The American Journal of Pathology

Background: General-purpose large language models (LLMs) are increasingly evaluated in diagnostic pathology, but prior studies have largely emphasized diagnostic accuracy rather than how models fail. We evaluated four LLMs for diagnostic performance, pathology-relevant errors and hallucinations, their burden, and potential clinical impact across multi-organ pathology cases. Design: In this retrospective multicenter study, 153 pathology cases from two institutions spanning 20 organs were evaluated using ChatGPT-5.3 (LLM1), Gemini 3 (LLM2), Grok 4.20 (LLM3), and Claude Opus 4.6 (LLM4). Each LLM received multi-magnification histologic images with clinical context and generated a microscopic description and diagnosis. No data splitting, training, or fine-tuning was performed. Twenty-one pathologists assessed 612 outputs for diagnostic correctness, error and hallucination type and burden, clinical impact (0-4), and overall performance (1-5). Results: Strict diagnostic accuracy was 48.9% overall (60.1% including partially correct diagnoses) and ranged from 36.6% to 58.2% across LLMs. Errors occurred in 82.0% of outputs and hallucinations in 76.6%; 90.2% contained at least one error or hallucination. Misinterpretation was the most frequent error (72.1%), while fabricated histologic features were the dominant hallucination type (74.5%). LLM2 had significantly lower misinterpretation rates than the other three LLMs, while LLM3 had significantly higher fabricated-feature hallucination rates than LLM1 and LLM2. Errors were more frequent in incorrect than correct diagnoses (97.5% vs 66.2%), as were hallucinations (95.1% vs 59.9%; both p < 0.001). Every strictly incorrect diagnosis contained an error and/or hallucination, while 79.9% of strictly correct diagnoses also contained at least one. In multivariable analysis, LLM3 was independently associated with higher odds of an incorrect diagnosis. Diagnostic correctness was the strongest determinant of high clinical impact; each one-point increase in hallucination burden increased the odds by 63% (adjusted OR, 1.63). Conclusion: Diagnostic accuracy alone substantially underestimates the safety limitations of general-purpose LLMs in histopathology. Errors and hallucinations were common even when the final diagnosis was correct, and their burden was independently associated with clinical impact. Evaluation frameworks should therefore assess both diagnostic correctness and the reliability and potential consequences of accompanying generated content.

Pediatric endoscopy is performed only when clinically indicated, limiting access to healthy duodenal tissue. Biopsies with duodenal no pathologic abnormality (NPA) are often used as controls despite the presence of symptoms or inflammatory disease found elsewhere in the gastrointestinal (GI) tract. We characterized pediatric duodenal NPA tissue across clinical, histologic, cellular, and transcriptomic domains. Methods Archival duodenal NPA biopsies were obtained with clinical metadata and hematoxylin-and-eosin whole-slide images (WSIs). Duodenal mRNA-seq data were analyzed from a subset of patients with duodenal NPA. Clinical metadata and WSIs underwent machine-learning analysis, cell populations were quantified from WSIs, and RNA-seq data underwent differential expression and pathway-enrichment analyses. Results The primary cohort included 195 patients with duodenal NPA. Comparisons between patients with non-duodenal GI disease and those with no GI disease showed differences in inflammatory biomarkers and follow-up utilization. Unsupervised clinical clustering identified three clusters with partial enrichment for IBD with colonic inflammation and Eosinophilic Esophagitis (EoE) with esophageal inflammation. Supervised clinical classification showed modest discrimination. WSI clustering showed limited disease-status discrimination, and cell quantification showed no significant group differences. In the separate RNA-seq cohort of 43 patients, differential-expression and pathway-enrichment analyses identified transcriptional and pathway-level differences between disease-status groups. Conclusions This multi-level characterization indicates that pediatric duodenal NPA tissue should not be treated as a uniform control category. Clinical metadata and transcriptomics revealed clinical and molecular heterogeneity, while histologic and cell analyses showed limited disease-status separation, supporting a refined definition of control tissue.

