Hasil untuk "Diseases of the digestive system. Gastroenterology"

M. Sevo, J. Bornschein

The link between Helicobacter pylori (H. pylori) infection and gastric cancer is well estab­lished. Recent studies shed further light on the effects of an H. pylori screen-and-treat approach on primary prevention of gastric cancer. The residual risk following eradication highlights again the need to further define the ‘point of no return’ when the preventive effect of eradication is impaired. This also includes data on the impact of H. pylori eradication on the risk of metachronous and recurrent gastric cancer after endo­scopic resection of early lesions. Further studies explored the relevance of H. pylori infection in the context of systemic gastric cancer treatment. Recent research has offered new perspectives into clinical characteristics of gastric mucosa-associated lymphoid tissue lymphoma and the role of H. pylori eradication treatment in H. pylori-negative cases. The emerging issue of antibiotic resistance in the treatment of these patients was also addressed. The risk between H. pylori infection and colorectal cancer and its premalignant lesions has also been explored with studies mainly focusing on causality, whilst additionally exploring potential modulatory effects of eradica­tion therapy on these risks. In this article, we have summarized the most noteworthy findings on the topic, focusing primarily on the stud­ies with significant clinical implications published in the period between April 2024 and March 2025.

Shengwei Li, Yumeng Zhang, Fanyu Zhou et al.

Abstract Background Cryoablation simulation based on Finite Element Method (FEM) can facilitate preoperative planning for liver tumors. However, it has limited application in clinical practice due to its time-consuming process and improvable accuracy. We aimed to propose a FEM-based simulation model for rapid and accurate prediction of the iceball size during the hepatic cryofreezing cycle. Methods A 3D simulation model was presented to predict the iceball size (frozen isotherm boundaries) in biological liver tissues undergoing cryofreezing based on the Pennes bioheat equation. The simulated results for three cryoprobe types were evaluated in the ex vivo porcine livers and clinical data. In ex vivo experiments, CT-based measurements of iceball size were fitted as growth curves and compared to the simulated results. Eight patient cases of CT-guided percutaneous hepatic cryoablation procedures were retrospectively collected for clinical validation. The Dice Score Coefficient (DSC) and Hausdorff distance (HD) were used to measure the similarity between simulation and ground truth segmentation. Results The measurements in the ex vivo experiments showed a close similarity between the simulated and experimental iceball growth curves for three cryoprobe models, with all mean absolute error<2.9 mm and coefficient of determination>0.85. In the clinical validation, the simulation model achieved high accuracy with a DSC of 0.87 ± 0.03 and an HD of 2.0 ± 0.4 mm. The average computational time was 23.2 s for all simulations. Conclusion Our simulation model achieves accurate iceball size predictions within a short time during hepatic cryoablation and potentially allows for the implementation of the preoperative cryoablation planning system.

Yuchong Li, Xiaojun Zeng, Chihua Fang et al.

Hepato-pancreato-biliary (HPB) disorders represent a global public health challenge due to their high morbidity and mortality. Although large language models (LLMs) have shown promising performance in general medical question-answering tasks, the current evaluation benchmarks are mostly derived from standardized examinations or manually designed questions, lacking HPB coverage and clinical cases. To address these issues, we systematically eatablish an HPB disease evaluation benchmark comprising 3,535 closed-ended multiple-choice questions and 337 open-ended real diagnosis cases, which encompasses all the 33 main categories and 465 subcategories of HPB diseases defined in the International Statistical Classification of Diseases, 10th Revision (ICD-10). The multiple-choice questions are curated from public datasets and synthesized data, and the clinical cases are collected from prestigious medical journals, case-sharing platforms, and collaborating hospitals. By evalauting commercial and open-source general and medical LLMs on our established benchmark, namely ClinBench-HBP, we find that while commercial LLMs perform competently on medical exam questions, they exhibit substantial performance degradation on HPB diagnosis tasks, especially on complex, inpatient clinical cases. Those medical LLMs also show limited generalizability to HPB diseases. Our results reveal the critical limitations of current LLMs in the domain of HPB diseases, underscoring the imperative need for future medical LLMs to handle real, complex clinical diagnostics rather than simple medical exam questions. The benchmark will be released at https://clinbench-hpb.github.io.

