Abstract Background The impact of direct acting antivirals (DAAs) on hepatitis C virus related hepatocellular carcinoma (HCV-HCC) incidence after sustained virologic response (SVR) remains controversial. We aimed to compare long-term HCC incidence after SVR induced by interferon (IFN) and DAA therapy, also to construct a nomogram model for predicting long-term HCC incidence after HCV cure. Methods A total of 405 patients treated with IFN or DAA were analyzed. 200 patients who achieved SVR were assigned to IFN and DAA group by propensity score matching. The COX regression, LASSO logistic regression, and best subset regression (BSR) were adopted to select variables independently associated with HCC incidence and construct nomogram. Results After a median follow-up period of 72.0 (interquartile range 57.5–90.0) months, HCC developed in 7 (7%) and 15 (15%) patients in IFN and DAA groups, respectively. The cumulative HCC rates were comparable between the two groups (HR: 0.917, 95% CI: 0.262–1.968, P = 0.520). Out of all three models constructed by COX, LASSO and BSR, model 2 and 3 both contained gender, age, platelet count and alkaline phosphatase, exhibited the minimum Akaike Information Criterion (174.54) and the best predictive performance for 8-year HCC incidence (AUROC = 0.905). Using these four factors, a novel nomogram was established to predict 8-year HCC occurrence following HCV eradication. It showed significant clinical benefit according to decision curve analysis. Conclusions DAAs exert the same long-term protect role against HCC as IFN treatment, though the development of HCC is still inevitable. Herein, we developed a 8-year HCC-predicting nomogram that can be used to guide clinical screening strategies.
Diseases of the digestive system. Gastroenterology
Introduction. Chronic non-communicable diseases (NCDs) account for 75% of global mortality, while traditional treatment paradigm demonstrates inability to contain epidemiological burden. Artificial intelligence (AI) technologies combined with telemedicine enable healthcare transformation: from reactive treatment to proactive health management through personalized prevention. Russian school of pre-nosological diagnostics, focused on identifying pre-pathological states through assessment of body’s functional reserves, creates methodological foundation for personalized approach that can be significantly enhanced by modern machine learning methods. Objective: to develop methodology for remote questionnaire-based screening of NCDs using AI with integration of holistic approach to pre-nosological diagnostics, providing generation of personalized prevention recommendations, and evaluate its effectiveness in young adults. Material and methods. Study included 3,155 university students from St. Petersburg (mean age 19.6±1.5 years) from 83 regions of Russian Federation. AI-based technology for remote screening was developed using holistic approach. System verifies risk factors by five pathology profiles (cardiology, gastroenterology, pulmonology, endocrinology, oncology). Questionnaire contains 198 information requests. Decision rules system (1,098 rules) was applied. Systematic literature review in PubMed, Scopus, Web of Science, eLibrary for 2020–2025 was conducted; RCTs, systematic reviews, WHO and Food and Drug Administration regulatory documents, methodological guidelines were analyzed. Results. Low NCD risk detected in 57.4%, moderate in 30.9%, high in 11.7% of examined individuals. Most frequent complaints related to endocrine (28.9%), digestive (21.8%), respiratory (21.1%), and cardiovascular systems (20.1%). More than 75% showed signs of polymorbidity. Statistical analysis confirmed significant consistency between system and physician assessments (p < 0.001). Cohen’s kappa showed substantial agreement for cardiology and pulmonology profiles, moderate for gastroenterology and endocrinology. System generates personalized recommendations considering age, gender, anthropometric data, harmful habits, and psychological state. Physician time savings reached 20%. User satisfaction – 96.6%, healthcare workers – 91.7%. Conclusion. Developed methodology for remote questionnaire-based AI screening with holistic approach showed high effectiveness for early risk factor detection in young adults. Integration of Russian pre-nosological diagnostics experience through pathology profiles with modern machine learning technologies creates conditions for transition to personalized prevention focused on correction of body’s functional reserves. System demonstrates significant social and economic effectiveness.
A.Yu. Goncharenko, Fedor V. Gorbovskoi, Ekaterina A. Kargina
et al.
