Multiomics and Machine Learning Identify Immunometabolic Biomarkers for Active Tuberculosis Diagnosis Against Nontuberculous Mycobacteria and Latent Tuberculosis Infection.

This study utilized multiomics combined with a comprehensive machine learning-based predictive modeling approach to identify, validate, and prioritize circulating immunometabolic biomarkers in distinguishing tuberculosis (TB) from nontuberculous mycobacteria (NTM) infections, latent tuberculosis infection (LTBI), and other lung diseases (ODx). Functional omics data were collected from two discovery cohorts (76 patients in the TB-NTM cohort and 72 patients in the TB-LTBI-ODx cohort) and one validation cohort (68 TB patients and 30 LTBI patients). Mutiomics integrative analysis identified three plasma multiome biosignatures that could distinguish active TB from non-TB with promising performance, achieving area under the receiver operating characteristic curve (AUC) values of 0.

Endoplasmic reticulum stress inhibition preserves mitochondrial function and cell survival during the early onset of isoniazid-induced oxidative stress.

A comprehensive understanding of isoniazid (INH)-mediated hepatotoxic effects is essential for developing strategies to predict and prevent severe liver toxicity in tuberculosis treatment. In this study, we used multi-omics profiling in vitro to investigate the toxic effects of INH, revealing significant involvement of endoplasmic reticulum (ER) stress, mitochondrial impairment, redox imbalance, and altered metabolism. Additional analysis using transcriptomics data from repeated time-course INH treatments on human hepatic microtissues revealed that cellular responses to ER stress and oxidative stress happened prior to disturbances in mitochondrial complexes.

Advancements in Mass Spectrometry-Based Targeted Metabolomics and Lipidomics: Implications for Clinical Research.

Targeted metabolomics and lipidomics are increasingly utilized in clinical research, providing quantitative and comprehensive assessments of metabolic profiles that underlie physiological and pathological mechanisms. These approaches enable the identification of critical metabolites and metabolic alterations essential for accurate diagnosis and precision treatment. Mass spectrometry, in combination with various separation techniques, offers a highly sensitive and specific platform for implementing targeted metabolomics and lipidomics in clinical settings.

Discovery of urinary biosignatures for tuberculosis and nontuberculous mycobacteria classification using metabolomics and machine learning.

Nontuberculous mycobacteria (NTM) infection diagnosis remains a challenge due to its overlapping clinical symptoms with tuberculosis (TB), leading to inappropriate treatment. Herein, we employed noninvasive metabolic phenotyping coupled with comprehensive statistical modeling to discover potential biomarkers for the differential diagnosis of NTM infection versus TB. Urine samples from 19 NTM and 35 TB patients were collected, and untargeted metabolomics was performed using rapid liquid chromatography-mass spectrometry.

Application of the Hollow-Fiber Infection Model to Personalized Precision Dosing of Isoniazid in a Clinical Setting.

Background: The hollow-fiber infection model (HFIM) is a valuable tool for evaluating pharmacokinetics/pharmacodynamics relationships and determining the optimal antibiotic dose in monotherapy or combination therapy, but the application for personalized precision medicine in tuberculosis treatment remains limited. This study aimed to evaluate the efficacy of adjusted antibiotic doses for a tuberculosis patient using HFIM.

Methods: Model-based Bayesian forecasting was utilized to assess the proposed reduction of the isoniazid dose from 300 mg daily to 150 mg daily in a patient with an ultra-slow-acetylation phenotype.

Omics analysis unveils changes in the metabolome and lipidome of Caenorhabditis elegans upon polydopamine exposure.

Polydopamine (PDA) is an insoluble biopolymer with a dark brown-black color that forms through the autoxidation of dopamine. Because of its outstanding biocompatibility and durability, PDA holds enormous promise for various applications, both in the biomedical and non-medical domains. To ensure human safety, protect health, and minimize environmental impacts, the assessment of PDA toxicity is important.

Metabolic phenotyping and global functional analysis facilitate metabolic signature discovery for tuberculosis treatment monitoring.

Tracking alterations in polar metabolite and lipid levels during anti-tuberculosis (TB) interventions is an emerging biomarker discovery and validation approach due to its sensitivity in capturing changes and reflecting on the host status. Here, we employed deep plasma metabolic phenotyping to explore the TB patient metabolome during three phases of treatment: at baseline, during intensive phase treatment, and upon treatment completion. Differential metabolites (DMs) in each period were determined, and the pathway-level biological alterations were explored by untargeted metabolomics-guided functional interpretations that bypassed identification.

Alterations of lipid-related genes during anti-tuberculosis treatment: insights into host immune responses and potential transcriptional biomarkers.

Background: The optimal diagnosis and treatment of tuberculosis (TB) are challenging due to underdiagnosis and inadequate treatment monitoring. Lipid-related genes are crucial components of the host immune response in TB. However, their dynamic expression and potential usefulness for monitoring response to anti-TB treatment are unclear.

Comprehensive lipid profiles investigation reveals host metabolic and immune alterations during anti-tuberculosis treatment: Implications for therapeutic monitoring.

