Journal of Cystic Fibrosis

Background and AimMASLD affects 30-38% of Indian adults, yet the contribution of genetic risk variants to disease susceptibility and fibrosis progression remains poorly characterised. We investigated the association of 12 candidate SNPs with MASLD susceptibility and fibrosis severity in North Indian patients, benchmarking allele frequencies against IndiGenomes and global populations. MethodsSixty-nine MASLD patients (75.4% male; median BMI 29.8 kg/m{superscript 2}) from a tertiary care liver clinic in New Delhi were genotyped for 12 SNPs using Illumina custom BeadChip array and Sanger sequencing. Patients were stratified by liver stiffness measurement (LSM): significant fibrosis ([&ge;]8 kPa, n=38) versus no significant fibrosis (<8 kPa, n=31). Allele frequencies were compared with IndiGenomes ([~]1,020 Indian individuals) and 1000 Genomes populations. ResultsPNPLA3 rs738409 G allele was the strongest within-cohort predictor of significant fibrosis (allelic OR 2.89, 95% CI 1.35-6.19, P=0.006; dominant model OR 3.94, P=0.008), with carriers demonstrating higher LSM (median 15.6 vs. 7.5 kPa, P=0.005). SAMM50 rs3761472 (OR 2.12, P=0.065) and FTO rs9939609 (OR 2.08, P=0.089) showed non-significant trends. In the population-level comparison, APOC3 rs2854116 T allele was the only variant significantly enriched after Bonferroni correction (64.0% vs. 47.9%; OR 1.93, 95% CI 1.35-2.77, P<0.001), followed by PNPLA3 (33.3% vs. 24.1%, OR 1.57, P=0.019) and SAMM50 (31.2% vs. 22.6%, OR 1.55, P=0.028). Notably, APOC3 showed no association with fibrosis (OR 0.96, P=1.000), suggesting a role in susceptibility rather than progression. All SNPs were in Hardy-Weinberg equilibrium. ConclusionsThis study reveals a dissociation between genetic determinants of MASLD susceptibility and fibrosis progression in North Indian patients. APOC3 rs2854116 predisposes to MASLD at the population level, while PNPLA3 rs738409 drives fibrosis severity within established disease, underscoring the need for ancestry-specific genetic risk stratification. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=112 SRC="FIGDIR/small/26347059v1_ufig1.gif" ALT="Figure 1"> View larger version (69K): org.highwire.dtl.DTLVardef@187f189org.highwire.dtl.DTLVardef@25d3borg.highwire.dtl.DTLVardef@13704e9org.highwire.dtl.DTLVardef@1238cce_HPS_FORMAT_FIGEXP M_FIG C_FIG

Background Short-chain fatty acids (SCFAs) are widely used as functional readouts of gut microbial activity in vivo. The growing adoption of decentralised study designs and self-collection protocols has amplified the need for reliable room-temperature storage and shipment strategies. However, SCFAs volatility and the persistence of post-collection microbial metabolism raise concerns regarding pre-analytical stability and the interpretability of measured concentrations. Methods We assessed the temporal stability of fatty acids (FAs) across intestinal and systemic matrices under room-temperature storage. Untreated stool was compared with two nucleic acid stabilisation devices (eNAT and OMNIgene-GUT), while whole blood, plasma and dried blood spots (DBS) were evaluated as minimally invasive systemic sampling strategies. Profiles were quantified using complementary GC-MS and LC-MS/MS workflows. Results Untreated stool showed fermentation-driven increases in major SCFAs, whereas immediate freezing preserved baseline profiles. eNAT maintained faecal FA stability for up to 21 days, while OMNIgene-GUT exhibited baseline and time-dependent alterations. In systemic matrices, plasma and whole blood showed upward drift, whereas DBS declined initially before stabilising after approximately 14 days. Conclusions FA measurements are highly matrix- and device-dependent. Our findings provide practical guidance for the selection of sampling strategies in microbiome-associated FA studies and emphasise the need for controlled pre-analytical conditions in decentralised microbiome studies.

