Hasil untuk "Diseases of the digestive system. Gastroenterology"

Hye Kyung Jeon, Gwang Ha Kim

Accurate lesion size measurement is essential in endoscopic practice as it influences treatment strategies, surveillance decisions, and clinical outcomes, especially in colorectal polyps. Traditional measurement techniques, including visual estimation and biopsy forceps, have significant interobserver variability and procedural inefficiencies. Recent advancements in digital measurement technologies, including virtual scale endoscopy (VSE) and artificial intelligence (AI)-assisted virtual rulers, have addressed these limitations. VSE projects a virtual scale onto endoscopic images, enhancing measurement precision and reducing variability. Several studies have demonstrated its superior accuracy compared with conventional methods; however, limitations such as increased procedure time and operator training requirements persist. AI-assisted virtual rulers utilize deep learning algorithms to automate lesion size estimation, significantly improving reproducibility and diagnostic reliability. Although these technologies offer promising improvements, challenges remain, including real-time integration, standardization, and regulatory approval. Future research should focus on refining AI models, expanding validation studies, and optimizing their usability in routine practice. A hybrid approach that combines AI automation with real-time digital tools may enhance the precision and efficiency of endoscopic lesion assessment, ultimately improving patient outcomes.

Kazuhiro Nouso, Akiko Wakuta, Shohei Shiota et al.

ABSTRACT Aim The gut microbiome modulates immune responses, and butyrate‐producing bacteria have been linked to improved immune checkpoint inhibitor (ICI) efficacy. Conversely, proton pump inhibitors (PPIs) may negatively impact ICI outcomes by altering gut microbiota. This study aims to elucidate their effects in hepatocellular carcinoma (HCC). Methods This retrospective multicenter cohort study included 208 HCC patients treated with durvalumab plus tremelimumab at 25 hospitals in Japan. Patients were classified into a butyric acid group (n = 27), who ingested drugs containing butyrate‐producing enterobacteria, and a non‐butyric acid group (n = 181), as well as a PPI group (n = 107) and a non‐PPI group (n = 101). Overall survival (OS) was analyzed using inverse probability of treatment weighting, and risk factors were assessed with Cox proportional hazards modeling. Tumor response was evaluated by RECIST v1.1. Results No significant OS differences were observed between the butyric acid and non‐butyric acid groups (p = 0.921), or between PPI and non‐PPI groups (p = 0.917). The objective response rate was 3.7% in the butyric acid group versus 15.5% in the non‐butyric acid group (p = 0.543) and 15.8% in the PPI group versus 12.1% in the non‐PPI group (p = 0.222). Disease control rates were comparable. Multivariate analysis identified ECOG performance status (p = 0.019) and ALBI score (p < 0.001) as independent prognostic factors, while butyrate‐producing bacteria and PPI use were not associated with survival outcomes. Conclusion Neither butyrate‐producing bacteria nor PPI use significantly influenced the efficacy of durvalumab plus tremelimumab in HCC. The liver's immunotolerant microenvironment may limit the impact of microbiome modulation on ICI efficacy.

Rachel L. Hill, Dhiraj Yadav, Phil A. Hart et al.

