Journal of Clinical Pathology

BackgroundPlacental growth and function are imperative for healthy fetal growth; data on placentas can inform research and clinical care. Measuring placental size after delivery should be easy, but current methods are hard to standardize and error prone. We developed PlacentaVision using artificial intelligence (AI)-based models, to automatically, accurately, and precisely measure placentas from digital photographs. ObjectiveWe aimed to compare placental disc morphology between gross pathology examination (human measurements) and our automated PlacentaVision model (AI measurements). MethodsPlacentaVision is a multi-site study to assess placental morphology, features, and pathologies from digital photographs. We built a large dataset of digital placenta photographs and clinical data from singleton births at three large hospitals: Northwestern Memorial (Chicago; n=24,933), UPMC Magee-Womens (Pittsburgh; n=1198) and Mbarara Regional Referral (Uganda, n=1715). Data and images were from the medical record for Northwestern, part of a biobank study for Magee, and from our prospective studies for Mbarara. We compared long and short disc axis length (defined by Amsterdam criteria) between human and AI-based PlacentaVision measurements by calculating the difference and using Bland-Altman; we stratified by site, disc shape, infant sex, and term/preterm birth. ResultsMean (SD) disc length was 19.2 (3.1) and 18.6 (3.1) cm from PlacentaVision and human measurement, respectively, with a difference of 0.57 (2.19) cm. Disc width was 16.3 (2.3) cm and 16.1 (2.4) cm from PlacentaVision and human measurement, respectively, with a difference of 0.25 (1.85) cm. Bland-Altman limits of agreement were -3.7 to 4.9 cm for length and -3.4 to 3.9 cm for width. Irregularly-shaped placentas had a greater difference between PlacentaVision and human measurements compared to those with round/oval shapes (length differences of 1.53 and 0.45 cm respectively). Further, there were length differences by site (Northwestern 0.6, Magee 0.0, and Mbarara 0.4) and gestational age at birth (preterm 0.71, term 0.53 cm), but similar results for male and female placentas. Results for width were similar to length. ConclusionsAI-based measurements were less than a cm from human measurements overall. Our findings of larger differences for irregular shapes and preterm may indicate it is difficult for humans to measure irregular or small placentas according to protocol. PlacentaVision can automate and standardize the process.

ObjectiveTo explore the application value of dual-staining for specific AT sequence binding protein 2 (SATB2) immunohistochemistry and elastic lamina in detecting elastic lamina invasion (ELI) in pT3 colon cancer, and to assess its association with clinicopathological characteristics, staging, and prognosis. MethodsThis retrospective cohort study enrolled 176 pT3 colon cancer patients who underwent radical resection at Affiliated Jinhua Hospital Zhejiang University School of Medicine. The deepest tumor-infiltrated paraffin blocks were collected for SATB2 immunohistochemistry and elastin dual-staining. Correlations between ELI status and clinicopathological characteristics and prognosis were analyzed. Survival data of 74 pT4a stage patients were collected for comparative analysis. ResultsELI (+) was positively associated with high tumor budding grade, vascular invasion, lymph node metastasis, and reduced tumor infiltrating lymphocytes (TILs) (all P < 0.001). No correlations were observed with age, gender, tumor location, histological subtype, tumor grade, or perineural invasion (all P > 0.05). The ELI (+) group exhibited significantly shorter disease-free survival (DFS) and overall survival (OS) compared to ELI (-) group (P < 0.05). Additionally, the ELI (+) group demonstrated inferior OS than the pT4a group, though DFS did not differ significantly. ConclusionDual-staining of SATB2 immunohistochemistry and elastic lamina provides a reproducible and objective method for assessing ELI. ELI correlates with key clinicopathological features and functions as an independent adverse prognostic indicator in pT3 colon cancer.

