Journal of Clinical Pathology

BackgroundNeonatal sepsis is a major cause of morbidity and mortality, particularly in low- and middle-income countries such as Senegal, where incidence is 78-104 per 1,000 live births and mortality exceeds 20 per 1,000, with case fatality rates around 36%. Diagnosis is difficult due to non-specific clinical signs and lack of molecular biomarkers, highlighting the need for improved early diagnostic molecular markers that could be applied even outside of hospital settings. ObjectivesCompare neonatal and adult serum proteomes to establish a reference and identify serum protein biomarkers of neonatal sepsis. MethodsSerum samples from Senegalese neonates and adults were analyzed using data-independent acquisition (DIA) proteomics on neat serum (Evosep-timsTOF HT platform). The cohort comprised 6 neonates with non-confirmed sepsis (NCS), 22 with confirmed sepsis (CS), 17 healthy newborn controls (HC), 6 unclassified and 20 healthy adults. Downstream analyses included differential protein abundance testing, unsupervised clustering, weighted gene co-expression network analysis (WGCNA), and correlation analyses with clinical parameters. ResultsWe identified 979{+/-}20 proteins in newborns versus 718{+/-}40 in adults. Newborns showed reduced immune-response proteins, a narrower dynamic range, and increased structural proteins such as collagens, consistent with immune immaturity and tissue development. Unsupervised WGCNA analysis led to a 53-protein cluster discriminated CS from NCS/HC. Some of these dysregulated proteins identified have already been reported in independent studies using different approaches in neonatal and/or adult sepsis. Our larger panel however of identified markers maps to three major biological processes involved in sepsis: (i) pathogen sensing (LBP, CD14), and acute-phase inflammation (e.g. CRP, SAA1/2, ORM1/2); (ii) innate immune activation and leukocyte recruitment (e.g., FCGR3A, CSF1R, CD163, CD206) and final platelet exhaustion and metabolic dysregulation, (e.g., PF4, PPBP, THBS1, GP5); (iii) endothelial injury and microvascular dysfunction with tissue remodeling (e.g., ICAM1, VCAM1, VWF, SPARC) and loss of protective lipoproteins and serpins (e.g., APOA1, APOA2, APOM, SERPINA4, SERPINA5) ConclusionThis study provides a very comprehensive neonatal serum proteome characterization and identifies, for the first time, a protein panel of proteins mapped to three major processes in sepsis.

Background: Wide-spread mast cell (MC)-associated symptoms and MC activation syndrome (MCAS) are often reported in patients with hypermobile Ehlers-Danlos syndrome (hEDS) and hypermobility spectrum disorders (HSD). The goal of this study was to develop a novel MC score based on 11 self-reported MC-related conditions with clinical and research utility to better understand MC symptoms in hEDS and HSD patients. Methods: From November 1, 2019, to June 13, 2025, patients (n=2,141) filled out an Intake Questionnaire at the Mayo Clinic Florida EDS Clinic that included 11 self-reported questions related to categories of MC-related conditions for a MC score ranging from 0/11 to 11/11. Based on the MC score distribution in hEDS and HSD patients, a MC score of 0-1 was considered a low MC score and [&ge;]5 was considered a high MC score. Symptoms/comorbidities were compared between patients with high vs. low MC scores. Results: From the 2,141 hEDS/HSD patients, 535 (25.0%) had a MC score [&ge;]5 (Hi MC). MCAS-specific symptoms such as nausea and vomiting were reported more often in hEDS/HSD patients with a high vs. low MC score (p<0.0001). Random clinical blood tryptase and urinary MC markers were not elevated in patients with high MC scores (n=50/group), although high MC scores were found to significantly reduce urinary creatinine levels indicating that the protein used to normalize data was affected by MC activity. In contrast, random blood IgE, tryptase and major basic protein (MBP) by ELISA were increased in patients with high MC scores (e.g., IgE hEDS p=0.0004, HSD p=0.003). Of note, the percentage of patients reporting abuse or post-traumatic stress disorder was nearly doubled in patients with high vs. low MC scores (Abuse and PTSD: hEDS p < 0.0001; HSD p < 0.0001). Overall, 109/135 (80.7%) in hEDS and 129/135 (95.6%) in HSD reported more symptoms/comorbidities if they had a high MC score. Conclusions: We found that hEDS/HSD patients with high MC scores self-reported more widespread symptoms/comorbidities and higher MC-related blood markers than patients with low MC scores indicating the utility of this tool to evaluate the level of widespread MC activity in hEDS, HSD and other patients.

