Demystifying furan formation in foods: Implications for human health, detection, and control measures: A review.

Furan (C₄H₄O), an unintended hazardous compound, is formed in various thermally processed foods through multiple pathways, raising concerns due to its potential carcinogenicity in humans. The aim of this comprehensive review was to synthesize and evaluate the latest research on furan, from its formation by different precursors to its presence in diverse food matrices, as well as the emerging methods for its detection and mitigation. Emphasizing the toxicity of furan, it explored evidence from in vitro and in vivo studies, including reproductive toxicity, carcinogenic effects, and related biomarkers.

Large Language Models in Worldwide Medical Exams: Platform Development and Comprehensive Analysis.

Background: Large language models (LLMs) are increasingly integrated into medical education, with transformative potential for learning and assessment. However, their performance across diverse medical exams globally has remained underexplored.

Objective: This study aims to introduce MedExamLLM, a comprehensive platform designed to systematically evaluate the performance of LLMs on medical exams worldwide.

Enhanced Analysis of Carcinogens and Nutritional Profile of by Employing Pulsed Electric Field.

Solid Phase Microextraction-Gas Chromatography Triple Quadrupole Mass Spectrometry (SPME-GC-TQ/MS) was optimized and validated to specifically analyze aldehydes and furans after drying by conventional as well as modern pre-drying technique i.e. pulsed electric field (PEF).

Bibliometric and visual analysis of human microbiome-breast cancer interactions: current insights and future directions.

The composition of the gut microbiome differs from that of healthy individuals and is closely linked to the progression and development of breast cancer. Recent studies have increasingly examined the relationship between microbial communities and breast cancer. This study analyzed the research landscape of microbiome and breast cancer, focusing on 736 qualified publications from the Web of Science Core Collection (WoSCC).

Unveiling the Potential of Natural Resources-Derived Therapeutics for Improved Malaria Management: Computational to Experimental Studies.

Malaria kills millions of people annually, and it is one of the major causes of preventable mortality in the world. Of the different plasmodium species that induce malaria, Plasmodium falciparum and Plasmodium vivax account for the most severe form of malarial disease in humans. This review focuses on understanding preventive measures, mutation-based disease evolution, malaria-related biomarkers, and potential plant bioactive components for the treatment and management of malaria.

Development of transient ischemic attack risk prediction model suitable for initializing a learning health system unit using electronic medical records.

Background: Patients with transient ischemic attack (TIA) face a significantly increased risk of stroke. However, TIA screening and early detection rates are low, especially in developing countries. This study aims to develop an inclusive and practical TIA risk prediction model using machine learning (ML) that performs well in both hospital and resource-limited clinic settings.

Evidence-based advancements in breast cancer genetic counseling: a review.

Purpose: Understanding individuals at high risk of breast cancer, as well as patients and survivors, underscores the critical role of genetic counseling in the diagnosis and treatment of breast cancer.

Methods: This systematic review adhered to the guidelines outlined in the Reporting Items for Systematic Review and Meta-Analysis (PRISMA). The review process was managed using Covidence systematic review software, facilitating data extraction according to predefined eligibility criteria by two independent reviewers.

Comprehensive human respiratory genome catalogue underlies the high resolution and precision of the respiratory microbiome.

The human respiratory microbiome plays a crucial role in respiratory health, but there is no comprehensive respiratory genome catalogue (RGC) for studying the microbiome. In this study, we collected whole-metagenome shotgun sequencing data from 4067 samples and sequenced long reads of 124 samples, yielding 9.08 and 0.

PAHFKB: A knowledge base to support personalized exercise prescription recommendations in prevention and intervention of heart failure.

Background And Aims: Guidelines for exercise recommendations are typically designed for the population as a whole and do not account for individual differences, making it challenging to provide personalized exercise training for individuals with complex conditions. To address this issue, this study aimed to develop PAHFKB (Physical Activity-Heart Failure Knowledge Base), a knowledge-based system for personalized exercise prescription (EP) for heart failure (HF), by mining, analyzing, and organizing existing literature and data on the relationship between physical activity (PA) and HF.

Methods: Firstly, 3186 citations on PAHF were gathered from PubMed.

An integrated bioinformatics approach to early diagnosis, prognosis and therapeutics of non-small-cell lung cancer.

Non-small-cell lung cancer (NSCLC) is one of the most deadly tumors characterized by poor survival rates. Advances in therapeutics and precise identification of biomarkers can potentially reduce the mortality rate. Thus, this study aimed to identify a set of common and stable gene biomarkers through integrated bioinformatics approaches that might be effective for NSCLC early diagnosis, prognosis, and therapies.

Unraveling the nexus of age, epilepsy, and mitochondria: exploring the dynamics of cellular energy and excitability.