BackgroundHepatocellular carcinoma (HCC) is a leading cause of cancer-related death in sub-Saharan Africa (SSA). Differentiating primary HCC from metastatic liver tumors remains a significant diagnostic challenge. Understanding the prevalence and clinical predictors of HCC is crucial for improving diagnosis and patient care. This study examined the prevalence of hepatitis B virus (HBV), hepatitis C virus (HCV), and HCC, and clinical predictors of HCC. MethodsWe used immunohistochemical markers on archived liver tumor biopsies and analyzed the data using descriptive and logistic regression analysis. ResultsAmong 58 liver carcinoma cases, 37.9% had HCC, and 62% had metastatic liver carcinoma (MLC). HCC was most common (61.5%) among middle-aged adults (50-59 years). HCC was more frequent in males (47.2%) than in females (22.7%). Over half of the patients (51.7%) tested positive for HBV. HCC was more prevalent in HBV-positive patients than HBV-negative ones (43.3% vs 32.1%). Hepatic fibrosis was identified in 27.6% of cases. HCC was more common in patients with fibrosis (56.2%) than in those without (31%). HCV infection was rare (6.9%) in this study. In multivariable logistic regression analysis, none of the examined predictors reached statistical significance (P>0.05). Patients aged 50-59 years, males, those with HBV infection, and hepatic fibrosis showed higher odds of HCC. Hepatocyte Paraffin-1 (Hep Par-1) demonstrated 97% specificity and a 95% positive predictive value (PPV) for differentiating HCC from MLC. The combined marker pattern of Hep Par-1 positive and AE1/AE3 negative was highly predictive of HCC (100% specificity, 100% PPV, and 93.2% diagnostic accuracy). ConclusionsOur findings indicate that while the assessed risk factors tend to show directional association with HCC, as expected, larger studies are needed to determine their independent effects. The combined Hep Par-1 AE1/AE3 immunophenotype is more accurate than either marker alone. Therefore, this combined test is a valuable diagnostic tool for confirming HCC in resource-limited settings.

BackgroundIn 2004, South Africas public health system faced the dual challenge of rapidly scaling up antiretroviral therapy (ART) while reducing the cost of laboratory monitoring. At the time, conventional CD4 testing methods were expensive, labour-intensive, and impractical for sustaining a national testing network. This study aimed to assess the financial impact and cost savings associated with the implementation of the PanLeucogated CD4 (PLG/CD4) enumeration method between 2004 and 2024 in the public-sector in South Africa. MethodsA longitudinal cost analysis was conducted using annual test volumes and state tariffs for PLG/CD4 testing and the 4-colour CD3/CD4/CD8/CD45 T-cell enumeration reference method. Annual cost savings were calculated in United States Dollars (USD) by applying historical South African Rands (ZAR) to United States Dollars (USD) exchange rates. The state prices for tariff codes PLG/CD4 and the reference method were provided by calendar year in ZAR and converted to USD based on the prevailing exchange rate. The USD test prices were multiplied by annual test volumes. Cost savings were calculated by multiplying annual test volumes and the difference in test prices in USD (difference between PLG/CD4 and the reference method). ResultsThere were 50,745,848 PLG/CD4 tests performed over 20-years. The cost-per-test of PLG/CD4 was consistently lower than the reference method, ranging from $4,06 to $9,40, compared to $13,06 to $28,21. Cumulative national savings amounted to USD 626 million. The peak annual savings of $64,6 million occurred in 2011, coinciding with the height of ART enrolment. Cost savings persisted despite a doubling in the exchange rate over the study period. ConclusionThe PLG/CD4 implementation enabled cost-efficient, scalable, quality-assured CD4 testing as part of the national HIV response, reducing reliance on complex/costly technologies while improving coverage. These findings support the critical role of context-specific diagnostic innovation to strengthen health system resilience.

BackgroundHistologic descriptors such as lymphovascular invasion (LVI), visceral pleural invasion (VPI), spread through air spaces (STAS), and grading system have each been associated with adverse outcomes in lung adenocarcinoma (LUAD). However, with the exception of VPI, these features are not formally incorporated into the TNM staging system. We evaluated the prognostic value and incremental contribution of these histologic descriptors within the framework of the 9th edition TNM staging system. MethodsIn total, 1,745 individuals diagnosed with stage I-III invasive non-mucinous lung adenocarcinoma (NM-LUAD) were included in this study, comprising 1139 French-Canadian patients who underwent surgical resection at IUCPQ-Universite Laval (discovery cohort) and 606 patients from the National Cancer Center Hospital in Tokyo, Japan (validation cohort). The objective of this study was to assess the prognostic contribution of histologic descriptors, including STAS, and LVI, as complements to conventional 9th edition TNM staging. ResultsGrade 3 tumors, LVI, and STAS were identified in 880 (50.4%), 809 (46.4%), and 775 (44.4%) of 1745 cases, respectively. Histologic grade and LVI demonstrated the strongest associations, particularly in early-stage disease, while STAS exhibited a stage-dependent effect, being more impactful in stages II-III. VPI showed less consistent prognostic value. Incorporating these histologic descriptors into TNM staging improved prognostic model performance, with the largest gains driven by histologic grade and LVI, while STAS provided additional, complementary prognostic refinement. ConclusionThese findings demonstrate that key histologic descriptors--including grading system, LVI, and STAS--represent robust and reproducible prognostic parameters. Importantly, these descriptors provide complementary, stage-dependent information that may enhance risk stratification and inform refinement of future TNM staging frameworks, including the forthcoming 10th edition.

