Potential biomarkers for tuberculous meningitis diagnosis using metabolomics and proteomics: a systematic review
Bibliographic record
Abstract
Tuberculosis (TB) is the leading cause of death from a single infectious agent, with approximately 1.2 million deaths reported in 2023. While TB primarily affects the lungs, it can also spread to other organs, where it is classified as extrapulmonary TB. Tuberculous meningitis (TBM) is the most severe form of extrapulmonary TB, affecting 1–5% of TB cases. Delayed diagnosis contributes to its high mortality and severe neurological complications, with approximately 10% of affected individuals dying or suffering permanent neurological damage. When combined with adjunctive therapy, early detection and treatment can significantly improve survival outcomes. Currently, many studies have identified potential biomarkers of TBM; however, to date, there is no clear consensus on the markers altered in TBM. Hence, we conducted a systematic review aimed at identifying metabolites and proteins that are significantly altered in TBM when compared with healthy controls. Three databases — PubMed, Scopus, and Web of Science — were scanned by two independent reviewers for potential articles that met our inclusion and exclusion criteria. After quality assessment, 17 studies were included, comprising a total of 963 participants (healthy control, n = 576; TBM, n = 387). Metabolites and proteins identified as being significantly altered across studies included alanine, isoleucine, myo-inositol, valine, arachidonate 5-lipoxygenase (ALOX5), apolipoprotein B (APOB), and glial fibrillary acidic protein (GFAP), which were detected in serum, urine, brain tissue, and cerebrospinal fluid samples. These markers have potential diagnostic value for TBM. However, further validation is needed to determine their specificity to reliably distinguish TBM from other neurological infections, which could improve early diagnosis and patient outcomes in TBM.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".