The American Journal of Pathology

BackgroundCOVID-19 disease, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has spread globally, and no proven treatments are available. Convalescent plasma therapy has been used with varying degrees of success to treat severe microbial infections for more than 100 years. MethodsPatients (n=25) with severe and/or life-threatening COVID-19 disease were enrolled at the Houston Methodist hospitals from March 28 - April 14, 2020. Patients were transfused with convalescent plasma obtained from donors with confirmed SARS-CoV-2 infection and had been symptom free for 14 days. The primary study outcome was safety, and the secondary outcome was clinical status at day 14 post-transfusion. Clinical improvement was assessed based on a modified World Health Organization 6-point ordinal scale and laboratory parameters. Viral genome sequencing was performed on donor and recipient strains. ResultsAt baseline, all patients were receiving supportive care, including anti-inflammatory and anti-viral treatments, and all patients were on oxygen support. At day 7 post-transfusion with convalescent plasma, nine patients had at least a 1-point improvement in clinical scale, and seven of those were discharged. By day 14 post-transfusion, 19 (76%) patients had at least a 1-point improvement in clinical status and 11 were discharged. No adverse events as a result of plasma transfusion were observed. The whole genome sequencing data did not identify a strain genotype-disease severity correlation. ConclusionsThe data indicate that administration of convalescent plasma is a safe treatment option for those with severe COVID-19 disease. Randomized, controlled trials are needed to determine its efficacy.

ObjectivesTo describe histopathologic findings in the placentas of women with COVID-19 during pregnancy. MethodsPregnant women with COVID-19 delivering between March 18, 2020 and May 5, 2020 were identified. Placentas were examined and compared to historical controls and women with placental evaluation for a history of melanoma. Results16 placentas from patients with SARS-CoV-2 were examined (15 with live birth in the 3rd trimester 1 delivered in the 2nd trimester after intrauterine fetal demise). Compared to controls, third trimester placentas were significantly more likely to show at least one feature of maternal vascular malperfusion (MVM), including abnormal or injured maternal vessels, as well as delayed villous maturation, chorangiosis, and intervillous thrombi. Rates of acute and chronic inflammation were not increased. The placenta from the patient with intrauterine fetal demise showed villous edema and a retroplacental hematoma. ConclusionsRelative to controls, COVID-19 placentas show increased prevalence of features of maternal vascular malperfusion (MVM), a pattern of placental injury reflecting abnormalities in oxygenation within the intervillous space associated with adverse perinatal outcomes. Only 1 COVID-19 patient was hypertensive despite the association of MVM with hypertensive disorders and preeclampsia. These changes may reflect a systemic inflammatory or hypercoagulable state influencing placental physiology. Key PointsO_LIThe placentas of women infected with SARS-CoV2 have higher rates maternal vascular malperfusion features compared to controls. C_LIO_LIMaternal vascular malperfusion has been associated with adverse perinatal outcomes, such as preeclampsia, fetal growth restriction, preterm birth, and stillbirth. C_LIO_LIAs the placentas of women with SARS-CoV2 show reproducible histopathologic abnormalities, these findings suggest increased antenatal surveillance for women with COVID-19 may be warranted. C_LI

BACKGROUNDSevere Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) and its associated clinical syndrome COVID-19 are causing overwhelming morbidity and mortality around the globe, disproportionately affecting New York City. A comprehensive, integrative autopsy series that advances the mechanistic discussion surrounding this disease process is still lacking. METHODSAutopsies were performed at the Mount Sinai Hospital on 67 COVID-19 positive patients and data from the clinical records were obtained from the Mount Sinai Data Warehouse. The experimental design included a comprehensive microscopic examination carried out by a team of expert pathologists, along with transmission electron microscopy, immunohistochemistry, RNA in situ hybridization, as well as immunology and serology assays. RESULTSLaboratory results of our COVID-19 cohort show elevated inflammatory markers, abnormal coagulation values, and elevated cytokines IL-6, IL-8 and TNF. Autopsies revealed large pulmonary emboli in four cases. We report microthrombi in multiple organ systems including the brain, as well as conspicuous hemophagocytosis and a secondary hemophagocytic lymphohistiocytosis-like syndrome in many of our patients. We provide electron microscopic, immunofluorescent and immunohistochemical evidence of the presence of the virus and the ACE2 receptor in our samples. CONCLUSIONSWe report a comprehensive autopsy series of 67 COVID-19 positive patients revealing that this disease, so far conceptualized as a primarily respiratory viral illness, also causes endothelial dysfunction, a hypercoagulable state, and an imbalance of both the innate and adaptive immune responses. Novel findings reported here include an endothelial phenotype of ACE2 in selected organs, which correlates with clotting abnormalities and thrombotic microangiopathy, addressing the prominent coagulopathy and neuropsychiatric symptoms. Another original observation is that of macrophage activation syndrome, with hemophagocytosis and a hemophagocytic lymphohistiocytosis-like disorder, underlying the microangiopathy and excessive cytokine release. We discuss the involvement of critical regulatory pathways.