Current state of the art computational pathology foundation models attain satisfying accuracy most general pathology tasks including detection of pancreatic ductal adenocarcinoma (PDAC) but fail to provide sufficient explainability, operating as "black boxes". Some interpretability effort has been made, with models providing attention maps that project back on the slide the models high attended areas that pushed for the given prediction result, without further naming or reasoning explanation which can limit pathologists trust toward the prediction. We developed C3PRO, a cell-centric framework attempting to provide deeper understanding and explainability, by using embeddings extracted using a pathology foundation model, from cell centered tiles containing both the cell and its close neighborhood, which were then clustered into recurrent morphological phenotypes. A spatial graph was additionally constructed on top of the back-projected vocabulary to account for recurrent motifs. The evaluation of the model performance involved a cohort of 809 patients (825 diagnostic biopsies) using nested cross validation. C3PRO was benchmarked against four different multiple instance learning frameworks and achieved on-par performance for diagnostic (0.958 AUC), survival (0.617) and resectability (0.696) prediction, while providing additional insights compared to the strongest baseline models. Each clustered phenotype was probed and extracted as a representative tile mosaic and annotated by two expert pathologists, constituting a phenotype vocabulary available for statistical analysis. These phenotypes were scored by pathologists with physical brightfield microscopy and replicated the independent statistical significance impact on overall survival prediction from diagnostic biopsies.

Stem cell-derived (SC) islets hold promise as a transformative therapy for type 1 diabetes (T1D). To enhance graft uniformity, SC-islet organoids are often dissociated and reaggregated into standardized sizes. However, how endocrine cell composition varies with islet size in native human islets remains largely unknown. Defining this relationship is essential for establishing physiological benchmarks for islet engineering for transplantation. In this study, we examined the relationships among islet size, endocrine cell composition, and spatial distribution throughout whole pancreata from 10 age-matched, non-diabetic male subjects. Insulin-, glucagon-, and somatostatin-positive endocrine cells were identified as single-, double-, and triple-hormone populations ranging from non-islet single endocrine cells and small clusters (niSSCs) to large islets. Approximately 80% of the total endocrine area was contained in islets positive for all three hormones. Islet size strongly correlated with endocrine composition. Specifically, larger islets had increased alpha-cell fractions and reduced beta-cell fractions, while delta-cell fractions were consistent across sizes. Spatial analysis revealed that niSSCs, mono-, and tri-hormone islets frequently exhibit a clustered distribution. Additionally, tri-hormone islets were positioned near the core of the pancreas, whereas mono-somatostatin islets were often localized in the periphery. These findings were consistent across all three pancreatic regions (head, body and tail).

Achromatopsia (ACHM) is a rare autosomal recessive retinal disorder characterized by absent cone photoreceptor function from early life, leading to severe visual impairment. Mutations in genes involved in the cone phototransduction cascade frequently result in elevated cyclic guanosine monophosphate (cGMP) levels and activation of stress pathways, including endoplasmic reticulum (ER) stress and the unfolded protein response. Targeting common downstream mechanisms rather than individual mutations may provide a broadly applicable therapeutic strategy. Here, we investigated whether pharmacological inhibition of valosin-containing protein (VCP), a key regulator of ER and protein homeostasis, can prevent cone degeneration in the spontaneous cone photoreceptor function loss 1 (cpfl1) mouse model of ACHM. Organotypic culture of retinal explants from cpfl1 mice were treated with the selective VCP inhibitor ML240. Cone survival, cell death, opsin expression and localization were assessed by TUNEL assay, immunohistochemistry, and quantitative image analysis. ML240 treatment significantly increased cone density and improved cone opsin expression and trafficking to the outer segments (OSs) in cpfl1 explants compared to controls. Importantly, rhodopsin trafficking in rod photoreceptors was unaffected, indicating that VCP inhibition did not impair normal rod phototransduction. These findings demonstrate that VCP inhibition by ML240 effectively preserves cone photoreceptors and improves cone-specific functional markers in the cpfl1 model. Targeting VCP may represent a mutation-independent therapeutic strategy for preventing cone death in ACHM.