Background and aimsPopulation screening for liver disease in high-risk groups is recommended. Community diagnosis of liver disease is a challenge due to the asymptomatic nature of disease until very advanced stages. Moreover, regional variation in testing availability can result in people with clinically significant liver disease being missed. Machine learning (ML) has been proposed as a method to reduce diagnostic error and automate screening. We present a novel machine learning derived algorithm (ID LIVER-ML) designed to predict the risk of clinically significant liver disease in a high-risk community population to identify those needing further investigations or specialist referral. MethodsUsing data from 2039 patients recruited to two UK cohorts, we created a parsimonious model using investigations that would be available in primary care using liver stiffness measurement as reference standard. The performance of ID LIVER-ML was compared against FIB-4 score in a second unseen hold out cohort (n=327). ResultsID LIVER-ML performed well at identifying patients at risk of clinically significant liver fibrosis (sensitivity 0.90, Specificity 0.43, PPV 0.54, NPV 0.86, AUC 0.83) and outperformed conventional risk scoring systems (FIB-4: AUC 0.65; NAFLD Fibrosis Score: AUC 0.66; APRI: AUC 0.53; BARD: AUC 0.58). ConclusionMachine learning derived algorithms can help screen high risk populations in a community setting for liver fibrosis. ClinicalTrials.gov ID: NCT04666402 Impact and ImplicationsThe prevalence of steatotic liver disease is rising globally and is an increasingly significant challenge for healthcare systems. Existing risk stratification scores are not validated in a real-world cohort where patients have risk factors for multiple aetiologies of liver disease. Our work shows that a machine learning model can predict the risk of clinically significant liver disease using routine primary care data, better than existing non-invasive risk stratification tools in a real-world cohort. This highlights a potential role for machine learning in the automation of fibrosis risk assessment in primary care. Highlights- Machine learning derived algorithms can predict the risk of clinically significant liver disease in an at risk community population with a mixed aetiology of liver diseases. - The performance of the ML algorithm (ID LIVER-ML) is not affected by metabolic, alcohol, or mixed aetiologies. - ID LIVER-ML outperforms traditional risk stratification scoring systems such as FIB-4 and NAFLD fibrosis scores. - Compared to the FIB-4 score, the use of Machine Learning can reduce the need for secondary care investigations by 59%.

PurposeHirschsprung-associated enterocolitis remains a major postoperative complication of Hirschsprungs disease (HD), and impaired epithelial barrier integrity has been proposed as a contributing factor. In this study, we investigated whether 12-hydroxyheptadecatrienoic acid (12-HHT), an endogenous leukotriene B4 receptor 2 (BLT-2) agonist, enhances the epithelial barrier and exerts anti-inflammatory effects in patient-derived colonic organoids. MethodsNormoganglionic specimens from rectal/rectosigmoid HD at pull-through (HD-N; n = 8) and transverse colon specimens from anorectal malformation (ARM) at colostomy closure (n = 10) were used to generate colonic organoids. Epithelia were isolated using ethylenediaminetetraacetic acid and subsequently embedded in Matrigel. Baseline expression of TJP1, TJP2, F11R (encoding junctional adhesion molecule-A), JAM2, CLDN1, CLDN3, CLDN4) and LTB4R2 (encoding BLT-2) was assessed by qPCR and immunoblotting. Organoids were then treated with 12-HHT (0.4, 2, or 10 M) for 7 days, followed by qPCR. Additional experiments assessed cytokine expression (IL1B, IL6) and TJPs after 24 h with tumor necrosis factor- (TNF-, 100 ng/mL) plus phosphate buffered saline or 12-HHT. Barrier function was evaluated using FITC-dextran influx assays. ResultsHD-N and ARM organoids exhibited similar growth efficiencies. Baseline expression for F11R, JAM2, CLDN1, CLDN3, CLDN4, and LTB4R2 was significantly lower in HD-N than in ARM. TJPs were upregulated by 12-HHT at 2 and 10 M in both groups, with stronger effects in ARM. In HD-N organoids, 10 M 12-HHT suppressed TNF--induced IL1B and IL6 elevation mitigated tight junction proteins (TJPs) downregulation more effectively than 2 M. 12-HHT attenuated TNF--induced FITC-dextran influx in HD-N organoids. Conclusion12-HHT may exert anti-inflammatory effects by integrating TJPs of HD-N.

