Faris Chater, Maja Kopczynska, Mahdi Saeidinejad
et al.
ABSTRACT Objectives The success of anti‐TNFα agents has changed the landscape of treatment for inflammatory bowel diseases (IBD). One major limitation to this success is the secondary loss of response (sLOR) phenomenon. Combination therapy with immunomodulating agents has been shown to be effective in reducing the sLOR process. The primary aim of this study was to evaluate the rates of sLOR to anti‐TNFα therapy in IBD patients on mono versus combination therapy, using real‐world data. Methods This was a retrospective study of 200 patients with IBD treated with anti‐TNFα agents from 2000 to 2023. Data was collected on patient demographics, IBD phenotype, drug therapy, clinical and biochemical response to treatment. Results Overall, there was no significant difference in median duration of response for infliximab (IFX) vs. adalimumab (ADA) (15 months, IQR 7–30 vs. 17 months, IQR 8–31; p = 0.53). In total, 41/200 (20.6%) of patients developed sLOR. Rates of sLOR were similar between IFX (23/106, 21.7%) and ADA (18/94, 19.1%, p = 0.76). Combination therapy (used in 69/200, 34.8% patients) was associated with a significantly lower risk of sLOR compared with monotherapy (HR 0.41, 95% CI: 0.19–0.87; p = 0.020). This effect was observed in patients receiving IFX (p = 0.0095), whilst no significant difference was seen with ADA (p = 0.15). Safety outcomes were comparable between both groups, with no signal for increased risk of infection or malignancy with combination therapy. Conclusion Combination therapy significantly reduced sLOR compared with monotherapy. There was no significant difference in sLOR between IFX and ADA.
Diseases of the digestive system. Gastroenterology
Rowzatul Zannat, Abdullah Al Shafi, Abdul Muntakim
Increased access to reliable health information is essential for non-English-speaking populations, yet resources in Bangla for disease prediction remain limited. This study addresses this gap by developing a comprehensive Bangla symptoms-disease dataset containing 758 unique symptom-disease relationships spanning 85 diseases. To ensure transparency and reproducibility, we also make our dataset publicly available. The dataset enables the prediction of diseases based on Bangla symptom inputs, supporting healthcare accessibility for Bengali-speaking populations. Using this dataset, we evaluated multiple machine learning models to predict diseases based on symptoms provided in Bangla and analyzed their performance on our dataset. Both soft and hard voting ensemble approaches combining top-performing models achieved 98\% accuracy, demonstrating superior robustness and generalization. Our work establishes a foundational resource for disease prediction in Bangla, paving the way for future advancements in localized health informatics and diagnostic tools. This contribution aims to enhance equitable access to health information for Bangla-speaking communities, particularly for early disease detection and healthcare interventions.
Daniël Docter, Bernadette S. de Bakker, Jaco Hagoort
et al.
Background and Aims: Patients born with anorectal malformations (ARMs) might experience constipation and fecal incontinence. During ARM surgery (anterior and posterior sagittal anorectoplasty procedure), the distal part of the bowel (fistula) is usually resected. Microfocus computed tomography (micro-CT) imaging, capable of imaging samples in ultra-high 3-dimensional resolution, can be used to learn from this resected material. Through this technique, we aim to investigate whether or not structures, such as the internal anal sphincter (IAS), are present within this fistula. Methods: Pediatric patients undergoing surgical reconstruction for ARM were eligible for inclusion. Resected fistulas were fixed using 4% paraformaldehyde and stained with 3.75% B-Lugol for 48 hours to improve soft tissue contrast. Scans were performed on a Phoenix Nanotom micro-CT with a voxel size between 4–6 μm. Samples were destained for subsequent histopathological examination. Outcomes were presence of structures like the IAS, epithelial transition zone and ganglia. ARM fistulas were compared with a fetal anal canal sample derived from the Dutch Fetal Biobank. Results: Eleven ARM fistulas were analyzed. All samples showed evidence of normal development of the rectal wall. Columnar epithelium and stratified squamous epithelium were observed. Muscle fibers were present, arranged in circular pattern that expanded toward the distal end, becoming the intrinsic sphincter (IAS). Ganglia were present with normal appearance. Conclusion: We present micro-CT imaging to research resected material to provide new insights in microscale anatomy. The fistula, currently resected during surgical reconstruction for ARM, contains vital structures like the IAS, normal epithelial transition zone and normal ganglion cells. Although clinical functionality should be studied in the future, our results indicate that the fistula has a normal anal canal morphology and should be spared during ARM reconstruction if possible.