Kidney disease affects over 850 million people and leads to degradation of the kidney cortical compartments and kidney failure. Interstitial fibrosis and tubular atrophy (IFTA) is a universal feature of chronic kidney disease, yet the sequence of tubulointerstitial matrix protein (matrisome) changes that underlie the development and progression of IFTA are not fully understood. In particular, post-translational modifications which may stabilize key matrisome components and result in irreversible and functionally detrimental interstitial fibrosis or tubular basement membrane thickening, have yet to be characterized. Post-translational modifications (e.g., acetylation) have been shown to contribute to kidney diseases, yet they have not been investigated in chronic kidney disease progression. Herein, we established feasibility of matrisome protein quantification in the tubulointerstitium, with concurrent matrisome protein post-translational modification quantification, in archived diagnostic human kidney biopsies, using laser capture microdissection, liquid chromatography tandem mass spectrometry (LC-MS/MS), and bioinformatics analysis. In this pilot study, we focused on two conditions associated with IFTA progression: diabetic nephropathy and kidney transplantation. We examined both healthy and chronically damaged tubulointerstitium in two human kidney biopsies, one representing each condition. We identified signature protein markers of tubulointerstitium (e.g., UMOD, SLC12A1) in abundance, confirming sample composition, and observed uniformity in protein abundance across replicates from the same biopsies. We found increases in certain collagen isoforms (COL1A1, COL6A3) and other ancillary proteins in areas of IFTA. We also successfully quantified post-translational modifications, confirming that hydroxylproline was the most abundant modification in healthy tubulointerstitium from the kidney transplant biopsy, and that hydroxyllysine was also highly abundant and could be confidently quantitated on two collagen isoforms (COL1A1, COL4A2). Lastly, we performed power analysis to determine the required sample size to achieve significant power and false discovery rate. Several proteins exhibited large Hedge effect sizes in the tubulointerstitium (ANPEP, PRODH2), indicating their potential usefulness as fibrosis signatures. Our study is a first step toward comprehensive characterization of tubulointerstitial matrisome changes, including post-translational modifications seen with IFTA progression. Subsequent studies will potentially reveal new molecular markers of key early events in chronic kidney disease progression, and new therapeutic targets for mitigating this process. Data are available via ProteomeXchange identifier PXD079025.

Background. Activation of de novo lipogenesis (DNL) is a major hallmark of steatotic liver disease (SLD), a highly prevalent group of diseases ranging from a harmless steatosis to more severe steatohepatitis, cirrhosis, and hepatocellular carcinoma. We interrogated herein the early alterations in liver proteome, transcriptome, lipidome, inflammation, fibrosis, and oxidative status in a mouse model of progressive SLD induced by constitutive activation of PI3K-AKT signaling, the major inducer of DNL. Methods. Male, 8 weeks-old mice bearing phosphatase and tensin homolog (Pten) deletion and therefore constitutive activation of PI3K-AKT in hepatocytes (albumin-Cre) and littermate controls were evaluated for liver steatosis, injury, lipid peroxidation, metabolism, proteome, lipidome and transcriptome either upon feeding with a chow or high-fat diets supplemented or not with linoleic acid or after treatment with vehicle or acetyl-CoA carboxylase (ACC) inhibitor (ND-630, 20 mg/kg/day, i.p.) Results. Mice bearing Pten deletion and therefore constitutive activation of PI3K-AKT in hepatocytes display steatohepatitis characterized by enhanced DNL, severe steatosis, hepatocyte ballooning, inflammation, fibrosis and hepatomegaly, which, unexpectedly, was associated with reduced lipid peroxidation, increased GSH content and enhanced rates of triacylglycerol turnover, fatty acid {beta}-oxidation, tricarboxylic acid cycle flux, and mitochondrial respiration, altogether promoting a robust lipidome remodeling mainly defined by enrichment of DNL-derived fatty acids in detriment of a broad depletion of essential fatty acids (linoleic and -linolenic acids) and cardiolipins. Dietary restoration of liver linoleic acid content mildly attenuated liver inflammation, without however affecting lipid peroxidation and improving steatosis and fibrosis, whereas pharmacological acetyl-CoA carboxylase (ACC) and DNL inhibition markedly reduced steatosis, inflammation and fibrosis, despite further lowering hepatic linoleic acid content. Conclusions. Collectively, these findings support a harmful role of essential fatty acids depletion fomenting liver inflammation and identify DNL as a major culprit of steatohepatitis induced by constitutive PI3K-AKT activation.

