Host Protein Biomarkers Identify Active Tuberculosis in HIV Uninfected and Co-infected Individuals
Bibliographic record
Abstract
Biomarkers for active tuberculosis (TB) are urgently needed to improve rapid TB diagnosis. The objective of this study was to identify serum protein expression changes associated with TB but not latent Mycobacterium tuberculosis infection (LTBI), uninfected states, or respiratory diseases other than TB (ORD). Serum samples from 209 HIV uninfected (HIV(-)) and co-infected (HIV(+)) individuals were studied. In the discovery phase samples were analyzed via liquid chromatography and mass spectrometry, and in the verification phase biologically independent samples were analyzed via a multiplex multiple reaction monitoring mass spectrometry (MRM-MS) assay. Compared to LTBI and ORD, host proteins were significantly differentially expressed in TB, and involved in the immune response, tissue repair, and lipid metabolism. Biomarker panels whose composition differed according to HIV status, and consisted of 8 host proteins in HIV(-) individuals (CD14, SEPP1, SELL, TNXB, LUM, PEPD, QSOX1, COMP, APOC1), or 10 host proteins in HIV(+) individuals (CD14, SEPP1, PGLYRP2, PFN1, VASN, CPN2, TAGLN2, IGFBP6), respectively, distinguished TB from ORD with excellent accuracy (AUC = 0.96 for HIV(-) TB, 0.95 for HIV(+) TB). These results warrant validation in larger studies but provide promise that host protein biomarkers could be the basis for a rapid, blood-based test for TB.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".