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Machine learning approaches in diagnosing tuberculosis through biomarkers - A systematic review

Balakrishnan V, Kherabi Y, Ramanathan G, Paul SA, Tiong CK

Prog Biophys Mol Biol, 2023 May;179:16-25.
PMID: 36931609 DOI: 10.1016/j.pbiomolbio.2023.03.001

Biomarker-based tests may facilitate Tuberculosis (TB) diagnosis, accelerate treatment initiation, and thus improve outcomes. This review synthesizes the literature on biomarker-based detection for TB diagnosis using machine learning. The systematic review approach follows the PRISMA guideline. Articles were sought using relevant keywords from Web of Science, PubMed, and Scopus, resulting in 19 eligible studies after a meticulous screening. All the studies were found to have focused on the supervised learning approach, with Support Vector Machine (SVM) and Random Forest emerging as the top two algorithms, with the highest accuracy, sensitivity and specificity reported to be 97.0%, 99.2%, and 98.0%, respectively. Further, protein-based biomarkers were widely explored, followed by gene-based such as RNA sequence and, Spoligotypes. Publicly available datasets were observed to be popularly used by the studies reviewed whilst studies targeting specific cohorts such as HIV patients or children gathering their own data from healthcare facilities, leading to smaller datasets. Of these, most studies used the leave one out cross validation technique to mitigate overfitting. The review shows that machine learning is increasingly assessed in research to improve TB diagnosis through biomarkers, as promising results were shown in terms of model's detection performance. This provides insights on the possible application of machine learning approaches to diagnose TB using biomarkers as opposed to the traditional methods that can be time consuming. Low-middle income settings, where access to basic biomarkers could be provided as compared to sputum-based tests that are not always available, could be a major application of such models.
Fulltext Paraquat poisoning: experience in Hospital Taiping (year 2008 - october 2011)

Tan JT, Letchuman Ramanathan G, Choy MP, Leela R, Lim BK

Med J Malaysia, 2013 Oct;68(5):384-8.
PMID: 24632866 MyJurnal

INTRODUCTION: Paraquat is a quaternary nitrogen herbicide which is highly toxic to human. Death is usually from respiratory failure and may occur within days up to a month after exposure. It is easily available and commonly abused to commit suicide.

METHODOLOGY: This is a retrospective study describing the demographic characteristics, clinical features and outcomes of paraquat poisoning cases admitted to Hospital Taiping from 1st January 2008 to 30th October 2011. Medical records of 79 patients were reviewed.

RESULT: Majority were of the Indian ethnicity (72.2%) followed by Chinese (13.9%) and Malay (10.1%). Majority was male (73.4%) and between 20 to 29 years old (34.2%). The median age of the patients was 30 years old. The mean length of stay was 6.2 days. Most exposures were intentional (69.6%) and presented to the hospital early at less than 6 hours after exposure (72.2%). Patients with positive urine paraquat result had significantly higher mortality rate compared to patients with negative results (47.4% vs 15.2% respectively). We found that neither hemofiltration nor immunosuppressive therapies help to improve survival.

CONCLUSION: The non-survivor characteristics of patients with paraquat poisoning are intentional exposure, delay from exposure to hospital admission, urine paraquat positivity and manifestation of respiratory failure. The demographic characteristics, reasons for exposure and mortality rate are similar to previous reports. Urine paraquat may be used to assess severity of the exposure as well as prognosis. Hemofiltration and immunosuppression therapy do not improve patients' survival and paraquat remains a lethal killer.
Allopurinol-induced severe cutaneous adverse drug reactions: Risk minimization measures in Malaysia

Panickar R, Wo WK, Ali NM, Tang MM, Ramanathan GRL, Kamarulzaman A, et al.

Pharmacoepidemiol Drug Saf, 2020 10;29(10):1254-1262.
PMID: 33084196 DOI: 10.1002/pds.5033

PURPOSE: To describe risk minimization measures (RMMs) implemented in Malaysia for allopurinol-induced severe cutaneous adverse drug reactions (SCARs) and examine their impact using real-world data on allopurinol usage and adverse drug reaction (ADR) reports associated with allopurinol.
METHODS: Data on allopurinol ADR reports (2000-2018) were extracted from the Malaysian ADR database. We identified RMMs implemented between 2000 and 2018 from the minutes of relevant meetings and the national pharmacovigilance newsletter. We obtained allopurinol utilization data (2004-2018) from the Pharmaceutical Services Programme. To determine the impact of RMMs on ADR reporting, we considered ADR reports received within 1 year of RMM implementation. We used the Pearson χ2 test to examine the relation between the implementation of RMMs and allopurinol ADR reports.
RESULTS: The 16 RMMs for allopurinol-related SCARs implemented in Malaysia involved nine risk communications, four prescriber or patient educational material, and three health system innovations. Allopurinol utilization decreased by 21.5% from 2004 to 2018. ADR reporting rates for all drugs (n = 144 507) and allopurinol (n = 1747) increased. ADR reports involving off-label use decreased by 6% from 2011. SCARs cases remained between 20% and 50%. RMMs implemented showed statistically significant reduction in ADR reports involving off-label use for August 2014 [χ2(1, N = 258) = 5.32, P = .021] and October 2016 [χ2(1, N = 349) = 3.85, P = .0499].
CONCLUSIONS: RMMs to promote the appropriate use of allopurinol and prescriber education have a positive impact. We need further measures to reduce the incidence and severity of allopurinol-induced SCARs, such as patient education and more research into pharmacogenetic screening.
A 'one stone, two birds' approach with mesenchymal stem cells for acute respiratory distress syndrome and Type II diabetes mellitus

Sababathy M, Ramanathan G, Abd Rahaman NY, Ramasamy R, Biau FJ, Qi Hao DL, et al.

Regen Med, 2023 Dec;18(12):913-934.
PMID: 38111999 DOI: 10.2217/rme-2023-0193

This review explores the intricate relationship between acute respiratory distress syndrome (ARDS) and Type II diabetes mellitus (T2DM). It covers ARDS epidemiology, etiology and pathophysiology, along with current treatment trends and challenges. The lipopolysaccharides (LPS) role in ARDS and its association between non-communicable diseases and COVID-19 are discussed. The review highlights the therapeutic potential of human umbilical cord-derived mesenchymal stem cells (hUC-MSCs) for ARDS and T2DM, emphasizing their immunomodulatory effects. This review also underlines how T2DM exacerbates ARDS pathophysiology and discusses the potential of hUC-MSCs in modulating immune responses. In conclusion, the review highlights the multidisciplinary approach to managing ARDS and T2DM, focusing on inflammation, oxidative stress and potential therapy of hUC-MSCs in the future.