ObjectivesTo characterize ultra-processed food (UPF) circulating metabolic signatures associated with Crohns disease (CD) and to localize key metabolic mediators linking UPF intake to CD risk. DesignProspective cohort study. SettingTwo large multi-center cohorts (UK Biobank [UKB] and Whitehall II [WHII] study) across the UK and an Eastern multi-center cohort ONE-IBD Study from China. ParticipantsUK Biobank discovery cohort (n=10,229) for signature derivation, internal validation cohort (n=91,306), external validation cohort Whitehall-II (n=7,893), and three additional cohorts (two Western and ONE-IBD) for validation of key metabolic drivers. Main outcome measuresPrimary outcomes were UPF-related circulating metabolic signatures and their associations with CD risk; secondary outcomes included evidence supporting causal roles of candidate metabolites and genetic pathways assessed by Mendelian randomization, colocalization, and gene-environment analysis. ResultsA UPF metabolic signature of 73 metabolites was constructed and validated across cohorts (Spearman {rho}: 0.20-0.25). More pronounced UPF metabolic signature was associated with increased CD risk (HRper SD=2.65, 95% CI 1.57-4.48). WGCNA revealed a cluster enriched in fatty acids. Within this cluster, docosahexaenoic acid (DHA) emerged as the strongest, which mediated 17.1% of the UPF-CD association. External validation in ONE-IBD supported DHA as the strongest associated metabolite with UPF and CD. Mendelian randomization supported a causal protective effect of DHA on CD (OR=0.72, 95% CI 0.61- 0.83; P<0.001), with colocalization implicating rs174546 in the FADS1 gene. ConclusionThe adverse effects of UPF on CD risk may be driven by a relative deficiency of protective metabolites such as DHA, apart from additive harm to metabolic depletion. This reframes UPF-related risk and highlighting potential targets for precision nutrition in CD prevention.

Respiratory monitoring in daily-life settings is important for health assessment, yet extracting physiologically interpretable information from breathing signals under natural conditions remains challenging, as breathing is inherently dynamic and strongly modulated by behavior. Here, a portable breathing monitoring device based on a flexible lead zirconate titanate sensor is developed to address this challenge. By exploiting polarity-opposed piezoelectric and pyroelectric responses through sensor orientation, the recorded breathing waveform exhibits a characteristic dual-component structure, consisting of a narrow transient spike followed by a broad quasi-steady peak within each breathing phase. This intrinsic waveform structure enables phase-resolved quantification of how breathing effort is distributed between transient and quasi-steady components during inhalation and exhalation. Pilot measurements in healthy subjects and patients with chronic obstructive pulmonary disease or asthma reveal systematic shifts toward transient-enhanced breathing in patients, providing clearer differentiation than conventional descriptors based on breathing duration or amplitude. By transforming complex breathing dynamics into stable and physiologically meaningful signal components under daily-life conditions, this dual-response sensing approach enables more robust access to function-related changes in natural breathing.

BackgroundGestational exposures may contribute to the newborns lifetime risk of inflammatory bowel disease (IBD). While gestational influences are associated with IBD onset, the causality and confounding of such exposures are difficult to ascertain. The neonatal metabolome provides a metabolic snapshot of gestational influences. ObjectiveWe tested the neonatal metabolomes ability to predict future IBD, to assess whether gestational exposures are reflected in early molecular precursors of the disease. MethodsWe profiled dried blood spots from 520 newborns who later developed IBD and matched controls using high-resolution untargeted mass spectrometry metabolomics (1,350 QC-passing metabolites). Genotyping was available for 1,009 of these individuals. PERMANOVA confirmed assay sensitivity to gestational exposures, gradient boosting was used for prediction. ResultsThe neonatal metabolome significantly captured maternal smoking, birth weight, and gestational age (p < 0.001), but explained minimal variance in IBD status (R2 = 0.09%, p = 0.390) and showed no predictive power for IBD (AUC = 0.51, 95% CI 0.50-0.52, p = 0.585). Stratifying by disease subtype and age of onset did not improve performance. In contrast, genetic risk scores were modestly predictive (CD: AUC = 0.64, p < 5.11x10-14; UC: AUC = 0.63, p < 7.65x10-{superscript 1}{superscript 2}), but uncorrelated with neonatal metabolomic profiles (CD: p = 0.650; UC: p = 0.970), suggesting a later-age effect. ConclusionsUsing a large, comprehensively profiled cohort, we demonstrate that neonatal metabolomic profiles sensitively capture gestational signatures, but not the overall future IBD risk. Our findings suggest that most IBD risk accumulates later in life, beyond gestational molecular imprints.