PURPOSEPancreatic ductal adenocarcinoma (PDAC) is a deadly malignancy with poor prognosis. A transcriptomic-based algorithm called Purity Independent Subtyping of Tumors (PurIST) relies on expression profiling of a defined set of genes to stratify PDAC into classical and basal-like molecular subtypes and assigns a confidence level to the subtypes as either "strong," "likely," or "lean." In this study, we apply PurIST to Appalachian and non-Appalachian patient cohorts and assess demographic variables between groups that may influence treatment outcomes. PATIENTS AND METHODSThis 13-site study includes 780 PDAC patients. Patients were divided into Appalachian and non-Appalachian cohorts according to Appalachian Regional Commission guidelines, and differences in PurIST classification, tobacco use, and alcohol use were analyzed. RESULTSIndividuals from Appalachian regions were more likely to receive a subtype classification from PurIST with "strong" confidence as opposed to "likely" or "lean" (P=0.0175; odds ratio=1.66). We did not observe any significant difference between basal and classical subtype classification in Appalachia (P=0.1457), yet an exploratory analysis showed that individuals aged [&ge;]85 were significantly more likely to have basal-like tumor classifications (P=0.005817; odds ratio=3.79). Appalachian patients were more likely to have a history of smoking (59.6% vs 49.9%; P=0.01756) and less likely to report alcohol consumption (50.5% vs 65.6%; P=0.000229). CONCLUSIONWe conducted the largest comparison of the PurIST algorithm in Appalachia to date and found PurIST is more likely to have higher confidence in molecular subtype classifications for Appalachian PDAC tumors compared to non-Appalachian tumors. If PurIST is to be considered as a prognostic or decision-making tool for PDAC patients, we must account for variability in different populations and model the likelihood that tumors will have a strong molecular signature.

Introduction: Adipose tissue fibrosis has been associated with metabolic dysfunction-associated steatotic liver disease (MASLD) and suboptimal weight loss after bariatric surgery. The objective was to characterize changes in adipose tissue fibrosis and extracellular matrix (ECM) components in adipose tissues from two abdominal fat compartments, subcutaneous and omental, in response to bariatric surgery; and determine the effect of weight loss on the association between adipose tissue fibrosis and MASLD feature changes with weight loss. Methods: Liver and adipose tissue biopsy samples were obtained before and after weight loss from patients who underwent two-stage bariatric surgery. Adipose tissue pericellular fibrosis was evaluated histologically with picrosirius red staining for collagen quantification and normalized to adipocyte count per microscope field. Transcript levels of ECM remodeling and inflammatory genes were measured by RNA sequencing in subcutaneous adipose tissue pre- and post-surgery. Results: At baseline, adipose tissue pericellular fibrosis did not associate with MASLD features, metabolic dysfunction-associated steatohepatitis or hepatic fibrosis in patients with severe obesity. Changes in liver histology after surgery were unrelated to changes in adipose tissue fibrosis. Transcripts for most genes involved in ECM remodeling and inflammation were downregulated in subcutaneous fat one year after bariatric surgery. Omental, but not subcutaneous, adipose tissue fibrosis decreased after surgery when adjusted for cell number (mean {+/-} SD: 0.41 {+/-} 0.12 vs 0.32 {+/-} 0.08 {micro}m2/cell, p<0.005). Higher subcutaneous adipose tissue fibrosis correlated with reduced weight loss (estimate {+/-} SE: -2.04 {+/-} 0.99, p<0.05) and BMI loss (-0.73 {+/-} 0.35, p<0.05), while increased omental adipose tissue fibrosis after weight loss was associated with worsening hepatic fibrosis (0.31 {+/-} 0.14, p<0.05). Conclusion: In individuals with severe obesity undergoing bariatric surgery, adipose tissue fibrosis is poorly correlated with MASLD features. Notably, pericellular fibrosis in subcutaneous abdominal, but not omental, adipose tissue was associated with a reduced weight-loss response to surgery.

