Mycobacterium tuberculosis (Mtb), the causative agent of tuberculosis (TB), is a uniquely successful pathogen due in large part to its complex lipid-rich cell envelope. Comprising nearly 40% of its dry weight, Mtb lipids-such as mycolic acids, phthiocerol dimycocerosates (PDIM), trehalose dimycolate (TDM), and sulfolipids (SLs)-play crucial roles in infection, immune evasion, intracellular persistence, granuloma formation, transmission, and drug resistance. These lipids modulate host-pathogen interactions by altering host membrane biophysics, hijacking phagosome maturation, and interfering with host immune pathways, including autophagy and inflammatory signaling. Upon inhalation, Mtb surface lipids inhibit pulmonary surfactant function and mask pathogen-associated molecular patterns, facilitating uptake by permissive macrophage subsets. Intracellularly, lipoglycans like mannose-capped lipoarabinomannan block phagolysosome fusion, while PDIM and TDM promote phagosomal escape and subversion of vesicular trafficking. Lipid-mediated modulation of autophagy pathways further enhances bacterial survival within host cells. In addition to shaping host immune responses, Mtb lipids orchestrate granuloma development and promote pathological features such as foam cell formation and caseation, which are central to transmission. Specifically, phenolic glycolipids and SLs stimulate neuronal pathways, triggering cough, thereby facilitating aerosol spread. Finally, the lipid-rich envelope acts as a formidable barrier to antibiotics, with resistance partly driven by the altered lipid composition and architecture in multidrug-resistant strains. Targeting lipid biosynthesis and transport pathways offers promising avenues for novel anti-TB therapies. This review highlights the multifaceted roles of Mtb lipids at the host-pathogen interface, recent technical advances enabling these insights, and emerging challenges in translating lipid biology into improved TB control.

Tuberculosis among Shanghai's homeless: a retrospective cohort study highlighting critical care gaps.

Synergistic interaction between tuberculosis and depression: Changes in miRNA expression.

Mathematical modeling of tuberculosis with two strains, seasonality, and age heterogeneity.

Vit-Ensemble: Probabilistic voting based ensemble of Vision Transformers for tuberculosis detection using radiographs.

Tuberculosis (TB) remains a major public health concern in Pakistan, with the country ranked sixth among high-burden developing nations. Significant regional disparities in TB incidence emphasize the importance of localized surveillance and targeted interventions. Objectives: To assess the prevalence of TB in District Abbottabad, explore variations by age and gender, and evaluate trends in TB cases reported by healthcare facilities between January 2022 and December 2023. Methods: A retrospective cross-sectional study was conducted from April to October 2024, utilizing secondary data from the TB Control Programme, Khyber Pakhtunkhwa. Purposive sampling included all registered pulmonary tuberculosis patients with positive sputum smear results (PTBSS). Data were entered into Microsoft Excel and analyzed using frequency distributions, histograms, and bar charts to highlight case patterns and temporal trends. Results: In 2022, 810 new bacteriologically confirmed TB cases were reported in Abbottabad, increasing to 843 in 2023, representing a 4% rise. Male patients were more frequently affected, with male-to-female ratios of 1.07 and 1.17 in 2022 and 2023, respectively. The PPM ACD center reported the largest share of cases. Moreover, relapsed pulmonary TB cases rose from 78 to 96 between the two years, showing a 23% increase. The overall relapse rate (23%) was higher than the 11.2% previously reported from Pakistan. Conclusion: District Abbottabad demonstrates a persistent TB burden with a notable 23% rise in relapse cases, indicating gaps in treatment outcomes. Strengthening follow-up, improving adherence, and enhancing case detection in high-risk groups remain crucial for effective TB control.

