Cross‐Talk between CD31 and the Signaling Lymphocytic Activation Molecule–Associated Protein during Interferon‐γ Production against<i>Mycobacterium tuberculosis</i>
Bibliographic record
Abstract
Effective host defense against tuberculosis requires Th1 cytokine responses. We studied the regulation of interferon (IFN)- gamma production during tuberculosis by investigating the role of CD31, a receptor that attenuates T cell receptor signals. After antigen stimulation, CD3(+)CD31(+) blood lymphocytes decreased in healthy donors and in tuberculosis patients with robust Th1 responses to Mycobacterium tuberculosis and IFN- gamma was secreted only by CD31(-) T cells. In contrast, in patients with weak Th1 cytokine responses to M. tuberculosis, the level of CD3(+)CD31(+) lymphocytes was increased and IFN- gamma production was low. Furthermore, the inverse relationship between CD31 expression and IFN- gamma production was in contrast to signaling lymphocytic activation molecule (SLAM) expression, an IFN- gamma inducer in tuberculosis. Interestingly, CD31 bound to SLAM-associated protein (SAP), an IFN- gamma inhibitor in tuberculosis, and when CD31 and SAP were coexpressed in lymphocytes, their association inhibited the IFN- gamma response to M. tuberculosis. Thus, CD31, when binding to SAP, interferes with Th1 responses, suggesting that CD31 has a key regulatory role in the signaling pathway(s) leading to the IFN- gamma response to M. tuberculosis.
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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 | 0.000 | 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".