Proteomic profiling of eccrine sweat reveals its potential as a diagnostic biofluid for active tuberculosis
Bibliographic record
Abstract
PURPOSE: Excessive sweating is a common symptom of the disease and an unexplored biofluid for TB diagnosis; we conducted a proof-of-concept study to identify potential diagnostic biomarkers of active TB in eccrine sweat. EXPERIMENTAL DESIGN: We performed a global proteomic profile of eccrine sweat sampled from patients with active pulmonary TB, other lung diseases (non-TB disease), and healthy controls. A comparison of proteomics between Active-TB, Non-TB, and Healthy Controls was done in search for potential biomarkers of active TB. RESULTS: Sweat specimens were pooled from 32 active TB patients, 27 patients with non-TB diseases, and 24 apparently healthy controls, all were negative for HIV. Over 100 unique proteins were identified in the eccrine sweat of all three groups. Twenty-six proteins were exclusively detected in the sweat of patients with active TB while the remaining detected proteins overlapped between three groups. Gene ontology evaluation indicated that the proteins detected uniquely in sweat of active TB patients were involved in immune response and auxiliary protein transport. Gene products for cellular components (e.g. ribosomes) were detected only in active TB patients. Data are available via ProteomeXchange with identifier PXD003224. CONCLUSIONS AND CLINICAL RELEVANCE: Proteomics of sweat from active TB patients is a viable approach for biomarker identification, which could be used to develop a nonsputum-based test for detection of active 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".