INTRODUCTION: Pancreatitis-associated osteopathy is a clinically significant but mechanistically underexplored complication of pancreatic disease. We aimed to characterize stage-specific alterations in bone remodeling biomarkers across the spectrum of disease: recurrent acute pancreatitis (RAP) and chronic pancreatitis (CP). METHODS: In a cross-sectional analysis of North American Pancreatitis Study 2 participants, we measured serum mechanistic (sclerostin, dickkopf-1, receptor activator of nuclear factor κβ ligand, and osteoprotegerin), hormonal (fibroblast growth factor 23, insulin, and leptin), and modulatory (osteopontin, oncostatin, and osteoactivin) markers in controls (n = 30), RAP (n = 40), and CP (n = 40) using a multiplex assay. Group differences were assessed with ANOVA, Fisher exact test, and Kruskal-Wallis; multivariable regression identified predictors of biomarker variation. RESULTS: Eight of 10 biomarkers differed significantly among groups. Sclerostin, dickkopf-1, receptor activator of nuclear factor κβ ligand, and osteoprotegerin were elevated in RAP and CP vs controls, with the highest values in CP. The RANKL/OPG ratio was greatest in CP. Fibroblast growth factor 23 was increased in RAP, while insulin was reduced in CP. Osteopontin and oncostatin were elevated in pancreatitis groups, with osteopontin increasing progressively from control to RAP to CP. Several bone biomarker patterns varied by sex, tobacco, and alcohol use. Stepwise regression identified several significant predictors. DISCUSSION: These findings represent the most comprehensive bone metabolism biomarker profiling in pancreatitis to date, revealing stage-specific dysregulation of bone remodeling. Findings suggest a shift toward increased bone resorption and impaired formation with disease progression. Larger longitudinal studies are needed for marker validation, to clarify mechanisms, and guide targeted interventions to reduce bone loss and fracture risk in this high-risk population.

Shahira Abdelaziz Ali Ahmed, Amira Bakr Mokhtar, Samar Farag Mohamed et al.

Rena Yadlapati, Lorijane Robles, Mary K. Clarke et al.

Jami L. Saloman, Bahiyyah Jefferson, Samuel Han et al.

INTRODUCTION: Chronic pancreatitis (CP) is a disease associated with chronic inflammation, fibrosis, and pain. There is a lack of tools available that facilitate early diagnosis, when intervention could prevent irreversible damage. Pilot data suggested prostaglandin E2 (PGE2) as a candidate biomarker for early CP. PGE2 activates signaling pathways that promote inflammation, pain, and fibrosis. METHODS: We assessed PGE2, metabolites, and downstream targets in pancreatic fluid collected endoscopically 0–10 (n = 110) and 10–20 (n = 111) minutes after intravenous secretin administration. PGE2 and metabolites were measured in plasma (n = 75) and urine (n = 71) from the same subjects. Subjects were enrolled in the PROCEED study and classified symptomatic controls, acute/recurrent acute pancreatitis (AP/RAP), or CP. RESULTS: A significant main effect was detected in 10–20 minutes pancreas fluid ( P = 0.027) and plasma ( P = 0.046); post hoc testing showed PGE2 was lower in the AP/RAP group compared with symptomatic controls. There was also trend toward lower PGE2 in urine ( P = 0.062). To elucidate the active downstream pathways, calcitonin gene-related peptide, substance P, and matrix metalloproteinases (MMPs) 1, 2, 3, 7, 9, and 13 were measured in pancreas fluid. A significant difference between the 3 groups was detected for both MMP7 and MMP9. MMP7 was elevated in individuals with CP vs AP/RAP ( P = 0.012) for samples collected early but both time points for MMP9 ( P = 0.027, P = 0.002). DISCUSSION: While PGE2 is detectable in pancreas fluid, these data suggest that it may not be sensitive enough to distinguish between AP/RAP and CP. However, MMPs may distinguish between stages of pancreatitis and require further testing as potential diagnostic biomarkers.

Soo Kyung Park, Darwin L. Conwell, Phil A. Hart et al.

