B cell/antibody tolerance to our own antigens
Bibliographic record
Abstract
The lymphoid system normally mounts damaging responses to infectious pathogens while avoiding equally damaging responses to self. A notable number of antibodies to self antigens are formed but normally remain at levels below the damaging threshold, only temporarily rising to damaging levels during protective responses against infectious nonself. Many mechanisms regulate the level of autoantibodies and anti-self B cells including deletion, anergy, ignorance for antigen, receptor editing, coinhibition, competition for resources to sustain B cell responses, and apoptotic denouement of damaging responses following the ejection or containment of foreign invaders. While infectious events may encourage immune responses to self antigens, infectious events tend also to strengthen regulatory mechanisms. When regulatory mechanisms do not function properly, abnormal damaging responses to self antigens may occur. While defects in a single regulatory mechanism may result in autoimmunity, this eventuality usually happens only on permissive genetic backgrounds; this indicates that weakness in other regulatory mechanisms may be necessary to result in the emergence of damaging responses to self antigens. The immune system and its regulatory mechanisms are not simple, as one would expect of a homoeostatic process that also has the ability to expand enormously when challenged and to contract rapidly when threats pass. These processes that avoid damaging anti-self B cells are much more complicated than that envisaged in standard two signal models. Simple signals through the B cell antigen-receptor probably encourage B cell survival and receptivity, while other signals (costimulatory or coinhibitory) promote B cell stimulation or non-stimulation/inactivation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".