On how the immune system preferentially interacts with antigen‐specific molecules bound to antigen over unbound molecules, with emphasis on B cell receptor signalling
Bibliographic record
Abstract
Antigen-specific molecules of the immune system, namely antibodies, the membrane immunoglobulins (mIgs) of B cells and T cell receptors (TcRs), can all signal their interaction with antigen. There are different mechanisms by which this signalling could occur. These mechanisms can be divided into two general categories: allosteric and non-allosteric. In allosteric mechanisms, the monovalent binding of the antigen to the receptor triggers a conformational change at the binding site that is propagated to an invariant part of the receptor, a change recognized by a sensing unit. We argue allosteric mechanisms are implausible. Non-allosteric mechanisms depend on steric effects due to the antigen's size and/or multivalency. We consider two non-allosteric mechanisms by which the mIg of B cells has been envisaged to signal its interaction with antigen: the popular cross-linking model and the dissociation activation model. We argue, on the basis of both experimental observations and physiological considerations, that the dissociation activation model, developed by Reth and his colleagues, is uniquely plausible.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".