Sarah Laouedj, Yuzhe Wang, Jesus Villalba et al.

In this study, we explored the use of spectrograms to represent handwriting signals for assessing neurodegenerative diseases, including 42 healthy controls (CTL), 35 subjects with Parkinson's Disease (PD), 21 with Alzheimer's Disease (AD), and 15 with Parkinson's Disease Mimics (PDM). We applied CNN and CNN-BLSTM models for binary classification using both multi-channel fixed-size and frame-based spectrograms. Our results showed that handwriting tasks and spectrogram channel combinations significantly impacted classification performance. The highest F1-score (89.8%) was achieved for AD vs. CTL, while PD vs. CTL reached 74.5%, and PD vs. PDM scored 77.97%. CNN consistently outperformed CNN-BLSTM. Different sliding window lengths were tested for constructing frame-based spectrograms. A 1-second window worked best for AD, longer windows improved PD classification, and window length had little effect on PD vs. PDM.

Umakanta Maharana, Sarthak Verma, Avarna Agarwal et al.

Large language models (LLMs) offer a promising pre-screening tool, improving early disease detection and providing enhanced healthcare access for underprivileged communities. The early diagnosis of various diseases continues to be a significant challenge in healthcare, primarily due to the nonspecific nature of early symptoms, the shortage of expert medical practitioners, and the need for prolonged clinical evaluations, all of which can delay treatment and adversely affect patient outcomes. With impressive accuracy in prediction across a range of diseases, LLMs have the potential to revolutionize clinical pre-screening and decision-making for various medical conditions. In this work, we study the diagnostic capability of LLMs for Rheumatoid Arthritis (RA) with real world patients data. Patient data was collected alongside diagnoses from medical experts, and the performance of LLMs was evaluated in comparison to expert diagnoses for RA disease prediction. We notice an interesting pattern in disease diagnosis and find an unexpected \textit{misalignment between prediction and explanation}. We conduct a series of multi-round analyses using different LLM agents. The best-performing model accurately predicts rheumatoid arthritis (RA) diseases approximately 95\% of the time. However, when medical experts evaluated the reasoning generated by the model, they found that nearly 68\% of the reasoning was incorrect. This study highlights a clear misalignment between LLMs high prediction accuracy and its flawed reasoning, raising important questions about relying on LLM explanations in clinical settings. \textbf{LLMs provide incorrect reasoning to arrive at the correct answer for RA disease diagnosis.}

Kristyn Gumpper-Fedus, Kaylin Chasser, Valentina Pita-Grisanti et al.

INTRODUCTION: Chronic pancreatitis (CP) is a progressive fibroinflammatory disorder lacking therapies and biomarkers. Neutrophil gelatinase-associated lipocalin (NGAL) is a proinflammatory cytokine elevated during inflammation that binds fatty acids (FAs) such as linoleic acid. We hypothesized that systemic NGAL could serve as a biomarker for CP and, with FAs, provide insights into inflammatory and metabolic alterations. METHODS: NGAL was measured by immunoassay, and FA composition was measured by gas chromatography in plasma (n = 171) from a multicenter study, including controls (n = 50), acute and recurrent acute pancreatitis (AP/RAP) (n = 71), and CP (n = 50). Peripheral blood mononuclear cells (PBMCs) from controls (n = 16), AP/RAP (n = 17), and CP (n = 15) were measured by cytometry by time-of-flight. RESULTS: Plasma NGAL was elevated in subjects with CP compared with controls (area under the curve [AUC] = 0.777) or AP/RAP (AUC = 0.754) in univariate and multivariate analyses with sex, age, body mass index, and smoking (control AUC = 0.874; AP/RAP AUC = 0.819). NGAL was elevated in CP and diabetes compared with CP without diabetes (P < 0.001). NGAL+ PBMC populations distinguished CP from controls (AUC = 0.950) or AP/RAP (AUC = 0.941). Linoleic acid was lower, whereas dihomo-γ-linolenic and adrenic acids were elevated in CP (P < 0.05). Linoleic acid was elevated in CP with diabetes compared with CP subjects without diabetes (P = 0.0471). DISCUSSION: Elevated plasma NGAL and differences in NGAL+ PBMCs indicate an immune response shift that may serve as biomarkers of CP. The potential interaction of FAs and NGAL levels provide insights into the metabolic pathophysiology and improve diagnostic classification of CP.