IntroductionKidney biopsy reports contain rich information that is clinically actionable and useful for research. However, the narrative format hinders scalable reuse. We here investigated whether open-source large language models (LLMs) can extract relevant, standardized readouts from native kidney biopsy pathology reports. MethodsGerman free-text native kidney biopsy reports were parsed with three open-source LLMs (Llama3 70B, Llama3 8B, MedGemma) to generate structured JSON outputs covering relevant report elements (e.g., diagnosis, glomerular counts, histopathological patterns). Two independent observers manually curated the same report elements; disagreements between the two were resolved by an experienced nephropathologist to create the final ground truth. Performance was assessed using strict and soft matching and summarized accuracy. Inter-rated agreement was quantified using Cohens and Lights Kappa with 95% confidence intervals via 1000-times bootstrapping. ResultsLlama3 70B achieved the highest overall accuracy (93.3% strict, 97.1% soft), followed by MedGemma. These larger models showed near perfect performance for explicit and discrete variables and positivity of immunohistochemistry markers, while accuracy decreased for report elements requiring interpretation (e.g., primary diagnosis, interstitial inflammation in fibrosis vs. non-fibrotic cortex). Human raters showed strong agreement for the primary diagnosis ({kappa} = 0.74, 95% CI 0.64-0.84). Adding Llama3 70B or MedGemma as a third rater increased overall agreement (0.82, 95% CI 0.74-0.89 and 0.78, 95% CI 0.69-0.85, respectively), whereas Llama3 8B reduced it. ConclusionsOpen-source LLMs can accurately transform narrative nephropathology reports into a structured and machine-readable format, potentially supporting scalable retrospective cohort building. While some report elements can be extracted without supervision, interpretation-dependent elements should be supervised by a human observer. Lay SummaryRetrospective data collection from nephropathology reports is essential for building informative cohorts in computational nephropathology research, yet manual processing of narrative reports is time-consuming and limits scalability. In this study, we demonstrate that open-source large language models can reliably extract key diagnostic, quantitative, and descriptive data elements from kidney biopsy reports with high accuracy. While factual and clearly stated report elements can be extracted automatically, findings that require contextual or interpretative judgment still benefit from expert supervision. Overall, this approach substantially reduces manual effort and enables efficient generation of structured datasets from diagnostic routine, facilitating the development of kidney registries and future computational nephropathology research. In addition, such systems could be implemented into the routine diagnostic workflow, to directly transform narrative reports into structured data.

Drug-induced liver injury (DILI) is an acute inflammatory liver disease caused not only by prescription and over-the-counter medications but also by health foods and dietary supplements. Typically, DILI patients recover once the causative substance is identified and discontinued. In contrast, autoimmune hepatitis (AIH) results from the immune-mediated destruction of hepatocytes due to a breakdown of self-tolerance mechanisms. Patients presenting with acute-onset AIH often lack characteristic clinical features, such as autoantibodies, and require prompt steroid treatment to prevent progression to liver failure. Liver biopsy currently remains the gold standard to differentiate acute DILI from AIH; however, general pathologists face significant diagnostic challenges due to overlapping histopathological features. This study integrates pathology expertise with deep learning-based artificial intelligence (AI) to differentiate DILI from AIH using histopathological images. Our AI model demonstrates promising classification accuracy (Accuracy 74%, AUC 0.81). This paper presents a detailed pathological analysis alongside AI methods, discusses the current model performance and limitations, and proposes directions for future improvements.

T-cell lymphomas are often histologically indistinguishable from benign T-cell infiltrates. Clonality testing is frequently required for diagnosis. It lacks the spatial context and is slow and expensive, relying on complex, multiplexed PCR reactions, interpreted by experienced scientists or pathologists. We previously published details of a pair of highly specific monoclonal antibodies against the two alternatively used, but very similar, T-cell receptor {beta} constant regions, TCR{beta}1 and TCR{beta}2. We demonstrated the feasibility of immunohistochemical detection of TCR{beta}1 and TCR{beta}2 in formalin-fixed, paraffin-embedded (FFPE) tissue as a novel diagnostic strategy for T-cell lymphomas. Here we validate an improved pairing of TCR{beta}1/2 rabbit monoclonal antibodies, and demonstrate their utility for single and double immunostaining, including with a chimeric mouse anti-TCR{beta}2 antibody. Finally, we show that this staining is amenable to automated cell counting, permitting accurate calculation of the TCR{beta}2:TCR{beta}1 ratio.