INTRODUCTION: Chronic Pancreatitis Prognosis Score (COPPS) was developed to discriminate disease severity and predict risk for future hospitalizations. In this cohort study, we evaluated if COPPS predicts the likelihood of hospitalization(s) in an American cohort. METHODS: The Chronic Pancreatitis, Diabetes, and Pancreatic Cancer consortium provided data and serum from subjects with chronic pancreatitis (N = 279). COPPS was calculated with baseline data and stratified by severity (low, moderate, and high). Primary endpoints included number and duration of hospitalizations during 12-month follow-up. RESULTS: The mean ± SD COPPS was 8.4 ± 1.6. COPPS correlated with all primary outcomes: hospitalizations for any reason (number: r = 0.15, P = 0.01; duration: r = 0.16, P = 0.01) and pancreas-related hospitalizations (number: r = 0.15, P = 0.02; duration: r = 0.13, P = 0.04). The severity distribution was 13.3% low, 66.0% moderate, and 20.8% high. 37.6% of subjects had ≥1 hospitalization(s) for any reason; 32.2% had ≥1 pancreas-related hospitalizations. All primary outcomes were significantly different between severity groups: hospitalizations for any reason (number, P = 0.004; duration, P = 0.007) and pancreas-related hospitalizations (number, P = 0.02; duration, P = 0.04). The prevalence of continued drinking at follow-up (P = 0.04) was higher in the low and moderate groups. The prevalence of anxiety at enrollment (P = 0.02) and follow-up (P < 0.05) was higher in the moderate and high groups. DISCUSSION: Statistically, COPPS significantly correlated with hospitalization outcomes, but the correlations were weaker than in previous studies, which may be related to the outpatient nature of the PROspective Evaluation of Chronic Pancreatitis for EpidEmiologic and Translational StuDies cohort and lower prevalence of high severity disease. Studies in other prospective cohorts are needed to understand the full utility of COPPS as a potential tool for clinical risk assessment and intervention.

Cevriye Cansiz Ersöz, Siyar Ersöz, Berna Savas et al.

<b>Background:</b> Low-grade appendiceal mucinous neoplasms (LAMNs) are rare lesions of the vermiform appendix and characterized by mucinous epithelial proliferation, extracellular mucin, and the absence of destructive invasion. Appendiceal diverticulum (AD) is also an uncommon condition that may be challenging to differentiate from acute appendicitis when it is superimposed by diverticulitis or perforation. Some recently published studies emphasized that complicated AD with mucosal hyperplasia can be confused with LAMNs, leading to overdiagnosis. The present study aimed to determine the histopathological features which can be used in the differential diagnosis of LAMNs and ADs, particularly complicated diverticula, in a large cohort. <b>Methods:</b> Cases comprising LAMNs and ADs diagnosed between 2011 and 2021 were included in the study. All cases were evaluated for the epithelial lining, the wall of the lesions, and the presence of cellular or acellular mucin, with its localization in terms of level and site of involvement within the appendix also recorded. <b>Results:</b> The hypermucinous epithelium characteristic of LAMNs, fibrosis, and calcification in the wall and the absence of lamina propria and muscularis mucosa proved to be the most discriminatory features in the differential diagnosis of LAMNs and ADs. <b>Conclusions:</b> The distinction between mucinous neoplasia and its mimics is critically important, since mucinous neoplasia requires surveillance imaging and potential surgery or chemotherapy depending on the extent of the disease, whereas non-neoplastic lesions are treated by an appendectomy and require no future intervention or surveillance.

Bimarsha Khanal, Paras Poudel, Anish Chapagai et al.

Plant diseases significantly impact our food supply, causing problems for farmers, economies reliant on agriculture, and global food security. Accurate and timely plant disease diagnosis is crucial for effective treatment and minimizing yield losses. Despite advancements in agricultural technology, a precise and early diagnosis remains a challenge, especially in underdeveloped regions where agriculture is crucial and agricultural experts are scarce. However, adopting Deep Learning applications can assist in accurately identifying diseases without needing plant pathologists. In this study, the effectiveness of various computer vision models for detecting paddy diseases is evaluated and proposed the best deep learning-based disease detection system. Both classification and detection using the Paddy Doctor dataset, which contains over 20,000 annotated images of paddy leaves for disease diagnosis are tested and evaluated. For detection, we utilized the YOLOv8 model-based model were used for paddy disease detection and CNN models and the Vision Transformer were used for disease classification. The average mAP50 of 69% for detection tasks was achieved and the Vision Transformer classification accuracy was 99.38%. It was found that detection models are effective at identifying multiple diseases simultaneously with less computing power, whereas classification models, though computationally expensive, exhibit better performance for classifying single diseases. Additionally, a mobile application was developed to enable farmers to identify paddy diseases instantly. Experiments with the app showed encouraging results in utilizing the trained models for both disease classification and treatment guidance.

Ahmed Akib Jawad Karim, Muhammad Zawad Mahmud, Samiha Islam et al.