In this study, we investigated the lipidome of tuberculosis patients during standard chemotherapy to discover biosignatures that could aid therapeutic monitoring. UPLC-QToF MS was used to analyze 82 baseline and treatment plasma samples of patients with pulmonary tuberculosis. Subsequently, a data-driven and knowledge-based workflow, including robust annotation, statistical analysis, and functional analysis, was applied to assess lipid profiles during treatment.

Development of population pharmacokinetics model and Bayesian estimation of rifampicin exposure in Indonesian patients with tuberculosis.

Background: Interindividual variability in the pharmacokinetics (PK) of anti-tuberculosis (TB) drugs is the leading cause of treatment failure. Herein, we evaluated the influence of demographic, clinical, and genetic factors that cause variability in RIF PK parameters in Indonesian TB patients.

Methods: In total, 210 Indonesian patients with TB (300 plasma samples) were enrolled in this study.

Quantitative Analysis of Isoniazid and Its Four Primary Metabolites in Plasma of Tuberculosis Patients Using LC-MS/MS.

Isoniazid and its metabolites are potentially associated with hepatotoxicity and treatment outcomes in patients who receive antituberculosis (TB) therapy. To further understand the pharmacokinetic profiles of these molecules, a method based on LC-MS/MS was developed to determine the concentration of these compounds in human plasma. Isoniazid, acetylisoniazid, and isonicotinic acid were directly analyzed, whereas hydrazine and acetylhydrazine were determined after derivatization using p-tolualdehyde.

Comprehensive lipid and lipid-related gene investigations of host immune responses to characterize metabolism-centric biomarkers for pulmonary tuberculosis.

Despite remarkable success in the prevention and treatment of tuberculosis (TB), it remains one of the most devastating infectious diseases worldwide. Management of TB requires an efficient and timely diagnostic strategy. In this study, we comprehensively characterized the plasma lipidome of TB patients, then selected candidate lipid and lipid-related gene biomarkers using a data-driven, knowledge-based framework.

Student generation and peer review of examination questions in the dental curriculum: Enhancing student engagement and learning.

Background: Writing, sharing, answering, discussing and rating examination questions are a way to involve students in creating content and applying their knowledge. The PeerWise online question-setting platform facilitates student communities in this activity. This mixed-methods study asks the question: Does students' writing and answering examination questions enhance their engagement and learning of Neurology as a Life Science topic?

Methods: Over a 2-year period, self-assembled groups of 3-4 students submitted 1-2 multiple-choice questions (MCQs) every 2 weeks into the PeerWise portal for review by their peers.

Storage media and not extraction method has the biggest impact on recovery of bacteria from the oral microbiome.

Next Generation sequencing has greatly progressed the exploration of the oral microbiome's role in dental diseases, however, there has been little focus on the effect of sample storage conditions and their interaction with DNA extraction method. Dental plaque samples collected from 20 healthy participants were pooled and stored in either 75% ethanol or Bead solution for up to 6-months at -80 °C, prior to DNA extraction with either QIAamp (non-bead beating) or PowerSoil (bead-beating) kit, followed by Illumina sequencing of 16S rRNA gene. We found that storage media and not extraction method had the biggest influence on the diversity and abundance of the oral microbiota recovered.

A novel function of artesunate on inhibiting migration and invasion of fibroblast-like synoviocytes from rheumatoid arthritis patients.

Introduction: Anti-malarial drug artesunate can suppress inflammation and prevent cartilage and bone destruction in collagen-induced arthritis model in rats-suggesting it may be a potent drug for rheumatoid arthritis (RA) therapy. We aimed to investigate its effect on the invasive property of fibroblast-like synoviocytes (FLS) from patients with RA.

Methods: Synovial tissues were obtained by closed needle biopsy from active RA patients, and FLS were isolated and cultured in vitro.

Proteolytic processing and activation of gingipain zymogens secreted by T9SS of Porphyromonas gingivalis.

Porphyromonas gingivalis uses a type IX secretion system (T9SS) to deliver more than 30 proteins to the bacterial surface using a conserved C-terminal domain (CTD) as an outer membrane translocation signal. On the surface, the CTD is cleaved and an anionic lipopolysaccharide (A-PLS) is attached by PorU sortase. Among T9SS cargo proteins are cysteine proteases, gingipains, which are secreted as inactive zymogens requiring removal of an inhibiting N-terminal prodomain (PD) for activation.

Conjugated carbon quantum dots: Potent nano-antibiotic for intracellular pathogens.

A common theme in the persistence of microbial infections involves intracellular survival of microbial pathogens within host cells where they stay sheltered from attack by antimicrobial agents. In order to improve antimicrobial access inside host cells, we developed nanoparticles intracellular delivery of antibiotics. Using an intracellular infection model with the periodontal pathogen, Porphyromonas gingivalis (P.

Characterization of a diet-induced obesity rat model for periodontal research.

Objectives: Obesity is associated with periodontitis, but the mechanisms underlying this association have yet to be unraveled. The present investigation was to evaluate a common rat model, in which obesity is induced by high-fat, high-sucrose diet (HFSD), for its applicability in periodontal research.

Materials And Methods: Ten male Wistar rats were fed a 3-month HFSD along with a matching control group.