PurposeOvarian clear cell carcinoma (OCCC) is strongly associated with endometriosis and shows geographic variation in incidence. We investigated whether OCCC and adjacent endometriosis exhibit distinct transcriptional states and whether these patterns differ between United Kingdom (UK) and Japanese cohorts. Experimental DesignWe performed whole-transcriptome spatial profiling on specimens from 16 OCCC cases (8 UK, 8 Japan) in which tumor and endometriosis were both present. Gene expression was analyzed in tumor, endometriosis and stroma. ARID1A status was assessed by immunohistochemistry. ResultsMedian age was 59 years (range 26-82). 13/16 cases (81.3%) had early-stage disease. Tissue compartment rather than cohort of origin was the dominant source of variation across endometriosis and tumor regions. Endometriosis was enriched for inflammatory and immune-related pathways compared to tumor, whilst there was greater representation of chromatin and protein-DNA complex assembly pathways in tumor regions. These patterns were conserved across both cohorts and after stratification by ARID1A status. Mesenchymal-associated gene expression scores also significantly differed across stroma, endometriosis and tumor with clear compartmental separation. Cell type deconvolution analyses showed clear compositional differences between stromal and epithelial disease compartments. ConclusionsOCCC and coexisting endometriosis are transcriptionally distinct, with the dominant contrast being compartmental rather than geographic. ARID1A alone is unlikely to account for the principal spatial transcriptional states identified here. Further analyses will be required to ascertain whether these differences reflect genuine biological differences between OCCC and coexisting endometriosis or represent different stages of endometriosis-associated tumorigenesis. Translational RelevanceOvarian clear cell carcinoma often arises in association with endometriosis, yet the biological transition between these lesions remains poorly understood. Using spatial transcriptomics in matched tumor and adjacent endometriosis from Japanese and UK cohorts, we showed that endometriosis is characterized by inflammatory and antigen-presentation features, whereas tumor regions showed chromatin-organization and oncogenic transcriptional states. These patterns were largely maintained irrespective of ARID1A status and geographic background. In addition, spatial deconvolution suggested differences in local immune composition, with tumor regions showing relatively greater neutrophil- and T cell-associated signals. Together, our data suggest that OCCC and coexisting endometriosis share a spatially linked tissue context, but that tumor regions have distinct transcriptional profile and microenvironment that may be involved in the malignant transformation and inform interpretation of molecular classification in endometriosis-associated OCCC.

Background Coeliac disease (CD) diagnosis on duodenal biopsies is limited by interobserver variability. We have previously demonstrated pathologist-level performance with our artificial intelligence (AI) model for the histopathological diagnosis of adult CD, but not in paediatric practice. As paediatric CD screening programmes expand internationally, accurate and scalable diagnostic tools are needed. We investigated whether an AI model trained exclusively on adult whole-slide images (WSIs) can generalise to paediatric CD diagnosis across independent centres. Methods A training and validation dataset of 9,958 WSIs from 8,421 adult patients (961 CD) from five centres was used to develop an ensemble of multiple-instance learning models using features from a foundation model. Testing was performed on 708 consecutive paediatric patients (86 CD) from two centres (Edinburgh and Southampton) not included in training. Model calibration was assessed, and probability outputs were grouped into clinically interpretable categories. Findings In adult cross-validation, the AI model achieved an area under the receiver operating characteristic curve (AUC) of 98.7%, sensitivity of 84.9%, specificity of 99.0%, and negative predictive value (NPV) of 98.1%. On testing (paediatric) datasets, performance remained high (AUC 98.8%, sensitivity 80.2%, specificity 98.4%, NPV 97.3%). Restricting analysis to predictions outside the intermediate-probability range (predicted CD probability <10% or [&ge;]65%; 85.3% of cases) improved sensitivity to 100% and specificity to 98.7%. No misclassifications were observed among high-confidence predictions (<2% or [&ge;]85%; 66.0% of cases). The expected calibration error was 0.03. Performance improved significantly when biopsies from both duodenal sites (bulb [D1] and descending [D2/3]) were considered. Interpretation Our AI model, trained on adult biopsies, generalises to paediatric CD diagnosis across centres and scanner platforms. Well-calibrated probability outputs provide clinically interpretable measures of diagnostic confidence and could support safe identification of CD-negative biopsies within defined thresholds. These findings demonstrate the feasibility of applying adult-derived AI models in paediatric populations and reinforce the importance of multi-site (D1 & D2) biopsy sampling.