Irritable bowel syndrome (IBS) is a widespread bowel disease, associated with a significant decrease in patients' quality of life. The etiology, pathogenesis, clinical symptoms and treatment strategies of IBS have not been studied sufficiently. Current clinical guidelines list antispasmodics as medications for abdominal pain management, the main symptom of IBS. Both in Russian and foreign clinical guidelines of IBS, among the antispasmodic drugs, hyoscine butylbromide is regarded as an effective and safe medicine for abdominal pain management. Hyoscine butylbromide has a broad spectrum of applications in gastroenterology. This fact determines its advantages in terms of drug choice for treating patients with comorbidities involving digestive system pathologies. The use of hyoscine butylbromide is especially relevant in light of the frequent occurrence of a combination of IBS and functional disorders of the biliary tract, since the antispasmodic is also recommended for biliary pain management.
T. Koo, Venkata C. K. Sunkesula, R. Chowdhary
et al.
Background Diverticular disease (DD) is a common gastrointestinal condition in the United States (US) associated with significant morbidity and mortality. The COVID-19 pandemic posed new challenges that might exacerbate DD-related outcomes. This study analyzed the trends in all-cause, digestive system (DGS), and cardiovascular system (CVS) mortality associated with DD from 1999-2020, focusing on the impact of COVID-19 on age-adjusted mortality rates (AAMRs) and disparities across demographics and geography. Methods Data from adults aged ≥25 years were extracted from the Centers for Disease Control WONDER database. AAMRs per 100,000 people were standardized using the 2000 US census. AAMRs were assessed from 1999-2020 for context, while the primary comparative analysis focused on the pre-COVID-19 (2016-2019) and post-COVID-19 (2019-2022) periods using linear regression models. AAMRs were stratified by age, sex, race/ethnicity and geographic region. Note: 2021-2022 trends were extrapolated, as finalized mortality records were not available at the time of analysis. Results Between 1999 and 2020, 115,009 DD-related deaths occurred (AAMR 2.4/100,000), including 70,648 DGS-related deaths (AAMR 1.5) and 17,405 CVS-related deaths (AAMR 0.4). Females (AAMR 2.6), elderly individuals (AAMR 11.1), and non-Hispanic whites (AAMR 2.5) had the highest mortality rates. Post-COVID-19, AAMRs increased from 1.8 to 2.0, with significant increases among rural populations. DGS-related deaths were most prevalent, particularly in non-metropolitan areas. Conclusions DD-related mortality has increased in the post-COVID-19 period, especially in vulnerable populations, such as the elderly, rural residents and females. These findings highlight the need for equitable healthcare interventions and the continued monitoring of pandemic-era health disparities.
F. M. G. Alotaibi, Abduljabbar.S.Ba Mahel, Kaixuan Zhang
et al.
Gastrointestinal (GI) diseases encompass a wide range of conditions that impact various parts of the digestive system, with symptoms ranging from mild discomfort to lifethreatening complications. Wireless capsule endoscopy (WCE) has emerged as a non-invasive and patient-friendly method to visualize the GI tract, especially for detecting abnormalities such as ulcers and arteriovenous malformations (AVMs). In this study, we propose a deep learning-based classification approach for analyzing WCE images and identifying three GI conditions: Normal, Ulcer, and a new class, AVM. Its classification is very important as it improves the diagnosis of gastrointestinal diseases. The King Abdulaziz University Hospital-Capsule (KAUHC) dataset, captured using the OMOMWCE system, was used for training and evaluating five ResNet variants. Images were preprocessed and augmented to enhance generalization, and models were trained using transfer learning and optimized with a standardized configuration. Evaluation metrics, including accuracy, precision, recall, and F1-score, demonstrated that the ResNet-34, ResNet-50, and ResNet-101 models achieved the best overall performance, with classification accuracy reaching up to $\mathbf{9 9. 4 \%}$. A comparative analysis with traditional machine learning models (e.g., Decision Trees, KNN, SVM) revealed that deep learning significantly outperformed these methods in both consistency and classification accuracy. These findings highlight the effectiveness of integrating capsule endoscopy with deep learning to enhance diagnostic precision in gastroenterology and support more accurate, non-invasive medical decision-making.
