A006: Liver Function and Risk of Non-Alcoholic Fatty Liver Disease in Patients with Sarcopenia: A Meta-Analysis

Background/Aim: Nonalcoholic fatty liver disease (NAFLD) has become increasingly prevalent as one of the most common chronic liver diseases worldwide. Sarcopenia, defined by the decline in muscle mass, power, strength, and performance, has been linked to NAFLD in recent epidemiological research, although findings have been inconclusive. This meta-analysis aimed to consolidate and determine the impact of sarcopenia on liver function, as well as the occurrence and severity of NAFLD in individuals with sarcopenia. Methods: A comprehensive search was conducted in databases including PubMed, Medline, Embase, Scopus, the Cochrane Library, and the Web of Science were searched up to November 20, 2023. Relevant data from the identified articles were extracted and subjected to quality and risk of bias assessment. Meta-analysis was performed utilizing random-effect models. Results: Twenty-two articles were utilized for the analysis. Individuals with sarcopenia exhibited elevated levels of serum alanine aminotransferase (ALT) (SMD = 0.131, 95% CI: 0.039-0.224) and serum aspartate aminotransferase (AST) (SMD = 0.083, 95% CI: 0.043-0.124) compared to healthy control groups. Furthermore, a significantly increased risk of NAFLD (OR=1.538; 95% CI: 1.355-1.744) and notable fibrosis related to NAFLD (OR=1.611; 95% CI: 1.406-1.846) was observed in sarcopenia patients. Conclusion/Discussion: The serum levels of both ALT and AST were found to be elevated in individuals with sarcopenia compared to those in the normal population. Additionally, sarcopenia patients had a higher risk of developing NAFLD, and NAFLD-associated significant fibrosis. In contrast to earlier research, this study incorporated the most recent findings and was the first to investigate potential differences in serum ALT and AST level between sarcopenia patients and healthy control groups. The remaining results aligned with previous studies. These findings emphasize the significance of routine monitoring of liver function in patients with sarcopenia and highlight the need for longitudinal studies to investigate the potential causal relationship between sarcopenia and NAFLD

I Still See You: Why Existing IoT Traffic Reshaping Fails

The Internet traffic data produced by the Internet of Things (IoT) devices are collected by Internet Service Providers (ISPs) and device manufacturers, and often shared with their third parties to maintain and enhance user services. Unfortunately, on-path adversaries could infer and fingerprint users' sensitive privacy information such as occupancy and user activities by analyzing these network traffic traces. While there's a growing body of literature on defending against this side-channel attack-malicious IoT traffic analytics (TA), there's currently no systematic method to compare and evaluate the comprehensiveness of these existing studies. To address this problem, we design a new low-cost, open-source system framework-IoT Traffic Exposure Monitoring Toolkit (ITEMTK) that enables people to comprehensively examine and validate prior attack models and their defending approaches. In particular, we also design a novel image-based attack capable of inferring sensitive user information, even when users employ the most robust preventative measures in their smart homes. Researchers could leverage our new image-based attack to systematize and understand the existing literature on IoT traffic analysis attacks and preventing studies. Our results show that current defending approaches are not sufficient to protect IoT device user privacy. IoT devices are significantly vulnerable to our new image-based user privacy inference attacks, posing a grave threat to IoT device user privacy. We also highlight potential future improvements to enhance the defending approaches. ITEMTK's flexibility allows other researchers for easy expansion by integrating new TA attack models and prevention methods to benchmark their future work.Comment: EWSN'24 paper accepted, to appea

Mechanistic insight on water dissociation on pristine low-index TiO2 surfaces from machine learning molecular dynamics simulations

Water adsorption and dissociation processes on pristine low-index TiO$_{2}$ interfaces are important but poorly understood outside the well-studied anatase (101) and rutile (110). To understand these, we construct three sets of machine learning potentials that are simultaneously applicable to various TiO$_{2}$ surfaces, based on three density-functional-theory approximations. Here we show the water dissociation free energies on seven pristine TiO$_{2}$ surfaces, and predict that anatase (100), anatase (110), rutile (001), and rutile (011) favor water dissociation, anatase (101) and rutile (100) have mostly molecular adsorption, while the simulations of rutile (110) sensitively depend on the slab thickness and molecular adsorption is preferred with thick slabs. Moreover, using an automated algorithm, we reveal that these surfaces follow different types of atomistic mechanisms for proton transfer and water dissociation: one-step, two-step, or both. These mechanisms can be rationalized based on the arrangements of water molecules on the different surfaces. Our finding thus demonstrates that the different pristine TiO$_{2}$ surfaces react with water in distinct ways, and cannot be represented using just the low-energy anatase (101) and rutile (110) surfaces