Deepak Madhu, MD, MRCP, DM, Yohei Minato, MD, Susumu Banjoya, MD et al.

Tianchen Huang, Yong Zhang, Yachao Wu et al.

Background: Cuproptosis is a novel pathway that differs from other forms of cell death and has been confirmed to be applicable for predicting tumor prognosis and clinical treatment response. However, the mechanism underlying the resistance of colorectal cancer (CRC) to cuproptosis at the molecular level has not been elucidated. Methods: Using bioinformatics analysis, the expression of CCAAT/enhancer-binding protein beta (CEBPB) in CRC tissues and its enrichment in biological processes were detected. Quantitative reverse transcription polymerase chain reaction and western blotting (WB) were employed to test the expression of CEBPB in CRC cells. WB was utilized to assess the levels of proteins related to cuproptosis and the phosphatidylinositol 3-kinase/protein kinase B/mammalian target of rapamycin (PI3K/AKT/mTOR) pathway. The MTT assay was used to test cell viability. Cell proliferation was assessed by a colony formation assay. Transwell assays were used to measure cell migration and invasion ability. DLAT-aggregate formation was determined by immunofluorescence. Results: CEBPB was highly upregulated in CRC cells to enhance cell viability, proliferation, migration, and invasion. CEBPB was strongly implicated in copper ion homeostasis and the mTOR signaling pathway in CRC. In a CRC cuproptosis cell model, rescue experiments revealed that a PI3K/AKT/mTOR pathway inhibitor attenuated the promoting effect of CEBPB overexpression on the PI3K/AKT/mTOR pathway and rescued the sensitivity of CRC to cuproptosis. Conclusion: This work demonstrated that CEBPB can activate the PI3K/AKT/mTOR signaling pathway, thereby decreasing the sensitivity of CRC to cuproptosis. These data suggested that targeting CEBPB or the PI3K/AKT/mTOR pathway may enhance the sensitivity of CRC patients to cuproptosis, providing a combined therapeutic strategy for cuproptosis-induced therapy.

Han Hee Lee, Siddharth Singh

Daniel A. Kuppers, Jonathan Linton, Sergio Ortiz Espinosa et al.

Human CD34 + hematopoietic stem and progenitor cells (HSPCs) are a standard source of cells for clinical HSC transplantations as well as experimental xenotransplantation to generate “humanized mice”. To further extend the range of applications of these humanized mice, we developed a protocol to efficiently edit the genomes of human CD34 + HSPCs before transplantation. In the past, manipulating HSPCs has been complicated by the fact that they are inherently difficult to transduce with lentivectors, and rapidly lose their stemness and engraftment potential during in vitro culture. However, with optimized nucleofection of sgRNA:Cas9 ribonucleoprotein complexes, we are now able to edit a candidate gene in CD34 + HSPCs with almost 100% efficiency, and transplant these modified cells in immunodeficient mice with high engraftment levels and multilineage hematopoietic differentiation. The result is a humanized mouse from which we knocked out a gene of interest from their human immune system.

Karen Woo, Laura Gascue, Keith Norris et al.

