Hasil untuk "Diseases of the digestive system. Gastroenterology"

Miéllio Melo Galdino, Alexandre Kyoshi Hidaka, Matheus Cardoso Morrone et al.

Prostate cancer is one of the most prevalent neoplasms among men, and radical prostatectomy is a common treatment. While urinary incontinence and erectile dysfunction are frequently discussed postoperative complications, the incidence of inguinal hernias (IHs) has received less attention. Retzius-sparing robot-assisted radical prostatectomy (RS-RALP) has emerged as a technique that may reduce the risk of IH compared to conventional approaches (C-RALP). We conducted a systematic review and meta-analysis following PRISMA guidelines to evaluate whether different surgical approaches influence the incidence of IHs. From 670 initially identified studies, four met the inclusion criteria. The meta-analysis showed that RS-RALP was associated with a significantly lower incidence of IH compared to C-RALP (odds ratio = 0.31; confidence interval = 0.18–0.55; P < 0.001). Preservation of the Retzius space appears to reduce the risk of postoperative IH, whereas reconstructive measures within this space do not seem to offer additional protection.

Jun Zhu, Meixia Wang, Yizhi Zhang et al.

Monica T. Dayao, Aaron T. Mayer, Alexandro E. Trevino et al.

Jae Won Shin, Eun-Jung Lee, Sung Sil Park et al.

Purpose The incidence of rectal neuroendocrine tumors (NETs) is increasing owing to a rise in colonoscopy screening. For the endoscopic removal of NETs, complete resection including the submucosal layer is essential. Therefore, appropriate endoscopic resection techniques are of critical importance. This study aimed to analyze data on rectal NETs and help provide guidance for their endoscopic treatment. Methods A retrospective analysis was conducted on data from patients who underwent resection for rectal NETs at 6 institutions between 2010 and 2021. Results A total of 1,406 tumors were resected from 1,401 patients. During a mean follow-up period of 55.4 months, there were 8 cases (0.5%) of recurrence. Overall, a complete resection was achieved in 77.6% of the patients, with modified endoscopic mucosal resection (mEMR) and endoscopic submucosal dissection (ESD) showing the highest rate at 86.0% and 84.9%, respectively, followed by conventional EMR (cEMR; 68.7%) and snare polypectomy (59.0%). In the subgroup analysis, statistically significant differences were observed in complete resection rates based on tumor size. ESD and mEMR demonstrated significantly higher complete resection rates compared with cEMR. Univariate and multivariate analyses showed that tumor location of the lower rectum and advanced techniques (mEMR and ESD) were significant prognostic factors for complete resection rates. Conclusion When encountering rectal subepithelial lesions on endoscopic examination, endoscopists should consider the possibility of NETs and carefully decide on the endoscopic treatment method. Therefore, it is advisable to perform mEMR or ESD to achieve complete resection, especially for rectal NETs measuring ≤10 mm.

Xiangyang Ye, Rongxian Qiu, Xiongzhi He et al.

Abstract Pegylated interferon alfa (pegIFN-α) is one of the main therapeutic strategies for chronic hepatitis B (CHB) infection. Although interferon-based treatment strategies have great potential in combating CHB, they have significant side effects, and many patients do not respond. In this study, nontargeted metabolomics was performed to investigate the effects of interferon therapy on metabolites and their correlations with clinical indicators. Each group of 20 patients received PEG-IFN alfa-2b for 0, 1, 3, or 6 months. At the end of the IFN-α treatment, patient serum samples were processed for clinical, biological, and metabolomic analyses. The results revealed that in all four groups, the most affected metabolic pathways were ABC transporter, caffeine metabolism, and protein digestion and absorption. Notably, among the top 8 differentially abundant metabolites with high ROC AUC values (≥ 0.95), three were related to pyrimidine nucleoside metabolites (cytidine, 3-methyluridine and ribothymidine), indicating the pivotal role of altered nucleoside metabolism in HBV infection and suggesting that IFN-α treatment might partially correct this dysregulation. The top 20 metabolites were strongly associated with clinical indicators, with significant differences after IFN-α treatment, and may serve as promising biomarkers for monitoring HBV progression and the therapeutic effect of IFN-α treatment for chronic HBV infection.

