Latent Tuberculosis Infection (LTBI) and Its Potential Targets: An Investigation into Dormant Phase Pathogens

Abstract

One-third of the world’s population harbours the latent tuberculosis infection (LTBI) with a lifetime risk of reactivation. Although, the treatment of LTBI relies significantly on the first-line therapy, identification of novel drug targets and therapies are the emerging focus for researchers across the globe. The current review provides an insight into the infection, diagnostic methods and epigrammatic explanations of potential molecular targets of dormant phase bacilli. This study also includes current preclinical and clinical aspects of tubercular infections and new approaches in antitubercular drug discovery.

Explainable machine learning for breast cancer diagnosis from mammography and ultrasound images: a systematic review

“…DALEX21 22 is a library used by R Studio. It only supports a few functionalities (ie, local post-hoc and global post-hoc), whereas AIX36012 is a library used by Python. This toolkit supports all functionalities (ie, data explanations, directly interpretable, local post-hoc, global post-hoc and persona-specific explanations) including the evaluation matrix.…”

“…There are different taxonomies for machine learning explainability. An interactive explanation allows consumers to drill down or ask for different types of explanations until they are satisfied, while a static explanation refers to one that does not change in response to feedback from the consumer 12. A local explanation is for a single prediction, whereas a global explanation describes the behaviour of the entire model.…”

“…No single method is always the best for interpreting machine learning 12. For this reason, it is necessary to have the skills and equipment to fill the gap from research to practice.…”

“…For this reason, it is necessary to have the skills and equipment to fill the gap from research to practice. To do so, XAI toolkits like AIX360,12 Alibi,14 Skater,15 H2O,16 17 InterpretML,18 19 EthicalML-XAI,19 20 DALEX,21 22 tf-explain,23 Investigate 24. Most interpretations and explanations are post hoc (local interpretable model-agnostic explanations (LIME) and SHapley Additive exPlanations (SHAP).…”

Impact of diabetes mellitus on immunity to latent tuberculosis infection

“…More recently, a meta-analysis reported that based on the interferon-γ release assays (IGRAs) and tuberculin skin test (TST), the current prevalence rate of LTBI was 24.8% and 21.2%, respectively which accounts for one-fourth of the world’s population ( 6 ). Although the total reactivation rate of LTBI is around 10% ( 7 , 8 ), the risk of reactivation is several fold greater in immunosuppressed patients and also patients with DM ( 9 , 10 ). However, the exact mechanism of the progress of LTBI to active TB in the presence of DM is still unclear.…”