub-id><ext-link ext-link-type="uri" xlink:href="https://doi.org/10.1097/rli.0000000000001061">https://doi.org/10.1097/rli.0000000000001061</ext-link></mixed-citation>
          <element-citation publication-type="other">
            <person-group person-group-type="author">
              <string-name>Bodard, S.</string-name>
              <string-name>Denis, L.</string-name>
              <string-name>Chabouh, G.</string-name>
              <string-name>Battaglia, J.</string-name>
              <string-name>Anglicheau, D.</string-name>
            </person-group>
            <year>2024</year>
            <article-title>Visualization of Renal Glomeruli in Human Native Kidneys with Sensing Ultrasound Localization Microscopy</article-title>
            <source>Investigative Radiology</source>
            <volume>59</volume>
            <pub-id pub-id-type="doi">10.1097/rli.0000000000001061</pub-id>
            <pub-id pub-id-type="pmid">38214557</pub-id>
          </element-citation>
        </citation-alternatives>
      </ref>
      <ref id="B27">
        <label>27.</label>
        <citation-alternatives>
          <mixed-citation publication-type="other">Mammas, C.S., Lazaris, A., Kostopanagiotou, G., Lemonidou, C. and Patsouris, E. (2015) The Digital Microscopy in Organ Transplantation: Ergonomics of the Tele-pathological Evaluation of Renal and Liver Grafts. <italic>Studies in Health Technology and Informatics</italic>, 213, 287-290.</mixed-citation>
          <element-citation publication-type="other">
            <person-group person-group-type="author">
              <string-name>Mammas, C.S.</string-name>
              <string-name>Lazaris, A.</string-name>
              <string-name>Kostopanagiotou, G.</string-name>
              <string-name>Lemonidou, C.</string-name>
              <string-name>Patsouris, E.</string-name>
            </person-group>
            <year>2015</year>
            <article-title>The Digital Microscopy in Organ Transplantation: Ergonomics of the Tele-pathological Evaluation of Renal and Liver Grafts</article-title>
            <source>Studies in Health Technology and Informatics</source>
            <volume>213</volume>
          </element-citation>
        </citation-alternatives>
      </ref>
      <ref id="B28">
        <label>28.</label>
        <citation-alternatives>
          <mixed-citation publication-type="other">Zhang, J. and Zhang, A. (2023) Deep Learning-Based Multi-Model Approach on Electron Microscopy Image of Renal Biopsy Classification. <italic>BMC</italic><italic>Nephrology</italic>, 24, Article No. 132. https://doi.org/10.1186/s12882-023-03182-6 <pub-id pub-id-type="doi">10.1186/s12882-023-03182-6</pub-id><pub-id pub-id-type="pmid">37161367</pub-id><ext-link ext-link-type="uri" xlink:href="https://doi.org/10.1186/s12882-023-03182-6">https://doi.org/10.1186/s12882-023-03182-6</ext-link></mixed-citation>
          <element-citation publication-type="other">
            <person-group person-group-type="author">
              <string-name>Zhang, J.</string-name>
              <string-name>Zhang, A.</string-name>
            </person-group>
            <year>2023</year>
            <article-title>Deep Learning-Based Multi-Model Approach on Electron Microscopy Image of Renal Biopsy Classification</article-title>
            <source>BMC Nephrology</source>
            <volume>24</volume>
            <elocation-id>No</elocation-id>
            <pub-id pub-id-type="doi">10.1186/s12882-023-03182-6</pub-id>
            <pub-id pub-id-type="pmid">37161367</pub-id>
          </element-citation>
        </citation-alternatives>
      </ref>
      <ref id="B29">
        <label>29.</label>
        <citation-alternatives>
          <mixed-citation publication-type="other">Hacking, S. and Bijol, V. (2021) Deep Learning for the Classification of Medical Kidney Disease: A Pilot Study for Electron Microscopy. <italic>Ultrastructural</italic><italic>Pathology</italic>, 45, 118-127. https://doi.org/10.1080/01913123.2021.1882628 <pub-id pub-id-type="doi">10.1080/01913123.2021.1882628</pub-id><pub-id pub-id-type="pmid">33583322</pub-id><ext-link ext-link-type="uri" xlink:href="https://doi.org/10.1080/01913123.2021.1882628">https://doi.org/10.1080/01913123.2021.1882628</ext-link></mixed-citation>
          <element-citation publication-type="other">
            <person-group person-group-type="author">
              <string-name>Hacking, S.</string-name>
              <string-name>Bijol, V.</string-name>
            </person-group>
            <year>2021</year>
            <article-title>Deep Learning for the Classification of Medical Kidney Disease: A Pilot Study for Electron Microscopy</article-title>
            <source>Ultrastructural Pathology</source>
            <volume>45</volume>
            <pub-id pub-id-type="doi">10.1080/01913123.2021.1882628</pub-id>
            <pub-id pub-id-type="pmid">33583322</pub-id>
          </element-citation>
        </citation-alternatives>
      </ref>
    </ref-list>
  </back>
</article>