Background: Interstitial lung abnormalities (ILA) are radiologic findings of increased lung density or fibrosis in individuals without clinical interstitial lung disease (ILD) and are associated with increased mortality and progression to ILD. Understanding physiologic trajectories of lung function preceding ILA diagnosis may illuminate early mechanisms of lung injury. Methods: We recruited participants from the Coronary Artery Risk Development in Young Adults (CARDIA) Lung Study, a prospective cohort of adults enrolled at ages 18 to 30 years and followed longitudinally for 25 years. Percent predicted forced vital capacity (ppFVC) was measured at five study visits over 20 years. Individual ppFVC trajectories were estimated using random coefficient models. Person specific slopes were incorporated into logistic regression models to examine associations with visually detected ILA on chest CT at exam year 25. Models were adjusted for age, sex, race, body mass index, pack years of smoking, and study center. Results: Among 3,136 participants with complete data, 57 (1.8%) had ILA at mean age 51 years. In univariable and multivariable models, individuals with ILA had greater cumulative decline in ppFVC over the 20 years preceding diagnosis. Each 10% absolute decline in ppFVC was associated with more than twice the odds of ILA (adjusted OR 2.21; 95% confidence interval 1.47, 3.31; p = 0.0001). Conclusions: Greater longitudinal decline in FVC from early adulthood was strongly associated with the presence of ILA at midlife. These findings suggest that physiologic impairments precede radiologic evidence of subclinical parenchymal lung abnormalities, underscoring the potential of life course lung function trajectories to identify individuals at risk for developing ILD.

BackgroundCurrent British Society of Gastroenterology (BSG) guidelines misclassify metachronous lesion risk after polypectomy in approximately 40% of patients. Building on evidence that immune exclusion drives progression of adenomas to colorectal cancer, this study examined immune profiles in screen-detected adenomas as a predictive biomarker for metachronous lesion risk. MethodsPatients undergoing polypectomy within the Scottish Bowel Screening Programme, with surveillance colonoscopy between 6 months and 6 years were included. Chromogenic immunohistochemistry (IHC; n=2642), 6-plex multiplex immunofluorescence (mIF; n=334), and spatially resolved 6000-plex single cell transcriptomics (n=7) were applied to adenoma microarrays. Cell density and location were measured using QuPath. Hierarchical then K-means clustering was used to define immune cell density-based clusters, which were compared to future lesion events using Kaplan-Meier curves and the log rank test. ResultsAfter adjustment for age, sex, site, size and dysplasia, adenoma CD3+ T cell density was significantly associated with future colorectal neoplasia (HR 1.43, 95% CI 1.19-1.71, p<0.001). Using mIF three immune cell density clusters were identified; 1) high T cell density, low macrophage density, 2) low T cell density, low macrophage density, and 3) high T cell, macrophage and SMA density, with significant differences in future lesion risk (Cluster 1: 22%, Cluster 2: 41%, Cluster 3: 36%, p=0.032). Bulk RNAseq and spatial transcriptomic analysis revealed significant variation in T cell and macrophage co-location and gene expression profiles between clusters. ConclusionAdenoma immune contexture emerges as a determinant of future metachronous lesion risk, offering a novel biomarker to refine surveillance and reduce disease burden. SummaryWhat is already known on this topic: O_LIPost-polypectomy surveillance is currently recommended to patients with high-risk pathological features to detect metachronous lesions and cancer. However current guidelines misclassify risk in a proportion of patients, leading to unnecessary surveillance for some, whilst falsely reassuring others. C_LI What this study adds: O_LIAnalysis of this large post-polypectomy surveillance cohort reveals that adaptive immune responses within removed index adenomas predicts low risk of metachronous lesions, while an immune excluded phenotype signals higher risk, independent of pathological characteristics, and patient risk factors. C_LI How this study might affect research, practice or policy: O_LIDefining immune cell spatial distributions and interactions that drive future adenoma and cancer risk will enable more precise risk stratification for surveillance, informing surveillance guidelines and shaping targeted colorectal cancer prevention strategies. C_LI