Diseases of the digestive system. Gastroenterology
A. I. Ulyanin, E. A. Poluektova, A. V. Kudryavtseva
et al.
Aim: to investigate the relationship between tryptophan metabolism features, gut microbiota composition, systemic inflammation markers, cortisol levels, quality of life, and psychoemotional and cognitive status in female patients with functional constipation (FC).Materials and methods. The study included 64 female patients with FC and 26 age- and BMI-matched women without FC (p > 0.05). All participants underwent assessment of gut microbiota composition in stool samples (via 16S rRNA sequencing), health-related quality of life (SF-36), psychoemotional status (4DSQ, Spielberger — Hanin test, Hamilton scale), and cognitive function (BACS cognitive tests). Tryptophan metabolism was evaluated by measuring levels of interleukin-1β, cortisol, brain-derived neurotrophic factor (BDNF), tryptophan, kynurenine, kynurenic acid, and serum and platelet serotonin.Results. Compared to women without FC, female patients with FC had higher levels of cortisol (325 [266; 403] vs. 275[255; 304] nmol/L; p = 0.025), interleukin-1β (10.0 [9.2; 11.2] vs. 7.2 [6.5; 7.8] pg/mL; p < 0.001), and blood kynurenine (0.65 [0.54; 0.82] vs. 0.44 [0.35; 0.48] μg/mL; p < 0.001), as well as lower plasma serotonin levels (108 [85; 134] vs. 163 [117; 190] ng/mL; p < 0.001). No differences were found between groups in plasma tryptophan, BDNF, kynurenic acid, or platelet serotonin. Patients with FC exhibited more pronounced depression (Hamilton scale: 8 [6; 9] vs. 3 [2; 3] points; p < 0.001) and somatization (9 [7; 12] vs. 5 [3; 9] points; p < 0.001); lower cognitive function scores (50 [45; 54] vs. 54 [53; 56] points; p < 0.001), particularly in auditory-verbal memory (p < 0.001) and information processing speed (p < 0.001); and reduced quality of life (SF-36) in physical functioning (90 [83; 95] vs. 95 [95; 95] points; p < 0.001) and bodily pain (60 [50; 70] vs. 75 [56; 85] points; p < 0.001). Cortisol levels positively correlated with bodily pain (r = 0.379; p = 0.003), while interleukin-1β levels inversely correlated with bodily pain (r = –0.391; p = 0.002), physical functioning (r = –0.448; p < 0.001), and verbal memory (r = –0.252; p = 0.046), and positively correlated with depression (r = 0.311; p = 0.013) and somatization (r = 0.266; p = 0.035). Cortisol levels correlated positively with Oscillospira (r = 0.45; p = 0.01), while kynurenine levels correlated with Alistipes (r = 0.36; p = 0.04) abundance. Plasma serotonin positively correlated with Haemophilus (r = 0.37; p = 0.03) and inversely with Bacteroides plebeius (r = –0.40; p = 0.02) abundance. Physical functioning (SF-36) positively correlated with Lachnospiraceae NK4B4 group (r = 0.35; p = 0.04), while depression severity (4DSQ) inversely correlated with Alistipes abundance (r = –0.37; p = 0.03). Information processing speed is inversely correlated with abundance of Bacilli (r = –0.48; p = 0.004), Lactobacillales (r = –0.48; p = 0.004), Pasteurellales (r = –0.36; p = 0.03), Pasteurellaceae (r = –0.36; p = 0.03), Streptococcaceae (r = –0.47; p = 0.006), Haemophilus (r = –0.41; p = 0.02), and Streptococcus (r = –0.38; p = 0.02).Conclusion. The findings indicate that women with functional constipation exhibit altered tryptophan metabolism and gut microbiota dysbiosis, associated with depression, somatization, cognitive impairment, and reduced health-related quality of life.