Hongtao Hao, Joseph L. Austerweil

Chronic diseases often progress differently across patients. Rather than randomly varying, there are typically a small number of subtypes for how a disease progresses across patients. To capture this structured heterogeneity, the Subtype and Stage Inference Event-Based Model (SuStaIn) estimates the number of subtypes, the order of disease progression for each subtype, and assigns each patient to a subtype from primarily cross-sectional data. It has been widely applied to uncover the subtypes of many diseases and inform our understanding of them. But how robust is its performance? In this paper, we develop a principled Bayesian subtype variant of the event-based model (BEBMS) and compare its performance to SuStaIn in a variety of synthetic data experiments with varied levels of model misspecification. BEBMS substantially outperforms SuStaIn across ordering, staging, and subtype assignment tasks. Further, we apply BEBMS and SuStaIn to a real-world Alzheimer's data set. We find BEBMS has results that are more consistent with the scientific consensus of Alzheimer's disease progression than SuStaIn.

Balram Singh, Ram Prakash Sharma, Somnath Dey

Plant diseases pose a significant threat to global food security, necessitating accurate and interpretable disease detection methods. This study introduces an interpretable attention-guided Convolutional Neural Network (CNN), CBAM-VGG16, for plant leaf disease detection. By integrating Convolution Block Attention Module (CBAM) at each convolutional stage, the model enhances feature extraction and disease localization. Trained on five diverse plant disease datasets, our approach outperforms recent techniques, achieving high accuracy (up to 98.87%) and demonstrating robust generalization. Here, we show the effectiveness of our method through comprehensive evaluation and interpretability analysis using CBAM attention maps, Grad-CAM, Grad-CAM++, and Layer-wise Relevance Propagation (LRP). This study advances the application of explainable AI in agricultural diagnostics, offering a transparent and reliable system for smart farming. The code of our proposed work is available at https://github.com/BS0111/PlantAttentionCBAM.

Pierrick Coupé, Boris Mansencal, José V. Manjón et al.

The differential diagnosis of neurodegenerative diseases, characterized by overlapping symptoms, may be challenging. Brain imaging coupled with artificial intelligence has been previously proposed for diagnostic support, but most of these methods have been trained to discriminate only isolated diseases from controls. Here, we develop a novel machine learning framework, named lifespan tree of brain anatomy, dedicated to the differential diagnosis between multiple diseases simultaneously. It integrates the modeling of volume changes for 124 brain structures during the lifespan with non-linear dimensionality reduction and synthetic sampling techniques to create easily interpretable representations of brain anatomy over the course of disease progression. As clinically relevant proof-of-concept applications, we constructed a cognitive lifespan tree of brain anatomy for the differential diagnosis of six causes of neurodegenerative dementia and a motor lifespan tree of brain anatomy for the differential diagnosis of four causes of parkinsonism using 37594 MRI as a training dataset. This original approach enhanced significantly the efficiency of differential diagnosis in the external validation cohort of 1754 cases, outperforming existing state-of-the art machine learning techniques. Lifespan tree holds promise as a valuable tool for differential diagnostic in relevant clinical conditions, especially for diseases still lacking effective biological markers.

Gustavo A. Sousa, Diogo L. M. Souza, Enrique C. Gabrick et al.

