Abstract The steatotic liver disease (SLD) landscape has seen a paradigm shift in recent years with a revitalization of the nomenclature following a multi-society Delphi consensus. The terms metabolic dysfunction-associated steatotic liver disease (MASLD) and metabolic dysfunction-associated steatohepatitis (MASH) were introduced to address several of the challenges and limitations associated with the former terminology. By transitioning away from stigmatizing and ambiguous terms, the nomenclature has adopted inclusionary language that emphasizes the underlying risk factors that drive disease progression and are accompanied by distinct diagnostic criteria. With SLD prevalence steadily increasing over the past few decades, affecting over 30% of the global population, accurate classification of the spectrum of conditions that fall under this overarching term is essential. Most importantly, the introduction of combined metabolic and alcohol-associated liver disease (MetALD) as a novel subclassification of SLD has shifted the diagnostic approach, raised awareness of disease prevalence, and paved the way for therapeutic management and multidisciplinary approaches to patient care. By recognizing the distinct clinical entity that is MetALD and the synergistic interplay between the cardiometabolic risk factors and alcohol use, clinicians are better equipped to effectively care for this patient population. In this review, we aim to discuss the catalysts for the SLD nomenclature changes, the dynamic nature of its subclasses, the natural history and disease burden, and the implications for clinical practice and research, with a particular focus on MetALD.
Kacey Chae, Amie F Bettencourt, Denise K Houston
et al.
Abstract Background Intentional weight loss improves physical function among older adults with obesity, despite the associated lean mass loss. However, prior studies have not assessed the impacts of weight loss on physical function and body composition among older adults with type 2 diabetes mellitus and obesity, a population at high risk for sarcopenia and functional decline. Our objective was to examine differences in body composition changes by physical function status among middle-aged and older adults with type 2 diabetes mellitus and overweight/obesity participating in an intensive weight-loss intervention of diet and exercise over 12 months. Methods We conducted a secondary analysis of 12-month data from the Look AHEAD dual-energy X-ray absorptiometry substudy among participants randomized to intervention (n = 603). Independent variables included dual-energy X-ray absorptiometry-derived percent change in appendicular lean mass and fat mass. The dependent variable was SF-36 physical function subscale change categorized as worsened (decrease ≥ 5), stable (change ± 4), or improved (increase ≥ 5). We examined the associations using ANOVA. Results Overall, participants had a mean age of 58.3 (SD 6.7) and 63% were women—8% had worsened, 69% stable, and 22% improved physical function. Differences in mean percent appendicular lean mass change between physical function groups were nonsignificant (worsened −3.7%; stable −4.8%; improved −5.6%; p = .05). Mean percent fat mass change was significantly different across physical function groups (worsened −9.3%; stable −14.6%; improved −17.9%; p < .01). Conclusions Lean mass loss associated with lifestyle weight-loss intervention does not negatively affect physical function, rather the intervention appears to improve physical function by reducing adiposity among adults with type 2 diabetes mellitus and overweight/obesity.
Fusobacterium nucleatum (Fn) is commonly enriched in colorectal cancer (CRC) and associated with poor outcomes, though its mechanisms remain unclear. Our study investigated how Fn affects the tumor microenvironment through single-cell transcriptomic analyses of 42 CRC patient tissues, comparing Fn-positive and Fn-negative tumors. We discovered that Fn impairs IgA plasma cell development and secretory IgA (sIgA) production by disrupting communication with tumor-associated macrophages. Additional experiments in germ-free mice, together with our re-analysis of a publicly available single-cell RNA-seq data set from a CRC mouse model with an intact gut microbiome–both models having been orally gavaged with Fn–jointly validated the causal role of Fn in impairing sIgA induction. We identified a dysregulated IgA maturation (IGAM) module in Fn-positive patients, indicating compromised mucosal immunity and increased bacterial infiltration. This IGAM signature effectively stratified Fn-positive patients, suggesting potential for targeted therapeutic approaches. Our findings reveal that Fn disrupts sIgA production, increasing tumor microbial burden and worsening prognosis through chronic inflammation in Fn-positive CRC.
