Somatic DNA mutations in the blood of normal and autoimmune individuals.
Bibliographic record
Abstract
We have analyzed blood from normal and autoimmune individuals, finding a previously unrecognized level of somatic DNA mutations. Using a PCR‐cloning technique we have found an average mutational frequency of 10 −3 mutations/bp in two marker genes (a mitochondrial [ND1] and nuclear gene [DLD]). These somatic mutational data from normal and autoimmune individuals suggests a need for re‐evaluation of self tolerance models to incorporate mutated self. Negative selection requires the presentation of self antigens – but what happens when self mutates? Among the possibilities are: a) there may be no B and T cells capable of launching a reaction against the mutated self because no new epitopes were generated by the mutant antigen, b) an immune cell may recognize the mutant antigen as non‐self and react to it but the number of identical mutant antigens may be too small to promote an auto‐immune reaction, or c) the immune cells may bind and react to the mutant antigen and in doing so form a memory response that can cross‐react to the nonmutant self antigen precipitating or supporting autoimmunity. A model of autoimmunity precipitated and/or supported by mutations in self will be presented. Research funded by CIHR.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".