Epilepsy, a complex neurological condition marked by recurring seizures, is increasingly recognized for its intricate relationship with mitochondria, the cellular powerhouses responsible for energy production and calcium regulation. This review offers an in-depth examination of the interplay between epilepsy, mitochondrial function, and aging. Many factors might account for the correlation between epilepsy and aging.

Exploring cytokines dynamics: Uncovering therapeutic concepts for metabolic disorders in postmenopausal women- diabetes, metabolic bone diseases, and non-alcohol fatty liver disease.

Menopause is an age-related change that persists for around one-third of a woman's life. Menopause increases the risk of metabolic illnesses such as diabetes, osteoporosis (OP), and nonalcoholic fatty liver disease (NAFLD). Immune mediators (pro-inflammatory cytokines), such as interleukin-1 (IL-1), IL-6, IL-17, transforming growth factor (TGF), and tumor necrosis factor (TNF), exacerbate the challenges of a woman undergoing menopause by causing inflammation and contributing to the development of these metabolic diseases in postmenopausal women.

Developing natural microcapsules by encapsulating peptides for preserving Zanthoxylum Bungeanum.

Natural edible microcapsules, were developed to improve the shelf life of Zanthoxylum bungeanum. Antimicrobial peptides, extracted from seeds of Sichuan pepper corn by ultrasound and microwave assisted extraction were encapsulated with nisin using water-in-oil-in-water (W/O/W) microencapsulation technique. Prepared microcapsules exhibited maximum encapsulation efficiency (ω %) of 30.

Development of Lung Cancer Risk Prediction Machine Learning Models for Equitable Learning Health System: Retrospective Study.

Background: A significant proportion of young at-risk patients and nonsmokers are excluded by the current guidelines for lung cancer (LC) screening, resulting in low-screening adoption. The vision of the US National Academy of Medicine to transform health systems into learning health systems (LHS) holds promise for bringing necessary structural changes to health care, thereby addressing the exclusivity and adoption issues of LC screening.

Objective: This study aims to realize the LHS vision by designing an equitable, machine learning (ML)-enabled LHS unit for LC screening.

OligoM-Cancer: A multidimensional information platform for deep phenotyping of heterogenous oligometastatic cancer.

Patients with oligometastatic cancer (OMC) exhibit better response to local therapeutic interventions and a more treatable tendency than those with polymetastatic cancers. However, studies on OMC are limited and lack effective integration for systematic comparison and personalized application, and the diagnosis and precise treatment of OMC remain controversial. The application of large language models in medicine remains challenging because of the requirement of high-quality medical data.

Advancing Chinese biomedical text mining with community challenges.

Objective: This study aims to review the recent advances in community challenges for biomedical text mining in China.

Methods: We collected information of evaluation tasks released in community challenges of biomedical text mining, including task description, dataset description, data source, task type and related links. A systematic summary and comparative analysis were conducted on various biomedical natural language processing tasks, such as named entity recognition, entity normalization, attribute extraction, relation extraction, event extraction, text classification, text similarity, knowledge graph construction, question answering, text generation, and large language model evaluation.

Proteomics profiling reveals lipid metabolism abnormalities during oogenesis in unexplained recurrent pregnancy loss.

Background: Unexplained recurrent pregnancy loss (URPL) is a clinical dilemma in reproductive fields. Its diagnosis is mainly exclusionary after extensive clinical examination, and some of the patients may still face the risk of miscarriage.

Methods: We analyzed follicular fluid (FF) from fertilization (IVF) in eight patients with URPL without endocrine abnormalities or verifiable causes of abortion and eight secondary infertility controls with no history of pregnancy loss who had experienced at least one normal pregnancy and delivery by direct data-independent acquisition (dDIA) quantitative proteomics to identify differentially expressed proteins (DEPs).

Shaping the future of gastrointestinal cancers through metabolic interactions with host gut microbiota.

Gastrointestinal (GI) cancers represent a significant global health challenge, driving relentless efforts to identify innovative diagnostic and therapeutic approaches. Recent strides in microbiome research have unveiled a previously underestimated dimension of cancer progression that revolves around the intricate metabolic interplay between GI cancers and the host's gut microbiota. This review aims to provide a comprehensive overview of these emerging metabolic interactions and their potential to catalyze a paradigm shift in precision diagnosis and therapeutic breakthroughs in GI cancers.

Intelligent medicine in focus: the 5 stages of evolution in robot-assisted surgery for prostate cancer in the past 20 years and future implications.

Robot-assisted surgery has evolved into a crucial treatment for prostate cancer (PCa). However, from its appearance to today, brain-computer interface, virtual reality, and metaverse have revolutionized the field of robot-assisted surgery for PCa, presenting both opportunities and challenges. Especially in the context of contemporary big data and precision medicine, facing the heterogeneity of PCa and the complexity of clinical problems, it still needs to be continuously upgraded and improved.