VirulentHunter : deep learning-based virulence factor predictor illuminates pathogenicity in diverse microbial contexts

Virulence factors (VFs) are critical determinants of bacterial pathogenicity, but current homology-based identification methods often miss novel or divergent VFs, and many machine learning approaches neglect functional classification. Here, we present VirulentHunter, a novel deep learning framework that enable simultaneous VF identification and classification directly from protein sequences by leveraging the crucial step of fine-tuning pretrained protein language model. We curate a comprehensive VF database by integrating diverse public resources and expanding VF category annotations. Our benchmarking results demonstrate that VirulentHunter outperforms existing methods, particularly in identifying VFs lacking detectable homologs. Additionally, strain-level analysis using VirulentHunter highlights distinct pathogenicity profiles between Mycobacterium tuberculosis and Mycobacterium avium, revealing enrichment in VFs related to adherence, effector delivery systems, and immune modulation in M. tuberculosis, compared to biofilm formation and motility in M. avium. Furthermore, metagenomic profiling of gut microbiota from inflammatory bowel disease patient reveals a depletion of VFs associated with immune homeostasis. These results underscore the versatility of VirulentHunter as a powerful tool for VF analysis across diverse applications. To facilitate broader accessibility, we provide a freely accessible web service for VF prediction (http://www.unimd.org/VirulentHunter), accommodating protein sequences, genomes, and metagenomic data

Elucidating the molecular mechanisms of ozone therapy for neuropathic pain management by integrated transcriptomic and metabolomic approach

Introduction: Neuropathic pain remains a prevalent and challenging condition to treat, with current therapies often providing inadequate relief. Ozone therapy has emerged as a promising treatment option; however, its mechanisms of action in neuropathic pain remain poorly understood.Methods: In this study, we investigated the effects of ozone treatment on gene expression and metabolite levels in the brainstem and hypothalamus of a rat model, using a combined transcriptomic and metabolomic approach.Results: Our findings revealed significant alterations in key genes, including DCST1 and AIF1L, and metabolites such as Aconitic acid, L-Glutamic acid, UDP-glucose, and Tyrosine. These changes suggest a complex interplay of molecular pathways and region-specific mechanisms underlying the analgesic effects of ozone therapy.Discussion: Our study provides insights into the molecular targets of ozone treatment for neuropathic pain, laying the groundwork for future research on validating these targets and developing novel therapeutic strategies

Values of lymphocyte-related ratios in predicting the clinical outcome of acute ischemic stroke patients receiving intravenous thrombolysis based on different etiologies

BackgroundWhile neutrophil-to-lymphocyte ratio (NLR), lymphocyte-to-monocyte ratio (LMR), and platelet-to-lymphocyte ratio (PLR) have been associated with acute ischemic stroke (AIS) outcomes, their differential predictive value across etiological subtypes (TOAST classification) in thrombolysis-treated patients remains underexplored.MethodsIn this retrospective cohort study, we analyzed 381 AIS patients receiving intravenous thrombolysis. Hematological indices were calculated from pre-thrombolysis. Using multivariable logistic regression adjusted for age, NIHSS, and comorbidities, we assessed associations between baseline ratios and 90-day unfavorable outcomes (mRS 3–6). Receiver operating characteristic (ROC) analysis was used to determine optimal cutoffs stratified by TOAST subtypes.ResultsA total of 381 patients were included in the study. NLR showed superior predictive performance: large-artery atherosclerosis: AUC = 0.702 (aOR = 1.35, 95%CI = 1.14–1.61, p = 0.001), small-artery occlusion: AUC = 0.750 (aOR = 1.51, 95%CI = 1.08–2.10, p = 0.015), cardioembolic stroke: AUC = 0.679 (aOR = 1.82, 95%CI = 1.07–3.10, p = 0.028). LMR showed predictive value only in large-artery atherosclerosis (AUC = 0.632, p = 0.004). Optimal NLR cutoffs: 3.19 (large-artery), 3.94 (small-artery), 3.17 (cardioembolic stroke).ConclusionNLR emerged as a robust, subtype-specific predictor of post-thrombolysis outcomes, particularly in atherosclerotic stroke variants. These findings supported NLR’s clinical utility for risk stratification in thrombolysis-eligible AIS patients