Background Coeliac disease affects approximately 1% of the global population and remains substantially underdiagnosed. Histopathological assessment of duodenal biopsies is the diagnostic gold standard but is subject to approximately 20% inter-observer disagreement. While machine learning approaches show promise, most prior work relies on black-box models with limited interpretability, restricting clinical adoption. Methods We present an interpretable pipeline that follows established histopathological criteria by extracting clinically meaningful morphological features from H&E-stained whole-slide images. Five sequential stages perform pre-processing, semantic segmentation of villi, crypts, intraepithelial lymphocytes (IELs) and enterocytes, crypt morphometry, villus length estimation via a novel polyline-based keypoint model, and coeliac disease classification using three quantitative features: IEL-to-enterocyte ratio, villus-to-crypt area ratio, and villus-length-to-crypt-depth ratio. Training and validation used data from four institutions; independent testing used 1,357 WSIs from two further institutions including one with a previously unseen scanner manufacturer, spanning five diagnostic categories: coeliac disease, normal mucosa, chronic inflammation, gastric metaplasia, and gastric heterotopia. Results Semantic segmentation achieved villus and crypt precision and recall of 87-90%. Villus length estimation correlated strongly with expert annotations (Pearson's r=0.85, mean relative error 13.5% post-calibration). All three morphological features significantly separated coeliac disease from all non-coeliac diagnostic groups across internal and external datasets (p<0.01 in all comparisons). On the test set the diagnostic classifier achieved accuracy 94.5%, PPV 92.9%, NPV 94.7%, and AUC 0.982. Conclusions This interpretable framework achieves strong multi-centre diagnostic performance while producing quantitative morphological outputs, villus length, crypt depth, and IEL-to-enterocyte ratios, that directly reflect established histopathological criteria, representing a meaningful step towards standardised AI-assisted coeliac disease diagnosis.

Background. Definitive diagnosis of Hirschsprung's disease (HD) requires pathological identification of enteric ganglion cells. This process is time-consuming and subject to inter-observer variability. Artificial intelligence (AI) tools have the potential to standardize and accelerate this workflow, but no study has determined which AI approach best serves intraoperative HD pathology diagnostics. Method. This study compared the U-Net and You Only Look Once version 26 (YOLO26) frameworks for ganglion cell detection using a single-centre retrospective dataset of 54 whole-slide images (WSIs) from rectal biopsies. WSIs were tiled into 397,731 image patches (128x128 pixels), further partitioned into training (70%), validation (15%), and testing (15%) sets. Models were evaluated on tile- and patient-level diagnostic metrics and processing latency. Results. The U-Net achieved a tile-level sensitivity of 82.9%, showing no statistically significant difference compared to YOLO26 (79.1%; p = 0.097). However, YOLO26 demonstrated a statistically significant advantage in tile-level specificity (96.1% vs. 93.9%; p < 0.001) and reduced mean inference latency (7.64 ms vs. 11.57 ms/tile). At the patient level, both models achieved 100% diagnostic sensitivity. Despite low patient-level specificity (0.0% U-Net; 11.8% YOLO26), the tissue-level diagnostic burden of false positives was 6.00% for U-Net and 3.50% for YOLO26. Conclusion. The U-Net is preferred when nominal gains in sensitivity are prioritized, while the YOLO26 is an alternative that optimizes efficiency and false positive suppression. Both models serve as robust screening filters to augment the pathologist's workflow and should be selected based on workflow requirements. Prospective validation on larger, multi-centre datasets is required before clinical implementation.

Introduction: Preeclampsia (PE) is a leading cause of maternal and neonatal morbidity and mortality, and low-dose aspirin (LDA) prophylaxis is the cornerstone of evidence-based prevention. Despite guideline recommendations, LDA adherence remains poor, with 10-25% of moderate-risk patients taking aspirin. Objective personalized risk stratification using biomarkers has been shown to motivate behavior change in other disease contexts. Survey data suggest that patients are more motivated to take aspirin if informed by an objective predictive test. Here, we report real-world LDA adherence among patients who received a high-risk result from a cell-free RNA (cfRNA) PE risk prediction test. Methods: This retrospective, observational survey study included asymptomatic patients of advanced maternal age (AMA; [&ge;] 35 years at delivery) with singleton pregnancies without USPSTF-defined preexisting high-risk conditions for PE who received the cfRNA PE risk prediction test. Patients who opted in to receive text message surveys were asked about LDA use following receipt of test results. High adherence was defined as reporting LDA use on at least 6 of 7 days per week at least 85% of the time surveyed. The primary analysis included patients with a high-risk test result and at least one LDA frequency survey response following receipt of test result. The observed proportion of adherent patients was compared to a baseline estimate of 25% using an exact binomial test. Results: Of 166 patients who received a cfRNA PE risk prediction test result, 48 (28.9%) received a high-risk result. Of these, 29 (60%) opted in and responded to at least one survey, constituting the primary analysis population. Twenty-seven of the 29 (93.1%; 95% CI: 78.0-98.1%) were classified as highly adherent, significantly higher than the 25% baseline adherence estimate for moderate-risk patients (p < 0.0001). Conclusion: Among surveyed patients who received a high-risk cfRNA PE test result, the proportion classified as highly adherent to LDA (93%) substantially exceeded published estimates of adherence in a similar patient population and met the clinically meaningful threshold of [&ge;] 80% associated with reduced risk of preterm preeclampsia. These findings indicate that objective and personalized biomarker risk testing may be a powerful driver of behavior change that current guidelines have failed to produce.