Reben Raeman

Юрий Павлович Успенский, Олимбек Саидбекович Мирзоев

В современной гастроэнтерологии проблема функциональных заболеваний органов пищеварения является основной причиной заболеваемости и обращаемости к практикующим врачам. Перекрестный синдром чаще встречается среди пациентов, нежели изолированные функциональные поражения органов пищеварения. Целью данного исследования было изучение клинико -психологической характеристики больных с сочетанием функциональной диспепсии и синдрома раздраженного кишечника. В исследование включено 192 пациента. Сформированы 3 группы: «синдром раздраженного кишечника» - 82 человека; «функциональная диспепсия» - 54 человека и «сочетанные функциональные заболевания» - 56 человек по результатам обследования. Таким образом, распространенность симптомов диспепсии у пациентов с синдромом раздраженного кишечника составила 40,5 %. Доля пациентов с тяжелыми и выраженными клиническими симптомами в группе «сочетанные функциональные заболевания» была больше по сравнению с группами больных «синдром раздраженного кишечника» и «функциональная диспепсия». Кроме того, у лиц с сочетанной функциональной патологией отмечаются более низкие значения психологического статуса по сравнению с больными с синдромом раздраженного кишечника и с функциональной диспепсией. In modern gastroenterology, the problem of functional diseases of the digestive system is the main cause of morbidity and referral to practitioners. Cross syndrome is more common among patients than isolated functional lesions of the digestive system. The purpose of this study was to study the clinical and psychological characteristics of patients with a combination of functional dyspepsia and irritable bowel syndrome. The study included 192 patients. 3 groups of patients were formed: “irritable bowel syndrome” - 82 people; “functional dyspepsia” - 54 people and “combined functional diseases” - 56 people according to the results of the survey. Thus, the prevalence of dyspeptic symptoms in patients with irritable bowel syndrome was 40.5 %. The proportion of patients with severe and severe clinical symptoms in the “combined functional diseases” group was higher compared to the “irritable bowel syndrome” and “functional dyspepsia” groups of patients. In addition, individuals with combined functional pathology have lower psychological status values compared to patients with irritable bowel syndrome and functional dyspepsia.

Justin Engelmann, Alice D. McTrusty, Ian J. C. MacCormick et al.

Ultra-widefield (UWF) imaging is a promising modality that captures a larger retinal field of view compared to traditional fundus photography. Previous studies showed that deep learning (DL) models are effective for detecting retinal disease in UWF images, but primarily considered individual diseases under less-than-realistic conditions (excluding images with other diseases, artefacts, comorbidities, or borderline cases; and balancing healthy and diseased images) and did not systematically investigate which regions of the UWF images are relevant for disease detection. We first improve on the state of the field by proposing a DL model that can recognise multiple retinal diseases under more realistic conditions. We then use global explainability methods to identify which regions of the UWF images the model generally attends to. Our model performs very well, separating between healthy and diseased retinas with an area under the curve (AUC) of 0.9206 on an internal test set, and an AUC of 0.9841 on a challenging, external test set. When diagnosing specific diseases, the model attends to regions where we would expect those diseases to occur. We further identify the posterior pole as the most important region in a purely data-driven fashion. Surprisingly, 10% of the image around the posterior pole is sufficient for achieving comparable performance to having the full images available.

Jingwei Li, Ruitian Wu, Tzu-liang Huang et al.

Parkinson's Disease (PD) is a progressive nervous system disorder that has affected more than 5.8 million people, especially the elderly. Due to the complexity of its symptoms and its similarity to other neurological disorders, early detection requires neurologists or PD specialists to be involved, which is not accessible to most old people. Therefore, we integrate smart mobile devices with AI technologies. In this paper, we introduce the framework of our developed PD early detection system which combines different tasks evaluating both motor and non-motor symptoms. With the developed model, we help users detect PD punctually in non-clinical settings and figure out their most severe symptoms. The results are expected to be further used for PD rehabilitation guidance and detection of other neurological disorders.

Yucong Lin, Keming Lu, Sheng Yu et al.

Objective: Disease knowledge graphs are a way to connect, organize, and access disparate information about diseases with numerous benefits for artificial intelligence (AI). To create knowledge graphs, it is necessary to extract knowledge from multimodal datasets in the form of relationships between disease concepts and normalize both concepts and relationship types. Methods: We introduce REMAP, a multimodal approach for disease relation extraction and classification. The REMAP machine learning approach jointly embeds a partial, incomplete knowledge graph and a medical language dataset into a compact latent vector space, followed by aligning the multimodal embeddings for optimal disease relation extraction. Results: We apply REMAP approach to a disease knowledge graph with 96,913 relations and a text dataset of 1.24 million sentences. On a dataset annotated by human experts, REMAP improves text-based disease relation extraction by 10.0% (accuracy) and 17.2% (F1-score) by fusing disease knowledge graphs with text information. Further, REMAP leverages text information to recommend new relationships in the knowledge graph, outperforming graph-based methods by 8.4% (accuracy) and 10.4% (F1-score). Conclusion: REMAP is a multimodal approach for extracting and classifying disease relationships by fusing structured knowledge and text information. REMAP provides a flexible neural architecture to easily find, access, and validate AI-driven relationships between disease concepts.

Siyu Liu, Linfeng Liu, Xuan Vinh et al.