Tharaka Fonseka, Buddhi Wijenayake, Athulya Ratnayake
et al.
Understanding the relationship between population dynamics and disease-specific mortality is central to evidence-based health policy. This study introduces two novel metrics, PoPDivergence and PoPStat, one to quantify the difference between population pyramids and the other to assess the strength and nature of their association with the mortality of a given disease. PoPDivergence, based on Kullback-Leibler divergence, measures deviations between a countrys population pyramid and a reference pyramid. PoPStat is the correlation between these deviations and the log form of disease-specific mortality rates. The reference population is selected by a brute-force optimization that maximizes this correlation. Utilizing mortality data from the Global Burden of Disease 2021 and population statistics from the United Nations, we applied these metrics to 371 diseases across 204 countries. Results reveal that PoPStat outperforms traditional indicators such as median age, GDP per capita, and Human Development Index in explaining the mortality of most diseases. Noncommunicable diseases (NCDs) like neurological disorders and cancers, communicable diseases (CDs) like neglected tropical diseases, and maternal and neonatal diseases were tightly bound to the underlying demographic attributes whereas NCDs like diabetes, CDs like respiratory infections and injuries including self-harm and interpersonal violence were weakly associated with population pyramid shapes. Notably, except for diabetes, the NCD mortality burden was shared by constrictive population pyramids, while mortality of communicable diseases, maternal and neonatal causes and injuries were largely borne by expansive pyramids. Therefore, PoPStat provides insights into demographic determinants of health and empirical support for models on epidemiological transition. Code and scripts: https://github.com/Buddhi19/DevisingPoPStat.git
Jorge Tapias Gomez, Nishant Nadkarni, Lando S. Bosma
et al.
Objective: Clinical implementation of deformable image registration (DIR) requires voxel-based spatial accuracy metrics such as manually identified landmarks, which are challenging to implement for highly mobile gastrointestinal (GI) organs. To address this, patient-specific digital twins (DT) modeling temporally varying motion were created to assess the accuracy of DIR methods. Approach: 21 motion phases simulating digestive GI motion as 4D sequences were generated from static 3D patient scans using published analytical GI motion models through a semi-automated pipeline. Eleven datasets, including six T2w FSE MRI (T2w MRI), two T1w 4D golden-angle stack-of-stars, and three contrast-enhanced CT scans. The motion amplitudes of the DTs were assessed against real patient stomach motion amplitudes extracted from independent 4D MRI datasets. The generated DTs were then used to assess six different DIR methods using target registration error, Dice similarity coefficient, and the 95th percentile Hausdorff distance using summary metrics and voxel-level granular visualizations. Finally, for a subset of T2w MRI scans from patients treated with MR-guided radiation therapy, dose distributions were warped and accumulated to assess dose warping errors, including evaluations of DIR performance in both low- and high-dose regions for patient-specific error estimation. Main results: Our proposed pipeline synthesized DTs modeling realistic GI motion, achieving mean and maximum motion amplitudes and a mean log Jacobian determinant within 0.8 mm and 0.01, respectively, similar to published real-patient gastric motion data. It also enables the extraction of detailed quantitative DIR performance metrics and rigorous validation of dose mapping accuracy. Significance: The pipeline enables rigorously testing DIR tools for dynamic, anatomically complex regions enabling granular spatial and dosimetric accuracies.
Afsana Ahsan Jeny, Masum Shah Junayed, Md Robel Mia
et al.
