Dataset Information

Advances in computational frameworks in the fight against TB: The way forward.

ABSTRACT: Around 1.6 million people lost their life to Tuberculosis in 2021 according to WHO estimates. Although an intensive treatment plan exists against the causal agent, Mycobacterium Tuberculosis, evolution of multi-drug resistant strains of the pathogen puts a large number of global populations at risk. Vaccine which can induce long-term protection is still in the making with many candidates currently in different phases of clinical trials. The COVID-19 pandemic has further aggravated the adversities by affecting early TB diagnosis and treatment. Yet, WHO remains adamant on its "End TB" strategy and aims to substantially reduce TB incidence and deaths by the year 2035. Such an ambitious goal would require a multi-sectoral approach which would greatly benefit from the latest computational advancements. To highlight the progress of these tools against TB, through this review, we summarize recent studies which have used advanced computational tools and algorithms for-early TB diagnosis, anti-mycobacterium drug discovery and in the designing of the next-generation of TB vaccines. At the end, we give an insight on other computational tools and Machine Learning approaches which have successfully been applied in biomedical research and discuss their prospects and applications against TB.

SUBMITTER: Naidu A

PROVIDER: S-EPMC10106641 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

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Advances in computational frameworks in the fight against TB: The way forward.

Naidu Akshayata A Nayak Smruti Sudha SS Lulu S Sajitha S Sundararajan Vino V

Frontiers in pharmacology 20230403

Around 1.6 million people lost their life to Tuberculosis in 2021 according to WHO estimates. Although an intensive treatment plan exists against the causal agent, <i>Mycobacterium Tuberculosis</i>, evolution of multi-drug resistant strains of the pathogen puts a large number of global populations at risk. Vaccine which can induce long-term protection is still in the making with many candidates currently in different phases of clinical trials. The COVID-19 pandemic has further aggravated the adv ...[more]

PMID: 37077815

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Project description:BACKGROUND:With India preparing for the next decennial Census in 2021, we compared the disability estimates and data collection methodology between the Census 2011 and the most recent population-level survey for India and its states, to highlight the issues to be addressed to improve robustness of the disability estimates in the upcoming Census. METHODS:Data from the Census 2011 and from two complementary nationally representative household surveys that covered all Indian states with the same methodology and survey instruments-the District-Level Household Survey-4 (DLHS-4, 2012-2013) and the Annual Health Surveys (AHS three rounds, 2010-11, 2011-12 and 2012-13) were used. Data from DLHS-4 and AHS 2012-13 round were pooled to generate estimates for the year 2012-13. Data collection methodology between the sources was compared, including the review of definitions of each type of disability. The overall, mental, visual, hearing, speech, and movement disability rate (DR) per 100,000 population were compared between the sources for India and for each state, and the percent difference in the respective rates was calculated. We explored the reliability of these estimates comparing yearly data from the AHS for three successive rounds. RESULTS:Survey data were collected through proxy reporting, however, it is not entirely clear whether the data were proxy- or self-reported or a mix of both in the Census. The overall DR was 25.1% higher in the Census (2,242; 95% CI 2,241-2,243) than the survey (1,791; 95% CI 1,786-1,797) per 100,000 population, with the state-level difference ranging from -64% in Tamil Nadu to 107% in Sikkim state. Despite both sources using nearly similar definitions for overall disability and disability by type, the difference in DR was 125.5%, 54.2%, -25.7%, -19.7%, and 21.9% for hearing, speech, mental, movement, and visual DR, respectively. At the state-level, the difference in disability-specific estimates ranged from -84% to 450%. The extent of variations in the disability-specific estimates in AHS successive rounds ranged from -25% to 929% at the state-level. CONCLUSIONS:There is momentum globally towards building disability measurement that is consistent with the data required for monitoring of the Sustainable Development Goals to ensure robust estimation of disability. The current estimates from the Census and surveys seem much lower than would be expected at the population level. We make recommendations that India needs to take serious note of in order to improve the validity and reliability of India's disability estimates.

Dataset Information

Advances in computational frameworks in the fight against TB: The way forward.

Publications

Advances in computational frameworks in the fight against TB: The way forward.

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