Background and AimsCholedocholithiasis (CDL) is a common condition that can lead to serious complications, requiring effective risk stratification for timely intervention. While current guidelines use clinical predictors, imaging, and laboratory markers for risk assessment, the role of glutamate dehydrogenase (GLDH) in CDL remains poorly understood. This study aims to evaluate its potential as a clinical biomarker for identifying patients with CDL. MethodsThis single-center cohort study identified 23,103 patients who presented to the emergency department of the University Medical Center Hamburg-Eppendorf and underwent routine abdominal laboratory testing between May 2021 and December 2023. Patients were classified into CDL and other diagnoses. To assess the predictive value of age, sex and laboratory markers for CDL, we developed a random forest machine learning model, conducted a backward stepwise logistic regression and performed receiver operating characteristic (ROC) analysis. Results152 patients were diagnosed with CDL and 22,951 with other diagnoses. In the random forest machine learning model, GLDH emerged as the most significant feature for predicting CDL. ROC analysis revealed that GLDH had the highest area under the curve of 0.93 among laboratory markers. At the upper limit of normal, GLDH demonstrated the best sensitivity (92%) compared to aspartate aminotransferase (AST), alanine aminotransferase (ALT) and bilirubin. High GLDH levels exceeding 150 U/L demonstrate the highest specificity (99%) for CDL, outperforming AST, ALT and bilirubin. ConclusionGLDH outperforms AST, ALT and bilirubin as a screening and predictive marker for CDL, supporting its inclusion in clinical guidelines for risk stratification.

Background and AimsThe prognosis of interstitial lung diseases (ILDs) other than idiopathic pulmonary fibrosis (IPF) has not been studied as extensively as IPF. This study aimed to evaluate baseline factors associated with mortality in non-IPF ILD, including demographic characteristics, respiratory function test (RFT), comorbidities, and ILD subtypes. MethodsThis retrospective cohort study analysed prospectively collected data of patients with non-IPF ILD at a single tertiary centre in Malaysia (2010-2023). Patients without baseline RFT or HRCT were excluded. Survival was assessed using Kaplan-Meier analysis, and mortality predictors were identified using Cox regression. ResultsThe mean age was 60 {+/-} 15 years, with a male-to-female ratio of 1:3. Indian ethnicity constituted the largest group (n = 109, 47.6%). The mean baseline forced vital capacity (FVC) was 53.3 {+/-} 21% predicted. An FVC <50% predicted, age [&ge;]50 years at diagnosis, specific ILD subtypes, and ethnicity were independently associated with mortality. Compared with Malays, both Chinese (hazard ratio [HR] 9.86, 95% confidence interval [CI] 1.27-76.89, p = 0.037) and Indians (HR 8.59, 95% CI 1.14-64.69, p = 0.001) were associated with a higher risk of death. Kaplan-Meier analysis demonstrated significant differences in survival across non-IPF ILD subtypes (log-rank p = 0.048), with hypersensitivity pneumonitis showing the poorest prognosis (mean survival 6.1 years). ConclusionEthnicity emerged as an independent prognostic factor for mortality in non-IPF ILD. The underlying mechanisms remain unclear and may reflect differences in genetic variation, cultural factors, or environmental exposures. Larger prospective studies are required to validate these findings.

Background and ObjectiveThe dysbiosis of human gut microbiome has been increasingly seen to have a relation in the development of autoimmune diseases, with specific microbial signatures having causative association with specific conditions. Inflammatory bowel disease (IBD) is one such autoimmune ailment. This paper proposes a predictive tool that can identify the IBD status of an individual based on the composition of the gut microbiome using machine learning and AI agents driven techniques. The technology can strengthen the suspicion of a potential IBD diagnosis a patient may have based on their gut microbiome profile. MethodsThe tool processes patient gut metagenome using integrated Kneaddata and MetaPhlAn to generate taxonomic profiles. These are fed into an XGBoost classifier to predict IBD or healthy status. Dysbiotic taxa are identified via Z-score and fold change. CrewAI delivers personalized probiotic recommendations based on diagnosis and dysbiosis. ResultsThe tuned XGBoost model achieved 86.6% accuracy. On validation using single ulcerative colitis sample, the tool correctly predicted IBD status but misclassified it as Crohns disease(possibly due to overlapping microbial signatures), identifying Faecalibacterium and Flavonifractor as dysbiotic taxa.The probiotic recommended was Faecalibacterium prausnitzii, backed with reasoning basedon scientific literature. ConclusionsDespite limited validation sample size, the high accuracy, correct IBD detection, dysbiosis analysis and elaborate probiotic recommendation suggest promising potential; further validation needed