The placenta undergoes morphological and functional adaptions to adverse exposures during pregnancy. The effects of suboptimal maternal body mass index (BMI), preterm birth, and infection on placental histopathological phenotypes remain unclear, despite the association between these conditions and poor offspring outcomes. We hypothesized that suboptimal maternal prepregnancy BMI and preterm birth (with and without infection) would associate with altered placental maturity and morphometry, and that altered placental maturity would associate with poor birth outcomes. Clinical data and human placentae were collected from 96 pregnancies where mothers were underweight, normal weight, overweight, or obese, without other major complications. Placental histopathological characteristics were scored with an anatomical pathologist. Associations between maternal BMI, placental pathology (immaturity and hypermaturity), placental morphometry, and infant outcomes were investigated at term and preterm, with and without infection. Fetal vascular endothelium volumetric proportion was decreased, whereas syncytial knot volumetric proportion was increased, in placentae from preterm pregnancies with chorioamnionitis compared to term placentae. At term and preterm, pregnancies with overweight and obesity had a high percentage increase in proportion of immature placentae compared to normal weight. Placental maturity did not associate with infant birth outcomes. We observed placental hypermaturity and altered placental morphometry among preterm pregnancies with chorioamnionitis, suggestive of altered placental development, which may inform about pregnancies susceptible to preterm birth and infection. Our data increase our understanding of how common metabolic exposures and preterm birth, in the absence of other comorbidities or perinatal events, potentially contribute to poor pregnancy outcomes and the programming of offspring development.

BackgroundThe novel coronavirus, SARS-CoV2 that causes COVID-19 has resulted in the death of more than 2.31 million people within the last year and yet no cure exists. Whereas passive immunization with COVID-19 convalescent plasma (CCP) provides a safe and viable option, selection of optimal units for therapy and lack of clear therapeutic benefit from transfusion remain as barriers to the use of CCP. Study design and methodsTo identify plasma that is expected to benefit recipients, we measured anti-SARS-CoV2 antibody levels using clinically available serological assays and correlated with the neutralizing activity of CCP from donors. Neutralizing titer of plasma samples was measured by assaying infectivity of SARS-CoV-2 spike protein pseudotyped retrovirus particles in the presence of dilutions of plasma samples. We also used this assay to identify evidence of passive transfusion of neutralizing activity in CCP recipients. ResultsViral neutralization and anti-spike protein antibodies in 109 samples from 87 plasma donors were highly varied but modestly correlated with each other. Recipients who died of COVID-19 were found to have been transfused with units with lower anti-spike antibody levels and neutralizing activity. Passive transfer of neutralization activity was documented in 62% of antibody naive plasma recipients. ConclusionsSince viral neutralization is the goal of CCP transfusion, our observations not only support the use of anti-spike SARS-CoV2 serology tests to identify beneficial CCP units, but also support the therapeutic value of convalescent plasma with high titers of anti-spike antibodies.

