35 papers
This paper introduces MyGeneRisk Colon, a publicly accessible web-based tool that enhances colorectal cancer risk prediction by integrating polygenic risk scores with demographic, family history, and lifestyle factors to provide personalized risk profiles and prevention education.
This study demonstrates that PFAS exposure disrupts steroid hormone pathways in a sex-specific manner, particularly suppressing steroidogenesis in females, which subsequently mediates alterations in hepatic lipids and bile acids that drive the severity of metabolic dysfunction-associated steatotic liver disease (MASLD).
This study demonstrates that combining 30-day medical-grade wearable monitoring with symptom surveys reveals distinct autonomic and blood pressure instability profiles in individuals with hypermobile Ehlers-Danlos Syndrome compared to healthy controls, supporting a hybrid screening approach to significantly reduce diagnostic delays.
This study from the GASTRIC consortium identifies that while the absence of endoscopic findings is reassuring for CDH1 carriers, the presence of specific abnormalities like masses or signet ring cell foci are independent predictors of advanced gastric cancer, suggesting endoscopic surveillance may be a viable alternative to prophylactic gastrectomy for those without worrisome mucosal findings.
This study demonstrates that whole-genome sequencing identified a high-risk *RET* enhancer haplotype as a key modifier of Hirschsprung disease penetrance in Mowat-Wilson syndrome, explaining the variable clinical expressivity observed in patients with *ZEB2* haploinsufficiency.
This study demonstrates that while reduced meal volumes significantly lower gastric amplitude metrics in healthy controls, key rhythm-based measures like Principal Gastric Frequency and the Gastric Alimetry Rhythm Index remain stable and reliable regardless of nutritional state.
This study demonstrates that plasma-based cfChIP-seq can non-invasively detect hepatocyte-specific transcriptional signatures to accurately diagnose and monitor autoimmune hepatitis, offering a promising alternative to invasive liver biopsies.
This study demonstrates that bowel sound patterns, when analyzed alongside physiological parameters like meal timing and medication intake, serve as promising quantitative indicators of gastrointestinal health, showing significant differences between healthy individuals and patients that can be effectively classified using machine learning.
This prospective multicenter study introduces and validates the Endo-HRS model, a novel endoscopic scoring system based on hemorrhoid count, diameter, color, and patient sex, to effectively stratify recurrence risk and guide personalized management following foam sclerotherapy for grade I internal hemorrhoids.
This prospective cohort study of over 430,000 UK Biobank participants found that regular use of antithrombotic drugs, particularly clopidogrel, is associated with an increased risk of nonbiliary acute pancreatitis, with warfarin use specifically elevating risk in non-diabetic individuals.
This paper proposes MCA-UNet, an improved U-Net architecture incorporating a multi-scale context convolution block and an attention-guided feature fusion module, which significantly enhances the accuracy and robustness of colorectal polyp segmentation in endoscopic images compared to baseline models.
This Mendelian randomization study provides evidence for a potential causal relationship between obesity-related traits and acute appendicitis risk, specifically demonstrating that a higher waist-to-hip ratio independently increases susceptibility to the condition even after adjusting for body mass index.
This study demonstrates that adults with type 2 diabetes and obesity who participated in an individualized, carbohydrate-reduced telemedicine nutrition program (VINT) experienced significantly lower risks of developing new-onset MASLD, MASH, and advanced liver disease compared to those receiving usual care, with the greatest protection observed in participants achieving substantial weight loss.
Using a validated microsimulation model, this study demonstrates that triennial next-generation multitarget stool DNA testing (ng mt-sDNA) yields superior 10-year cumulative effectiveness in detecting colorectal cancer and precancerous lesions, reducing mortality, and gaining life-years compared to a single decennial colonoscopy, primarily due to higher real-world adherence.
This study describes the development and validation of the Food Expectation Questionnaire (FEX-Q), a reliable 44-item instrument using food images that successfully measures individual food-related symptom expectations in patients with irritable bowel syndrome (IBS) and demonstrates strong validity for use in personalized dietary management.
By leveraging metabolomic signatures of established clinical risk factors in a large UK Biobank cohort, this study identified and validated specific plasma biomarkers that can predict hepatocellular carcinoma and intrahepatic cholangiocarcinoma years before clinical diagnosis, offering a promising foundation for early detection and precision risk stratification.
This systematic review and meta-analysis of 54,410 patients indicates that potassium-competitive acid blockers (PCABs) are as effective as proton-pump inhibitors (PPIs) in preventing rebleeding and mortality after endoscopic hemostasis for high-risk peptic ulcers, while significantly reducing hospital length of stay.
The paper proposes BEGA-UNet, a boundary-aware segmentation architecture integrating explicit edge modeling, dual-path attention, and multi-scale feature aggregation to achieve state-of-the-art accuracy and superior cross-domain robustness in colonoscopic polyp segmentation.
This study evaluates the pre-analytical stability of microbiome-derived fatty acids in self-collected stool and blood samples stored at room temperature, revealing that stability is highly dependent on the specific matrix and preservation device, with untreated stool and certain blood formats showing significant degradation or fermentation artifacts compared to stabilized alternatives.
This study demonstrates that a novel machine learning algorithm (ID LIVER-ML), trained on routine primary care data, significantly outperforms conventional risk scores like FIB-4 in detecting clinically significant liver disease within a real-world, mixed-etiology population, offering a more effective tool for community screening and reducing unnecessary secondary care referrals.