Background & AimsThe increasing global prevalence of metabolic dysfunction-associated steatotic liver disease (MASLD) including metabolic dysfunction-associated steatohepatitis (MASH) creates an urgent need for objective methods of histopathological assessment. Conventional histological approaches are time-consuming and rely on interpreters experience. Therefore, the results obtained may suffer from high variability and only offer coarse categorisation. In this study, we propose a fully automated, deep-learning-based pipeline for the segmentation and characterisation of histological liver features for MASH/MASLD assessment. MethodsSegmentation was applied to H&E sections from 45 mice and 44 humans with MASH/MASLD. The method, which we named qHisto (quantitative histology), utilises the nnU-Net framework and quantifies key histological components of the MASH score, including macro- and microvesicular steatosis, fibrosis, inflammation, hepatocellular ballooning and glycogenated nuclei. Additionally, we characterized the tissue using novel features that are inaccessible through manual histology, such as the distribution of fat droplet sizes, aspect ratio of nuclei and heatmaps. ResultsqHisto parameters showed strong positive correlations with conventional histology scores (fat area R=0.91, inflammation density R=0.7, ballooning density R=0.49) and also with quantitative magnetic resonance imaging (fat area vs. hepatic fat fraction R=0.87). Our novel scores showed that deformation of nuclei is driven by large fat droplets rather than the overall amount of fat. ConclusionsA key advantage of our method is spatially resolved, precise histological quantification. These features provide a finely resolved assessment of disease severity than conventional categorical scoring. By automating time-consuming and repetitive readouts, qHisto improves standardisation and reproducibility of MASH/MASLD feature quantification and provides scalable, slide-wide readouts that can support histopathologists and enhance clinical assessment and therapeutic development. Impact and ImplicationsThe proposed method provides an objective, automatic tool for comprehensive, histological liver analysis of MASH/MASLD, which can be extended to other diseases and organs. By offering classic and novel quantitative parameters and scores, our method could support histologists in their daily routines and provide researchers with further insight into steatotic liver diseases.

ObjectiveBrain branks preserve extensive material relevant to neurodegenerative disease research. As these collections age, tissue becomes archival, raising the question of whether long-term fixed and stored human brain tissue remains suitable for contemporary immunohistochemical analyses. Materials and MethodsForty-one autopsy brains collected between 1946 to 1980 were examined. For each case, midbrain and hippocampus were available both as original paraffin-embedded blocks and as tissue stored long term in fixative. New paraffin blocks were prepared from the long-term fixated tissue. Sections from original and newly prepared blocks were immunohistochemically stained for -synuclein, hyperphosphorylated tau and amyloid-{beta}. Immunoreactivity was assessed using semi-quantitative scoring. ResultsOriginal blocks consistently showed good staining intensity and morphological preservation for each protein pathology. Newly prepared blocks showed slightly lower semi-quantitative scores for Lewy-related pathology, without statistically significant differences, except for astrocytic -synuclein in the substantia nigra in cases from the 1960s. Tau pathology displayed modestly reduced labelling, particularly of the neuropil threads and neurofibrillary tangles, most evident in cases from the 1950s. Amyloid-{beta}-positive senile plaques showed similar or slightly higher scores in newly prepared blocks, with no significant differences across regions. ConclusionHuman brain tissue preserved as paraffin-embedded blocks or stored in fixative for up to 78 years remains suitable for immunohistochemical analyses. Adequate-to-good detection of aggregated of -synuclein, hyperphosphorylated tau and amyloid-{beta} is achievable, indicating preserved pathological hallmarks of Lewy Body Disease and Alzheimers Disease in archival tissue.

Meningiomas are the most common primary brain tumors and, despite their benign reputation, often behave aggressively. Meningiomas are morphologically heterogeneous, yet the full significance of their histologic diversity is unclear. This is in large part because many features are not readily quantifiable by traditional observer-based light microscopy. Molecular testing improves prognostic stratification, but is not universally accessible. We therefore sought to determine whether an artificial intelligence (AI)-trained program could predict specific genomic and epigenomic patterns in meningiomas, and whether it could extract more prognostic information out of standard hematoxylin and eosin (H&E) histopathology than the current WHO classification. To do this, we developed Morphologic Set Enrichment (MSE), an interpretable computational pathology framework that quantifies statistical enrichment of morphologic patterns, cells, and tissue architecture from H&E whole-slide images. The MSE meningioma histology program was able to accurately predict DNA methylation subtypes and concurrent chromosome 1p/22q losses, in the process identifying specific morphologic patterns associated with key genomic and epigenomic alterations. It also added prognostic value independent of standard clinical and pathological variables. These results demonstrate that AI-based quantitative morphologic profiling can capture clinically and biologically relevant information that redefines risk stratification for meningiomas, incorporating histological information not included in existing grading schemes.