The prognostic relevance of peritumoral SULF1 transcript signal in oral squamous cell carcinoma (OSCC) is unclear. We assembled a retrospective cohort of 133 patients with 218 primary-site tissue-microarray cores; 131 patients had evaluable boundary features. Multiplex fluorescence imaging combined RNAscope SULF1, CK-pan, and DAPI. Tumor-stroma boundaries were annotated with the SULF1 channel hidden. We screened 50 exported boundary features across cumulative stromal bands of 35-300 um against recurrence-free survival (RFS), overall survival (OS), and disease-specific survival (DSS), with false-discovery-rate correction. The strongest DSS association was signal SD within 150 um (n = 107; 23 disease-specific deaths; HR = 2.20 per SD; 95% CI, 1.64-2.94; q = 2.1 x 10^-6). In 50 repeated 5-fold cross-validations with feature selection repeated within training folds, adding a boundary feature to AJCC stage increased mean held-out DSS c-index from 0.613 to 0.754 (delta C = +0.141; 2.5th-97.5th percentiles across repeats, +0.105 to +0.192). These percentiles describe split variability, not confidence intervals. The 150-um SD feature was selected in 39.2% of DSS training folds. The DSS association persisted after adjustment for mean signal, although the measurements were strongly correlated. Peritumoral SULF1 signal dispersion was associated with outcome beyond an ordinal AJCC-stage model; independent validation is required.

Background: Transcardial perfusion fixation is widely used to preserve rodent brains for histological and ultrastructural analysis, but protocols can vary in the use of pre-fixation washout, fixative formulation, and osmotic additives. Relatively few studies have directly compared how the chemical composition of the perfusate affects tissue preservation. Methods: Male Sprague-Dawley rats underwent transcardial perfusion using a series of aldehyde-based fixative formulations that varied in fixative composition, use of a phosphate buffered saline washout, and the addition of mannitol or polyethylene glycol 35 kDa (PEG35). Perfusion outcomes were assessed using gross brain morphology, semi-quantitative grading of vascular blood clearance, quantitative detection of residual erythrocytes in whole-slide histological images, and light microscopic measures of cellular visualization and morphology. Perfusate osmolality was measured, and selected specimens were examined by electron microscopy. Results: Substantial vascular blood clearance was frequently achieved with fixative-only perfusion, indicating that a pre-fixation washout was not required to achieve high levels of blood clearance under the conditions tested. Routine light microscopy-based measures of cellular visibility and morphology were broadly similar across treatment groups. Adding mannitol and PEG35 to the fixative solution both led to concentration-dependent gross tissue shrinkage, particularly with PEG35, without correspondingly large or consistent changes on light microscopy. A substantial degree of case-to-case variation in perfusion quality was also observed among animals undergoing nominally similar procedures. Conclusions: Substantial variation in perfusate composition produced relatively modest differences in the light microscopy outcomes examined, despite pronounced effects of osmotic additives on gross brain morphology. There were some cases where perfusion with fixative alone led to near-complete blood vessel clearance, and the use of a preceding phosphate buffered saline washout was not associated with higher vascular clearance scores. The degree of case-to-case variability observed within treatment conditions suggests that procedural factors can play an important role on perfusion quality in addition to perfusate composition.

Introduction Timely diagnosis of kidney allograft rejection is critical for post-transplant management, yet treatment decisions may need to be made empirically while awaiting the final pathology report. We previously demonstrated that biopsy transport medium (BTM) contains molecular signals reflective of allograft rejection. Building on these findings, we developed a rapid BTM qPCR workflow using a targeted gene panel for early molecular assessment of rejection. Methods Kidney transplant biopsies assessed at Leiden University Medical Center from April 2023 to November 2024 were eligible. A predefined gene panel was quantified in BTM by qPCR, while matched formalin-fixed paraffin-embedded tissues were profiled using Tempo-Seq. Elastic Net, Random Forest, and support vector machine models were developed to classify rejection and predict continuous histological indices directly from the BTM. Results A total of 134 paired BTM samples and biopsy tissues were analyzed. The best-performing BTM gene-panel models demonstrated good discrimination for rejection, with a median AUC of 0.80, and good calibration (median slope, 0.92; median intercept, 0.08). For continuous histological indices, the best-performing BTM gene-panel models achieved R square values of 0.33 for Activity, 0.31 for TCMR/TI, and 0.27 for AMR/MVI. Corresponding biopsy gene-panel models showed similar patterns across diagnostic outcomes. Conclusions The BTM qPCR workflow captures molecular features of kidney allograft rejection and enables prediction of rejection status along the spectrum of continuous activity indices. By providing same-day molecular information while definitive biopsy interpretation is pending, this rapid approach has the potential to complement conventional histopathological diagnostics and support early clinical decision-making.