ObjectiveMyositis-associated interstitial lung disease (myositis-ILD) consists of two predominant radiologic patterns of lung injury--nonspecific interstitial pneumonia (NSIP) and organizing pneumonia (OP)--that oftentimes coexist. However, it remains unclear whether either is associated with clinical outcomes. We aimed to assess the therapeutic response in patients with NSIP-compared to those with OP-predominant myositis-ILD. MethodsThis retrospective, single-center cohort study recruited participants from the Northwestern University ILD Registry with a circulating myositis-associated antibody, ILD, and at least 6 months of follow-up while on immunomodulatory therapy during a 24-month observation period after diagnosis. Two thoracic radiologists determined the predominant radiologic pattern (NSIP or OP). The primary outcome was the absolute change in forced vital capacity (FVC) at 24 months post-diagnosis. Secondary outcomes included changes in the diffusing capacity of the lung for carbon monoxide (DLCO) and radiologic qualitative and quantitative measures of lung injury. ResultsForty-one participants were included in analyses. 71% had an OP-predominant while 29% had an NSIP-predominant radiologic pattern of lung injury. Both exposure cohorts had improvement in mean absolute FVC (OP cohort = +0.18L [p=0.005], NSIP cohort = +0.24L [p=0.07]) over the 24-month observation period. The OP (p<0.05) but not the NSIP cohort (p=0.20) had an increase in DLCO. The OP cohort demonstrated improvement in the qualitative assessment of follow-up imaging (p<0.05), driven by quantitative improvement in groundglass/consolidative opacities (p=0.006). A subset of participants demonstrated features of NSIP/OP overlap and had greater baseline radiologic severity of lung injury. ConclusionPatients with circulating myositis-associated antibodies and an OP-predominant pattern of lung injury may have a more favorable response to therapy than those with NSIP. Further studies are needed to validate our findings and delineate other features cognate with these associations. Significance and InnovationsO_LIRadiologic phenotyping may predict therapeutic response in myositis-ILD. This study demonstrates that an OP-predominant computed tomography (CT) pattern of lung injury is associated with greater improvement in lung function and radiologic signs of inflammation over 24 months on at least 6 months of immunomodulatory therapy compared with an NSIP-predominant pattern, suggesting that CT pattern may provide clinically meaningful prognostic information. C_LIO_LIFirst study to integrate blinded qualitative radiologic adjudication with quantitative CT scoring in myositis-ILD. By combining dual-radiologist review with Kazerooni quantitative scoring and longitudinal pulmonary function testing, this study offers a rigorous and multidimensional assessment of treatment response. C_LIO_LIExpands risk stratification beyond antibody-based toward imaging-based phenotyping strategies. In a heterogeneous population defined by diverse myositis-associated antibodies, this work introduces radiologic pattern as a practical and accessible framework for anticipating treatment responsiveness. C_LIO_LIProvides hypothesis-generating data for precision management in myositis-ILD. The findings support the concept that imaging-defined subgroups may exhibit differential therapeutic trajectories, laying groundwork for future multicenter studies integrating CT phenotype, antibody profile, and treatment strategy. C_LI

Identification of early interventions to reduce/eliminate asthma - the most common chronic disease among children - could significantly reduce burden on the healthcare system. Large-scale asthma Exposome-Wide Association Studies (ExWAS) could identify potential interventions, however integration of diverse data is required to address association confounders. The CHILD Cohort Study has followed 3,454 healthy Canadian children and their families from early pregnancy, collecting exceptionally diverse data including 24,852 variables from participant questionnaires, clinical data, household and neighbourhood-level exposures, and sample-derived chemical analytic/omic datasets. Here, we report integration of these datasets into the CHILDdb database platform, and use these data to perform ExWAS and machine learning analyses, identifying and further characterizing associations between childhood asthma and 2,954 diverse early exposures (pregnancy to age 5). Significant asthma associations include antibiotic use, human milk components, DEHP phthalate, and mothers prenatal cleaning product/disinfectant exposure. Subsequent analysis revealed epigenetic changes in the cord blood at birth, after prenatal cleaner exposure, and different microbiome and/or inflammatory cytokine changes associated with different asthma-associated exposures in the child. Collective results support asthma as a heterogeneous condition involving multiple etiologies, with associated endotypes, including prenatal exposures with potential transgenerational effects, and suggest targets for early interventions.