3D-DXA reconstructs DXA hip scans to 3-dimensional images allowing measurement of trabecular and cortical bone parameters. Given the higher image quality of GE Healthcare iDXA than GE Healthcare Prodigy, it could be hypothesized that the reconstruction might differ, thereby affecting 3D-DXA results. The aim of the study was to assess agreement and precision of 3D-DXA cortical and trabecular femur parameters between Prodigy and iDXA densitometers in adult subjects. The study cohort was composed of 391 men and women recruited from 3 clinical centers (USA and Brazil). All subjects were scanned on either Prodigy or iDXA scanners. Short-term precision was assessed on two Prodigy and two iDXA densitometers. 3D-DXA analyses were performed using 3D-Shaper software version 2.14. Agreement between densitometers was assessed by regression and Bland-Altman analyses. Short-term precision was determined following International Society for Clinical Densitometry recommendations. Strong agreements for 3D-DXA parameters were obtained between devices regardless of the center or the DXA device model (all R2 > 0.96). Bland-Altman analyses demonstrated statistically (p < 0.05), but not clinically, significant difference between both aBMD and 3D-DXA measurements obtained using Prodigy and iDXA scanners. Short-term precision of areal BMD and 3D-DXA parameters was similar between densitometers. This study demonstrated excellent 3D-DXA measurement agreement and similar precision between iDXA and Prodigy densitometers. These data provide evidence that no adjustments are required when using 3D-Shaper software on iDXA or Prodigy instruments. Mini AbstractWe assessed agreement and precision of 3D-DXA parameters between GE Healthcare Prodigy and iDXA densitometers in adults. Strong agreement was observed between devices, and short-term precision was comparable. Findings indicate that no adjustment is needed when using 3D-DXA with GE Healthcare densitometers.

ObjectivePreterm birth (PTB) is a leading cause of neonatal morbidity and mortality worldwide, with India alone contributing nearly 27% of the global PTB burden. Although alterations in the vaginal microbiome have been implicated in PTB, its association in the Indian context is underexplored. This study aimed to investigate the association of vaginal microbiome and PTB in Indian women at the time of delivery. Study designThe vaginal swabs were collected at the time of delivery from 72 women (31 term, 41 preterm) admitted to a tertiary care hospital in Western India. Microbial DNA was extracted, and the V3-V4 region of the 16S rRNA gene was sequenced. Community composition, alpha and beta diversity, and differential taxonomic abundance were assessed using bioinformatics pipelines. ResultsAt the time of delivery, there were no significant differences in alpha or beta diversity between term and preterm groups. Principal coordinate and unsupervised clustering analyses showed no group-wise segregation. The relative abundance of individual Lactobacillus species, including L. iners and L. helveticus, did not differ significantly between the two groups. However, a modest difference in the relative abundance of Streptococcus was observed between the two groups after adjustment. ConclusionThis study found no major microbial shifts in the vaginal microbiome associated with preterm birth in this cross sectional cohort of Indian women, suggesting that vaginal dysbiosis at the time of delivery may not be a principal driver of PTB in this population. These findings underscore the need for larger, longitudinal, and ethnically diverse studies using standardized methodologies better to understand the microbiomes role in PTB risk.

BackgroundSepsis is a major public health challenge, and reliable biomarkers are essential for distinguishing sepsis from other conditions. Neutrophil Gelatinase-Associated Lipocalin (Neutrophil gelatinase-associated lipocalin (NGAL)) has shown promise as a diagnostic marker due to its role in the immune response. This study evaluates plasma NGAL as a diagnostic tool at the time of ICU admission. MethodsWe analysed plasma NGAL and C-reactive protein (CRP) levels in 4732 adult patients admitted to four ICUs between 2015 and 2018. All patients were retrospectively screened for Sepsis-3 criteria at ICU admission. The discriminative performance of NGAL and CRP for sepsis was assessed using receiver operating characteristic (ROC) analysis, with NGAL levels adjusted for Chronic kidney disease (CKD) and age. Patients were stratified by renal function. ResultsPlasma NGAL levels were significantly higher in septic patients (p<0.001). For the whole cohort, NGAL alone yielded an Area under the curve (AUC) of 0.67 (Confidence interval (CI) 0.66-0.69), CRP yielded an AUC of 0.72 (CI 0.71-0.73, p<0.001), and combining NGAL with CRP nominally improved discriminative performance (AUC 0.74 vs 0.72, p<0.001). Stratified analyses indicated that NGAL, together with CRP, significantly outperformed CRP alone in patients with no kidney injury and those with Acute Kidney Injury (AKI) only. In contrast, differences were not significant in patients with CKD only or CKD and AKI. ConclusionIn this large cohort, NGAL showed modest discrimination for sepsis, with a nominal improvement when combined with CRP. These findings do not indicate that NGAL meaningfully improves sepsis diagnosis in the ICU.

