Bibliographic record
Abstract
CD4 T helper (Th) cell differentiation into distinct T cell subsets is critical to the normal function of the immune system. Until recently, the paradigm held that naïve T cells differentiated into distinct subsets under the guidance of environmental cues (e.g., cytokines) and that once polarized, these cells were committed to a particular functional state. However, the existence of transdifferentiated T cell populations, which express signature transcription factors and cytokines associated with more than one Th subset, challenges the immutability of T helper subsets and suggests that plasticity is a feature of multifaceted immune responses. How this process impacts immune dysregulation in diseases such as inflammatory bowel diseases (IBD) and the machinery that underlies this process is far from fully understood. Interleukin (IL)-17 secreting helper T (Th17) cells have been heavily implicated in tissue-specific immune pathology including murine models of IBD, human Crohn's disease and ulcerative colitis. Plasticity within this subset is suggested by the existence of IL-17 secreting cells, which, can also secrete interferon-γ, the signature cytokine for Th1 cells or, can co-express the anti-inflammatory transcription factor forkhead box p3, a signature transcription factor of regulatory T cells. In this review we mainly discuss evidence for Th17 plasticity, mechanisms, which govern it, and highlight the potential to therapeutically target this process in human IBD.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".