The World Health Organization (WHO) “Global Tuberculosis Report 2024” underscores the ongoing challenges and progress in the global fight against tuberculosis (TB). Despite a slowdown in the annual increase in TB cases and a decline in TB-related deaths, the disease remains a significant public health threat with 10.8 million new cases reported in 2023. The report reveals a persistently high burden of TB, especially in low- and middle-income countries, and highlights the escalating issue of drug-resistant TB. The report also emphasizes the critical need for increased funding, improved diagnostics, and more effective TB treatment regimens. The impact of the COVID-19 pandemic on TB control has been substantial, disrupting healthcare services and exacerbating existing challenges. This paper provides an in-depth interpretation of the report, analyzes global TB trends, updates the status of drug-resistant TB, discusses the intersection between TB and HIV, and summarizes the implications of the pandemic on TB control efforts. The report concludes with a call for intensified global action, including increased investment in research and innovation, to achieve the United Nations’ Sustainable Development Goals related to TB control. The stakes are high, but with concerted efforts, the goal of ending the TB epidemic is within reach.

Background: The incidence of non-tuberculous mycobacterial pulmonary disease (NTM-PD) has increased in recent years. However, the clinical and immunologic characteristics of NTM-PD patients have received little attention.Methods: NTM strains, clinical symptoms, underlying diseases, lung CT findings, lymphocyte subsets, and drug susceptibility tests (DSTs) of NTM-PD patients were investigated. Then, the counts of immune cells of NTM-PD patients and their correlation were evaluated using principal component analysis (PCA) and correlation analysis.Results: 135 NTM-PD patients and 30 healthy controls (HCs) were enrolled from 2015 to 2021 in a certain tertiary hospital in Beijing. The number of NTM-PD patients increased every year, and Mycobacterium intracellulare (M. intracellulare), M. abscessus, M. avium, and M. kansasii were the major pathogens of NTM-PD. The main clinical symptoms of NTM-PD patients were cough and sputum production, and the primary lung CT findings were thin-walled cavity, bronchiectasis, and nodules. In addition, we identified 23 clinical isolates from 87 NTM-PD patients with strain records. The DST showed that almost all of M. abscessus and M. avium and more than half of the M. intracellulare and M. avium complex groups were resistant to anti-tuberculosis drugs tested in this study. M. xenopi was resistant to all aminoglycosides. M. kansasii was 100% resistant to kanamycin, capreomycin, amikacin, and para-aminosalicylic acid, and sensitive to streptomycin, ethambutol, levofloxacin, azithromycin, and rifamycin. Compared to other drugs, low resistance to rifabutin and azithromycin was observed among NTM-PD isolates. Furthermore, the absolute counts of innate and adaptive immune cells in NTM-PD patients were significantly lower than those in HCs. PCA and correlation analysis revealed that total T, CD4⁺, and CD8⁺ T lymphocytes played an essential role in the protective immunity of NTM-PD patients, and there was a robust positive correlation between them.Conclusion: The incidence of NTM-PD increased annually in Beijing. Individuals with bronchiectasis and COPD have been shown to be highly susceptible to NTM-PD. NTM-PD patients is characterized by compromised immune function, non-specific clinical symptoms, high drug resistance, thin-walled cavity damage on imaging, as well as significantly reduced numbers of both innate and adaptive immune cells.

Background: Machine learning is a potentially effective method for predicting the response to platinum-based treatment for ovarian cancer. However, the predictive performance of various machine learning methods and variables is still a matter of controversy and debate. Objective: This study aims to systematically review relevant literature on the predictive value of machine learning for platinum-based chemotherapy responses in patients with ovarian cancer. Methods: Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, we systematically searched the PubMed, Embase, Web of Science, and Cochrane databases for relevant studies on predictive models for platinum-based therapies for the treatment of ovarian cancer published before April 26, 2023. The Prediction Model Risk of Bias Assessment tool was used to evaluate the risk of bias in the included articles. Concordance index (C-index), sensitivity, and specificity were used to evaluate the performance of the prediction models to investigate the predictive value of machine learning for platinum chemotherapy responses in patients with ovarian cancer. Results: A total of 1749 articles were examined, and 19 of them involving 39 models were eligible for this study. The most commonly used modeling methods were logistic regression (16/39, 41%), Extreme Gradient Boosting (4/39, 10%), and support vector machine (4/39, 10%). The training cohort reported C-index in 39 predictive models, with a pooled value of 0.806; the validation cohort reported C-index in 12 predictive models, with a pooled value of 0.831. Support vector machine performed well in both the training and validation cohorts, with a C-index of 0.942 and 0.879, respectively. The pooled sensitivity was 0.890, and the pooled specificity was 0.790 in the training cohort. Conclusions: Machine learning can effectively predict how patients with ovarian cancer respond to platinum-based chemotherapy and may provide a reference for the development or updating of subsequent scoring systems.

Tuberculosis (TB) is a chronic bacterial disease, as well as a complex immune disease. The occurrence, development, and prognosis of TB are not only related to the pathogenicity of Mycobacterium tuberculosis (Mtb), but also related to the patient’s own immune state. The research and development of immunotherapy drugs can effectively regulate the body’s anti-TB immune responses, inhibit or eliminate Mtb, alleviate pathological damage, and facilitate rehabilitation. This paper reviews the research progress of immunotherapeutic compounds for TB, including immunoregulatory compounds and repurposing drugs, and points out the existing problems and future research directions, which lays the foundation for studying new agents for host-directed therapies of TB.