GCMS Based Detection of Lipid Biomarkers of Mycobacteriumtuberculosis in the Serum Specimen
Bibliographic record
Abstract
AIM: The present study is focused on the identification of high abundant and low abundant biomarkers of Mycobacterium tuberculosis from serum specimen using Gas chromatography and Mass spectroscopy. METHODS: The TB positive and negative sera were screened on the basis of sputum smear microscopy and the in house developed liposome based antibody detection kit. The lipid fraction was isolated from the collected sera and derivatized. In GCMS analysis, the derivatized lipid samples were analyzed through Gas Chromatograph Mass spectrometer (Shimadzu QP-2010 plus with Thermal Desorption system TD 20). Split or splitless mode of sample injection was performed to identify the high abundant and low abundant biomarkers of tuberculosis. RESULTS : The study identified lipid biomarkers specific for M. tuberculosis in the serum of tuberculosis positive subjects. The study revealed that lipids like C17 H34, C21H52O6, C29H60, C34H70, C44H90 was identified in the split mode of sample injection, whereas C29H60, C34H70, C44H90, C14H23BrO, C11H24O2, C18H44O5, C14H30O3S and C24H39N were identified in the splitless mode of sample injection. Under split injection mode of analysis, C17H34, C21H52O6 and C34H70 lipids were identified as low abundant lipids. The molecules like C14H23BrO, C11H24O2, C18H44O5 and C22H39N were identified as low abundant lipids even after the splitless mode of analysis. DISCUSSIONS: The GCMS analysis revealed the presence of lipid biomarkers of Mycobacterial tuberculosis in the circulation of selected tb positive sera samples. The study further identified the low abundant and high abundant biomarkers of tuberculosis. The detection of characterized low abundant biomarkers may help in identifying the disease in sputum smear negative cases.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".