Deregulated Tyrosine–Phenylalanine Metabolism in Pulmonary Tuberculosis Patients
Bibliographic record
Abstract
Metabolic profiling of biofluids from tuberculosis (TB) patients would help us in understanding the disease pathophysiology and may also be useful for the development of novel diagnostics and host-directed therapy. In this pilot study we have compared the urine metabolic profiles of two groups of subjects having similar TB symptoms and categorized as active TB (ATB, n = 21) and non-TB (NTB, n = 21) based on GeneXpert test results. Silylation, gas chromatography mass spectrometry, and standard chemometric methods were employed to identify the important molecules and deregulated metabolic pathways. Eleven active TB patients were followed up on longitudinally for comparative urine metabolic profiling with healthy controls (n = 11). A set of 42 features qualified to have a variable importance parameter score of > 1.5 of a partial least-squares discriminate analysis model and fold change of > 1.5 at p value < 0.05 between ATB and NTB. Using these variables, a receiver operating characteristics curve was plotted and the area under the curve was calculated to be 0.85 (95% CI: 0.72-0.96). Several of these variables that represent norepinephrine, gentisic acid, 4-hydroxybenzoic acid, hydroquinone, and 4-hydroxyhippuric acid are part of the tyrosine-phenylalanine metabolic pathway. In the longitudinal study we observed a treatment-dependent trend in the urine metabolome of follow-up samples, and subjects declared as clinically cured showed similar metabolic profile as those of asymptomatic healthy subjects. The deregulated tyrosine-phenylalanine axis reveals a potential target for diagnostics and intervention in TB.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".