Editorial: Environmental factors in autoimmunity
Bibliographic record
Abstract
Environmental factors in autoimmunityWhile the T and B lymphocyte repertoires are designed to be able to recognize foreign antigen (pathogens) to protect the host, significant portions of these repertoires can recognize self-antigens.Normally this is not a problem for the host because such autoreactive lymphocytes are removed (clonal deletion), redeemed by B cell receptor (BCR) editing, anergized, or actively suppressed in the periphery by regulatory T and B cells (1, 2).Despite these mechanisms, an estimated 7-9% of humans are diagnosed with an autoimmune disease (3).Puzzlingly, an equal percentage of healthy people have autoreactive anti-nuclear antibodies in their blood despite having no clinical evidence of autoimmunity (4).Classic studies that reported on the concordance of autoimmunity in monozygotic and dizygotic twins indicated that a large fraction of autoimmune risk (>50%) is conferred by environmental factors that interact with genetic factors to initiate disease (5).Thus, autoimmunity may be largely preventable if the specific causal environmental factors are identified, as seminal studies have shown that initial autoimmune pathogenic events take place prior to clinical manifestations.This Research Topic aimed to provide new insights into environmental risk factors in autoimmunity and includes reviews and articles that describe how dietary factors and obesity, maternal and early life factors, viruses, the microbiome, and inhaled environmental toxins influence the development of autoimmunity. Dietary factors and obesity in autoimmunityPast studies have identified obesity as a common risk factor for many autoimmune diseases including multiple sclerosis (MS), systemic autoimmune erythematosus (SLE), and Alopecia Areata (AA) (Touil et al.; Correale and Marrodan).Correale and Marrodan reviewed how the adipose tissue becomes inflamed with obesity and how this leads to the development of pro-inflammatory adipokine production that can enhance neuroimmune mechanisms in MS and in animal models of MS.Touil et al. added to this theme by reviewing how other lifestyle factors including diet quality and dietary deficiencies potentially contribute to autoimmunity development.The general conclusion from this Frontiers in Immunology frontiersin.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.036 | 0.023 |
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".