Facial acne is a common disease, especially among adolescents, negatively affecting both physically and psychologically. Classifying acne is vital to providing the appropriate treatment. Traditional visual inspection or expert scanning is time-consuming and difficult to differentiate acne types. This paper introduces an automated expert system for acne recognition and classification. The proposed method employs a machine learning-based technique to classify and evaluate six types of acne diseases to facilitate the diagnosis of dermatologists. The pre-processing phase includes contrast improvement, smoothing filter, and RGB to L*a*b color conversion to eliminate noise and improve the classification accuracy. Then, a clustering-based segmentation method, k-means clustering, is applied for segmenting the disease-affected regions that pass through the feature extraction step. Characteristics of these disease-affected regions are extracted based on a combination of gray-level co-occurrence matrix (GLCM) and Statistical features. Finally, five different machine learning classifiers are employed to classify acne diseases. Experimental results show that the Random Forest (RF) achieves the highest accuracy of 98.50%, which is promising compared to the state-of-the-art methods.
In Bangladesh, tomatoes are a staple vegetable, prized for their versatility in various culinary applications. However, the cultivation of tomatoes is often hindered by a range of diseases that can significantly reduce crop yields and quality. Early detection of these diseases is crucial for implementing timely interventions and ensuring the sustainability of tomato production. Traditional manual inspection methods, while effective, are labor-intensive and prone to human error. To address these challenges, this research paper sought to develop an automated disease detection system using Convolutional Neural Networks (CNNs). A comprehensive dataset of tomato leaves was collected from the Brahmanbaria district, preprocessed to enhance image quality, and then applied to various deep learning models. Comparative performance analysis was conducted between YOLOv5, MobileNetV2, ResNet18, and our custom CNN model. In our study, the Custom CNN model achieved an impressive accuracy of 95.2%, significantly outperforming the other models, which achieved an accuracy of 77%, 89.38% and 71.88% respectively. While other models showed solid performance, our Custom CNN demonstrated superior results specifically tailored for the task of tomato leaf disease detection. These findings highlight the strong potential of deep learning techniques for improving early disease detection in tomato crops. By leveraging these advanced technologies, farmers can gain valuable insights to detect diseases at an early stage, allowing for more effective management practices. This approach not only promises to boost tomato yields but also contributes to the sustainability and resilience of the agricultural sector, helping to mitigate the impact of plant diseases on crop production.
BACKGROUND Gastroesophageal Reflux Disease (GERD) is a prevalent condition in Brazil, affecting 12% to 20% of the urban population, with significant implications for patient quality of life and potential for complications. OBJECTIVE This paper focuses on the recent update of the Brazilian guidelines for GERD, a necessary revision due to advancements in knowledge and practice since the last publication over a decade ago. The update pays particular attention to the role and safety of proton pump inhibitors (PPIs), acknowledging the growing concerns about their long-term use, adverse events, and overprescription. METHODS The methodology of the guideline update involved an extensive literature review in multiple languages (English, French, Italian, Spanish, and Portuguese), drawing from major databases such as Medline, Embase, and SciELO-Lilacs. RESULTS This comprehensive approach resulted in a carefully curated selection of studies, systematic reviews, and meta-analyses, specifically focusing on PPIs and other therapeutic strategies for GERD. The updated guidelines are presented in a user-friendly question-and-answer format, adhering to the PICO system (Population, Intervention, Comparison, Outcomes) for clarity and ease of interpretation. The recommendations are supported by robust scientific evidence and expert opinions, enhancing their practical applicability in clinical settings. To ensure the reliability and clarity of the recommendations, the GRADE system (Grading of Recommendations Assessment, Development, and Evaluation) was employed. This system categorizes the strength of recommendations as strong, weak, or conditional and classifies evidence quality as high, moderate, low, or very low. These classifications provide insight into the confidence level of each recommendation and the likelihood of future research impacting these guidelines. CONCLUSION The primary aim of these updated guidelines is to offer practical, evidence-based advice for the management of GERD in Brazil, ensuring that healthcare professionals are equipped with the latest knowledge and tools to deliver optimal patient care. BACKGROUND •Gastrointestinal specialists rely heavily on guidelines to manage digestive pathologies effectively. The Brazilian clinical guideline for therapeutic management of gastroesophageal reflux disease (GERD) is an invaluable tool for these specialists. BACKGROUND •It critically analyzes practical aspects of therapy through 12 questions covering a wide range of topics, from behavioral measures to surgical and endoscopic indications. BACKGROUND •The recommendations in this guideline are justified using the GRADE system (Grading of Recommendations Assessment, Development, and Evaluation), and experienced experts provide comments and suggestions at the end of each question.