Background: Murine models are widely used to investigate cardiovascular disease (CVD), yet many established models rely on invasive surgery or genetic manipulation to induce hyperlipidemia and therefore may not fully capture the gradual cardiovascular consequences of mutant hematopoiesis. Tie2-Cre/-FF1/- (Tie2FF1) mice express JAK2V617F mutation in both hematopoietic and endothelial cells and develop spontaneous CVD on a standard chow diet. Methods: We applied a comprehensive, non-invasive cardiovascular phenotyping protocol to 1-year-old Tie2FF1 mice and age-matched controls. Assessments included transthoracic echocardiography, coronary flow reserve by Doppler ultrasound, quantitative analysis of coronary arteriolar structure and perivascular fibrosis, endocardial histology and subendocardial collagen deposition, and histologic evaluation of pulmonary and hepatic congestion. Results: Tie2FF1 mice demonstrated left ventricular dilation, increased cardiac mass, and mildly reduced systolic function without obstructive epicardial coronary disease. Coronary microvascular remodeling was evident by arteriolar narrowing and increased perivascular fibrosis, while coronary flow reserve was markedly reduced in an exploratory cohort. Endocardial endothelial disruption and increased subendocardial fibrosis were also observed. In addition, Tie2FF1 mice showed increased lung and liver weights, pulmonary fluid accumulation, alveolar septal thickening, pulmonary vascular fibrosis, hepatic sinusoidal dilatation, and centrilobular hepatocyte swelling, providing objective evidence of clinically relevant heart failure. Significance: These findings establish Tie2FF1 mice as a spontaneous, non-invasive model with features of coronary microvascular and endocardial endothelial dysfunction and heart failure with mildly reduced left ventricular ejection fraction. The integrated approach described here provides a practical framework for comprehensive murine cardiovascular phenotyping beyond traditional atherosclerosis- and surgery-based models.

Background: Machine Learning (ML) has shown promise in histopathological diagnosis, but rare tumours, like duodenal epithelial neoplasms, are challenging because large, labelled datasets are difficult to assemble. We propose a data-efficient ML pipeline for detecting duodenal epithelial neoplasia in whole-slide images. Methods: We assembled a five-hospital dataset of haematoxylin and eosin-stained duodenal biopsy whole slide images (WSIs) at 10x magnification, comprising 8,027 normal and 401 neoplastic biopsies, including adenomas, carcinomas and neuroendocrine tumours. WSIs were divided into patches and encoded using the Hibou-B foundation model. We compared global standardisation with unsupervised site-specific standardisation of patch embeddings. Our pipeline combined principal component analysis, a Gaussian mixture model and a Random Forest classifier. We assessed generalisability using leave-one-hospital-out cross-validation across four hospitals and on an independent test set from a fifth hospital. Results: In leave-one-hospital-out cross-validation, the pipeline achieved a mean accuracy of 90%, sensitivity of 96%, specificity of 83% and an AUC of 0.96. On the external test hospital, it achieved 94% accuracy, 93% sensitivity, 95% specificity, and AUC of 0.97. Site-specific standardisation improved mean cross-validation accuracy by 20 percentage points compared with global standardisation, while the Gaussian mixture pipeline improved accuracy by 12 percentage points compared with mean pooling of patch embeddings. Conclusions: We present a data-efficient ML approach for detecting duodenal epithelial neoplasia from WSIs. Strong performance was maintained across hospitals and on an independent external test cohort despite the limited number of neoplastic training cases, supporting the feasibility of foundation-model-based approaches for rare histopathological diagnoses.

Chronic alcohol consumption is associated with increased breast cancer risk; however, the long-term effects of alcohol exposure on DNA damage response (DDR) signaling and therapeutic vulnerability in breast epithelial cells remain incompletely understood. In the present study, we established a chronic alcohol exposure model using immortalized human breast epithelial MCF-10A cells to investigate persistent alterations in DNA damage signaling and sensitivity to poly (ADP-ribose) polymerase (PARP) inhibition. MCF-10A cells that chronically exposed to 0.2% alcohol for 20 weeks and then withdrew alcohol treatment for 4 weeks still retained elevated {gamma}H2AX levels and sustained activation of ATM/ATR-associated DDR pathways. Chronic alcohol-exposed cells also exhibited increased sensitivity to the PARP inhibitors olaparib and veliparib, as demonstrated by reduced cell viability, decreased clonogenic survival, and enhanced apoptosis. Mechanistically, olaparib induced greater G2/M checkpoint activation and replication-associated DNA damage in chronic alcohol-exposed cells compared with control cells. In addition, chronic alcohol exposure increased PARP expression and enhanced activation of DDR signaling following PARP inhibition. These findings demonstrate that chronic alcohol exposure induces persistent DNA repair stress and sensitizes breast epithelial cells to PARP inhibition. Our study suggests that chronic alcohol-associated genomic stress may create potential vulnerability to PARP inhibition.