In medical image analysis, the subtle visual characteristics of many diseases are challenging to discern, particularly due to the lack of paired data. For example, in mild Alzheimer's Disease (AD), brain tissue atrophy can be difficult to observe from pure imaging data, especially without paired AD and Cognitively Normal ( CN ) data for comparison. This work presents Disease Discovery GAN ( DiDiGAN), a weakly-supervised style-based framework for discovering and visualising subtle disease features. DiDiGAN learns a disease manifold of AD and CN visual characteristics, and the style codes sampled from this manifold are imposed onto an anatomical structural "blueprint" to synthesise paired AD and CN magnetic resonance images (MRIs). To suppress non-disease-related variations between the generated AD and CN pairs, DiDiGAN leverages a structural constraint with cycle consistency and anti-aliasing to enforce anatomical correspondence. When tested on the Alzheimer's Disease Neuroimaging Initiative ( ADNI) dataset, DiDiGAN showed key AD characteristics (reduced hippocampal volume, ventricular enlargement, and atrophy of cortical structures) through synthesising paired AD and CN scans. The qualitative results were backed up by automated brain volume analysis, where systematic pair-wise reductions in brain tissue structures were also measured

Marcely Zanon Boito, John Ortega, Hugo Riguidel et al.

This paper describes the ON-TRAC Consortium translation systems developed for two challenge tracks featured in the Evaluation Campaign of IWSLT 2022: low-resource and dialect speech translation. For the Tunisian Arabic-English dataset (low-resource and dialect tracks), we build an end-to-end model as our joint primary submission, and compare it against cascaded models that leverage a large fine-tuned wav2vec 2.0 model for ASR. Our results show that in our settings pipeline approaches are still very competitive, and that with the use of transfer learning, they can outperform end-to-end models for speech translation (ST). For the Tamasheq-French dataset (low-resource track) our primary submission leverages intermediate representations from a wav2vec 2.0 model trained on 234 hours of Tamasheq audio, while our contrastive model uses a French phonetic transcription of the Tamasheq audio as input in a Conformer speech translation architecture jointly trained on automatic speech recognition, ST and machine translation losses. Our results highlight that self-supervised models trained on smaller sets of target data are more effective to low-resource end-to-end ST fine-tuning, compared to large off-the-shelf models. Results also illustrate that even approximate phonetic transcriptions can improve ST scores.

A. Mitra, U. Mukherjee, Suman Mazumder et al.

Patients with multiple myeloma (MM), an age-dependent neoplasm of antibody-producing plasma cells, have compromised immune systems and might be at increased risk for severe COVID-19 outcomes. This study characterizes risk factors associated with clinical indicators of COVID-19 severity and all-cause mortality in myeloma patients utilizing NCATS' National COVID Cohort Collaborative (N3C) database. The N3C consortium is a large, centralized data resource representing the largest multi-center cohort of COVID-19 cases and controls nationwide (>16 million total patients, and >6 million confirmed COVID-19+ cases to date). Our cohort included myeloma patients (both inpatients and outpatients) within the N3C consortium who have been diagnosed with COVID-19 based on positive PCR or antigen tests or ICD-10-CM diagnosis code. The outcomes of interest include all-cause mortality (including discharge to hospice) during the index encounter and clinical indicators of severity (i.e., hospitalization/emergency department/ED visit, use of mechanical ventilation, or extracorporeal membrane oxygenation (ECMO)). Finally, causal inference analysis was performed using the propensity score matching (PSM) method. As of 05/16/2022, the N3C consortium included 1,061,748 cancer patients, out of which 26,064 were MM patients (8,588 were COVID-19 positive). The mean age at COVID-19 diagnosis was 65.89 years, 46.8% were females, and 20.2% were of black race. 4.47% of patients died within 30 days of COVID-19 hospitalization. Overall, the survival probability was 90.7% across the course of the study. Multivariate logistic regression analysis showed histories of pulmonary and renal disease, dexamethasone, proteasome inhibitor/PI, immunomodulatory/IMiD therapies, and severe Charlson Comorbidity Index/CCI were significantly associated with higher risks of severe COVID-19 outcomes. Protective associations were observed with blood-or-marrow transplant/BMT and COVID-19 vaccination. Further, multivariate cox proportional hazard analysis showed that high and moderate CCI levels, International Staging System (ISS) moderate or severe stage, and PI therapy were associated with worse survival, while BMT and COVID-19 vaccination were associated with lower risk of death. Finally, matched sample average treatment effect on the treated (SATT) confirmed the causal effect of BMT and vaccination status as top protective factors associated with COVID-19 risk among US patients suffering from multiple myeloma. To the best of our knowledge, this is the largest nationwide study on myeloma patients with COVID-19.