Up-regulation of interferon-a/APOBEC3G signal pathway potently inactivates HIV-1 infectivity in resting CD4-T cells

Poster Presentation

Novel Pan-ERR Agonists Ameliorate Heart Failure Through Enhancing Cardiac Fatty Acid Metabolism and Mitochondrial Function

BACKGROUND: Cardiac metabolic dysfunction is a hallmark of heart failure (HF). Estrogen-related receptors ERRα and ERRγ are essential regulators of cardiac metabolism. Therefore, activation of ERR could be a potential therapeutic intervention for HF. However, in vivo studies demonstrating the potential usefulness of ERR agonist for HF treatment are lacking, because compounds with pharmacokinetics appropriate for in vivo use have not been available. METHODS: Using a structure-based design approach, we designed and synthesized 2 structurally distinct pan-ERR agonists, SLU-PP-332 and SLU-PP-915. We investigated the effect of ERR agonist on cardiac function in a pressure overload-induced HF model in vivo. We conducted comprehensive functional, multi-omics (RNA sequencing and metabolomics studies), and genetic dependency studies both in vivo and in vitro to dissect the molecular mechanism, ERR isoform dependency, and target specificity. RESULTS: Both SLU-PP-332 and SLU-PP-915 significantly improved ejection fraction, ameliorated fibrosis, and increased survival associated with pressure overload-induced HF without affecting cardiac hypertrophy. A broad spectrum of metabolic genes was transcriptionally activated by ERR agonists, particularly genes involved in fatty acid metabolism and mitochondrial function. Metabolomics analysis showed substantial normalization of metabolic profiles in fatty acid/lipid and tricarboxylic acid/oxidative phosphorylation metabolites in the mouse heart with 6-week pressure overload. ERR agonists increase mitochondria oxidative capacity and fatty acid use in vitro and in vivo. Using both in vitro and in vivo genetic dependency experiments, we show that ERRγ is the main mediator of ERR agonism-induced transcriptional regulation and cardioprotection and definitively demonstrated target specificity. ERR agonism also led to downregulation of cell cycle and development pathways, which was partially mediated by E2F1 in cardiomyocytes. CONCLUSIONS: ERR agonists maintain oxidative metabolism, which confers cardiac protection against pressure overload-induced HF in vivo. Our results provide direct pharmacologic evidence supporting the further development of ERR agonists as novel HF therapeutics

Scientific Large Language Models: A Survey on Biological & Chemical Domains

Large Language Models (LLMs) have emerged as a transformative power in enhancing natural language comprehension, representing a significant stride toward artificial general intelligence. The application of LLMs extends beyond conventional linguistic boundaries, encompassing specialized linguistic systems developed within various scientific disciplines. This growing interest has led to the advent of scientific LLMs, a novel subclass specifically engineered for facilitating scientific discovery. As a burgeoning area in the community of AI for Science, scientific LLMs warrant comprehensive exploration. However, a systematic and up-to-date survey introducing them is currently lacking. In this paper, we endeavor to methodically delineate the concept of scientific language , whilst providing a thorough review of the latest advancements in scientific LLMs. Given the expansive realm of scientific disciplines, our analysis adopts a focused lens, concentrating on the biological and chemical domains. This includes an in-depth examination of LLMs for textual knowledge, small molecules, macromolecular proteins, genomic sequences, and their combinations, analyzing them in terms of model architectures, capabilities, datasets, and evaluation. Finally, we critically examine the prevailing challenges and point out promising research directions along with the advances of LLMs. By offering a comprehensive overview of technical developments in this field, this survey aspires to be an invaluable resource for researchers navigating the intricate landscape of scientific LLMs