The study of infectious disease propagation is essential for understanding and controlling epidemics. One of the most useful tools for gaining insights into the spread of infectious diseases is mathematical modelling. In terms of mathematical epidemiology, the main models are based on compartments, such as SI, SIR, and SEIR. These models offer mathematical frameworks for representing the proliferation dynamics of various diseases, for instance flu and smallpox. In this work, we explore these models using two distinct mathematical approaches, Cellular Automata (CA) and ODEs. They are able to reproduce the spread dynamics of diseases with their own individuality. CA models incorporate the local interaction among individuals with discrete time and space, while ODEs provide a continuous and simplified view of a disease propagation in large and homogeneous populations. By comparing these two approaches, we find that the shape of the curves of all models is similar for both representations. Although, the growth rates differ between CA and ODE. One of our results is to show that the CA yields a power-law growth, while the ODE growth rate is well-represented by an exponential function. Furthermore, a substantial contribution of our work is using a hyperbolic tangent to fit the initial growth of infected individuals for all the considered models. Our results display a strong correlation between simulated data and adjusted function. We mainly address this successful result by the fact that the hyperbolic function captures both growing: the power-law (when considered the first terms of infinite sums) and combinations of exponential (when the hyperbolic function is written via exponential). Therefore, our work shows that when modelling a disease the choice of mathematical representation is crucial, in particular to model the onset of an epidemic.

Lang Qin, Zijian Gan, Xu Cao et al.

Computational drug repurposing for rare diseases is especially challenging when no prior associations exist between drugs and target diseases. Therefore, knowledge graph completion and message-passing GNNs have little reliable signal to learn and propagate, resulting in poor performance. We present RareAgent, a self-evolving multi-agent system that reframes this task from passive pattern recognition to active evidence-seeking reasoning. RareAgent organizes task-specific adversarial debates in which agents dynamically construct evidence graphs from diverse perspectives to support, refute, or entail hypotheses. The reasoning strategies are analyzed post hoc in a self-evolutionary loop, producing textual feedback that refines agent policies, while successful reasoning paths are distilled into transferable heuristics to accelerate future investigations. Comprehensive evaluations reveal that RareAgent improves the indication AUPRC by 18.1% over reasoning baselines and provides a transparent reasoning chain consistent with clinical evidence.

Lu You, Lauric A. Ferrat, Richard A. Oram et al.

Chenxi Tang, Li Cen, Hang Zeng et al.

Background &amp; Aims: Acetaminophen (APAP) overdose is the most common cause of drug-induced liver injury worldwide. Uric acid (UA) is involved in sterile inflammation in many organs, but its role in APAP-induced liver injury remains elusive. Methods: We quantified the concentration of UA in the serum and liver tissues of APAP-overdosed mice and explored the changes in proteins involved in UA synthesis, absorption, and degeneration on APAP stimulation. We also examined the effects of inhibiting hepatocyte UA synthesis or reabsorption on APAP-induced liver injury in mice. Furthermore, we explored the process of UA clearance by peripheral macrophages. Results: APAP overdose significantly increased intrahepatic UA contents, which occurred earlier than apparent hepatocyte injury in APAP-overdosed mice. APAP overdose induced significant DNA leakage and may thereby increase the substrate of UA synthesis. APAP overdose also significantly increased the enzymatic activity of xanthine oxidase and urate oxidase and decreased the expression of the UA reabsorption transporter GLUT9 in hepatocytes. Inhibiting hepatocyte UA synthesis by febuxostat or reabsorption by hepatic-specific knockout of GLUT9 alleviated APAP-induced liver injury. Further experiments showed that monosodium urate but not soluble UA may be a major form of UA mediating hepatocyte injury. Additionally, monosodium urate further recruited circulating macrophages into the liver and then aggravated inflammation by increasing the levels of inflammatory factors and reactive oxygen species. Deletion of macrophages significantly ameliorated APAP-induced liver injury in mice. Conclusions: APAP overdose induces excessive UA production and leads to local high concentrations in the liver, which further injures cells and induces liver inflammation. Inhibiting the production of UA may be a potential therapeutic option for treating APAP-induced liver injury.

Zihao Zhao, Yi Jing, Fuli Feng et al.