In this research, we explored the improvement in terms of multi-class disease classification via pre-trained language models over Medical-Abstracts-TC-Corpus that spans five medical conditions. We excluded non-cancer conditions and examined four specific diseases. We assessed four LLMs, BioBERT, XLNet, and BERT, as well as a novel base model (Last-BERT). BioBERT, which was pre-trained on medical data, demonstrated superior performance in medical text classification (97% accuracy). Surprisingly, XLNet followed closely (96% accuracy), demonstrating its generalizability across domains even though it was not pre-trained on medical data. LastBERT, a custom model based on the lighter version of BERT, also proved competitive with 87.10% accuracy (just under BERT's 89.33%). Our findings confirm the importance of specialized models such as BioBERT and also support impressions around more general solutions like XLNet and well-tuned transformer architectures with fewer parameters (in this case, LastBERT) in medical domain tasks.

Cécile Trottet, Manuel Schürch, Ahmed Allam et al.

We propose a deep generative approach using latent temporal processes for modeling and holistically analyzing complex disease trajectories, with a particular focus on Systemic Sclerosis (SSc). We aim to learn temporal latent representations of the underlying generative process that explain the observed patient disease trajectories in an interpretable and comprehensive way. To enhance the interpretability of these latent temporal processes, we develop a semi-supervised approach for disentangling the latent space using established medical knowledge. By combining the generative approach with medical definitions of different characteristics of SSc, we facilitate the discovery of new aspects of the disease. We show that the learned temporal latent processes can be utilized for further data analysis and clinical hypothesis testing, including finding similar patients and clustering SSc patient trajectories into novel sub-types. Moreover, our method enables personalized online monitoring and prediction of multivariate time series with uncertainty quantification.

O. I. Gumeniuk, I. A. Glushakov, Yu. V. Chernenkov et al.

The article discusses the features of lesions of the digestive system in children with mitochondrial diseases. Mitochondria play an important role in cellular metabolism as they are responsible for producing the majority of cellular energy in the form of adenosine triphosphate. Mutations in mitochondrial genes responsible for the functioning of mitochondria can lead to various forms of mitochondrial diseases. These diseases may present with the following clinical symptoms: muscle weakness, movement disorders, neurological symptoms, impaired motility and absorption from the gastrointestinal tract. Diagnosis of mitochondrial diseases can be challenging due to genetic and clinical heterogeneity. Treatment currently remains a pressing problem, as research in this area is being actively conducted and new methods are emerging aimed at the therapy and treatment of these rare diseases.

Mohammad Zamani, Shaghayegh Alizadeh-Tabari, Puja Chitkara et al.

Jeremy Louissaint, Sasha Deutsch-Link, Elliot B. Tapper

Henry P. Parkman, Emily Sharkey, Richard W. McCallum et al.

Tennekoon Karunaratne, Yun Yan, Audrey Eubanks et al.

Jin Li , Shao-Na Chen , Yun-Yong Lin et al.

Mengjie Du, Xiang Fang, Jie Li

This technical report describes ChinaTelecom system for Track 1 (closed) of the VoxCeleb2023 Speaker Recognition Challenge (VoxSRC 2023). Our system consists of several ResNet variants trained only on VoxCeleb2, which were fused for better performance later. Score calibration was also applied for each variant and the fused system. The final submission achieved minDCF of 0.1066 and EER of 1.980%.

Koike Hiroaki, Teruaki Hayashi

The development and spread of information and communication technologies have increased and diversified information. However, the increase in the volume and the selection of information does not necessarily promote understanding. In addition, conventional evaluations of information transfer have focused only on the arrival of information to the receivers. They need to sufficiently take into account the receivers' understanding of the information after it has been acquired, which is the original purpose of the evaluation. In this study, we propose the concept of "information digestion," which refers to the receivers' correct understanding of the acquired information, its contents, and its purpose. In the experiment, we proposed an evaluation model of information digestibility using hierarchical factor analysis and extracted factors that constitute digestibility by four types of media.

Phil A. Hart, Dhiraj Yadav, Liang Li et al.