Objective In our earlier research, the gut microbiota profiles of Uygur populations in Xinjiang infected with Mycobacterium tuberculosis ( Mtb ) characterized. As the Han and Uygur ethnic groups represent the predominant demographics in Xinjiang, this follow-up study focuses on identifying characteristic gut microbial alterations in Han patients with active tuberculosis (TB) and those with latent tuberculosis infection (LTBI). The findings are expected to support tailored strategies for the regional prevention and control of tuberculosis. Methods A total of 51 cases of TB, 35 cases of LTBI and 51 healthy controls (HC) were recruited from the Infectious Disease Hospital of Xinjiang Uygur Autonomous Region. Fecal samples were collected and underwent 16S rRNA gene sequencing. Results The gut microbiota α diversity was significantly lower in the TB group compared to the LTBI and HC groups, with significant β diversity differences observed among all three groups. At the phylum level, Firmicutes was the most abundant in all groups. The most abundant genera in the TB, LTBI, and HC groups were Phocaeicola , Escherichia , and Bifidobacterium , respectively. Lefse analysis revealed that pro-inflammatory and opportunistic pathogenic genera were enriched in the TB group, whereas butyrate-producing and immune-modulating genera dominated the LTBI group. PICRUSt2 analysis identified only five differential metabolic pathways between the TB and HC groups, among which Clostridium showed the strongest positive correlation with PWY-6876 ( R = 0.79, P < 0.01). Conclusions This study clarified the diversity, microbial species composition profiles, and metabolic pathways of the gut microbiota in the Han population with different TB states in the Xinjiang region of China. These findings may provide a certain theoretical basis and reference for the precise prevention and control of TB in Xinjiang.

Xizang (formerly known as Tibet in English) has one of the highest tuberculosis (TB) incidence rates in China. The region's extreme altitudes (≥ 3000m) and hypoxic environment present substantial challenges for conventional diagnostic methods. Additionally, the clinical and biological characteristics of TB at high altitudes remain poorly understood. TB-seq, a third-generation nanopore-targeted sequencing method, was used to analyze sputum samples from 158 confirmed pulmonary TB patients in Xizang. Bacterial load was quantified, a drug-resistance gene landscape was generated, and these findings were correlated with clinical phenotypes. A matched low-altitude group (< 1000m) was included to investigate the effects of altitude on Mycobacterium tuberculosis biology. In Xizang, retreatment cases accounted for the majority of TB patients (58.23%). Type I TB was the most common form (79.75%), while non-type I forms were observed only in retreatment patients (P < 0.01). Bacterial load decreased significantly with increasing age (P < 0.001), was higher in retreatment cases (P = 0.013), and positively correlated with white blood cell and neutrophil counts (P < 0.05). Drug-resistant mutations were identified in 22.78% of patients, primarily as mono-resistance (63.9%), with key resistance-associated genes being rpsL (47.2%), rpoB (38.9%), and katG (33.3%). Compared to low-altitude controls, the high-altitude group had a significantly lower bacterial load (P < 0.01), reduced overall drug resistance (23.7% vs. 38.2%, P < 0.01), no rrs mutations, and a significantly lower pncA mutation rate (P < 0.05). Tuberculosis in Xizang is marked by a high proportion of retreatment cases, low pathogen burden, and a reduced rate of drug resistance-associated gene mutations. Altitude appears to suppress bacterial quantity and may select for specific resistance mechanisms. These findings support the need for targeted TB control strategies in Xizang and underscore the complex interactions among the environment, the pathogen, and the host.

The spread of tuberculosis (TB) is believed to be greatly influenced by environmental variables. Lung disease continues to be a public health problem. The objective of this study is to determine how environmental factors, including ventilation, lighting, housing density, and temperature, are associated with the prevalence of pulmonary TB in Sidomulyo. Using a case-control study paradineed gm, an analytical observational study was conducted. The study sample consisted of 21 individuals with pulmonary TB (cases) and 21 residents without TB (controls). Direct observations of housing conditions and structured questionnaire interviews were used to collect data. Bivariate data were analyzed to assess relationships between variables using the chi-square test. The findings showed a strong association between pulmonary TB (p=0.001) and incidence of lighting and housing density (p=0.000). Neither with home temperature (p=0.520).or ventilation (p=1.000). Based on these findings, public education on the importance of maintaining a healthy home environment, particularly regarding ventilation, lighting, and appropriate housing density, is recommended, in accordance with Ministry of Health guidelines year 2023.

