Exploring the multilayered response of TB bacterium Mycobacterial tuberculosis to lysosomal injury.

Mtb subverts host immune surveillance by damaging phagolysosomal membranes, exploiting them as replication niches. In response, host cells initiate a coordinated LDR, integrating membrane repair, selective autophagy, and de novo biogenesis. This review delineates a systems-level model of lysosomal quality control governed by three critical regulatory axes: LGALS3/8/9, TRIM E3 ubiquitin ligases, and the AMPK-TFEB signaling pathway.

Artificial Intelligence in Gestational Diabetes Care: A Systematic Review.

Background: Artificial intelligence (AI) has emerged as a transformative tool for advancing gestational diabetes mellitus (GDM) care, offering dynamic, data-driven methods for early detection, management, and personalized intervention.

Objective: This systematic review aims to comprehensively explore and synthesize the use of AI models in GDM care, including screening, diagnosis, management, and prediction of maternal and neonatal outcomes. Specifically, we examine (1) study designs and population characteristics; (2) the use of AI across different aspects of GDM care; (3) types of input data used for AI modeling; and (4) AI model types, validation strategies, and performance metrics.

Phagosomal escape and sabotage: The role of ESX-1 and PDIMs in pathogenesis.

() employs diverse virulence factors to evade immune defenses and persist intracellularly. The ESAT-6 secretion system-1 (ESX-1) type VII secretion system (T7SS) releases EsxA, EspA, and EspB, inducing phagosomal rupture and cytosolic access while triggering host defenses, including galectin recruitment and stress granule formation. To counteract host responses, utilizes phthiocerol dimycocerosates (PDIMs) to inhibit autophagy and LC3-associated phagocytosis (LAP) by suppressing NADPH oxidase (NOX2) recruitment and reactive oxygen species (ROS) production.

Integrating constructed wetlands with maize cobs biological trickling filter for antibiotic removal: An innovative waste-to-treatment approach.

The pervasive discharge of residual antibiotics into aquatic environments poses a significant ecological risk. This study pioneered a novel waste to treatment approach integrating a maize cob biological trickling filter (MC-BTF) with bamboo biochar-amended vertical wetlands (IVCWs) for enhanced sulfamethoxazole (SMX) and tetracycline (TC) removal. The hybrid system demonstrated sequential treatment efficiency: MC-BTF pre-treatment significantly increased dissolved oxygen from 4.

Artificial intelligence models for predicting the mode of delivery in maternal care.

Background: Accurate prediction of the mode of delivery is critical in maternal care to improve prenatal counseling, optimize clinical decision-making, and reduce maternal and neonatal complications.

Objectives: This study aims to evaluate and compare the predictive accuracy of AI algorithms in predicting the mode of delivery (vaginal or cesarean) using routinely collected antepartum data from electronic health records (EHRs).

Methods: A retrospective dataset of 16,651 pregnancies monitored at St.

Wearable Artificial Intelligence for Sleep Disorders: Scoping Review.

Background: Worldwide, 30%-45% of adults have sleep disorders, which are linked to major health issues such as diabetes and cardiovascular disease. Long-term monitoring with traditional in-lab testing is impractical due to high costs. Wearable artificial intelligence (AI)-powered solutions offer accessible, scalable, and continuous monitoring, improving the identification and treatment of sleep problems.

Deep learning applications for human embryo assessment using time-lapse imaging: scoping review.

Background: The integration of deep learning (DL) and time-lapse imaging technologies offers new possibilities for improving embryo assessment and selection in clinical Fertilization (IVF).

Objectives: This scoping review aims to explore the range of deep learning model applications in the evaluation and selection of embryos monitored through time-lapse imaging systems.

Methods: A total of 6 electronic databases (Scopus, MEDLINE, EMBASE, ACM Digital Library, IEEE Xplore, and Google Scholar) were searched for peer-reviewed literature published before May 2024.

AI Driven Wearables and Large Language Models for Student Well-Being: A Preliminary Study.

This short communication presents preliminary findings on the integration of Large Language Models (LLMs) and wearable technology to generate personalized recommendations aimed at enhancing student well-being and academic performance. By analyzing diverse student data profiles, including metrics from wearable devices and qualitative feedback from academic reports, we conducted sentiment analysis to assess students' emotional states. The results indicate that LLMs can effectively process and analyze textual data, providing actionable insights into student engagement and areas needing improvement.

Leveraging Large Language Models for Sentiment Analysis in Educational Contexts.

This short communication presents preliminary findings on the application of Large Language Models (LLMs) for sentiment analysis in educational settings. By analyzing qualitative descriptions derived from student reports, we aimed to assess students' emotional states and attitudes towards their academic performance. The sentiment analysis provided valuable insights into student engagement and areas requiring attention.

Deciphering the functional roles of PE18 and PPE26 proteins in modulating pathogenesis and immune response.

Introduction: Tuberculosis (TB), caused by (Mtb), remains a leading cause of mortality worldwide. A crucial factor in virulence is the ESX-5 secretion system, which transports PE/PPE proteins such as PE18 and PPE26. These proteins modulate host-pathogen interactions, immune responses, and intracellular survival mechanisms.

Leveraging LLMs and wearables to provide personalized recommendations for enhancing student well-being and academic performance through a proof of concept.

Traditional one-size-fits-all recommendations for student well-being and academic success may not be optimal. Personalized recommendations based on individual data hold promise. This study explores the potential of Large Language Models (LLMs) to generate personalized recommendations for 12 high school students to enhance their well-being and academic performance.

A machine learning driven computationally efficient horse shoe shaped antenna design for internet of medical things.