BACKGROUNDCirculating levels of proprotein convertase subtilisin/kexin type 9 (PCSK9), which regulates plasma cholesterol content by degrading LDL receptor, are correlated with the risk of acute myocardial infarction (AMI). Recent studies suggested that PCSK9 improves cardiac function beyond its effects on LDL cholesterol levels after cardiac ischemic injury, but its precise mechanism remains unclear. METHODSWe examined the interrelationship and functional significance of PCSK9 and cardiac myeloid cells in ischemic hearts from AMI-induced Pcsk9-/- and Lyz2crePcsk9fl/flmice, as well as in serum samples from coronary artery disease (CAD) patients treated with PCSK9 antibodies (Ab). Single-cell RNA sequencing (scRNA-seq) was conducted to identify heterogenous cardiac macrophage clusters and to investigate the impact of adaptive remodeling due to PCSK9 deficiency during AMI. Additionally, the regulatory effect of the myeloid-PCSK9/VEGF-C pathway was assessed in vitro as a potential therapeutic strategy. RESULTSOur study demonstrated that PCSK9 deficiency induces diverse changes in myeloid cells and macrophages, potentially offering cardiac protection following AMI, irrespective of LDL cholesterol homeostasis. The scRNA-seq identified a subset of PCSK9-dependent cardiac macrophages (PDCMs) enriched in activator protein-1 (AP-1)-related pathways, functioning as reparative macrophages. These PDCMs were shown to enhance vascular endothelial growth factor C (VEGF-C) secretion and activate Akt signaling in cardiac endothelial cells, leading to improved cardiac remodeling. Notably, CAD patients treated with PCSK9 inhibitors exhibited increased numbers of myeloid cells with PDCM-like features, including elevated VEGF-C levels, consistent with our findings in mice. COUNCLUSIONSTargeting PCSK9 in myeloid cells could offer cardioprotective effects by increasing AP-1 activity and VEGF-C expression of PDCMs, presenting a novel approach to preventing cardiac dysfunction in AMI. This strategy could expand the clinical use of existing PCSK9 inhibitors beyond just lowering LDL cholesterol. Clinical PerspectiveO_ST_ABSWhat is New?C_ST_ABSO_LIMyeloid-PCSK9 deficiency attenuated cardiac dysfunction post-acute myocardial infarction (AMI) without affecting plasma lipid levels. These findings position PCSK9 as a novel immune regulator of macrophages, revealing functions independent of its role in LDL cholesterol regulation. C_LIO_LIWe demonstrated PCSK9-dependent cardiac macrophages (PDCMs) that play a reparative role under ischemic conditions influenced by PCSK9, using single-cell RNA sequencing (scRNA-seq) of CD45+ leukocytes following AMI. C_LIO_LIStrong enrichment of AP-1 family proteins in PDCMs led to reparative VEGF-C signaling in endothelial cells and improved cardiac remodeling, independent of PCSK9s conventional role in cholesterol homeostasis. C_LIO_LIIn coronary artery disease (CAD) patients, PCSK9 inhibition augmented myeloid cell populations towards a reparative phenotype and elevated VEGF-C levels, aligning with our findings in mice. C_LI What Are the Clinical Implications?O_LIMyeloid-derived PCSK9 is pathobiologically significant, directly influencing immune functions and contributing to cardiac remodeling after AMI, suggesting that targeting myeloid-specific PCSK9 could be a valuable therapeutic approach. C_LIO_LIGiven that the reparative effects of PCSK9 inhibitors on macrophages are preserved in CAD patients, this strategy could broaden the clinical applications of existing PCSK9 inhibitors beyond LDL cholesterol regulation. C_LI

Computational analysis of histopathological specimens holds promise in identifying biomarkers, elucidating disease mechanisms, and streamlining clinical diagnosis. However, the application of deep learning techniques in vascular pathology remains underexplored. Here, we present a comprehensive evaluation of deep learning-based approaches to analyze digital whole-slide images of abdominal aortic aneurysm samples from 369 patients from three European centers. Deep learning demonstrated robust performance in predicting inflammatory characteristics, particularly in the adventitia, as well as fibrosis grade and remaining elastic fibers in the tunica media. Overall, this study represents the first comprehensive evaluation of computational pathology in vascular disease and has the potential to contribute to improved understanding of abdominal aortic aneurysm pathophysiology and personalization of treatment strategies, particularly when integrated with radiological phenotypes and clinical outcomes.