Background The World Health Organization encourages the development of novel diagnostic tools based on ‘non-sputum’ samples to meet global goals for tuberculosis (TB) control. We aimed to develop a machine learning-driven model for TB diagnosis, using long non-coding RNAs (lncRNAs) as biomarkers. Methods Peripheral blood mononuclear cells (PBMCs) from 10 TB patients, 10 latent TB infection individuals (LTBI), and 10 healthy controls (HCs) underwent microarray analysis, and the TB-related lncRNA modules were identified by weighted gene co-expression network analysis (WGCNA). Key lncRNAs were validated by qPCR and selected using LASSO regression. Five machine learning algorithms were employed to construct a diagnostic model, with the ROC analysis assessing its performance. Results Based on the differential lncRNA profile, WGCNA identified 12 key modules associated with TB. From the most significant modules, 45 candidate lncRNAs were validated by qPCR, with 14 showing differential expression among TB (n = 192), LTBI (n = 55), HC (n = 66), and NTB (n = 78) groups. Five lncRNAs demonstrating the greatest contribution to TB diagnosis were further selected by LASSO analysis. AdaBoost algorithm incorporating these five lncRNAs achieved optimal diagnostic performance, with an area under the curve (AUC) of 0.97 (95%CI: 0.95–0.98) in the training cohort (n = 272) and 0.91 (95%CI: 0.86–0.96) in the validation cohort (n = 119). Independent validation of the model in another cohort (n = 206) showed an AUC of 0.92 (95%CI: 0.88–0.95). Conclusions This study established a novel, blood-based diagnostic model incorporating five host-derived lncRNAs with an AdaBoost algorithm, offering a non-sputum approach to enhance TB diagnosis.

National Tuberculosis Reference Laboratories (NTRLs) are central to tuberculosis (TB) control programs. Between 2018 and 2024, the Republic of Congo, a country of 6 million inhabitants, achieved a transformative strengthening of its TB diagnostic system, coordinated by the NTRL. Strategic investments, supported mainly by international partners, enabled a substantial decentralization of services, expanding the diagnostic network from 38 to 113 diagnostic and testing centers and increasing GeneXpert sites from 3 to 31. The expansion of the diagnostic network and specimen referral system was associated with a reduced structural gap in diagnostic coverage by extending access to GeneXpert testing to a larger number of peripheral and previously underserved centers. Critically, the establishment of a BSL-3 laboratory and the deployment of advanced assays like Xpert MTB/XDR ended the reliance on overseas testing by introducing in-country capacity for multidrug-resistant and pre-extensively drug-resistant TB detection. These systemic improvements were associated with significant positive outcomes, including an annual molecular testing surging from 11,609 in 2022 to over 27,000 in 2024 and bacteriological confirmation rates rising from 34 to 73%. This comprehensive laboratory systems strengthening, which also facilitated cross-programmatic initiatives like HIV and Mpox testing integration, underscores how sustained investment in infrastructure, logistics, and quality management is fundamental to improving case detection, surveillance, and progress toward the WHO End TB Strategy milestones.

Background The emergence of extensively drug-resistant tuberculosis (XDR-TB) poses a serious challenge to global tuberculosis control, particularly in high-burden countries like Niger. In 2021, a new fully oral, shorter treatment regimen, named regimen C, was adopted nationally. Aim This study aimed to assess its effectiveness under programmatic conditions. Methods This was a retrospective, cross-sectional study conducted from April 2021 to December 2024. All patients with pre-XDR and XDR-TB treated in one of the four multidrug-resistant (MDR) TB units in Niger, who received the new standardized regimen and completed their treatment, were included in the study. Results A total of 16 patients with pre-XDR-TB were included in the study. Clinical, microbiological, and radiological data were collected. The median age was 30.5 [interquartile range (IQR) 25–39 years], and 62.5% of patients were male. All patients had pulmonary pre-XDR TB. At the end of treatment, a therapeutic success rate of 75.0% was observed. Adverse events occurred in 88.0% of patients, including 2 cases (14.3%) of grade 4 adverse reactions. Undernourished patients tended to have an increased risk of unfavorable treatment outcomes, although this association was not statistically significant ( p = 0.18). Conclusion These findings suggest that the regimen evaluated in this study appears to be effective for the management of pre-XDR tuberculosis in Niger, with a promising treatment success rate.