Medication recommendation systems have gained significant attention in healthcare as a means of providing tailored and effective drug combinations based on patients' clinical information. However, existing approaches often suffer from fairness issues, as recommendations tend to be more accurate for patients with common diseases compared to those with rare conditions. In this paper, we propose a novel model called Robust and Accurate REcommendations for Medication (RAREMed), which leverages the pretrain-finetune learning paradigm to enhance accuracy for rare diseases. RAREMed employs a transformer encoder with a unified input sequence approach to capture complex relationships among disease and procedure codes. Additionally, it introduces two self-supervised pre-training tasks, namely Sequence Matching Prediction (SMP) and Self Reconstruction (SR), to learn specialized medication needs and interrelations among clinical codes. Experimental results on two real-world datasets demonstrate that RAREMed provides accurate drug sets for both rare and common disease patients, thereby mitigating unfairness in medication recommendation systems.

Yitao Peng, Lianghua He, Die Hu

With the widespread application of deep learning technology in medical image analysis, the effective explanation of model predictions and improvement of diagnostic accuracy have become urgent problems that need to be solved. Attribution methods have become key tools to help doctors better understand the diagnostic basis of models, and are used to explain and localize diseases in medical images. However, previous methods suffer from inaccurate and incomplete localization problems for fundus diseases with complex and diverse structures. To solve these problems, we propose a weakly supervised interpretable fundus disease localization method called hierarchical salient patch identification (HSPI) that can achieve interpretable disease localization using only image-level labels and a neural network classifier (NNC). First, we propose salient patch identification (SPI), which divides the image into several patches and optimizes consistency loss to identify which patch in the input image is most important for the network's prediction, in order to locate the disease. Second, we propose a hierarchical identification strategy to force SPI to analyze the importance of different areas to neural network classifier's prediction to comprehensively locate disease areas. Conditional peak focusing is then introduced to ensure that the mask vector can accurately locate the disease area. Finally, we propose patch selection based on multi-sized intersections to filter out incorrectly or additionally identified non-disease regions. We conduct disease localization experiments on fundus image datasets and achieve the best performance on multiple evaluation metrics compared to previous interpretable attribution methods. Additional ablation studies are conducted to verify the effectiveness of each method.

Meng Wang, Tian Lin, Aidi Lin et al.

Previous foundation models for fundus images were pre-trained with limited disease categories and knowledge base. Here we introduce a knowledge-rich vision-language model (RetiZero) that leverages knowledge from more than 400 fundus diseases. For RetiZero's pretraining, we compiled 341,896 fundus images paired with texts, sourced from public datasets, ophthalmic literature, and online resources, encompassing a diverse range of diseases across multiple ethnicities and countries. RetiZero exhibits remarkable performance in several downstream tasks, including zero-shot disease recognition, image-to-image retrieval, AI-assisted clinical diagnosis,few-shot fine-tuning, and internal- and cross-domain disease identification. In zero-shot scenarios, RetiZero achieves Top-5 accuracies of 0.843 for 15 diseases and 0.756 for 52 diseases. For image retrieval, it achieves Top-5 scores of 0.950 and 0.886 for the same sets, respectively. AI-assisted clinical diagnosis results show that RetiZero's Top-3 zero-shot performance surpasses the average of 19 ophthalmologists from Singapore, China, and the United States. RetiZero substantially enhances clinicians' accuracy in diagnosing fundus diseases, in particularly rare ones. These findings underscore the value of integrating the RetiZero into clinical settings, where various fundus diseases are encountered.

Khadija Khatun, Chen Shen, Jun Tanimoto et al.

Understanding how cooperation emerges in public goods games is crucial for addressing societal challenges. While optional participation can establish cooperation without identifying cooperators, it relies on specific assumptions -- that individuals abstain and receive a non-negative payoff, or that non-participants cause damage to public goods -- which limits our understanding of its broader role. We generalize this mechanism by considering non-participants' payoffs and their potential direct influence on public goods, allowing us to examine how various strategic motives for non-participation affect cooperation. Using replicator dynamics, we find that cooperation thrives only when non-participants are motivated by individualistic or prosocial values, with individualistic motivations yielding optimal cooperation. These findings are robust to mutation, which slightly enlarges the region where cooperation can be maintained through cyclic dominance among strategies. Our results suggest that while optional participation can benefit cooperation, its effectiveness is limited and highlights the limitations of bottom-up schemes in supporting public goods.