Ming-Ling Chang, Jur-Shan Cheng, Puo-Hsien Le
et al.
Background: How antimitochondrial antibody (AMA)-positive patients evolve to have primary biliary cholangitis (PBC) in viral hepatitis–endemic areas is unknown. Objectives: We aimed to investigate this evolution in Taiwan. Design/methods: A 16-year medical center-based cohort study of 2,095,628 subjects was conducted in Taiwan, an Asian country endemic to viral hepatitis. AMA-positive subjects were those with positive AMA with alkaline phosphatase (ALP) ⩽1.5 times the upper limit of normal (ULN), and PBC was defined as positive AMA with ALP >1.5 × ULN. Results: AMA-positive subjects had a lower average age- and sex-adjusted prevalence than PBC patients (4.68/10 5 versus 11.61/10 5 , p = 0.0002), but their incidence was comparable (0.99/10 5 versus 1.12/10 5 , p = 0.36). The former group had a borderline significantly lower mean age (56.59 years versus 58.10 years, p = 0.06) and a lower female-to-male ratio (2.85:1 versus 5.44:1, p < 0.0001). Both AMA-positive subjects (prevalence change: 20.0%, p < 0.01; incidence change: −9.2%, p < 0.01) and PBC patients (prevalence change: 14.6%, p < 0.01; incidence change: −4.7%, p < 0.01) prevalence rate increased but the incidence rate decreased. Among the 423 AMA-positive subjects, 77 (18.2%) developed PBC, for a mean duration of 1.757 years. Compared with AMA-positive subjects, PBC patients had similar concurrent chronic hepatitis B (CHB) rates (2.7% versus 4.3%, p = 0.197) but lower chronic hepatitis C (CHC) rates (3.69% versus 15.60%, p < 0.01). Conclusion: PBC was more prevalent than AMA-positive subjects, and PBC patients had a higher female-to-male ratio than AMA-positive subjects, of whom 18.2% developed PBC (mean lag: 1.757 years). Upward trends in prevalence rates and downward trends in incidence rates were noted for both AMA-positive subjects and PBC. CHB was rare, CHC was more prevalent among PBC patients than the general population, and CHC was less prevalent among PBC than among AMA-positive subjects.
Diseases of the digestive system. Gastroenterology
Stine Johansen, Simon Langkjær, Ditlev Nytoft Rasmussen
et al.
Background & Aims: Infections are frequent in patients with cirrhosis and worsen prognosis. We evaluated the incidence of infections and their impact on decompensation and death in patients with early alcohol-related liver disease (ALD) during long-term follow-up. Methods: We performed a prospective cohort study of patients in secondary care with a history of excess alcohol intake, no prior decompensation, and with liver biopsies along with clinical investigations conducted at baseline. During follow-up, we reviewed the patients’ electronic healthcare records for cases of infections, hospitalizations, transient elastography measurements, decompensations, all-cause mortality, and alcohol intake. Results: We included 461 patients with a mean age of 56±10 years (76% males; fibrosis stage F0-1/F2/F3-4 = 259/107/93 [56%/23%/20%]). During a median follow-up of 4.5 years (IQR 2.9-6.3), 134 patients (29%) developed a total of 312 infections, most frequently pneumonia (106/312, 34%) and urinary tract infections (57/312, 18%). Excessive alcohol intake during follow-up, smoking ≥30 pack years, MELD score and elevated liver stiffness during follow-up were independent predictors of infections. Patients who developed at least one infection had a significantly increased risk of subsequent decompensation (hazard ratio 4.98, 95% CI 2.47-10.03) and death (hazard ratio 8.24, 95% CI 4.65-14.59). Infections increased the risk of decompensation and death independently of baseline fibrosis stage, age, gender, and MELD score. Conclusions: Almost one-third of patients with early ALD develop an infection, which worsens their prognosis by increasing the risk of decompensation and death. The risk of infections increases with liver disease severity and ongoing harmful use of alcohol. Impact and implications: This study reveals that infections significantly worsen the prognosis of patients with early alcohol-related liver disease (ALD), increasing the likelihood of decompensation and death by up to eight times. These findings, pertinent to healthcare providers, researchers, and policymakers, emphasize the importance of early prevention and management of infections in patients with ALD, even those in early stages who may be asymptomatic. It was observed that nearly one-third of patients with early-stage ALD developed infections over 4.5 years, with risk factors including alcohol overuse, smoking, and higher MELD scores. The research underscores the critical need to incorporate these insights into clinical practice and public health policies to improve patient outcomes and mitigate the impact of infections in patients with ALD.
