What Determines the Class of Immunity an Antigen Induces? A Foundational Question Whose Rational Consideration Has Been Undermined by the Information Overload
Bibliographic record
Abstract
Activated CD4 T helper cells are required to activate B cells to produce antibody and CD8 T cells to generate cytotoxic T lymphocytes. In the absence of such help, antigens inactivate B cells and CD8 T cells. Thus, the activation or inactivation of CD4 T cells determines whether immune responses are generated, or potentially ablated. Most consider that the activation of CD4 T cells requires an antigen-dependent signal, signal 1, as well as a critical costimulatory signal, initiated when a pattern recognition receptor (PRR) engages with a danger- or pathogen-associated molecular pattern (DAMP or PAMP). Most also envisage that the nature of the DAMP/PAMP signal determines the Th subset predominantly generated and so the class of immunity predominantly induced. I argue that this framework is implausible as it is incompatible with diverse observations of the variables of immunization affecting the class of immunity induced. An alternative framework, the threshold hypothesis, posits that different levels of antigen mediated CD4 T cell interactions lead to the generation of different Th subsets and so different classes of immunity, that it is compatible with these observations. This alternative supports a rational approach to preventing and treating diverse clinical conditions associated with infectious disease and, more speculatively, with cancer.
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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 | 0.005 | 0.006 |
| 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 | 0.008 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".