Diseases of the digestive system. Gastroenterology
The evolutionary origins of ageing and age-associated diseases continue to pose a fundamental question in biology. This study is concerned with a recently proposed framework, which conceptualises development and ageing as a continuous process, driven by genetically encoded epigenetic changes in target sets of cells. According to the Evolvable Soma Theory of Ageing (ESTA), ageing reflects the cumulative manifestation of epigenetic changes that are predominantly expressed during the post-reproductive phase. These late-acting modifications are not yet evolutionarily optimised but are instead subject to ongoing selection, functioning as somatic "experiments" through which evolution explores novel phenotypic variation. These experiments are often detrimental, leading to progressive physical decline and eventual death, while a small subset may produce beneficial adaptations, that evolution can exploit to shape future developmental trajectories. According to ESTA, ageing can be understood as evolution in action, yet old age is also the strongest risk factor for major diseases such as cardiovascular diseases, cancer, neurodegenerative disorders, and metabolic syndrome. We argue that this association is not merely correlational but causal: the same epigenetic process that drive development and ageing also underlie age-associated diseases. Growing evidence points to epigenetic regulation as a central factor in these pathologies, since no consistent patterns of genetic mutations have been identified, whereas widespread regulatory and epigenetic disruptions are observed. From this perspective, evolution is not only the driver of ageing but also the ultimate source of the diseases that accompany it, making it the root cause of most age-related pathologies.
Carson Dudley, Reiden Magdaleno, Christopher Harding
et al.
Infectious disease forecasting in novel outbreaks or low-resource settings is hampered by the need for large disease and covariate data sets, bespoke training, and expert tuning, all of which can hinder rapid generation of forecasts for new settings. To help address these challenges, we developed Mantis, a foundation model trained entirely on mechanistic simulations, which enables out-of-the-box forecasting across diseases, regions, and outcomes, even in settings with limited historical data. We evaluated Mantis against 48 forecasting models across six diseases with diverse modes of transmission, assessing both point forecast accuracy (mean absolute error) and probabilistic performance (weighted interval score and coverage). Despite using no real-world data during training, Mantis achieved lower mean absolute error than all models in the CDC's COVID-19 Forecast Hub when backtested on early pandemic forecasts which it had not previously seen. Across all other diseases tested, Mantis consistently ranked in the top two models across evaluation metrics. Mantis further generalized to diseases with transmission mechanisms not represented in its training data, demonstrating that it can capture fundamental contagion dynamics rather than memorizing disease-specific patterns. These capabilities illustrate that purely simulation-based foundation models such as Mantis can provide a practical foundation for disease forecasting: general-purpose, accurate, and deployable where traditional models struggle.
Abdullah Al Shafi, Rowzatul Zannat, Abdul Muntakim
et al.
Disease-symptom datasets are significant and in demand for medical research, disease diagnosis, clinical decision-making, and AI-driven health management applications. These datasets help identify symptom patterns associated with specific diseases, thus improving diagnostic accuracy and enabling early detection. The dataset presented in this study systematically compiles disease-symptom relationships from various online sources, medical literature, and publicly available health databases. The data was gathered through analyzing peer-reviewed medical articles, clinical case studies, and disease-symptom association reports. Only the verified medical sources were included in the dataset, while those from non-peer-reviewed and anecdotal sources were excluded. The dataset is structured in a tabular format, where the first column represents diseases, and the remaining columns represent symptoms. Each symptom cell contains a binary value, indicating whether a symptom is associated with a disease. Thereby, this structured representation makes the dataset very useful for a wide range of applications, including machine learning-based disease prediction, clinical decision support systems, and epidemiological studies. Although there are some advancements in the field of disease-symptom datasets, there is a significant gap in structured datasets for the Bangla language. This dataset aims to bridge that gap by facilitating the development of multilingual medical informatics tools and improving disease prediction models for underrepresented linguistic communities. Further developments should include region-specific diseases and further fine-tuning of symptom associations for better diagnostic performance
F. Argüelles-Arias, J. Poza Cordón, M. M. Martín Arranz
et al.