Objectives: To evaluate if direct access to a Pregnancy Early Access Center (PEACE) improves the timeliness and efficiency of pregnancy loss care. Methods: We conducted a retrospective cohort study of patients diagnosed with EPL from January 2017 to December 2022 within a single healthcare system. We included EPL patients treated with procedural or medication management who had been assessed for a related early pregnancy complaint in the thirty days prior. The exposure was direct utilization of PEACE (yes/no) between first EPL symptom visit and EPL management. The primary outcome was "care latency" defined as days from initial presentation for concerning early pregnancy symptoms to initiation of active management. Secondary outcomes included "care continuity," the number of care teams encountered, "care efficiency," the number of patient encounters, and the type of EPL management received. Results: The evaluable cohort included 2151 individuals, with 36.5% patients of Black race and 30.3% publicly insured. A total of 885 (41.1%) received any EPL care at PEACE and 246 (11.4%) initiated their care at PEACE. Patients initiating care through PEACE experienced a 5-day reduction in care latency compared to patients who did not access PEACE. Adjusting for age, race, and insurance type, patients whose index EPL visit was with PEACE initiated their treatment twice as quickly as those who never saw PEACE (aHR 2.36 [95% CI, 2.05-2.71]). Care efficiency (median 2 [1-3] encounters) and care continuity (median 4.5 [4-7] care teams) were also improved by an index visit with PEACE when compared with controls (3 [2-4] and 6 [4-8] p<0.01), respectively). Conclusions: The Pregnancy Early Access Center (PEACE) model is associated with reduced care latency and improved efficiency and continuity when compared with routine care. PEACE reduces barriers to timely, patient-centered early pregnancy care.

Background: Hypertensive disorders of pregnancy (HDP) may first be diagnosed antepartum, during labor, or postpartum. We utilized untargeted large-scale proteomics to identify pathways associated with HDP based on timing of onset. Methods: We performed a nested case-control study comparing differential protein expression, from the SomaScan 7K platform, based on timing of onset of HDP versus controls (referent) using first-trimester samples from the NuMoM2b-Heart Health Study, a multi-site cohort that followed nulliparous individuals from the first trimester. Associations of proteins with timing of onset of HDP, adjusted for co-variates, were assessed using logistic regression q value-based false discovery rates and pathway enrichment and differential expression analysis were conducted. Results: Of 1628 individuals included, 678 had HDP, of which 67% manifested antepartum (AP), 29% intrapartum (IP), and 3% postpartum (PP). After adjusting for co-variates, compared to controls, 698 proteins, 39 proteins, and 144 proteins were differentially expressed in those with HDP according to AP, IP, PP onset, respectively. There was little overlap in individual protein expression based on timing of HDP. Pathway enrichment and graphical summary analyses suggested distinct processes. Specifically, there was downregulation of angiogenic proteins in AP HDP, downregulation of immune-related proteins in IP HDP, and upregulation of complement activation promoting fibrotic changes leading to cardiac dysfunction in PP HDP. Conclusion: There are differences in first-trimester protein expression based on whether HDP first manifests AP, IP or PP. This raises the possibility that there may be distinct mechanistic phenotypes that could uniquely inform diagnostic and therapeutic targets for HDP.

Background: Complicated acute appendicitis carries a higher risk of postoperative morbidity relative to uncomplicated cases. It remains unclear whether surgical timing (night vs. day; weekend vs. weekday) or surgeon seniority influence short-term outcomes in this high-risk population. Methods: This was a retrospective analysis of the RIFT Turkey dataset restricted to histologically confirmed cases of complicated appendicitis who had undergone laparoscopic appendectomy. Primary exposures were surgical timing (day [n=92] vs. night [n=123]; weekday [n=172] vs. weekend [n=43]) and surgeon seniority (trainee [n=89] vs. consultant [n=126]). The primary outcome was unplanned readmission and/or reintervention within 60 days. Secondary outcomes were conversion to open surgery and length of stay (LOS) >3 days. Propensity score matching (PSM) using RIPASA score (caliper 0.05, SMD <0.1) was performed as a pre-specified sensitivity analysis for each comparison. Results: Night-time surgery was associated with higher frequencies of unplanned readmission / reintervention (12.2% vs. 6.5%; OR 1.99 [95% CI 0.74-5.35], p=0.166) and surgical conversion (9.8% vs. 3.3%; OR 3.21 [0.88-11.72], p=0.064) compared with daytime surgery, neither reaching significance. Trainee surgeons had significantly higher readmission/reintervention rates than consultants (15.7% vs. 5.6%; OR 0.32 [0.12-0.82], p=0.013). PSM-adjusted results also showed similar relationships: night vs. day (readmission OR 2.45 [0.85-7.03], p=0.09; conversion OR 2.84 [0.73-11.1], p=0.13), weekend vs. weekday (readmission OR 1.53 [0.24-9.72], p=0.65), and trainee vs. consultant (readmission OR 0.25 [0.08-0.79], p=0.013). Conclusion: Surgical timing was not significantly associated with short-term outcomes in complicated appendicitis, though night-time surgery showed a consistent trend towards higher complication rates. Surgeon seniority was the only factor independently and significantly associated with unplanned readmission and reintervention in both primary and PSM analyses, indicating the need for senior supervision during out-of-hours procedures. Keywords: complicated appendicitis; surgical timing; night surgery; weekend effect; surgeon seniority; propensity score matching; RIFT Turkey

