Immune Regulation in Human Health and Disease
Bibliographic record
Abstract
Abstract The immune system requires a homeostatic equilibrium among the mechanisms that assure self‐tolerance, those that control the capacity to mount life‐long immunity to pathogenic microbes and those that attenuate effector mechanisms from inducing immune pathology. There are multiple processes in place to ensure that healthy immune regulation and FOXP3+T regulatory (Treg) cells are thought to be the major players. Treg cells exercise their regulatory role through various contact‐dependent and ‐independent mechanisms, and FOXP3 is the master regulator of their various functions such as inhibiting T effector (Teff) cell proliferation and inflammatory cytokine production. Various autoimmune diseases such as IPEX occur when Treg‐cell function or numbers are abrogated. Although there is evidence that supports the involvement of Treg cells in the development of autoimmune disease, there are inconsistencies in the literature owing to the lack of Treg‐cell‐specific markers. Key Concepts The immune system employs multiple tolerance mechanisms to maintain immune homeostasis. Multiple specialised regulatory cells exist, FOXP3+ T regulatory (Treg) cells being one of the major players in the maintenance of immune tolerance. FOXP3 is the master transcription factor of Treg cells, controlling Treg‐cell phenotype and suppressive functions. Autoimmune diseases such as IPEX arise when Treg cells are defective or lacking, which can be due to mutations at theFoxp3gene locus. Multiple markers are currently being used to study the Treg‐cell population; however, these markers are not specific to Treg cells and therefore are the cause of inconsistencies in the literature on the topic of Treg‐cell function in health and disease. Disturbances in FOXP3+ Treg‐cell development, homeostasis and/or function are thought to occur in many autoimmune and chronic inflammatory diseases in humans.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".