Diseases of the digestive system. Gastroenterology
Introduction:
Inguinal hernia surgery, a common procedure worldwide, continues to develop to achieve minimal access and tension-free repairs. However, a universally accepted technique has yet to be developed. Our study introduces a new approach, a modified tumescent transabdominal pre-peritoneal (TAPP), to a low-cost setting. We then compare its safety and efficacy with the conventional TAPP, providing a new perspective on hernia repair methods.
Patients and Methods:
The study was conducted between April 2016 and September 2017 at the department of surgery in a medical college in Jammu. Sixty patients were randomly assigned to either the conventional TAPP group or the tumescent TAPP group using computer-generated randomisation. In the tumescent group, we carefully administered a tumescent solution into the pre-peritoneal space after creating pneumoperitoneum and then compared the effectiveness and safety of the two procedures.
Results:
Our study revealed significant differences in various aspects between the two groups. In the conventional group, 16.7% of patients experienced challenging peritoneal flap dissection, while none in the tumescent group faced this issue. In addition, none of the patients in the tumescent group had an intraoperative haemorrhage. The conventional group had a mean operating time of 100.4 ± 11.21 min. On the other hand, the tumescent group had a significantly shorter mean operating time of 84 ± 13.47 min. The complication rates were 16.7% in the tumescent group and 30% in the conventional group. After the surgery, 13.3% of patients in the conventional group reported persistent pain, compared to only one patient in the tumescent group, which was statistically significant.
Conclusion:
Our study demonstrates that tumescent TAPP can overcome the challenges of conventional TAPP surgery, offering practical benefits such as reduced bleeding, easier dissection, decreased post-operative pain and shorter operating time. Administering tumescent solution before TAPP repair of inguinal hernia provides technical and clinical advantages, suggesting the potential for shorter surgeries and a quicker learning curve.
Surgery, Diseases of the digestive system. Gastroenterology
Vikas J. Patel, Maher Homsi, Nicholas A. Orriols
et al.
Background and Aims: Internal fistulas found on cross-sectional imaging (CSI) performed during routine care of patients with Crohn’s disease (CD) are often considered incidental findings. This study aimed to assess outcomes in patients with internal fistulas on CSI. Methods: This is a single tertiary care center, retrospective case-control study of CD outcomes. Patients who had magnetic resonance enterography or computer tomography enterography performed between 2007 and 2017 were included. Electronic medical record data up to 2017 were included as variables in logistic regression analysis. CSI was scored by 3 abdominal radiologists blinded to the electronic medical record. Results: Subjects included 199 CD patients: 63 patients (cases) had internal fistulas on index scan and 136 had no internal fistula. The cases and controls were well-matched for age, race, smoking status, body mass index, and years of disease. During follow-up, cases had a more complicated disease course with higher incidence of intra-abdominal abscess formation (19.1% vs 3.7%; P < .001) and abdominal surgery (44.4% vs 24.3%; P < .001). Patients with fistula were more likely to require surgery (odds ratio 4.96, P < .001) and to develop intra-abdominal abscess (odds ratio 6.05, P < .001). The index scan of cases was more likely to demonstrate inflammation (95.2% vs 39.7%; P < .001) and stricture (27.0% vs 7.35%; P < .001) than controls though the presence of an internal fistula was the only independent variable predictive of intra-abdominal abscess. Conclusion: CD patients with internal fistulas identified by CSI have worse disease outcomes. Presence of internal fistula is the only independent risk factor for future intra-abdominal abscess regardless of the patient’s symptoms.
Diseases of the digestive system. Gastroenterology
Valentina Carbonari, Pierangelo Veltri, Pietro Hiram Guzzi
Recent advances in artificial intelligence, particularly large language models LLMs, have shown promising capabilities in transforming rare disease research. This survey paper explores the integration of LLMs in the analysis of rare diseases, highlighting significant strides and pivotal studies that leverage textual data to uncover insights and patterns critical for diagnosis, treatment, and patient care. While current research predominantly employs textual data, the potential for multimodal data integration combining genetic, imaging, and electronic health records stands as a promising frontier. We review foundational papers that demonstrate the application of LLMs in identifying and extracting relevant medical information, simulating intelligent conversational agents for patient interaction, and enabling the formulation of accurate and timely diagnoses. Furthermore, this paper discusses the challenges and ethical considerations inherent in deploying LLMs, including data privacy, model transparency, and the need for robust, inclusive data sets. As part of this exploration, we present a section on experimentation that utilizes multiple LLMs alongside structured questionnaires, specifically designed for diagnostic purposes in the context of different diseases. We conclude with future perspectives on the evolution of LLMs towards truly multimodal platforms, which would integrate diverse data types to provide a more comprehensive understanding of rare diseases, ultimately fostering better outcomes in clinical settings.