Cervical cytology screening relies on the visual assessment of nuclear and cellular morphology across thousands of cells per slide, a labor-intensive task that could benefit from quantitative computational support. Here we present CytoLens, a modular deep learning system that detects and counts nuclei, delineates nuclei and whole cells, measures the nuclear-to-cell area ratio (N:C), classifies cells from multifocal images and scores candidate abnormality. We trained and tested each module on a public dataset matched to its task, without pooling datasets. CytoLens detected nuclei with an F1 score of 0.931 (95% confidence interval (CI), 0.914-0.946) and counted 75 of 100 fields to within one nucleus. Point-prompted segmentation achieved mean Dice coefficients of 0.901 for nuclei and 0.847 for whole cells and, without retraining, transferred to a second dataset in which measured N:C increased with lesion grade in parallel with expert outlines. Multifocal hierarchical classification reached a mean accuracy of 0.947 across four Bethesda-aligned categories, and abnormality scores ranked annotated abnormal candidates with an area under the receiver operating characteristic curve (AUROC) of 0.865. These results support the evaluation of CytoLens on patient-linked clinical specimens.

Chimeric antigen receptor (CAR) T cells are patient-derived T cells engineered to recognize specific antigens on malignant cells, representing a major breakthrough in personalized cancer immunotherapy. While CAR-T cell therapy is an established treatment for relapsed or refractory hematologic malignancies, understanding the rationale and methodology behind its development is essential for training the next generation of oncology researchers. Here, we describe a novel, hands-on small-enrollment laboratory course designed to teach upper-level undergraduate and graduate students about immunotherapies and CAR-T cell technology by paralleling a typical bench-scale development process. Students isolated T cells from porcine peripheral blood mononuclear cells (PBMCs) via immunomagnetic selection, followed by flow cytometry to assess purity, viability, and yield. Students then used pre-engineered B7H3 CAR-T cells to perform co-culture assays with U87 glioblastoma cells to evaluate tumor cell killing efficacy. Cytotoxicity was qualitatively evaluated using immunofluorescence microscopy, while IFN-gamma release was quantified using Enzyme-linked Immunosorbent Assay (ELISA). Student performance was evaluated through electronic laboratory notebooks, discussion questions, a capstone project, and student-generated data. Our results demonstrate students high academic performance across assignments and the successful execution of complex laboratory workflows. Collectively, these findings establish the feasibility and implementation of a research-integrated curriculum providing foundational exposure to immuno-oncology research tools and CAR-T cell production techniques.

Objectives: To develop and validate pixel data analysis (PDA) for reconstructing pixel-level shear-wave velocity (SWV) from saved Holder-Optimized Elastography (HOE) elastograms and to determine whether whole-field subcutaneous heterogeneity provides a quantitative signature of lymphatic obstruction compared with visual high-velocity-area (HVA) counting and sparse manual region-of-interest (ROI) sampling. Materials and Methods: In this cross-sectional study, 110 women after breast cancer surgery underwent lymphoscintigraphy and HOE, a pressure-minimized acquisition framework that preserves conventionally captured SWV information while additionally revealing spatial abnormalities attenuated by handheld acquisition. Reconstruction validation used 200 paired ROIs from 49 PACS-retrieved images spanning nearly the full displayed SWV range; reproducibility was assessed across 96 matched anatomical sites from 6 participants undergoing independently reacquired HOE examinations. PDA, visual HVA counting, and manual ROI sampling were evaluated on the same HOE-acquired elastograms in task-specific common complete-case cohorts. Results: PDA reconstructed machine-reported SWV with near-zero error (mean difference, +0.0009 m/s; mean absolute error, 0.0075 m/s; 95% limits of agreement, -0.0202 to +0.0221 m/s). For subcutaneous variance, interobserver/intraobserver ICCs were 0.718/0.799 for PDA and 0.161/0.560 for manual ROI sampling. In the primary no-versus-partial comparison (n = 93), forearm-pooled PDA subcutaneous variance ratio yielded the highest observed AUC (0.859; 95% CI, 0.772 - 0.947), compared with 0.820 for visual HVA counting and 0.769 for manual ROI sampling. PDA's subcutaneous variance ratio remained a leading feature across all three comparisons (AUC, 0.859 - 0.878), including partial-versus-total grading, where all three frameworks showed high discrimination. Conclusions: PDA accurately reconstructs pixel-level SWV from saved HOE elastograms and enables reproducible whole-field quantification of superficial SWE heterogeneity. Subcutaneous variance ratio was the leading quantitative PDA feature across lymphatic-obstruction comparisons and was particularly informative in partial obstruction. The same approach also enables retrospective quantitative reanalysis of compatible archived elastograms.

