From immunology to artificial intelligence: revolutionizing latent tuberculosis infection diagnosis with machine learning

Latent tuberculosis infection(LTBI)has become a major source of active tuberculosis(ATB).Although the tuberculin skin test and interferon-gamma release assay can be used to diagnose LTBI,these methods can only differentiate infected individuals from healthy ones but cannot discriminate between LTBI and ATB.Thus,the diagnosis of LTBI faces many challenges,such as the lack of effective biomarkers from Mycobacterium tuberculosis(MTB)for distinguishing LTBI,the low diagnostic efficacy of biomarkers derived from the human host,and the absence of a gold standard to differentiate between LTBI and ATB.Sputum culture,as the gold standard for diagnosing tuberculosis,is time-consuming and cannot distinguish between ATB and LTBI.In this article,we review the pathogenesis of MTB and the immune mechanisms of the host in LTBI,including the innate and adaptive immune responses,multiple immune evasion mechanisms of MTB,and epigenetic regulation.Based on this knowledge,we summarize the current status and challenges in diagnosing LTBI and present the application of machine learning(ML)in LTBI diagnosis,as well as the advantages and limitations of ML in this context.Finally,we discuss the future development directions of ML applied to LTBI diagnosis.

Hyperprogression under treatment with immune-checkpoint inhibitors in patients with gastrointestinal cancer:A natural process of advanced tumor progression?

Immunotherapy has shown great promise in treating various types of malignant tumors.However,some patients with gastrointestinal cancer have been known to experience rapid disease progression after treatment,a situation referred to as hyperprogressive disease(HPD).This minireview focuses on the definitions and potential mechanisms of HPD,natural disease progression in gastrointestinal malignancies,and tumor immunological microenvironment.

Metatranscriptome of human lung microbial communities in a cohort of mechanically ventilated COVID-19 Omicron patients

The Omicron variant of the severe acute respiratory syndrome coronavirus 2(SARS‑CoV‑2)infected a substantial proportion of Chinese population,and understanding the factors underlying the severity of the disease and fatality is valuable for future prevention and clinical treatment.We recruited 64 patients with invasive ventilation for COVID-19 and performed metatranscriptomic sequencing to profile host transcriptomic profiles,plus viral,bacterial,and fungal content,as well as virulence factors and examined their relationships to 28-day mortality were examined.In addition,the bronchoalveolar lavage fluid(BALF)samples from invasive ventilated hospital/community-acquired pneumonia patients(HAP/CAP)sampled in 2019 were included for comparison.Genomic analysis revealed that all Omicron strains belong to BA.5 and BF.7 sub-lineages,with no difference in 28-day mortality between them.Compared to HAP/CAP cohort,invasive ventilated COVID-19 patients have distinct host transcriptomic and microbial signatures in the lower respiratory tract;and in the COVID-19 non-survivors,we found significantly lower gene expressions in pathways related viral processes and positive regulation of protein localization to plasma membrane,higher abundance of opportunistic pathogens including bacterial Alloprevotella,Caulobacter,Escherichia-Shigella,Ralstonia and fungal Aspergillus sydowii and Penicillium rubens.Correlational analysis further revealed significant associations between host immune responses and microbial compositions,besides synergy within viral,bacterial,and fungal pathogens.Our study presents the relationships of lower respiratory tract microbiome and transcriptome in invasive ventilated COVID-19 patients,providing the basis for future clinical treatment and reduction of fatality.