With bio-medical wearables becoming an essential part of Internet of Medical things (IoMT) for monitoring the health of workers, patients and others in different environments, antenna play a pivotal role in such wearables. In this communication, a novel Horse shoe shaped antenna (HSPA) meant for such wearables is presented. The vitals of the workers, patients etc.

Mechanisms of immune evasion by : the impact of T7SS and cell wall lipids on host defenses.

() is one of the most successful human pathogens, causing a severe and widespread infectious disease. The frequent emergence of multidrug-resistant (MDR) strains has exacerbated this public health crisis, particularly in underdeveloped regions. employs a sophisticated array of virulence factors to subvert host immune responses, both innate and adaptive.

Multimodal Large Language Models in Health Care: Applications, Challenges, and Future Outlook.

In the complex and multidimensional field of medicine, multimodal data are prevalent and crucial for informed clinical decisions. Multimodal data span a broad spectrum of data types, including medical images (eg, MRI and CT scans), time-series data (eg, sensor data from wearable devices and electronic health records), audio recordings (eg, heart and respiratory sounds and patient interviews), text (eg, clinical notes and research articles), videos (eg, surgical procedures), and omics data (eg, genomics and proteomics). While advancements in large language models (LLMs) have enabled new applications for knowledge retrieval and processing in the medical field, most LLMs remain limited to processing unimodal data, typically text-based content, and often overlook the importance of integrating the diverse data modalities encountered in clinical practice.

Understanding water dynamics in Dal Lake: a comprehensive analysis of physiological parameters and seasonal variations.

Maintaining the standard of water quality in an aquatic habitat necessitates continual assessment of its physicochemical properties. The purpose of this study was to evaluate physicochemical properties and to discuss the causes of spatiotemporal variability in key physicochemical parameters at five different locations of Dal Lake. Water samples were collected in four seasons for 3 years (i.

Revitalizing antimicrobial strategies: paromomycin and dicoumarol repurposed as potent inhibitors of M.tb's replication machinery via targeting the vital protein DnaN.

Despite the WHO's recommended treatment regimen, challenges such as patient non-adherence and the emergence of drug-resistant strains persist with TB claiming 1.5 million lives annually. In this study, we propose a novel approach by targeting the DNA replication-machinery of M.

Regulation of Type I Interferon and Autophagy in Immunity against Mycobacterium Tuberculosis: Role of CGAS and STING1.

Mycobacterium tuberculosis (M. tb) is a significant intracellular pathogen responsible for numerous infectious disease-related deaths worldwide. It uses ESX-1 T7SS to damage phagosomes and to enter the cytosol of host cells after phagocytosis.

Harnessing Artificial Intelligence to Predict Ovarian Stimulation Outcomes in In Vitro Fertilization: Scoping Review.

Background: In the realm of in vitro fertilization (IVF), artificial intelligence (AI) models serve as invaluable tools for clinicians, offering predictive insights into ovarian stimulation outcomes. Predicting and understanding a patient's response to ovarian stimulation can help in personalizing doses of drugs, preventing adverse outcomes (eg, hyperstimulation), and improving the likelihood of successful fertilization and pregnancy. Given the pivotal role of accurate predictions in IVF procedures, it becomes important to investigate the landscape of AI models that are being used to predict the outcomes of ovarian stimulation.

Expression of a unique DNA MTase Rv1509 in alters the gene expression pattern and enhances virulence.

() genome encompasses 4,173 genes, about a quarter of which remain uncharacterized and hypothetical. Considering the current limitations associated with the diagnosis and treatment of tuberculosis, it is imperative to comprehend the pathomechanism of the disease and host-pathogen interactions to identify new drug targets for intervention strategies. Using comparative genome analysis, we identified one of the genes, Rv1509, as a signature protein exclusively present in .

Activation of the lysosomal damage response and selective autophagy: the coordinated actions of galectins, TRIM proteins, and CGAS-STING1 in providing immunity against .

Autophagy is a crucial immune defense mechanism that controls the survival and pathogenesis of by maintaining cell physiology during stress and pathogen attack. The E3-Ub ligases (PRKN, SMURF1, and NEDD4) and autophagy receptors (SQSTM1, TAX1BP1, CALCOCO2, OPTN, and NBR1) play key roles in this process. Galectins (LGALSs), which bind to sugars and are involved in identifying damaged cell membranes caused by intracellular pathogens such as , are essential.

Burnout increased among university students during the COVID-19 pandemic: a systematic review and meta-analysis.

Generally, university students are at risk of burnout. This likely was exacerbated during the COVID-19 pandemic. We aimed to investigate burnout prevalence among university students during the COVID-19 pandemic and examine its distribution across countries, sexes, fields of study, and time-period.

The Performance of Wearable AI in Detecting Stress Among Students: Systematic Review and Meta-Analysis.

Background: Students usually encounter stress throughout their academic path. Ongoing stressors may lead to chronic stress, adversely affecting their physical and mental well-being. Thus, early detection and monitoring of stress among students are crucial.

Targeting of essential mycobacterial replication enzyme DnaG primase revealed Mitoxantrone and Vapreotide as novel mycobacterial growth inhibitors.

Tuberculosis (TB) is the second leading cause of mortality after COVID-19, with a global death toll of 1.6 million in 2021. The escalating situation of drug-resistant forms of TB has threatened the current TB management strategies.

Wearable Artificial Intelligence for Detecting Anxiety: Systematic Review and Meta-Analysis.

Background: Anxiety disorders rank among the most prevalent mental disorders worldwide. Anxiety symptoms are typically evaluated using self-assessment surveys or interview-based assessment methods conducted by clinicians, which can be subjective, time-consuming, and challenging to repeat. Therefore, there is an increasing demand for using technologies capable of providing objective and early detection of anxiety.