The rapid diagnosis of drug-resistant and drug-sensitive tuberculosis using mass spectrometry and machine learning MachineLearning MassSpectrometry Spectrometry Tuberculosis SSRN TheLancet
By Dr. Chinta SidharthanFeb 28 2023Reviewed by Aimee Molineux *Important notice: Preprints with The Lancet / SSRN publishes preliminary scientific reports that are not peer-reviewed and, therefore, should not be regarded as conclusive, guide clinical practice/health-related behavior, or treated as established information.
Current tuberculosis detection methods include immunological exams, detecting the etiological agent from sputum smears, and molecular biology methods. While sputum smear tests are fast, their specificity and sensitivity in detecting tuberculosis are low, whereas mycobacterial cultures, which are accurate, require longer processing times and are difficult for large-scale, same-day testing of tuberculosis patients in general hospitals.
Serum samples were collected for NPELDI MS analysis to determine metabolic fingerprints, which were processed using machine learning algorithms to identify biomarkers for drug-sensitive and drug-resistant tuberculosis. The biomarkers used to differentiate between rifampicin-sensitive and rifampicin-resistant tuberculosis patients included uric acid, taurine, ascorbic acid, and homocysteine, which were elevated in rifampicin-resistant tuberculosis patients. The researchers believe that sulfur amino acids such as homocysteine and taurine may indicate drug-resistant tuberculosis as they are associated with antioxidant and membrane-stabilizing activity.
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