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 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".