Frequent blood testing is a routine but burdensome reality for many children, particularly those with chronic, rare, or medically complex conditions. Repeated clinic, hospital, and laboratory visits can disrupt family life, increase stress for children and caregivers, and limit access to timely monitoring and research participation. Despite advances in pediatric care, blood collection has remained largely tethered to in-person clinical settings. This study validates a new model: safe, effective, parent-administered pediatric blood collection performed at-home. We evaluated the RedDrop ONE capillary blood collection device in a real-world, parent-administered home setting to determine whether non-clinical caregivers can reliably collect clinically meaningful blood samples from children without venipuncture, specialized training, or in-clinic support. Conducted under Institutional Review Board (IRB) oversight, this observational usability study enrolled 50 children aged 3-17 years across a geographically diverse U.S.-based pediatric population, including healthy and medically fragile children with chronic autoimmune and rare diseases. All study activities, including enrollment, consent, instruction, collection, and sample return, were completed remotely, reflecting real-world adoption conditions rather than controlled clinical environments. Parents successfully collected blood samples from their children at home with high consistency, low perceived pain, and strong overall acceptance. Across collections, blood and serum volumes were sufficient and reproducible, and laboratory analysis confirmed strong analytical concordance between samples collected from two different anatomical sites, arm and leg. Parents reported high confidence using the device, short collection times, and a high likelihood of completing collections on the first attempt. Importantly, both parents and children rated the overall experience as better than expected, and parents consistently reported that the RedDrop ONE experience was superior to traditional finger-prick and needle-based venous blood draws. Parents reported minimal child discomfort and greater flexibility by avoiding in-clinic phlebotomy visits. These benefits are especially meaningful for families managing chronic or rare pediatric conditions that require repeated blood monitoring. By enabling blood collection at-home, this model reduces travel burden, scheduling constraints, and procedural anxiety while maintaining analytical reliability. This study also demonstrated that parent-administered pediatric blood collection can support real-world clinical workflows beyond research. All samples were successfully shipped overnight at ambient temperature and processed by a CLIA-certified laboratory, supporting feasibility for remote pediatric patient monitoring and decentralized clinical trials. While lipid testing served as the representative clinical use case, the volumes and consistency achieved exceeded volume thresholds commonly required for advanced downstream applications, including proteomics, metabolomics, transcriptomics, and genomic analyses. Taken together, these findings validate parent-administered pediatric blood collection as a practical, scalable alternative to in-clinic phlebotomy for many use cases. By shifting blood collection from the clinic to the home, this approach has the potential to reduce reliance on in-person phlebotomy, integrate seamlessly into routine pediatric care, and expand access to monitoring and research for families who face geographic, logistical, or medical barriers. For health systems, researchers, and parents alike, this study supports a future in which clinically meaningful pediatric blood collection is no longer limited by healthcare facility location but instead centered on the child and family.

PurposePrognosis in metastatic non-seminomatous germ cell tumors (mNSGCT) is currently guided by the IGCCCG classification, which incorporates tumor markers, organs involved with metastatic disease, and primary site but not histologic subtype. We aimed to evaluate whether specific histological components provide additional prognostic information in a large international mNSGCT cohort. Patient and MethodsWe analyzed clinical, pathologic, and outcome data from 662 patients with mNSGCT across multiple international centers. Cox regression and multivariable stepwise models were used to evaluate the impact of age, tumor histology, serum markers, primary site of disease, chemotherapy, IGCCCG, and post-chemotherapy surgery on overall survival. Analyses were performed using both complete-case and imputed datasets to account for missing values. ResultsThe presence of any percentage of embryonal carcinoma (EC) was independently associated with improved overall survival HR 0.603 (95% CI: 0.37-0.98, p=0.040), whereas yolk sac tumor (YST) predicted worse prognosis in complete-case analysis HR 2.27 (95% CI: 1.43 - 3.61 p = 0.001). Choriocarcinoma was also associated with a HR 1.58 (95% CI: 1.08 - 2.32 p= 0.019) adverse outcomes. IGCCCG risk classification remained a strong predictor of mortality HR up to 8.9 for Poor vs Good risk, (95% CI: 4.63 - 17.09 p < 0.001), but histologic components added significant independent prognostic value. Post-chemotherapy retroperitoneal lymph node dissection (RPLND) conferred a substantial survival benefit HR 0.44 (95% CI: 0.258 - 0.754 p=0.003). Interestingly, teratoma was not associated with mortality but was linked to younger age, testicular primaries, and higher likelihood of residual disease requiring surgery. ConclusionsHistological composition, particularly the presence of EC or YST, has a significant and independent impact on survival in mNSGCT, beyond established risk classifications. Integration of histological subtypes may enhance prognostic accuracy and guide individualized treatment strategies in advanced germ cell tumors.