Diseases of the digestive system. Gastroenterology
Globally, the elderly population are more increasing each year. The enhancement of life expectancy is also followed by the enhancement of chronic illness, which one of them is liver disease. In elderly, there are also several physiological and biochemical changes in liver. Several studies show that the reduction of liver function will affect the severity of liver clinical manifestation in older people. This review article aims to discuss more about liver disease in older population. Hepatitis A in elderly has higher mortality and morbidity rates compared to young people. More over, the progressivity of acute hepatitis B to chronic hepatitis B is also greater in older people than young people. The treatments of hepatitis B and hepatitis C are safe and effective to be applied in elderly. Polypharmacy and fraility affects the elderly to be more susceptible to drug induced liver injury (DILI). This review aims to address knowledge gaps in understanding the morphological and functional changes in the aging hepatic system, their implications for disease progression, and the effectiveness of current therapeutic strategies. By critically analyzing recent evidence, we identify challenges in treating liver diseases in the elderly and highlight areas requiring further research.Keywords: Elderly, hepatocellular call carcinoma, liver disease
The intricate relationship between genetic variation and human diseases has been a focal point of medical research, evidenced by the identification of risk genes regarding specific diseases. The advent of advanced genome sequencing techniques has significantly improved the efficiency and cost-effectiveness of detecting these genetic markers, playing a crucial role in disease diagnosis and forming the basis for clinical decision-making and early risk assessment. To overcome the limitations of existing databases that record disease-gene associations from existing literature, which often lack real-time updates, we propose a novel framework employing Large Language Models (LLMs) for the discovery of diseases associated with specific genes. This framework aims to automate the labor-intensive process of sifting through medical literature for evidence linking genetic variations to diseases, thereby enhancing the efficiency of disease identification. Our approach involves using LLMs to conduct literature searches, summarize relevant findings, and pinpoint diseases related to specific genes. This paper details the development and application of our LLM-powered framework, demonstrating its potential in streamlining the complex process of literature retrieval and summarization to identify diseases associated with specific genetic variations.
Rania Kousovista, Georgina Cosma, Emeka Abakasanga
et al.
Identifying and understanding the co-occurrence of multiple long-term conditions (MLTC) in individuals with intellectual disabilities (ID) is vital for effective healthcare management. These individuals often face earlier onset and higher prevalence of MLTCs, yet specific co-occurrence patterns remain unexplored. This study applies an unsupervised approach to characterise MLTC clusters based on shared disease trajectories using electronic health records (EHRs) from 13069 individuals with ID in Wales (2000-2021). Disease associations and temporal directionality were assessed, followed by spectral clustering to group shared trajectories. The population consisted of 52.3% males and 47.7% females, with an average of 4.5 conditions per patient. Males under 45 formed a single cluster dominated by neurological conditions (32.4%), while males above 45 had three clusters, the largest characterised circulatory (51.8%). Females under 45 formed one cluster with digestive conditions (24.6%) as most prevalent, while those aged 45 and older showed two clusters: one dominated by circulatory (34.1%), and the other by digestive (25.9%) and musculoskeletal (21.9%) system conditions. Mental illness, epilepsy, and reflux were common across groups. These clusters offer insights into disease progression in individuals with ID, informing targeted interventions and personalised healthcare strategies.