IntroductionAccurate intraoperative assessment of macrovesicular steatosis in donor liver biopsies is critical for transplantation decisions but is often limited by inter-observer variability and freezing artifacts that can obscure histological details. Artificial intelligence (AI) offers a potential solution for standardized and reproducible evaluation. To evaluate the diagnostic performance of two self-supervised learning (SSL)-based foundation models, Prov-GigaPath and UNI, for classifying macrovesicular steatosis in frozen liver biopsy sections, compared with assessments by surgical pathologists. MethodsWe retrospectively analyzed 131 frozen liver biopsy specimens from 68 donors collected between November 2022 and September 2024. Slides were digitized into whole-slide images, tiled into patches, and used to extract embeddings with Prov-GigaPath and UNI; slide-level classifiers were then trained and tested. Intraoperative diagnoses by on-call surgical pathologists were compared with ground truth determined from independent reviews of permanent sections by two liver pathologists. Accuracy was evaluated for both five-category classification and a clinically significant binary threshold (<30% vs. [&ge;]30%). ResultsFor binary classification, Prov-GigaPath achieved 96.4% accuracy, UNI 85.7%, and surgical pathologists 84.0% (P = .22). In five-category classification, accuracies were lower: Prov-GigaPath 57.1%, UNI 50.0%, and pathologists 58.7% (P = .70). Misclassification primarily occurred in intermediate categories (5%-<30% steatosis). ConclusionsSSL-based foundation models performed comparably to surgical pathologists in classifying macrovesicular steatosis, at the clinically relevant <30% vs. [&ge;]30% threshold. These findings support the potential role of AI in standardizing intraoperative evaluation of donor liver biopsies; however, the small sample size limits generalizability and requires validation in larger, balanced cohorts.

SARS-CoV-2 has rapidly spread across the United States, causing extensive morbidity and mortality, though the histopathologic basis of severe disease cases has yet to be studied in detail. Over the past century, autopsy has contributed significantly to our understanding of numerous disease processes, but for several reasons, autopsy reports following deaths related to SARS- CoV-2 have thus far been limited across the globe. We report on the relevant cardiopulmonary findings in the first series of autopsies in the United States, with the cause of death being due to SARS-CoV-2 infection. These cases identify key pathologic states potentially contributing to severe disease and decompensation in these patients.

A pathologist typically diagnoses tissue samples by examining glass slides under a light microscope. The entire tissue specimen can be stored digitally as a Whole Slide Image (WSI) for further analysis. However, managing and diagnosing large numbers of images manually is time-consuming and requires specialized expertise. Consequently, computer-aided diagnosis of these pathology images is an active research area, with deep learning showing promise in disease classification and cancer cell segmentation. Robust deep learning models need many annotated images, but public datasets are limited, often constrained to specific organs, cancer types, or binary classifications, which limits generalizability. To address this, we introduce the UCF multi-organ histopathologic (UCF-MultiOrgan-Path) dataset, containing 977 WSIs from cadaver tissues across 15 organ classes, including lung, kidney, liver, and pancreas. This dataset includes [~]2.38 million patches of 512x512 pixels. For technical validation, we provide patch-based and slide-based approaches for patch- and slide-level classification. Our dataset, containing millions of patches, can serve as a benchmark for training and validating deep learning models in multi-organ classification.

BackgroundMortality from preeclampsia (PE) and PE-associated morbidities are 3-to 5-fold higher in persons of African ancestry than in those of Asian and European ancestries. The placenta is central to the etiology of PE. However, how and to what extent the placenta contributes to worse PE outcomes in persons of African ancestry is yet to be fully elucidated. ObjectiveWe aimed to identify molecular pathways that are unique or enriched in placentas of parturient persons of African ancestry with PE with severe features (sPE) compared to those of Asian and European ancestry with sPE. Study designBulk RNA sequencing was performed on 50 placentas from parturient persons with sPE of African (n=9), Asian (n=18) and European (n=23) ancestries and 73 normotensive controls of African (n=9), Asian (n=15) and European (n=49) ancestries. ResultsMetabolism, hormone regulation and hypoxia/angiogenesis genes, previously described to be upregulated in PE, including: LEP, PAPPA2, INHA, FSTL3, FLT1, PHYHIP and ENG, were upregulated in sPE across ancestries, with high expression of FSTL3 being additionally associated with intrauterine growth restriction (p = .0047). Notably, the upregulation of, FLT1, LEP and PHYHIP was significantly higher in sPE placentas from parturient persons of African versus Asian ancestry (p = .0.35, .020 and .012 respectively). Genes associated with allograft rejection and adaptive immune response were upregulated in placentas from parturients of African ancestry but not in those of Asian and European ancestries. Among the allograft rejection/adaptive immune response genes, IL3RA was of particular interest because the patient with the highest placental IL3RA level, a woman of African ancestry with IL3RA levels 4.5-fold above the average for African ancestry parturients with sPE, developed postpartum cardiomyopathy, and was the only patient out of 123, that developed this condition. Interestingly, the sPE patients with the highest IL3RA levels among parturients of Asian and European ancestries developed unexplained tachycardia peripartum, necessitating echocardiography in the European ancestry patient. The association between elevated placental IL3RA levels and unexplained tachycardia or peripartum cardiomyopathy was found to be significant in the 50 sPE patients (p = .0005). ConclusionsPlacentas from parturients of African ancestry express higher levels of metabolism (LEP) and hypoxia/angiogenesis (FLT1) genes, as well as allograft rejection/adaptive immune response genes, including IL3RA. High placental expression of IL3RA may predict worse maternal cardiovascular outcomes, including peripartum cardiomyopathy. Studies evaluating placental IL3RA levels in peripartum cardiomyopathy cohorts are therefore warranted, as are broader studies evaluating placental factors in maternal cardiovascular outcomes postpartum.