ContextPolycystic ovary syndrome (PCOS) is a heterogeneous endocrine disorder associated with reproductive dysfunction and long-term cardiometabolic risk. Traditional phenotype classifications based on diagnostic criteria may not fully capture the multidimensional biological variability underlying endocrine and metabolic risk profiles, particularly in young women. ObjectiveTo identify data-driven endocrine-metabolic phenotypes in young women with PCOS and evaluate their association with established cardiometabolic risk markers. Design and SettingCross-sectional study conducted at a tertiary Gynecological Endocrinology Clinic in Poland between January 2018 and May 2025. ParticipantsA total of 1300 young women diagnosed with PCOS according to Rotterdam criteria were included. The primary analytic cohort comprised 1032 participants aged 16-25 years with complete endocrine-metabolic biomarker data. Main Outcome MeasuresEndocrine-metabolic phenotypes were derived using principal component analysis followed by Gaussian mixture model clustering. Cardiometabolic risk endpoints included impaired glucose tolerance (2-hour plasma glucose during an oral glucose tolerance test [&ge;]140 mg/dL), an atherogenic lipid profile (triglycerides (TG)/high-density lipoproteins (HDL-C) ratio >3.50), elevated non-HDL cholesterol ([&ge;]130 mg/dL), and a composite outcome of any abnormality. ResultsPrincipal component analysis retained 10 components explaining 81.9% of total variance. Unsupervised clustering identified two stable phenotypes (silhouette = 0.392; ARI = 0.842). Cluster 0 (n=954; 92.4%) represented a mixed endocrine-metabolic profile, whereas cluster 1 (n=78; 7.6%) was enriched for thyroid/autoimmune features, with higher anti-thyroid peroxidase antibody levels and higher thyroid-stimulating hormone. Cluster 1 showed a higher prevalence of an atherogenic lipid profile compared with cluster 0, while differences in glucose intolerance and non-HDL cholesterol were modest. Logistic regression analyses suggested phenotype-specific variation in cardiometabolic risk markers. ConclusionsIn a large cohort of young women with PCOS, data-driven analysis identified two reproducible endocrine-metabolic phenotypes, including a distinct thyroid/autoimmune-enriched subgroup. These findings highlight clinically relevant heterogeneity beyond traditional diagnostic phenotypes and support the potential value of integrated endocrine-metabolic profiling for early risk stratification in PCOS.

BackgroundPreterm premature rupture of membranes (PPROM) is a leading contributor of adverse perinatal outcomes, particularly in low-resource and conflict-affected settings. Despite its clinical importance, prospective evidence on its impact on composite adverse perinatal outcomes in northern Ethiopia remains limited. This study examined the impact of Preterm premature rupture of membranes on composite adverse perinatal outcomes and identified associated predictors among pregnant women in public hospitals of Tigray, Northern Ethiopia. MethodsA hospital-based prospective cohort study was conducted among 578 singleton pregnancies (288 with Preterm premature rupture of membranes and 290 without it at [&ge;]28 weeks of gestation. Participants were followed from admission to delivery and to the early neonatal period. The primary outcome was a Composite adverse perinatal outcome, and the main exposure variable was Preterm premature rupture of membranes (PPROM). Modified Poisson regression with robust variance estimation was used to estimate adjusted relative risks (ARRs) with 95% confidence intervals (CIs) and a significant level was declared at p<0.05. ResultsOverall, 33.4% of neonates experienced at least one composite adverse perinatal outcome. The incidence was substantially higher among the PPROM group compared with the non-PPROM group (59.4% vs. 7.6%). After adjustment, PPROM was strongly associated with composite adverse perinatal outcomes (ARR = 7.22, 95% CI: 4.73-11.03). Independent predictors included previous pregnancy-related infection (ARR = 1.54; 95% CI: 1.08-2.22), absence of iron-folate supplementation during pregnancy (ARR=1.63; 95% CI: 1.153-2.29), pelvic pain (ARR = 2.09; 95% CI: 1.05-4.15), and latency period of 1-3 days (ARR = 1.41; 95% CI: 1.10-1.81) compared to <24 hours. Induced labor was protective (ARR=0.58; 95% CI: 0 .422-0.800). ConclusionPPROM markedly increases the risk of composite adverse perinatal outcomes in this post-conflict, resource-constrained setting. The first 72 hours following membrane rupture represent a particularly vulnerable period. Strengthening antenatal care, nutritional supplementation, infection prevention, and timely obstetric intervention could reduce preventable neonatal morbidity and mortality in similar contexts.