Mohamed Ziad, Ailine Canete Cruz, C. Ramirez et al.

SESSION TITLE: Management of COVID-19-Induced Complications SESSION TYPE: Rapid Fire Case Reports PRESENTED ON: 10/19/2022 12:45 pm - 1:45 pm INTRODUCTION: Up to 17% of patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have been shown to develop pancreatic lesions (1). We present 2 cases of coronavirus disease 2019 (COVID-19) patients that presented with pancreatic lesions. CASE PRESENTATION: Case1 A 47-year-old lady with a history of type 2 diabetes mellitus present to the emergency department (ED) with complaints of flu-like symptoms for ten days. She tested positive for COVID-19 by rapid PCR. Computed tomography (CT) scan without contrast on admission shows an incidental finding of a pancreatic mass (see Figure 1). Abdominal CT with contrast shows a large, multiloculated cystic mass in the pancreatic tail (see Figure 2). Laboratory examination depicted lipase: 27 U/L, CA19-9: 72 U/mL, CEA: 6.5 ng/mL, CA125: 24 U/mL, erythrocyte sedimentation rate (ESR):2 mm/h, Total Bilirubin: 0.6 mg/dl, Direct Bilirubin: 0.1 mg/dl. Following treatment, the patient recovered fully and is discharged from the hospital 10 days later with home oxygen therapy. Case2 An 81-year old Caucasian lady presented to the outpatient clinic with complaints of fecal incontinence. She tested positive for COVID-19, four months before her visit. CT scan of the abdomen with oral contrast revealed multiple hypodense masses on the pancreas measuring 0.3cm in diameter (see Figure 3). Laboratory tests reveal CA19-9: 57 U/mL, CA125: 8 U/mL, CEA: 1.9 ng/mL, erythrocyte sedimentation rate (ESR):11 mm/h, C-reactive protein: 0.7 mg/L, Total Bilirubin: 1.5 mg/dl, Direct Bilirubin: 1.3 mg/dl. Following outpatient treatment and follow-up, the patient's symptoms were relieved. DISCUSSION: Pancreatic lesions in COVID-19 patients can be caused directly by the cytopathic effects of the viral infection, or indirectly by systemic responses to inflammation or respiratory failure. Several studies have shown that ACE2 is the functional receptor used by SARS-CoV-2 to gain access to target cells (2) and ACE-2 receptors are expressed in significant amounts in the pancreas (3). In the first case, an incidental finding of a multi-cystic pancreatic mass on admission was reported. There was no pancreatic ductal dilation on the CT scan, which may indicate a direct injury caused by cytopathic effects of the virus rather than inflammation resulting in exocrine secretions forming cysts. In the second case, multiple masses on the pancreas were found after recovering from COVID-19. These lesions could be remnants of a previous pancreatic injury during the acute phase of the infection. CONCLUSIONS: COVID-19 infection may trigger pancreatic injury in some patients. Reference #1: Yong, Shin Jie. Long COVID or post-COVID-19 syndrome: putative pathophysiology, risk factors, and treatments. Infectious diseases. 2021 Oct;53(10): 737–754. Reference #2: Ma C, Cong Y, Zhang H. COVID-19, and the Digestive System. Vol. 115, American Journal of Gastroenterology. Wolters Kluwer Health;2020. p. 1003–6. Reference #3: Liu F, Long X, Zhang B, Zhang W, Chen X, Zhang Z. ACE2 Expression in Pancreatic Damage After SAERS-CoV-2 Infection. Gastroenterology. 2020 Aug 1;18(9): 2128 – 2130.e2. DISCLOSURES: No relevant relationships by Ailine Canete Cruz No relevant relationships by Claudia Ramirez No relevant relationships by Joseph Varon No relevant relationships by Mohamed Ziad