Mycobacterium tuberculosis-specific plasmablast levels are differentially modulated in tuberculosis infection and disease
Bibliographic record
Abstract
BACKGROUND: While T cell responses to Mycobacterium tuberculosis (Mtb) have been extensively studied, the role of B-cells and antibodies are less well characterised. The aim of this study was to assess levels of Mtb-specific IgG + plasmablasts across the Mtb infection spectrum. METHODS: Patients with active TB were analysed at baseline and 6 months of therapy (n = 20).Their exposed household contacts (HHC) included individuals with latent TB infection (LTBI; n = 20); evident at baseline; individuals with a negative Tuberculin Skin Test (TST) at baseline who became; positive at 6 months (converters; n = 11) and those who remained negative (non-converters; n = 10). An e x-vivo B-cell ELISPOT was performed to analyse plasmablast responses. RESULTS: Frequencies of ESAT-6/CFP-10 (EC)- but not Whole Cell Lysate (WCL)-specific plasmablasts were significantly higher in patients with active TB pre-treatment compared to post-treatment (p = 0.002) and compared to HHC with LTBI (p < 0.0001). Conversely, total IgG + plasmablasts were significantly decreased in TB patients at baseline. No difference was seen in levels of plasmablasts between TST converters and non-converters at baseline. CONCLUSIONS: We show that EC-specific plasmablast levels are differentially modulated during TB infection and disease, with levels highest during active TB. These data provide new insights into TB biomarker development and avenues for novel immune interventions.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".