Multi-Source Pathways of T Follicular Helper Cell Differentiation
Bibliographic record
Abstract
T follicular helper (Tfh) cells participate in humoral immune by promoting inflammation and aiding B cells survival, proliferation, maturation, and generation autoantibodies. The plasticity of Tfh cells enables the immune system to adjust the direction of differentiation according to the degree of the immune response, regulate the germinal center (GC) response and maintain homeostasis. Tfh differentiation involves several signaling factors, including multiple cytokines, receptors, transcription factors and genes. The signal transducer and activator of transcription (STAT) family signaling pathways are crucial for Tfh formation. However, because of the multi-factorial and multi-stage features of Tfh differentiation, every STAT member plays a role in Tfh differentiation, but is not completely depended on. With the gradual recognition of different Tfh subsets (Tfh1, Tfh2, Tfh17), the process of Tfh differentiation can no longer be explained by straight-line derivation models. In this review, we summarize the roles of different STATs in mediating Tfh subsets, analyze the contributions of mutual restraint and cooperation among cytokine-STAT signals to terminal Tfh differentiation, and clarify the multi-source pathways of Tfh differentiation with a three-dimensional illustration.
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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 | 0.001 |
| 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".