Bibliographic record
Abstract
Autoimmune hepatitis (AIH) is the inflammation of the liver with clear-cut interface hepatitis and piecemeal necrosis located at the boundary between portal areas and periportal hepatocytes, and characterized by autoimmunity to hepatocytes with an increase in the antinuclear antibody. After the disastrous SARS-CoV-2 pandemic flagellated several countries, several vaccines have been commercialized and have become a ground for social responsibility. The mRNA vaccines, issued by Pfizer-BioNTech (BNT162b2) and Moderna (mRNA-1273), do not use prebuilt viruses to supply the antigen in the subject’s body and are not perfect but have been useful in tackling the pandemic. Nevertheless, both myocarditis and AIH have been reported as side effects of the vaccination programs in addition to thromboembolic events. Here, we explore this topic and give a data-based perspective, gathering a comparison between the titin protein of the sarcomere and myocarditis. The isolation of a Drosophila gene using the serum from a patient with autoimmune scleroderma recognized an epitope on chromosomes (condensed mitotic form) in both human cultured cells and early Drosophila embryos. It revealed that this gene encodes a Drosophila homolog of the vertebrate titin (D-Titin). Moreover, anti-titin antibodies have been found in a subset of patients with myasthenia gravis, a neuromuscular junction disease that is mostly associated with autoimmune antibodies, such as the anti-acetylcholine receptor antibody. The co-existence of myasthenia gravis and autoimmune hepatitis is rare, and a cohort of patients with myasthenia gravis anti-titin antibodies seems to be highly relevant. In consideration of these data and the number of patients who may not be symptomatic, we postulated that autoimmune phenomena may not be exceedingly rare, following the administration of mRNA technology-based vaccines, and a balance between pros and cons in administrating boosters is critical.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".