William C Orr, Ronnie Fass, Shikha S Sundaram et al.

Lauren Bernard, Ashley R. Wang, Steven Menez et al.

L.M. Mosiychuk, L.V. Demeshkina, E.V. Zigalo

Background. Due to the need to assess the impact of comorbid pathology (GERD and Oddi sphincter dysfunction), it is not possible to use such well-known specific questionnaires as the GERD Health-Related Quality of Life Questionnaire, the Quality of Life in Reflux and Dyspepsia and others. The purpose: to study Health-Related Quality of Life (HRQoL) before and after treatment in patients with GERD, combined with dysfunction of the sphincter of Oddi, using Short Form Health Status Survey (SF-36) and the self-developed specific questionnaire. Materials and me­thods. 30 patients with comorbid diseases GERD and Oddi’s sphincter dysfunction were studied. SF-36 and the self-developed specific questionnaire were used. Results. The specific questionnaire for patients with gastoesophageal reflux disease combined with Sphincter of Oddi dysfunction was worked out. The evaluation of the questionnaire (patients comprehensiveness of questions, timesuming etc.) was explored. Using SF-36, it was significant improvement of HRQoL for all scales after treatment compared with initial data: physical functioning (PF, (77,1 ± 2,0) versus (71,4 ± 2,2)), role-physical functioning (RP, (52,1 ± 5,0) versus (30,6 ± 2,4)), bodily pain (BP, (65,8 ± 1,2) versus (48,7 ± 2,1)), social functioning (SF, (69,5 ± 2,0) versus (50,8 ± 2,1)), mental health (MH, (68,5 ± 2,1) versus (49,8 ± 1,8)), role emotional (RE, (65,2 ± 4,7) versus (35,8 ± 3,7)), vitality (VT, (63,3 ± 2,1) versus (43,2 ± 2,2)), general health (GH, (58,1 ± 2,4) versus (46,1 ± 1,7) points). All patients had decreased HRQoL due to comorbid GERD and sphincter dysfunction Oddi. Moderate or significant disorders of activity and rhythm of life due to these diseases was revealed, but after treatment there was an improvement in significant HRQoL in all patients, in addition to psycho-emotional perception of their diseases became more positive. Conclusions. All patients had decreased HRQoL due to comorbid GERD and sphincter dysfunction Oddi, and it improved after treatment in most of patients.

Sofia Saraiva, Sónia Bernardo, Samuel Fernandes et al.

Introduction: The incidence of primary colorectal lymphoma in the gastrointestinal tract is very low, the rectum being infrequently affected. The development of this entity in inflammatory bowel disease patients usually occurs in a context of immunosuppression-based therapy, with only a few case reports describing its development in patients presenting no known risk factors. Moreover, the clinical presentation of primary colorectal lymphomas may be difficult to distinguish from an acute flare of ulcerative colitis (UC). Case Presentation: We present a case of non-Hodgkin lymphoma of the rectum in a 42-year-old male with a 7-year history of UC and no previous exposure to immunomodulatory agents. He presented with a history of mucous diarrhoea, tenesmus, proctalgia and weight loss, refractory to optimized therapy. A lower gastrointestinal endoscopy was performed revealing a circumferential ulcerated lesion of the rectum, from which histopathological analysis established the diagnosis of a non-Hodgkin diffuse large B-cell lymphoma (DLBCL). Discussion/Conclusion: The present case suggests the existence of alternative mechanisms for the development of DLBCL in UC patients. The clinical presentation mimicking an acute flare of UC posed a diagnostic challenge, highlighting the complexity behind the management of UC patients.