The Spanish Society of Digestive Diseases (SEPD) and the Spanish Association of Digestive Ultrasound (AEED), as leading scientific societies in the field of Gastroenterology in Spain, have among their core objectives the continuous improvement of clinical practice, the training of specialists, and the promotion of diagnostic and therapeutic tools that contribute to higher quality, more efficient, and patient-centered healthcare. As a guarantor of the professional development of specialists, SEPD also acts as a technical liaison with health authorities, advocating for the recognition of competencies based on scientific evidence and real healthcare needs. Likewise, AEED is committed to advancing the use of ultrasound techniques, as well as research and training in ultrasound techniques. Both societies consider it essential to establish clinical ultrasound as a core competency of the gastroenterologist. This need arises from a clinical and educational reality strongly supported by scientific evidence, the current regulatory framework, and accumulated experience in other countries and specialties. In this context, SEPD and AEED issue this position statement to reinforce the role of clinical ultrasound as an essential skill of the digestive system specialist. This position is not driven by corporate interests but by an academic, responsible vision that serves patients and the healthcare system. It is further justified by growing scientific evidence, regulatory and training support, and the urgent need to ensure equal access to this technique in all Gastroenterology departments across the country, avoiding unjustified inequalities between regions or hospitals. It also seeks to address organizational gaps where ultrasound remains restricted to other specialties, even when the overall care of the patient lies with the gastroenterologist.
Optical coherence tomography (OCT) has emerged as a transformative imaging modality in gastroenterology, offering micrometer-scale resolution for visualizing gastrointestinal tract structures with unprecedented detail. The ability of OCT to provide "optical biopsy" capabilities without tissue removal has revolutionized the diagnostic approach to gastrointestinal diseases, particularly for early cancer detection and characterization of mucosal abnormalities. The clinical utility of OCT in gastroenterology spans multiple applications including disease detection, differentiation of pathological states, and guidance for endoscopic therapies. Unlike traditional histopathology which requires tissue removal and processing, OCT enables real-time, in vivo assessment of gastrointestinal mucosa and submucosa with near-histological resolution. Several key technological innovations have driven OCT's adoption in gastrointestinal imaging. The development of miniaturized endoscopic probes capable of high-speed circumferential scanning has enabled comprehensive evaluation of the gastrointestinal lumen. Furthermore, the integration of OCT with conventional endoscopy systems has facilitated its clinical translation, allowing gastroenterologists to combine macroscopic and microscopic evaluation during routine procedures. Recent advances in image processing algorithms, including spectral estimation techniques, have further enhanced resolution beyond the fundamental limits imposed by light source coherence length, enabling visualization of cellular-level features in gastrointestinal tissues.
Marie-Pier Bachand, Mohamed-Anas Chennouf, Mandy Malick
et al.
Objectives:. Long-term surveillance of branch-duct intraductal papillary mucinous neoplasms (BD-IPMN) remains controversial, particularly regarding cysts follow-up >5 years. The primary endpoint of this study was to assess the risk of malignant transformation of presumed BD-IPMN during follow-up and identify clinical and morphological predictors of malignancy.
Methods:. We performed a retrospective analysis of data from all patients with a presumed BD-IPMN diagnosis at the CIUSSS de l’Estrie CHUS, from 2004 to 2018.
Results:. The final database included 380 patients with presumed BD-IPMN with a median follow-up of 43.9 months (interquartile range [IQR] 28.6–73.3 months). Mean age at diagnosis was 65.5 years [27–90], 159 patients (42.8%) were male and 17 patients (4.5%) underwent resection of their lesion during their surveillance period. In our cohort, 132 patients (34.7%) had a follow-up of >5 years. Overall risk of malignancy was 2.1% [0.9%–4.1%]. During follow-up, neoplastic transformation was observed in 2 of 132 patients (1.5%) surveilled >5 years. Malignancy was significantly associated with cyst growth >2.5 mm/y (57.1% vs 5.8%; P < .001) dilated MPD (71.4% vs 4.9%; P < .001), solid component (71.4% vs 1.3%; P < .001), positive cytology (37.5% vs 0.5%; P < .001), development of high-risk stigmatas (87.5% vs 1.9%; P < .001), or worrisome features (87.5% vs 23.9%; P < .001) during follow-up and symptoms of jaundice (25% vs 0.5%; P = .002) and abdominal pain (50% vs 9.4%; P = .005).