Background: Chronic kidney disease (CKD) may progress to stage 5 CKD (end-stage renal disease, ESRD), but stage-associated molecular alterations remain incompletely characterized. This study aimed to identify candidate biomarkers associated with CKD and ESRD. Methods: Two discovery datasets comparing CKD or ESRD with healthy controls were analyzed using differential expression analysis and weighted gene co-expression network analysis. Candidate genes were identified by intersecting differentially expressed genes with key co-expression modules, followed by protein-protein interaction network analysis using Degree, Closeness, and Maximal Clique Centrality algorithms. Candidate genes were evaluated in independent GEO validation datasets and further examined by quantitative real-time PCR and western blotting using clinical blood samples. Results: Ten hub genes were identified, among which EPB42, KLF1, and H3F3A were prioritized for further evaluation. EPB42 showed decreased expression in CKD but increased expression in ESRD, indicating an opposite-direction pattern between the two disease states. KLF1 showed no significant difference between healthy controls and CKD but was increased in ESRD, whereas H3F3A was increased in CKD and further increased in ESRD. Functional enrichment analysis highlighted processes related to oxygen transport, mitochondrial function, muscle-associated processes, and inflammation, suggesting possible involvement of hypoxia-associated oxidative injury and ferroptosis-related processes. Conclusions: Integrated transcriptomic and experimental analyses identified EPB42, KLF1, and H3F3A as candidate stage-associated biomarkers in CKD and ESRD. In particular, the opposite-direction expression pattern of EPB42 may provide additional molecular information for distinguishing CKD from ESRD.

Certain glomerulopathies (GN) feature impaired protein homeostasis in the endoplasmic reticulum (ER) of podocytes. Protein misfolding causing ER stress activates the unfolded protein response (UPR), a protective signaling network. Mesencephalic astrocyte-derived neurotrophic factor (MANF) and ERdj3/DNAJB11 are ER chaperones that may be secreted from cells during the UPR. In rats with membranous nephropathy (MN) and focal segmental glomerulosclerosis (FSGS), secretion of MANF or ERdj3 from podocytes into urine identified glomerular ER stress/UPR activation. We address the biomarker potential of MANF and ERdj3 in human MN and FSGS. Glomerular gene expression of MANF and ERdj3 was increased in human MN and FSGS. Urinary MANF and ERdj3 were increased in proteinuric MN and FSGS compared to MN and FSGS in remission (low proteinuria), as well as other GNs, polycystic kidney disease, hypertension and healthy controls. In individuals with proteinuric MN or FSGS, amounts of urinary MANF and ERdj3 correlated weakly with proteinuria; correlation of MANF with ERdj3 was moderate. Males represented ~60% of persons in each group. eGFR in proteinuric MN and FSGS was >40 ml/min/1.73 m2. In MN, there were no significant differences in urinary MANF and ERdj3 between anti-PLA2R positive and negative individuals. In conclusion, proteinuric MN and FSGS show increased glomerular expression of MANF and ERdj3. High urinary levels of these ER chaperones correlate only weakly with proteinuria. In these GNs, urinary ER chaperones are thus non-invasive biomarkers of glomerular/podocyte ER stress. Such biomarkers may eventually be useful in identifying patients for therapies targeting ER protein misfolding.