Background: In resource-limited settings, a critical bottleneck in cervical cancer prevention is the lack of practical strategies to triage high-risk human papillomavirus (HR-HPV)- positive women. Therefore, this study aimed to develop and internally validate a genotype-specific risk stratification model. Methods: A cross-sectional study enrolled 555 women in Cameroon. Data collection integrated cervical cytology and HPV genotyping using Abbott m2000rt and Sacace multiplex systems. An iterative modeling approach with bootstrap validation was used to develop the model and address model instability. HR-HPV genotypes were transformed into a hierarchical risk variable due to sparsity and integrated with significant predictors. The final model was translated into a scoring system, and the risk gradients and performances were evaluated at two thresholds. Data was analyzed using SPSS 27.0. Results: The mean age was 44.8 years, and the prevalence of HR-HPV was 26.5% (147/555). The final model, incorporating HPV categories, age, and tobacco, demonstrated moderate discriminative ability (AUC=0.702, 0.642-0.762) with a good calibration (Hosmer-Lemeshow {chi}{superscript 2}=4.05, p=0.399). The scoring system assigned women to risk groups based on their total scores which produced a clear monotonic risk gradient; the observed probability of high-grade lesions/cancer ranged from 15% (score 0) to >65% (score [&ge;]4). At a conservative threshold ([&ge;]4 points), 4.7% (26/555) of women were classified as high-risk, concentrating 46% (6/13) of cancers (positive predictive value[PPV]=58%) while a sensitive threshold ([&ge;]3 points) had 16.8% (93/555) high-risk, concentrating 77% (10/13) cancers (PPV=38%). Both thresholds maintained a high negative predictive value (>95%). Conclusion: This bootstrap-validated, risk-stratification tool is a proof-of-concept in resource limited settings that assigns HR-HPV-positive women to distinct management pathways using three variables. After refining through a longitudinal study and external validation, this scoring system can improve the efficiency of cervical cancer screening programs in low-resource settings.

Background: Breast cancer is the most frequently diagnosed cancer and the leading cause of cancer death in women worldwide, and the great majority of these deaths are caused by metastatic disease. Whether the immunohistochemical (IHC) phenotype of breast cancer is associated with the anatomical site of metastasis has been characterized mainly in high-income, registry-based populations, while data from ecologically stressed and medically under-served regions such as the Lower Aral Sea basin are lacking. Methods: We retrospectively reviewed 652 women diagnosed with breast cancer at the Khorezm Branch of the Republican Specialized Scientific-Practical Medical Center of Oncology and Radiology (Uzbekistan) between 2020 and 2024, of whom 213 had metastatic disease (306 metastatic foci). Histological type was assessed on hematoxylin-eosin and van Gieson-stained sections; quantitative morphometry was performed in Fiji/ImageJ; and HER2, estrogen receptor (ER), progesterone receptor (PR) and Ki-67 were assessed by IHC. The association between marker expression and metastatic site (liver, lung, lymph node) was tested in 187 foci with adequate tissue using the chi-square test, with significance at p < 0.05. Results: Invasive ductal carcinoma predominated. Metastatic site was significantly associated with the IHC phenotype. Liver metastases showed the highest frequency of HER2 3+ (45.7%), ER-negativity (65.2%), PR-negativity (69.6%) and high proliferation (Ki-67 [&ge;] 60%; 47.8%), whereas lymph-node metastases were more often hormone-receptor-positive (ER+ 58.7%; PR+ 52.4%) with lower HER2 3+ (22.2%); lung metastases were intermediate (all p < 0.05). The combination of HER2 3+ and Ki-67 [&ge;] 60% was associated with multi-organ spread. Morphometry corroborated these patterns: liver lesions had larger atypical cells (up to 132.8 m), a higher nuclear-to-cytoplasmic ratio (0.76 vs 0.51) and more extensive necrosis and microvascularity than lymph-node lesions. A pragmatic 5-criterion morphological score (histological type, Ki-67, HER2, ER/PR status, atypical-cell size) stratified metastatic risk into three tiers. Conclusions: In this regional cohort, the IHC phenotype of breast cancer tracked the anatomical site of metastasis, with an aggressive HER2-driven, hormone-receptor-negative profile concentrated in liver metastases and a hormone-receptor-positive profile in lymph-node metastases. These findings reproduce established organotropism patterns in a previously uncharacterized population and support phenotype-aware, site-specific surveillance together with a low-cost morphological risk score for resource-limited settings.