Conclusion:. While overall malignancy risk remains low in presumed BD-IPMN, continuous surveillance should be pursued after 5 years in surgically fit individuals, particularly in patients who develop our identified risk factors.
Diseases of the digestive system. Gastroenterology
Background and aimFecal incontinence (FI) is defined as the unintended loss of solid or liquid stool. FI adversely affects the patient’s quality of life. However, due to stigma, lack of awareness, and underdiagnosis, there is a notable gap in the knowledge regarding its prevalence. This study aimed to conduct a systematic review and meta-analysis of published literature reporting on FI prevalence and estimate the number of people afflicted by FI.MethodsA systematic review was conducted following the PRISMA 2020 guidelines, using the Embase, MEDLINE, CINHAL, and PubMed databases to identify relevant publications in the English language. Two reviewers independently screened the articles and extracted data. The reference sections and content of the review papers were also evaluated. Thirty-two articles were selected and included. A meta-analysis of proportions was performed using RStudio software. A sub-analysis was conducted to account for the variation between sample population age groups to minimize heterogeneity. The pooled prevalence was extrapolated to the Canadian population and a sample of ten densely populated countries to estimate the number of people affected by FI.ResultsThe Mean pooled FI prevalence in men and women was 7% (95% CI: 6-9%) and 10% (95% CI: 8-12%), respectively. The sub-analysis mean pooled prevalence of FI in men and women was 8% (95% CI: 6-10%) and 10% (95% CI: 8-12%), respectively. The authors estimate that between 1 and 1.5 million Canadians and 320 to 500 million people in the ten most populous countries suffer from FI.ConclusionFecal incontinence is a prevalent underdiagnosed condition requiring appropriate and timely treatment to improve a patient’s quality of life.
Diseases of the digestive system. Gastroenterology
Yash V. Chauhan, Mahesh D. Hakke, Prudwiraj Sanamandra
et al.
Introduction:
The effect and mechanism of skipping breakfast on glycemic control in type 2 diabetes mellitus (T2DM) in Asian-Indians is unknown.
Methods:
Cross-over, within-group study recruiting 5 habitual breakfast eaters (BE) and 5 habitual breakfast skippers (BS) with uncontrolled T2DM (HbA1c 7-9%). Patients underwent testing after three days of following their usual breakfast habits and after seven days of crossing over to the other arm. Fasting values and incremental area under the curve (iAUC0-180) of post-lunch levels of glucose, insulin, C-peptide, glucagon-like peptide 1 (GLP-1), and glucagon were measured. Continuous glucose monitoring (CGM) parameters assessed were area under the curve (AUC0-180) of post-meal glucose values, 24-hour average blood glucose (ABG), time in range (TIR), and glycemic variability.
Results:
BS led to significantly higher fasting (133.5 ± 34.5 mg/dl vs 110 ± 31.50 mg/dl, P = 0.009) and peak post-lunch (214.6 ± 35.07 mg/dl vs 175.4 ± 39.26 mg/dl, P < 0.001) plasma glucose, and HOMA-IR (3.05 ± 3.89 vs 2.03 ± 1.76, P = 0.007) as compared to BE. Post-lunch iAUC0-180 during BS was significantly higher for plasma glucose (7623 ± 2947.9 mg/dl × min vs 1922.4 ± 1902.1 mg/dl × min, P < 0.001), insulin (2460 ± 1597.50 mIU/ml × mins vs 865.71 ± 1735.73 mIU/ml × mins, P = 0.028), C-peptide (418.4 ± 173.4 ng/ml × mins vs 127.8 ± 117.1 ng/ml × mins, P < 0.001) and glucagon (7272.7 ± 4077 pg/ml × mins vs 4568.8 ± 2074.9 pg/ml × mins, P = 0.044) as compared to BE, while GLP-1 (1812.7 ± 883 pmol/l × mins during BS vs 1643 ± 910 pmol/l × mins during BE, P = 0.255) did not significantly differ between the two visits. CGM revealed a higher post-lunch AUC0-180 during BS. There was no difference in post-dinner AUC0-180, ABG, TIR, or glycemic variability.