As a social being, we have an intimate bond with the environment. A plethora of things in human life, such as lifestyle, health, and food are dependent on the environment and agriculture. It comes under our responsibility to support the environment as well as agriculture. However, traditional farming practices often result in inefficient resource use and environmental challenges. To address these issues, precision agriculture has emerged as a promising approach that leverages advanced technologies to optimise agricultural processes. In this work, a hybrid approach is proposed that combines the three different potential fields of model AI: object detection, large language model (LLM), and Retrieval-Augmented Generation (RAG). In this novel framework, we have tried to combine the vision and language models to work together to identify potential diseases in the tree leaf. This study introduces a novel AI-based precision agriculture system that uses Retrieval Augmented Generation (RAG) to provide context-aware diagnoses and natural language processing (NLP) and YOLOv8 for crop disease detection. The system aims to tackle major issues with large language models (LLMs), especially hallucinations and allows for adaptive treatment plans and real-time disease detection. The system provides an easy-to-use interface to the farmers, which they can use to detect the different diseases related to coffee leaves by just submitting the image of the affected leaf the model will detect the diseases as well as suggest potential remediation methodologies which aim to lower the use of pesticides, preserving livelihoods, and encouraging environmentally friendly methods. With an emphasis on scalability, dependability, and user-friendliness, the project intends to improve RAG-integrated object detection systems for wider agricultural applications in the future.
We propose an epidemic model for the spread of vector-borne diseases. The model, which is built extending the classical susceptible-infected-susceptible model, accounts for two populations -- humans and vectors -- and for cross-contagion between the two species, whereby humans become infected upon interaction with carrier vectors, and vectors become carriers after interaction with infected humans. We formulate the model as a system of ordinary differential equations and leverage monotone systems theory to rigorously characterize the epidemic dynamics. Specifically, we characterize the global asymptotic behavior of the disease, determining conditions for quick eradication of the disease (i.e., for which all trajectories converge to a disease-free equilibrium), or convergence to a (unique) endemic equilibrium. Then, we incorporate two control actions: namely, vector control and incentives to adopt protection measures. Using the derived mathematical tools, we assess the impact of these two control actions and determine the optimal control policy.
The development of an intelligent agricultural decision-supporting system for crop selection and disease forecasting in Bangladesh is the main objective of this work. The economy of the nation depends heavily on agriculture. However, choosing crops with better production rates and efficiently controlling crop disease are obstacles that farmers have to face. These issues are addressed in this research by utilizing machine learning methods and real-world datasets. The recommended approach uses a variety of datasets on the production of crops, soil conditions, agro-meteorological regions, crop disease, and meteorological factors. These datasets offer insightful information on disease trends, soil nutrition demand of crops, and agricultural production history. By incorporating this knowledge, the model first recommends the list of primarily selected crops based on the soil nutrition of a particular user location. Then the predictions of meteorological variables like temperature, rainfall, and humidity are made using SARIMAX models. These weather predictions are then used to forecast the possibilities of diseases for the primary crops list by utilizing the support vector classifier. Finally, the developed model makes use of the decision tree regression model to forecast crop yield and provides a final crop list along with associated possible disease forecast. Utilizing the outcome of the model, farmers may choose the best productive crops as well as prevent crop diseases and reduce output losses by taking preventive actions. Consequently, planning and decision-making processes are supported and farmers can predict possible crop yields. Overall, by offering a detailed decision support system for crop selection and disease prediction, this work can play a vital role in advancing agricultural practices in Bangladesh.