Translocations involving FN1 have been described in a variety of neoplasms, which share the presence of cartilage matrix and a variable extent of calcification. Fusions of FN1 to FGFR1 or FGFR2 have been reported in nine soft tissue chondromas, mostly demonstrated indirectly by FISH analysis. Delineation of FN1 fusions with various partner genes will facilitate our understanding of the pathogenesis and diagnostic classification of these neoplasms. In this study, we present molecular, clinical and pathologic features of 9 cartilaginous soft tissue neoplasms showing a predilection for the TMJ region and the extremities. We analyzed for gene fusions with precise breakpoints using targeted RNA-seq with a 115-gene panel, including FN1, FGFR1 and FGFR2. All 9 cases were positive for a gene fusion, including two novel fusions, FN1-MERTK and FN1-TEK, each in one case, recurrent FN1-FGFR2 in 5 cases, FN1-FGFR1 without the Ig3 domain in one case, and FGFR1-PLAG1 in one case. The breakpoints in the 5 partner gene FN1 ranged from exons 11-48, retaining the domains of signal peptide, FN1, FN2, and/or FN3, while the 3partner genes retained the trans-membrane domain, tyrosine kinase domains and /or Ig domain. The tumors with FN1-FGFR1, FN1-FGFR2 and FN1-MERTK fusions are generally characterized by nodular/lobular growth of polygonal to stellate cells within a chondroid matrix, often accompanied by various patterns of calcification. These features resemble those as described for the chondroblastoma-like variant of soft tissue chondroma. Additional histologic findings include calcium pyrophosphate dehydrate deposition and features resembling tenosynovial giant cell tumor. Overall, while the tumors from our series show significant morphologic overlap with chondroblastoma-like soft tissue chondroma, we describe novel findings that expand the morphologic spectrum of these neoplasms and have therefore labeled them as "calcified chondroid mesenchymal neoplasms." These neoplasms represent a distinct pathologic entity given the presence of recurrent FN1-receptor tyrosine kinase fusions.

BackgroundConventional assessment of Focal Segmental Glomerulosclerosis and Minimal Change Disease focuses on the presence/extent of segmental (SS) and global (GS) glomerulosclerosis. While SS and GS represent ongoing and terminal process, encoded in non-SS/GS glomeruli is prognostic information that can be extracted before structural changes are visually discernable. This study applies computational image analysis to (a) automate the segmentation and classification of glomeruli into GS, SS and non-GS/SS, (b) extract subvisual pathomic characteristics from non-GS/SS glomeruli, and (c) assess their clinical relevance. MethodsLeveraging the NEPTUNE/CureGN Periodic acid Schiff-stained whole slide images, we (i) developed deep learning (DL) models for the segmentation and classification of glomeruli into GS, SS and non-GS/SS; (ii) compared the association with disease progression and proteinuria remission of DL-derived percent of GS and SS vs. human scoring; (iii) extracted pathomic features from non-GS/SS; (iv) assessed their prognostic value using ridge-penalized Cox regression, with pathomic features ranked by Maximum Relevance Minimum Redundancy algorithm; and (v) estimated associations between selected pathomic features and clinical outcomes using Cox proportional hazard models. ResultsAgreement between computer-aided and visual scoring was good for %GS (ICC = 0.889) and moderate for %SS (ICC = 0.592). The prognostic performance of Cox models of computer-aided visual scoring approaches was comparable (iAUCs 0.779 vs. 0.776 for disease progression and 0.811 vs. 0.817 for complete proteinuria remission, respectively). For non-GS/SS glomeruli, 3 and 4 pathomic features were selected and demonstrated modest prognostic performance for disease progression (iAUC = 0.684) and proteinuria remission (iAUC = 0.661), respectively. After adjusting for demographics, clinical characteristics, %GS and %SS, 2 pathomic features remained statistically significantly associated with proteinuria remission. ConclusionComputational pathology allows for automatic quantification of SS/GS glomeruli that is comparable to manual assessment for outcome prediction, and the uncovering of previously under-recognized clinically useful information from non-GS/SS glomeruli.