Conclusion:
Skipping breakfast led to higher post-lunch glucose excursions, possibly due to higher glucagon excursion and increased insulin resistance.
Diseases of the endocrine glands. Clinical endocrinology, Diseases of the digestive system. Gastroenterology
Rare diseases, despite their low individual incidence, collectively impact around 300 million people worldwide due to the vast number of diseases. The involvement of multiple organs and systems, and the shortage of specialized doctors with relevant experience, make diagnosing and treating rare diseases more challenging than common diseases. Recently, agents powered by large language models (LLMs) have demonstrated notable applications across various domains. In the medical field, some agent methods have outperformed direct prompts in question-answering tasks from medical examinations. However, current agent frameworks are not well-adapted to real-world clinical scenarios, especially those involving the complex demands of rare diseases. To bridge this gap, we introduce RareAgents, the first LLM-driven multi-disciplinary team decision-support tool designed specifically for the complex clinical context of rare diseases. RareAgents integrates advanced Multidisciplinary Team (MDT) coordination, memory mechanisms, and medical tools utilization, leveraging Llama-3.1-8B/70B as the base model. Experimental results show that RareAgents outperforms state-of-the-art domain-specific models, GPT-4o, and current agent frameworks in diagnosis and treatment for rare diseases. Furthermore, we contribute a novel rare disease dataset, MIMIC-IV-Ext-Rare, to facilitate further research in this field.
Existing plant disease classification models have achieved remarkable performance in recognizing in-laboratory diseased images. However, their performance often significantly degrades in classifying in-the-wild images. Furthermore, we observed that in-the-wild plant images may exhibit similar appearances across various diseases (i.e., small inter-class discrepancy) while the same diseases may look quite different (i.e., large intra-class variance). Motivated by this observation, we propose an in-the-wild multimodal plant disease recognition dataset that contains the largest number of disease classes but also text-based descriptions for each disease. Particularly, the newly provided text descriptions are introduced to provide rich information in textual modality and facilitate in-the-wild disease classification with small inter-class discrepancy and large intra-class variance issues. Therefore, our proposed dataset can be regarded as an ideal testbed for evaluating disease recognition methods in the real world. In addition, we further present a strong yet versatile baseline that models text descriptions and visual data through multiple prototypes for a given class. By fusing the contributions of multimodal prototypes in classification, our baseline can effectively address the small inter-class discrepancy and large intra-class variance issues. Remarkably, our baseline model can not only classify diseases but also recognize diseases in few-shot or training-free scenarios. Extensive benchmarking results demonstrate that our proposed in-the-wild multimodal dataset sets many new challenges to the plant disease recognition task and there is a large space to improve for future works.
Lung diseases have become a prevalent problem throughout the United States, affecting over 34 million people. Accurate and timely diagnosis of the different types of lung diseases is critical, and Artificial Intelligence (AI) methods could speed up these processes. A dual-stage vision transformer is built throughout this research by integrating a Vision Transformer (ViT) and a Swin Transformer to classify 14 different lung diseases from X-ray scans of patients with these diseases. The proposed model achieved an accuracy of 92.06% on a label-level when making predictions on an unseen testing subset of the dataset after data preprocessing and training the neural network. The model showed promise for accurately classifying lung diseases and diagnosing patients who suffer from these harmful diseases.
Heart disease is a serious worldwide health issue because it claims the lives of many people who might have been treated if the disease had been identified earlier. The leading cause of death in the world is cardiovascular disease, usually referred to as heart disease. Creating reliable, effective, and precise predictions for these diseases is one of the biggest issues facing the medical world today. Although there are tools for predicting heart diseases, they are either expensive or challenging to apply for determining a patient's risk. The best classifier for foretelling and spotting heart disease was the aim of this research. This experiment examined a range of machine learning approaches, including Logistic Regression, K-Nearest Neighbor, Support Vector Machine, and Artificial Neural Networks, to determine which machine learning algorithm was most effective at predicting heart diseases. One of the most often utilized data sets for this purpose, the UCI heart disease repository provided the data set for this study. The K-Nearest Neighbor technique was shown to be the most effective machine learning algorithm for determining whether a patient has heart disease. It will be beneficial to conduct further studies on the application of additional machine learning algorithms for heart disease prediction.