T helper cell polarisation as a measure of the maturation of the immune response
Bibliographic record
Abstract
BACKGROUND: T helper cell polarisation is important under chronic immune stimulatory conditions and drives the type of the evolving immune response. Mice treated with superantigens in vivo display strong effects on Th subset differentiation. The aim of the study was to detect the intrinsic capacity of T cells to polarise under various ex vivo conditions. METHODS: Purified CD4+ T cells obtained from super-antigen-treated mice were cultured under Th polarising conditions in vitro. By combining intracellular cytokine staining and subsequent flow cytometric analysis with quantitative cytokine measurements in culture supernatants by enzyme-linked immunosorbent assay (ELISA), the differential Th polarising capacity of the treatment can be detected in a qualitative and quantitative manner. RESULTS AND CONCLUSIONS: BALB/c mice were shown to be biased to develop strong Th2 polarised immune responses using Th0 stimulation of purified CD4+ T cells from phosphate-buffered saline-treated mice. Nevertheless, our analysis methodology convincingly showed that even in these mice, Toxic Shock Syndrome Toxin-1 treatment in vivo resulted in a significantly stronger Th1 polarising effect than control treatment. Our results indicate that populations of Th cells can be assessed individually for their differential Th1 or Th2 maturation capacity in vivo by analysing robust in vitro polarisation cultures combined with intracellular cytokine staining and ELISA.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".