The diagnosis of myelodysplastic neoplasms (MDS) requires examination of the bone marrow for morphologic evidence of dysplasia. We sought to determine if a self-supervised learning (SSL) AI image analysis approach may be utilized to reliably distinguish MDS from its clinically relevant mimics using bone marrow biopsies (BMBx). Whole slide images (WSIs) of H&E- and reticulin-stained BMBx sections from 243 unique patients (89 MDS, 55 non-MDS cytopenic controls [NMCC], and 99 negative control [NC] cases) were segmented into tiles and analyzed. These tiles were then processed using the Barlow Twins SSL model to generate histomorphologic phenotype clusters (HPCs). Review of the HPCs revealed the clusters enriched in MDS captured known histopathologic features of MDS including hypercellularity, dysplastic and clustered megakaryocytes, increased immature hematopoietic cells, increased vascularity, fibrosis, and cell streaming patterns. Assessment of 95 MDS BMBx images from a second institution showed consistent HPC enrichment patterns, validating the robustness of the model. The trained ensemble model using H&E- and reticulin-stained slides distinguished MDS from NCs with an AUC of 0.89, and from age-matched, NMCCs with an AUC of 0.84. These findings demonstrate the potential of SSL approaches to capture diagnostically relevant morphologic patterns and to improve the reproducibility of MDS diagnosis.

Genetic variants of the SARS-CoV-2 virus have become of great interest worldwide because they have the potential to detrimentally alter the course of the SARS-CoV-2 pandemic, and disease in individual patients. We recently sequenced 20,453 SARS- CoV-2 genomes from patients with COVID-19 disease in metropolitan Houston (population 7 million), dating from March 2020 to early February 2021. We discovered that all major variants of concern or interest are circulating in the region. To follow up on this discovery, we analyzed 8,857 genome sequences from patients in eight Houston Methodist hospitals dispersed throughout the metroplex diagnosed from January 1, 2021 to March 7, 2021. This sample represents 94% of Houston Methodist cases and 4.8% of all reported cases in metropolitan Houston during this period. We discovered rapid, widespread, and preferential increase of the SARS-CoV-2 UK B.1.1.7 throughout the region. The estimated case doubling time in the Houston area is 6.9 days. None of the 648 UK B.1.1.7 samples identified had the E484K change in spike protein that can cause decreased recognition by antibodies.

