The functional adaptation of effector Foxp3<sup>+</sup> regulatory T cells to pulmonary inflammation
Bibliographic record
Abstract
Abstract During infections, the timings of effector differentiation of pulmonary immune responses are of paramount importance, as pathogen persistence and unsuppressed inflammation can rapidly lead to a loss of function, increased frailty, and death. Thus, both an efficient clearance of the danger and a rapid resolution of inflammation are critical to host survival. We now know that tissue‐localized FoxP3+ regulatory T cells, a subset of CD4+ T cells, are highly attuned to the type of immune response, acquiring unique phenotypic characteristics that allow them to adapt their suppressive functions with the nature of inflammatory cells. To achieve this, activated effector TREG cells acquire specialized TH1, TH2, and TH17‐like characteristics that allow them to migrate, survive, and time their function(s) through refined mechanisms. Herein, we describe how this process requires a unique developmental path that includes the acquisition of master transcription factors and the expression of receptors adapted to sense local danger signals that are found during pulmonary inflammation. In turn, we offer an overview of how these characteristics promote the capacity of local effector TREG cells to proliferate, survive, and display suppressive strategies to resolve lung injury.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".