In clinical trials for ulcerative colitis (UC), pathologists assess disease severity through standardized histological indices, including the Geboes Score, Robarts Histopathology Index (RHI), and Nancy Histologic Index (NHI). Despite strong associations with clinical outcomes, histologic scoring suffers from inter- and intra-reader variability, and consensus criteria for histologic remission remain uncertain. Through a consortium approach, we developed an artificial intelligence-based measurement (AIM) tool for scoring histology in UC mucosal biopsies (AIM-HI UC). This model, trained on a large dataset of UC biopsies (N=10,230), utilizes additive multiple instance learning models leveraging PLUTO, a pathology foundation model, that predict each of the Geboes subgrades, from which the Geboes grade-level score, RHI, and NHI can be calculated. Evaluation of this model on a standalone verification set including clinical trial specimens established algorithm non-inferiority and/or superiority relative to standard qualified pathologists through comparison of algorithm-consensus and pathologist-consensus agreement metrics (non-inferior if difference >-0.1, superior if difference >0, inclusive of confidence intervals). AIM-HI UC was determined to be non-inferior to pathologists (N=3) for the prediction of all seven Geboes subgrades, grade-level Geboes, RHI, NHI, histologic improvement (GS<3.1), 2A histologic remission (GS<2A.0), and 2B histologic remission (GS<2B.0). AIM-HI UC was superior to pathologists for several Geboes subgrades (GS 0, GS 1, GS 2B, and GS 5), as well as grade-level Geboes, RHI, and positive percent agreement of 2A histologic remission. The model was shown to be greater than 99% repeatable for all histologic scoring metrics examined. Model-derived scores were shown to strongly correlate with canonical histologic features of inflammation, including the proportion of total epithelium that is inflamed (Spearman r=0.83; p<0.01), the proportion of neutrophils localized within crypt epithelium (Spearman r=0.83, p<0.01), and the amount of mucosal area classified as erosion or ulceration (Spearman r=0.80, p<0.01). Overall, these results suggest that AIM-HI UC has the potential to improve consistency of UC histology interpretation, providing a path toward standardization of UC histology scoring in clinical trials.

Background: Placenta accreta spectrum (PAS) is an important cause of severe maternal morbidity. Although prior cesarean delivery is a well-established risk factor, PAS also occurs in women without prior cesarean section (CS), in whom risk may be underestimated. This study evaluated routinely available clinical factors associated with PAS in this population and developed a clinical-history-based prediction model. Methods: We conducted a retrospective cohort study of women without prior CS who delivered at Peking University Third Hospital, China, from January 1, 2022, to December 31, 2023. PAS was diagnosed according to the 2019 International Federation of Gynecology and Obstetrics clinical and/or histopathological criteria. Multivariable logistic regression was used to identify independent risk factors. Model performance was assessed using receiver operating characteristic curves, calibration, decision curve analysis, and stratified 5-fold cross-validation. Analyses were repeated after stratification by placenta previa status. Results: Among 11,148 women without prior CS, 236 had PAS. Independent risk factors in the overall cohort were placenta previa, operative hysteroscopy, uterine curettage, in vitro fertilization, and multifetal pregnancy. The overall clinical prediction model showed an area under the curve of 0.838 (95% confidence interval, 0.81-0.87), with stable performance in internal validation. In stratified analyses, model discrimination was lower among women without placenta previa (area under the curve, 0.734) and those with placenta previa (area under the curve, 0.647). Conclusions: In this single-center cohort, routinely available clinical history was associated with PAS risk among women without prior CS. The proposed model may help identify patients who warrant targeted PAS imaging or specialist assessment, but external validation and integration with imaging features are needed before broad clinical implementation.