BackgroundComprehensive data on the prevalence of Helicobacter pylori infection and its associated risk factors among patients with gastrointestinal symptoms remain limited. Generating this evidence would help inform clinical management and improve antibiotic stewardship. H. pylori infection affects a substantial proportion of the global population, with prevalence varying widely across regions. In Uganda, previous studies have documented the presence of H. pylori infection. However, data specific to symptomatic patients are scarce. This study therefore aimed to determine the prevalence of H. pylori infection and associated factors among patients with gastrointestinal symptoms attending Mulago National Referral Hospital in Kampala, Uganda. MethodsA cross-sectional study was conducted among 353 patients with gastrointestinal symptoms attending Mulago Hospital. Data on socio-demographic characteristics, lifestyle and dietary habits, and medical history were collected using a semi-structured questionnaire. H. pylori infection status was determined using stool antigen tests. Proportions were used to determine the prevalence of H. pylori, and associated factors analyzed using STATA version 14 software by performing bivariate and multivariate analyses. ResultsAmong the 353 participants, majority were between 16 and 25 years old (69%), female (58%), and residing in peri-urban areas (74%). The prevalence of H. pylori infection in this population was 308 (87.3%). Multivariate analysis showed that H. pylori infection was significantly associated with having more than five income dependents (aPRR = 1.104, 95% CI: 1.025-1.189, p = 0.008), a history of previous H. pylori treatment (aPRR = 3.459, 95% CI: 2.138-5.595, p < 0.001), and a family history of H. pylori infection or gastrointestinal ulcers (aPRR = 1.135, 95% CI: 1.055-1.221, p = 0.001). ConclusionThis study demonstrated a high prevalence of Helicobacter pylori infection among patients presenting with gastrointestinal symptoms, with nearly nine out of ten individuals testing positive. The high burden observed suggests that routine screening for H. pylori, or carefully guided empirical treatment, may be clinically justified in symptomatic patients. These findings underscore the need for integrated clinical and public health strategies to improve diagnosis, treatment, and prevention of H. pylori infection in this setting.

Accurate polyp segmentation from colonoscopy images is critical for colorectal cancer prevention, yet the generalization of deep learning models under domain shift remains insufficiently explored. We propose Boundary-Explicit Guided Attention U-Net (BEGA-UNet), a boundary-aware segmentation architecture that introduces explicit edge modeling as a structural inductive bias to enhance both segmentation accuracy and cross-domain robustness. The framework integrates three components: an Edge-Guided Module (EGM) with learnable Sobel-initialized operators to capture boundary cues, a Dual-Path Attention (DPA) module that processes channel and spatial attention in parallel, and a Multi-Scale Feature Aggregation (MSFA) module to encode contextual information across multiple receptive fields. Evaluated on the combined Kvasir-SEG and CVC-ClinicDB benchmarks, BEGA-UNet achieves 88.53% Dice and 82.51% IoU, outperforming representative convolutional and transformer-based baselines. More importantly, cross-dataset evaluation demonstrates strong robustness under domain shift, with BEGA-UNet retaining 83.2% of its in-distribution performance-substantially higher than U-Net (64.5%), Attention U-Net (47.5%), and TransUNet (53.1%). In a zero-shot setting on an entirely unseen dataset, the model further maintains 72.6% performance retention. Comprehensive ablation studies indicate that explicit boundary modeling plays a central role in improving generalization, while multi-scale context aggregation further stabilizes performance across domains. Feature distribution analyses support this observation by showing that edge-oriented representations exhibit markedly reduced cross-domain variability compared to appearance-driven features. Overall, BEGA-UNet provides an effective and interpretable solution for robust polyp segmentation, demonstrating that explicit boundary modeling serves as a critical inductive bias for ensuring reliability under clinical domain shifts.