The recent focus on microbes in human medicine highlights their potential role in the genetic framework of diseases. To decode the complex interactions among genes, microbes, and diseases, computational predictions of gene-microbe-disease (GMD) associations are crucial. Existing methods primarily address gene-disease and microbe-disease associations, but the more intricate triple-wise GMD associations remain less explored. In this paper, we propose a Heterogeneous Causal Metapath Graph Neural Network (HCMGNN) to predict GMD associations. HCMGNN constructs a heterogeneous graph linking genes, microbes, and diseases through their pairwise associations, and utilizes six predefined causal metapaths to extract directed causal subgraphs, which facilitate the multi-view analysis of causal relations among three entity types. Within each subgraph, we employ a causal semantic sharing message passing network for node representation learning, coupled with an attentive fusion method to integrate these representations for predicting GMD associations. Our extensive experiments show that HCMGNN effectively predicts GMD associations and addresses association sparsity issue by enhancing the graph's semantics and structure.
Energy storage (ES) and virtual energy storage (VES) are key components to realizing power system decarbonization. Although ES and VES have been proven to deliver various types of grid services, little work has so far provided a systematical framework for quantifying their adequacy contribution and credible capacity value while incorporating human and market behavior. Therefore, this manuscript proposed a novel evaluation framework to evaluate the capacity credit (CC) of ES and VES. To address the system capacity inadequacy and market behavior of storage, a two-stage coordinated dispatch is proposed to achieve the trade-off between day-ahead self-energy management of resources and efficient adjustment to real-time failures. And we further modeled the human behavior with storage operations and incorporate two types of decision-independent uncertainties (DIUs) (operate state and self-consumption) and one type of decision-dependent uncertainty (DDUs) (available capacity) into the proposed dispatch. Furthermore, novel reliability and CC indices (e.g., equivalent physical storage capacity (EPSC)) are introduced to evaluate the practical and theoretical adequacy contribution of ES and VES, as well as the ability to displace generation and physical storage while maintaining equivalent system adequacy. Exhaustive case studies based on the IEEE RTS-79 system and real-world data verify the significant consequence (10%-70% overestimated CC) of overlooking DIUs and DDUs in the previous works, while the proposed method outperforms other and can generate a credible and realistic result. Finally, we investigate key factors affecting the adequacy contribution of ES and VES, and reasonable suggestions are provided for better flexibility utilization of ES and VES in decarbonized power system.
As road accident cases are increasing due to the inattention of the driver, automated driver monitoring systems (DMS) have gained an increase in acceptance. In this report, we present a real-time DMS system that runs on a hardware-accelerator-based edge device. The system consists of an InfraRed camera to record the driver footage and an edge device to process the data. To successfully port the deep learning models to run on the edge device taking full advantage of the hardware accelerators, model surgery was performed. The final DMS system achieves 63 frames per second (FPS) on the TI-TDA4VM edge device.
Abstract The importance of mindfulness in promoting mental health and well-being has been increasingly recognised in recent years. As a result, mindfulness-based interventions have been introduced to improve various aspects of life, including quality of life and social support. The aim of this study was to examine the effectiveness of a seven-week mindfulness-based workshop programme in improving quality of life and social support among participants in the intervention compared to a control group. A total of 257 participants (65+) were recruited and assigned to either the intervention group, which participated in the seven-week mindfulness-based workshop programme, or the control group, which received no intervention. The workshop programme combined two evidence-based programmes: The Chronic Disease Self-Management Programme (CDSMP) and the Mindfulness-based Living Programme. Participants completed two questionnaires (EQ-5D-5L and OSSS-3) to assess quality of life and social support before and after the intervention. Data were analysed using appropriate statistical tests to compare pre- and post-intervention outcomes between groups. The intervention group showed significant improvement in quality of life (p<.001) and social support scores (p = 0.002) after the seven-week mindfulness-based workshop programme. The control group, on the other hand, showed no significant changes in these measures. The significant improvement indicates the effectiveness of the mindfulness-based workshop programme. The results of this study show the positive effects of a seven-week mindfulness-based workshop programme on the quality of life and social support of older people. The results suggest that mindfulness-based interventions can be an effective tool for improving mental health and well-being by promoting quality of life and strengthening social support networks. Key messages • The seven-week mindfulness-based workshop programme improves the quality of life and social support of older people, which can have an impact on overall mental health and well-being in general. • The results of this study can ensure a sustainable impact on achieving better health outcomes and saving resources in the health care system through reduced and more effective use of services.