Objective To determine if it is possible to assess individual patient risk of the development of colorectal cancer (CRC) in people in high-risk groups due to their family history. Design/Method Retrospective observational study of prospectively collected data from consecutive patients referred for a colonoscopy. 2,478 consecutive patients were referred to a single colorectal surgical practice in Sydney, Australia between 1977 and 2018 for a colonoscopy because of a family history of CRC. Of these, 1,963 have been followed for more than 10 years and are the subject of this paper. Histopathological findings categorised as normal (N), non-advanced adenoma (NAA) or advanced neoplasia (AN) with AN proven to be the precursor to CRC. Intervention Colonoscopic screening on the basis of contemporary practice to 2006 and subsequently according to Australian National Health and Medical Research Council guidelines. Results Participants with normal or low-risk findings in the first decade remain at lower risk of CRC for 30 years from the commencement of screening. Conclusion It is possible to stratify individual patients in a high relative risk cohort into those with high or low personal risk of CRC based on colonoscopic findings in the first 10 years of surveillance. Those with no AN in the first ten years have a lower 30-year risk of developing AN than the general community. This offers the possibility of structuring surveillance programs around individual risk rather than group risk, lessening the need for multiple surveillance colonoscopies in the majority of such patients and improving the cost effectiveness of CRC screening at the population level.

Flow cytometry can establish T cell clonality by detecting a restricted expression pattern of the T cell receptor (TCR) {beta} constant region (TRBC), expressed in association with CD3. However, T cell neoplasms frequently lose surface expression of the CD3/TCR complex, posing a challenge to demonstrating T cell lineage and clonality. To address this challenge, here we present a 12-color flow cytometry panel, called cytoTCR, to characterize cytoplasmic expression of CD3/TCR complex components. We apply cytoTCR to 38 patient specimens with immunophenotypically abnormal T cell populations, demonstrating this approach can efficiently establish T cell lineage and clonality in challenging T cell neoplasms that have lost surface CD3 expression. While we show that natural killer (NK)-lineage neoplasms can express cytoplasmic CD3 at similar levels to T cells, we show that absent expression of cytoplasmic TCR components by mature lymphocytes can help confirm NK cell lineage. We demonstrate that cytoTCR can detect cytoplasmic TRBC-restriction in challenging cases of null-phenotype anaplastic large cell lymphoma, which lack surface expression of pan-T cell antigens. In cases of T-lymphoblastic leukemia, cytoTCR shows that cytoplasmic TRBC expression matches the expected developmental stage of the leukemia. Finally, we use cytoTCR to characterize atypical cCD3-CD7- T cells in a patient with a history of T-lymphoblastic leukemia as well as recent CAR-T therapy, showing that this atypical population is polytypic and represents CAR-T product rather than residual disease. Our study presents a broadly applicable flow cytometric approach to simultaneously assess T cell lineage and clonality in suspected T lineage populations with absent surface CD3 expression.

The Epic Sepsis Model version 2 (ESMv2) is a prediction model embedded into the electronic medical record used to warn clinicians which hospitalized patients are at risk for sepsis. We conducted a retrospective cohort study of 31,951 hospitalizations of 25,760 patients to compare analyses conducted at the commonly used patient-level (where a maximum prediction prior to the onset of sepsis is used to measure performance) vs novel prediction-level (where each prediction is used to measure performance). Sepsis, defined by the Sepsis 3 criteria occurred during 1,049 hospitalizations (3.3%). Patient-level analyses suggested excellent discrimination AUC 0.86; [IQR 0.85, 0.87], whereas prediction-level analyses demonstrated lower performance AUC 0.62; [IQR 0.57, 0.65]. Low estimates of the positive predictive value (14.5% at the patient level vs 4% at the prediction level) imply a high number of false alerts. Common evaluation approaches may overstate the performance of dynamic prediction models and mislead clinical decision-making.

Background Gastric cancer management is heterogeneous, and although the treating surgeon leads decisions across the pathway, surgeon level outcome variation remains poorly quantified. This study assessed surgeon identity as an independent predictor of survival after risk adjustment, introducing the Surgical Assessment and Healthcare (SAH) Index. Methods This single institution retrospective study (Ruijin Hospital, Shanghai Jiao Tong University; NCT07180966) included 692 patients undergoing curative-intent resection for gastric adenocarcinoma (pStage I ,II, III) in 2019 by eight consultant surgeons. Overall survival was modelled by multivariable Cox regression (primary model, 199 events, EPV 16.6; complete-case sensitivity model, N = 647). The SAH Index expressed surgeon * stage observed-to-expected ratios for five-year mortality and major morbidity (Clavien Dindo [&ge;] IIIa). Median follow up was 74.3 months. Results Independent predictors of survival were tumour stage (HR 2.979/step), age (HR 1.030/year), and non-distal gastrectomy (HR 1.498; all p [&le;] .006). After full adjustment, surgeon identity remained significant (Wald = 14.58, df = 7, p = .042): two surgeons carried roughly double the reference hazard S6 (HR 2.219, p = .003) and S8 (HR 2.034, p = .031) both with the cohort's lowest neoadjuvant chemotherapy rates (3.0% and 7.0% versus 17.6%), implicating pre-operative pathway decisions. The effect persisted in the sensitivity model (MSI also prognostic, HR 3.162, p = .007). Morbidity benchmarking flagged no surgeon for excess complications (no Tier 2 flags) and one survival-outlier cell (S6, Stage II; Tier 3). Conclusion Surgeon identity is independently associated with survival in gastric cancer beyond measurable case mix. The SAH Index offers a reproducible tool for institutional and inter-hospital benchmarking, with tier assignments stable across all four prespecified weighting scenarios confirming tier classification is independent of weight specification.