IntroductionFibrosis can affect organs throughout the body and is present in a wide range of diseases. Recent research has suggested that there could be shared biological mechanisms that lead to fibrosis in different organs. MethodsWe performed genome-wide association studies using UK Biobank for fibrosis in 12 different organ-systems and meta-analysed results with previously published studies of fibrotic diseases. We considered genetic associations that colocalised across [&ge;]3 organs as those likely to be involved in general fibrotic mechanisms and also identified novel genetic variants not previously reported as associated with fibrosis. Genetic correlation of fibrosis between organs was calculated using linkage disequilibrium score regression (LDSC). Discovery analyses were performed using European ancestry individuals and results were tested further in African, South Asian and East Asian ancestry groups. ResultsWe identified eight genetic loci that colocalised across three or more organs. One of these signals, located near the SH2B3 and ATXN2 genes, showed evidence of a shared causal variant for fibrosis across five organs. We also identified two novel fibrotic associations, one implicating alternative splicing of TFCP2L1 for urinary fibrosis and another implicating a missense variant in FAM180A for intestinal-pancreatic fibrosis. We observed significant genetic correlations for all organs, particularly for liver and skeletal fibrosis. ConclusionWe found evidence of shared genetic associations for fibrosis across organs, both at individual genetic loci and genome-wide. This highlights specific genes that may contribute to fibrosis across organs and diseases, which may facilitate the development of new therapies.

Background and AimsAvoidance of symptom-related situations is common in chronic gastrointestinal (GI) conditions, contributing to greater symptom severity, psychological distress, and reduced quality of life. However, no validated measure exists to comprehensively assess GI-specific avoidance. We developed and validated the GI-specific Avoidance Scale (GIAS), a self-report instrument measuring behavioral and cognitive avoidance specific to GI symptoms. MethodsFollowing literature review and multidisciplinary input, an initial pool of 58 items was generated and refined through expert and patient ratings, yielding 37 items. A sample of 102 adults (mean age 40.8 years) with medically diagnosed GI conditions completed the GIAS and validated measures of avoidance, psychological flexibility, illness shame, GI symptoms, distress, and quality of life. Exploratory factor analysis was used to determine factor structure. Internal consistency, convergent validity, incremental validity, and mediation analyses were conducted. ResultsFactor analysis supported a 20-item, three-factor solution: General Avoidance, Food Avoidance, and Intimacy/Body Exposure Avoidance. Internal consistency was excellent for the total scale ( = .94) and good-to-excellent for subscales ( = .82-.94). GIAS scores correlated positively with illness shame, GI symptoms, and distress, and negatively with psychological flexibility, self-compassion, and quality of life. GIAS showed incremental validity over a general illness avoidance measure (IBAS) in predicting GI symptoms and anxiety. Moreover, mediation models suggested that GI-specific avoidance partially mediates bidirectional associations between GI symptoms and psychological distress. ConclusionsThe GIAS is a novel, psychometrically robust, and multidimensional self-report questionnaire of GI-specific avoidance. It holds potential for clinical assessment, treatment planning, and evaluation of intervention mechanisms in GI populations.

Introduction Vonoprazan, a new oral potassium-competitive acid blocker (PCAB), has shown promise in terms of superior acid suppression when compared to Proton pump inhibitors (PPIs). We evaluated the efficacy of PCABs versus PPIs in preventing rebleeding in high-risk peptic ulcer patients after endoscopic hemostasis. Methods Following the Preferred Reporting Items for Systematic Reviews and Meta Analyses (PRISMA) guidelines, we conducted a comprehensive search for relevant studies across Medline, Embase, Web of Science, Cochrane Central Register of Controlled Trials and ClinicalTrials.gov, from inception till March 25, 2025. The primary outcome of interest was peptic ulcer rebleeding rate. Pooled risk ratios (RR) and mean difference (MD) with the corresponding 95% confidence intervals (CIs) were calculated. Results Three studies with 54,410 patients receiving endoscopic hemostasis for peptic ulcer bleeding were included in our analysis. The mean age of included participants was 71 years. There was no significant difference in rebleeding rates between patients receiving PPIs and PCABs (RR 0.827; 95 % CI: 0.5 to 1.3). We observed a significant reduction in length of hospital stay in the PCAB group when compared to the PPI group (MD: -0.44, 95% CI: -0.72 to -0.17), but no significant difference in all-cause mortality between both groups (RR: 0.90, 95% CI: 0.79 to 1.04). Conclusions Our study demonstrates comparable efficacy of PPIs and PCABs in preventing rebleeding in patients with high-risk peptic ulcers after successful endoscopic hemostasis. However, there was a significant reduction in hospital length of stay favoring PCABs. Keywords: Vonoprazan, Proton Pump inhibitors, peptic ulcer bleeding, Endoscopy

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.