Histological assessment is foundational to multi-omics studies of liver disease, yet conventional fibrosis staging lacks resolution, and quantitative metrics like collagen proportionate area (CPA) fail to capture tissue architecture. While recent AI-driven approaches offer improved precision, they are proprietary and not accessible to academic research. Here, we present a novel, interpretable AI-based framework for characterising liver fibrosis from picrosirius red (PSR)-stained slides. By identifying distinct data-driven collagen deposition phenotypes (CDPs) which capture distinct morphologies, our method substantially improves the sensitivity and specificity of downstream transcriptomic and proteomic analyses compared to CPA and traditional fibrosis scores. Pathway analysis reveals that CDPs 4 and 5 are associated with active extracellular matrix remodelling, while phenotype correlates highlight links to liver functional status. Importantly, we demonstrate that selected CDPs can predict clinical outcomes with similar accuracy to established fibrosis metrics. All models and tools are made freely available to support transparent and reproducible multi-omics pathology research. HighlightsO_LIWe present a set of data-driven collagen deposition phenotypes for analysing PSR-stained liver biopsies, offering a spatially informed alternative to conventional fibrosis staging and CPA available as open-source code. C_LIO_LIThe identified collagen deposition phenotypes enhance transcriptomic and proteomic signal detection, revealing active ECM remodelling and distinct functional tissue states. C_LIO_LISelected phenotypes predict clinical outcomes with performance comparable to fibrosis stage and CPA, highlighting their potential as candidate quantitative indicators of fibrosis severity. C_LI O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=98 SRC="FIGDIR/small/25334719v1_ufig1.gif" ALT="Figure 1"> View larger version (22K): org.highwire.dtl.DTLVardef@2d80aeorg.highwire.dtl.DTLVardef@15b5c1forg.highwire.dtl.DTLVardef@fd0a62org.highwire.dtl.DTLVardef@b4b0a4_HPS_FORMAT_FIGEXP M_FIG C_FIG

Assessment of interstitial fibrosis is essential in the diagnosis and prognosis of kidney diseases. However, current histologic scoring methods using trichrome-stained slides are limited by inter-observer variability and inconsistent stain reproducibility. To address these challenges, we developed DUET (DUal-mode Emission and Transmission) microscopy, a novel imaging platform that rapidly captures both brightfield and fluorescence images from H&E-stained slides to generate pixel-registered collagen images and virtual trichrome stains. In a cohort of 32 kidney transplant biopsies, four renal pathologists estimated the extent of interstitial fibrosis in real trichrome and DUET-derived virtual trichrome whole slide images, with the latter showing improved inter-pathologist agreement. A deep learning pipeline was trained to segment interstitial kidney regions from DUET-acquired images, enabling semi-automated computational fibrosis quantitation, which demonstrated a positive correlation with pathologists estimates of interstitial fibrosis. These findings highlight DUET as a rapid, cost-effective, and scalable alternative to traditional trichrome staining, offering both visual and computational advantages.

We sequenced the genomes of 5,085 SARS-CoV-2 strains causing two COVID-19 disease waves in metropolitan Houston, Texas, an ethnically diverse region with seven million residents. The genomes were from viruses recovered in the earliest recognized phase of the pandemic in Houston, and an ongoing massive second wave of infections. The virus was originally introduced into Houston many times independently. Virtually all strains in the second wave have a Gly614 amino acid replacement in the spike protein, a polymorphism that has been linked to increased transmission and infectivity. Patients infected with the Gly614 variant strains had significantly higher virus loads in the nasopharynx on initial diagnosis. We found little evidence of a significant relationship between virus genotypes and altered virulence, stressing the linkage between disease severity, underlying medical conditions, and host genetics. Some regions of the spike protein - the primary target of global vaccine efforts - are replete with amino acid replacements, perhaps indicating the action of selection. We exploited the genomic data to generate defined single amino acid replacements in the receptor binding domain of spike protein that, importantly, produced decreased recognition by the neutralizing monoclonal antibody CR30022. Our study is the first analysis of the molecular architecture of SARS-CoV-2 in two infection waves in a major metropolitan region. The findings will help us to understand the origin, composition, and trajectory of future infection waves, and the potential effect of the host immune response and therapeutic maneuvers on SARS-CoV-2 evolution. IMPORTANCEThere is concern about second and subsequent waves of COVID-19 caused by the SARS-CoV-2 coronavirus occurring in communities globally that had an initial disease wave. Metropolitan Houston, Texas, with a population of 7 million, is experiencing a massive second disease wave that began in late May 2020. To understand SARS-CoV-2 molecular population genomic architecture, evolution, and relationship between virus genotypes and patient features, we sequenced the genomes of 5,085 SARS-CoV-2 strains from these two waves. Our study provides the first molecular characterization of SARS-CoV-2 strains causing two distinct COVID-19 disease waves.

