Regulatory T-cell: Regulator of Host Defense in Infection
Bibliographic record
Abstract
Based on diverse activities and production of several cytokines, T lymphocytes and T helper cells are divided into Th1, Th2, Th17 and regulatory T-cell (T regs) subsets based on diverse activities and production of several cytokines. Infectious agents can escape from host by modulation of immune responses as effector T-cells and Tregs. Thus, regulatory T-cells play a critical role in suppression of immune responses to infectious agents such as viruses, bacteria, parasites and fungi and as well as preserving immune homeostasis. However, regulatory T-cell responses can advantageous for the body by minimizing the tissue-damaging effects. The following subsets of regulatory T-cells have been recognized: natural regulatory Tcells, Th3, Tr1, CD8+ Treg, natural killer like Treg (NKTreg) cells. Among various markers of Treg cells, Forkhead family transcription factor (FOXP3) as an intracellular protein is used for discrimination between activated T reg cells and activated T-cells. FOXP3 has a central role in production, thymocyte differentiation and function of regulatory Tcells. Several mechanisms have been indicated in regulation of T reg cells. As, the suppression of T-cells via regulatory T-cells is either mediated by Cell-cell contact and Immunosuppressive cytokines (TGF-Beta, IL-10) mediated.
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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 | 0.001 |
| 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 | 0.001 |
| 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".