Bridging maternal health and tuberculosis control: closing the data and policy gap.

Tuberculosis (TB) is the greatest cause of infectious disease deaths worldwide. In highly affected countries, effective TB control requires prompt identification and treatment of individuals with active disease. We examined the performance of TB case-finding in low- and middle-income countries based on a comprehensive analysis of TB diagnosis data reported to the World Health Organization. Using these data we estimated the total number of individuals correctly and incorrectly diagnosed with TB, for 111 countries with a collective 6.8 million TB notifications in 2023. Here we estimate that in 2023, 2.05 (1.83-2.27) million individuals were incorrectly diagnosed with TB (false-positives), and 1.00 (0.71-1.36) million received a false-negative diagnosis, at an assumed 25% disease prevalence among individuals evaluated for TB. As many as three of every ten TB notifications may not have TB, and many individuals with TB receive false-negative diagnoses. Compared to current diagnostic performance, scaling-up new polymerase chain reaction-based diagnostics would substantially reduce under-diagnosis but only produce a small reduction in false-positive diagnoses. Major improvements in TB diagnosis will likely require higher-sensitivity bacteriological tests combined with reduced reliance on clinical diagnosis.

This study aimed to analyze medication adherence among pulmonary tuberculosis patients by examining the roles of patients’ knowledge level and treatment duration within a TB DOTS clinical setting. Methodology: A quantitative analytic study with a cross-sectional design was conducted among 89 pulmonary tuberculosis patients at the TB DOTS Clinic of Pasar Minggu Regional General Hospital. Data were collected using structured questionnaires and clinical records. Univariate analysis described respondent characteristics, while bivariate analysis using the Chi-Square test examined the relationships between study variables. Findings: The results showed that most patients demonstrated poor knowledge of tuberculosis and associated with levels of medication adherence. A statistically significant relationship was found between patients’ knowledge and medication adherence, indicating that higher knowledge levels were associated with better adherence. In contrast, treatment duration was not significantly associated with medication adherence. Implications: These findings highlight the importance of strengthening patient education within tuberculosis control programs, as improved knowledge may enhance adherence and support successful treatment outcomes. Originality: This study provides empirical evidence from a real-world DOTS clinical setting by integrating patient knowledge and treatment duration in adherence analysis. The findings demonstrate that patient knowledge may play a more influential role than treatment duration in shaping medication adherence, offering a patient-centered contribution to tuberculosis care research.

Tuberculosis Risk in Diabetic Patients from the Shanghai Suburban Adult Cohort.

Exosomal cargoes as potential biomarkers for latent tuberculosis infection: a promising frontier in diagnosis?

Pathogen-specific drug metabolism in tuberculosis: Enzymes, metabolic pathways, and new horizons in therapeutic development.

Future research on Mycobacterium tuberculosis (Mtb) proteins is essential for advancing tuberculosis (TB) diagnosis, treatment, and control amid rising drug resistance and global health challenges. Recent studies focus on elucidating protein structures and interactions critical to bacterial survival, virulence, and drug resistance, enabling the identification of novel therapeutic targets. Cutting-edge technologies such as single-cell proteomics, cryo-electron microscopy, and spatial proteomics provide unprecedented resolution of protein localization, dynamics, and host-pathogen interactions. Posttranslational modifications and dynamic proteomic profiling reveal bacterial adaptive mechanisms, while integrative multi-omics combined with artificial intelligence (AI) and machine learning accelerate functional annotation and predictive modeling. Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)-based functional genomics complements these approaches by enabling precise gene regulation studies. Advances in AI-powered diagnostics and genomic surveillance are transforming TB detection and drug resistance profiling, improving patient outcomes, especially in resource-limited settings. Overall, integrating experimental and computational innovations promises to deepen understanding of Mtb biology, accelerate drug discovery, enhance diagnostics, and ultimately contribute to global efforts in combating tuberculosis.

Advances in tuberculosis co-infections: Mechanisms, diagnosis, and therapeutics.