BackgroundThe current semi-qualitative methods used to score sclerosis and hyalinosis in arteries and arterioles in clinical practice are limited in standardization and reproducibility. We developed a computational pipeline designed to accurately and consistently quantify prognostic arterial and arteriolar characteristics in digital kidney biopsies of patients with focal segmental glomerulosclerosis (FSGS) and minimal change disease (MCD) through segmentation and pathomic feature extraction. MethodsWe utilized one trichrome-stained WSI from 225 participants in the NEPTUNE/CureGN studies, comprising 127 cases of focal segmental glomerulosclerosis (FSGS) and 98 cases of minimal change disease (MCD). We developed, validated, and quality-controlled deep learning models to segment muscular vessels and their internal compartments (lumen, intima, media, and hyalinosis), including (i) arcuate arteries, (ii) interlobular arteries, and (iii) arterioles with two muscle layers. Arterioles, interlobular, and arcuate arteries were visually scored for sclerosis and hyalinosis on a scale of 0 to 3. Area- and thickness-based pathomic feature extraction was performed on each compartment (lumen, intima, media, and hyalinosis) through radial sampling and ray casting. A correlation study was performed between pathomic and visual semiquantitative visual scores, and the association of both visual scores and pathomic features with disease progression (40% eGFR decline or renal failure) was assessed. Summary statistics (maximum, median, and 75th percentile) were computed for each WSI and analyzed using LASSO-regularized Cox proportional hazards models, adjusted for clinical and demographic factors. ResultsA total of 1,499 arterioles, 686 interlobular arteries, and 131 arcuate arteries were segmented. Statistically significant correlations were found between pathologists visual scores and the average intima-media thickness ratio (Spearman {rho} = 0.27, p < 0.001 for arterioles; {rho} = 0.69, p < 0.001 for interlobular arteries; and {rho} = 0.80, p < 0.001 for arcuate arteries) and arteriolar hyalinosis ({rho} = 0.46, p < 0.001). Incorporating pathomic features from trichrome-stained WSIs improved the prediction of disease progression, enhancing the concordance index from 0.70 to 0.75 in arterioles and from 0.69 to 0.74 in arcuate arteries, compared to using demographics and clinical characteristics alone. ConclusionOur computational approach offers a novel and reliable method for segmenting and analyzing the pathomic features of sclerosis and hylalinosis in arteries and arterioles. This technique has demonstrated potential as a valuable tool for enhancing the clinical assessment performed by pathologists. Key PointsO_LIA computational pipeline was developed and validated to segment arteries and arterioles and to quantify lumen, intima, media, and hyalinosis in kidney biopsies from patients with FSGS and MCD. C_LIO_LIPathomic features, such as intima-media thickness ratio and hyalinosis area, significantly correlated with pathologists semi-quantitative sclerosis and hyalinosis scores. C_LIO_LIIntegrating pathomic features into clinical models improved disease progression prediction accuracy C_LI

Cutaneous squamous cell carcinoma (cSCC) poses significant clinical challenges due to its rising incidence and potential for metastasis. Histopathologic risk stratification is further limited by substantial inter-observer variability. Unsupervised AI approaches based on content-based image retrieval offer scalable and interpretable decision support for diagnostic pathology. The objective of this study was to evaluate the use of image retrieval within histopathology atlases to stratify cSCC tumor differentiation from whole-slide images (WSIs), while comparing different patch selection and feature extraction strategies. This retrospective study included 552 archived WSIs comprising 385 well-differentiated, 102 moderately differentiated, and 66 poorly differentiated cases collected across Mayo Clinic sites in Arizona, Florida, and Minnesota. Image atlases were constructed using multiple patch aggregation strategies (Mosaic, Collage, and Montage) and deep learning encoders (KimiaNet, PathDino, and H-Optimus-0). A leave-one-WSI-out evaluation framework was used to assess differentiation classification performance using accuracy, specificity, sensitivity, and F1 score. Mosaic combined with KimiaNet achieved the highest Top-1 accuracy (74.9%) and specificity (92.6%), while Mosaic with H-Optimus-0 yielded the best Top-5 accuracy (79.0%) and macro-F1 score (62.6%). Collage combined with KimiaNet produced the highest Top-5 specificity (99.5%). The generalizability of the evaluated AI models varied across hospitals, reflecting differences in imaging protocols, staining practices, and patient populations. Overall, unsupervised image search and retrieval provides effective, annotation-free support for cSCC differentiation and has the potential to enhance dermatopathology workflows when appropriate combinations of patch selection and feature ex-traction methods are employed.