Background: Citrin deficiency, caused by biallelic pathogenic variants in SLC25A13, must be identified early to prevent serious complications such as hyperammonemia and liver failure. However, clinical diagnosis is often delayed due to its nonspecific presentation and limited sensitivity of amino acid-based newborn screening methods. Although genome-based evaluations are being investigated to address these issues, concerns about their cost, turnaround time, variant interpretation ability, and data handling highlight the need for a more practical yet reliable alternative. We investigated the feasibility of applying proteomic approach on dried blood spots (DBS), which are routinely used in newborn screening. Methods: We performed untargeted liquid chromatography-tandem mass spectrometry to analyze the proteome of DBS using a previously developed "non-targeted analysis of non-specifically DBS-absorbed proteins" (NANDA) workflow. SLC25A13 protein abundance was quantified in individuals with biallelic loss-of-function mutations, compound loss-of-function/missense mutations, and heterozygous carriers; this was also evaluated in healthy and diseased controls representing relevant differential diagnoses. To leverage proteomic information, we derived a multivariate proteomic signature using feature selection and evaluated its performance with leave-one-out cross-validation. Biological relevance was assessed by enrichment analysis, and complementary transcriptomics was performed using RNA sequencing. Results: A total of 7,474 proteins, including SLC25A13, were consistently detected in DBS. SLC25A13 was undetectable in individuals with biallelic loss-of-function mutations. However, individuals with compound loss-of-function/missense genotypes showed reduced but measurable SLC25A13 levels, comparable to those observed in heterozygous carriers. In contrast, a compact 15-protein signature accurately identified individuals with compound loss-of-function/missense genotypes (AUC, 0.99; sensitivity, 1.00; specificity, 0.95). The signature was enriched for Ca2+-response, and transcriptomics showed downregulation of genes related to multimodal ion channels in affected individuals compared to controls. Conclusions: DBS-based proteomic profiling may assist in the diagnosis of citrin deficiency through SLC25A13-quantification and a biologically plausible multivariate signature. More broadly, this strategy offers a promising new diagnostic layer for protein disorders, providing a proteomic readout in a clinically practical DBS format with potential utility for future diagnostic and screening applications.

Background Complicated appendicitis (CA) increases morbidity and resource use.[1,2] In the emergency setting, risk stratification must rely on rapidly available data. Procalcitonin (PCT) is frequently obtained, but its incremental value beyond basic preoperative indicators remains uncertain.[5] We aimed to quantify PCTs incremental predictive value and develop a practical bedside score with temporal validation. Methods We conducted a retrospective cohort study of consecutive laparoscopic appendectomy patients (January 2023-December 2024). CA was defined by postoperative pathology (gangrene/necrosis, perforation, or peri-appendiceal inflammation/abscess; worst-category rule). We compared a base logistic model (age, WBC, neutrophil percentage, fever, symptom-to-surgery interval, shock index) with an extended model adding log-transformed PCT. Discrimination (AUC) and calibration were assessed. Temporal validation used 2023 for development and 2024 for testing. We also created a simple bedside score using pre-specified cutoffs and evaluated CA risk across score strata in 2024. Results In the overall complete-case cohort (n=1,792), 397 patients (22.2%) had CA. Adding PCT modestly improved discrimination in the full cohort (AUC 0.673 to 0.685). For temporal validation, 2023 included 870 patients (CA 26.9%) and 2024 included 921 patients (CA 17.7%); one otherwise eligible patient lacked a usable admission year. In the 2024 test set, discrimination was 0.662 (base) vs 0.673 (base+PCT) with a non-significant AUC difference (DeLong p=0.116); calibration slopes were near 1.0. A 7-item bedside score stratified 2024 CA risk: 9.1% (score 0-1), 14.7% (2-3), and 34.2% [&ge;]4). Using [&ge;]4 points identified a higher-risk subgroup (PPV 34.2%, NPV 87.5%, sensitivity 46.0%, specificity 81.0%). Conclusions PCT adds modest predictive information beyond simple preoperative indicators in the full cohort, but temporal validation suggests that this incremental gain is smaller and not statistically significant in later patients. A pragmatic bedside score can support CA risk stratification and prioritization in emergency care, whereas the role of routine PCT testing may be best reserved for selected situations in which uncertainty remains after initial assessment.