ObjectivesTo determine temporal trends in the rates of preterm birth and its sub-types in Sweden and assess the contribution of known-risk factors. DesignA population-based register study. SettingSweden. Participants (Instead of patients or subjects)3,264,146 pregnancies registered in the Swedish Medical Birth Registry with information on pregnancy duration and onset of labour (1991 - 2021). Main outcome measuresThe primary outcomes were the overall, spontaneous and iatrogenic preterm birth rates between 1991 - 2021, stratified on singleton and multiple births, as well as for extremely preterm (<28 weeks, <196 days), very preterm (28-31 weeks, 196 - 224 days), moderately preterm (32 - 33 weeks, 224 - 238 days), and late preterm (34 - 36 weeks, 238 - 259 days) births. Using logistic regression models, we investigated whether maternal age at conception, use of artificial reproductive technologies, smoking, parity, and maternal continent of birth were associated with the observed trends. ResultsThe overall preterm birth rate was stable between 1991 - 2005 at 5.50% (95% CI: 5.37%, 5.63% in 1991) but decreased thereafter to 4.78% (95% CI: 4.66%, 4.91%) in 2021, a finding confined to spontaneous preterm births. The largest decline was observed in late preterm births, from 3.92% (95% CI: 3.80%, 4.05%) in 2005 to 3.52% (95% CI: 3.41%, 3.63%) in 2021. Moderately preterm birth also declined (0.70%, 95% CI: 0.65%, 0.76% in 2005 to 0.53%; 95% CI: 0.49%, 0.58% in 2021), whereas very-extremely preterm birth did not. Decreased spontaneous preterm birth rates were observed in women born in European, Asian and African countries, with largest decline observed in the latter (rate in 1991 = 2.65%, 95% CI: 1.74%, 3.86%; rate in 2021 = 1.72%, 95% CI: 1.42%, 2.07%). Adjusting for maternal and obstetric risk factors didnt alter these associations. ConclusionsWhile rates of preterm birth have been stable or increased globally, they have decreased in Sweden from 2006 - 2021, despite the lack of any nation-wide preventive strategy during this period. Understanding the reasons for this decline will provide useful strategies to make the decline a rule, rather than an exception.

BackgroundMaternal participation in neurodevelopmental research involving neuroimaging and diverse biological samples is essential for understanding prenatal influences on early brain development, yet willingness in low-resource settings remains underexplored. MethodWe surveyed 300 mothers using a structured questionnaire to assess willingness to undergo brain health testing (with a focus on electroencephalography [EEG] and brain magnetic resonance imaging [MRI]), provide biological samples (blood, stool, urine, breast milk, placenta, amniotic fluid, vaginal/nasal fluid, saliva, tears), and consent to 10-year storage. Responses were analysed to examine associations between maternal sociodemographic factors and willingness to consent for each research component. ResultsNinety-two percent of participants expressed willingness for brain health testing, including [~]82% and [~]88% interest in EEG and MRI, respectively, even for untreatable conditions. Self-reported histories of foetal defects (5.3%) and birth defects (7.3%) were notably low. Biospecimen acceptance was highest (>95%) for routine samples (blood, stool, urine) but significantly low for sensitive specimens (breast milk, placenta, amniotic fluid: 51-55%) including (vaginal fluid, saliva, tears: 16-47%). Higher levels of maternal education consistently predicted consent across modalities, while being in a relationship increased willingness for stool, urine, placenta, amniotic fluid, MRI, and EEG. Low income reduced uptake for placenta, amniotic fluid, MRI, and EEG. Only 48% consented to 10-year storage of images and samples for future research. ConclusionThis study demonstrates high maternal willingness for neurodevelopmental research involving brain health testing and routine biospecimens in a low-resource setting. The findings highlight the feasibility of such protocols in a low-resource setting while exposing persistent inequities that risk underrepresenting disadvantaged mothers in maternal-child brain research. Contextually tailored consent models and capacity-building initiatives will be essential to ensure equitable, sustainable engagement across diverse LMIC populations.