Autoimmune/inflammatory syndrome induced by adjuvants (ASIA) in 2023
Bibliographic record
Abstract
In 2011, a syndrome entitled ASIA (Autoimmune/inflammatory Syndrome Induced by Adjuvants; Shoenfeld's syndrome) was first described. ASIA aimed to organize under a single umbrella, the existing evidence regarding certain environmental factors which possess immune stimulatory properties, in order to shed light on a common pathway of autoimmune pathogenesis. Such environmental immune stimulators, or adjuvants, include among others: aluminum salts as in vaccines, various medical implants, as well as various infectious agents. After the launch of the ASIA syndrome, the expansion and recognition of this syndrome by different researchers from different countries began. During the past decades, evidence had been accumulating that (auto)immune symptoms can be triggered by exposure to environmental immune stimulatory factors that act as an adjuvant in genetically susceptible individuals. A panoply of unexplained subjective and autonomic-related symptoms has been reported in patients with ASIA syndrome. The current review summarizes and updates accumulated knowledge from the past decades, describing new adjuvants- (e.g. polypropylene meshes) and vaccine- (e.g. HPV and COVID vaccines) induced ASIA. Furthermore, a direct association between inflammatory/autoimmune diseases with ASIA syndrome, will be discussed. Recent cases will strengthen some of the criteria depicted in ASIA syndrome such as clear improvement of symptoms by the removal of adjuvants (e.g. silicone breast implants) from the body of patients. Finally, we will introduce additional factors to be included in the criteria for ASIA syndrome such as: (1) dysregulated non-classical autoantibodies directed against G-protein coupled receptors (GPCRs) of the autonomic nervous system and (2)) small fiber neuropathy (SFN), both of which might explain, at least in part, the development of 'dysautonomia' reported in many ASIA patients.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".