Seropositivity for West Nile Virus Antibodies in Patients Affected by Myasthenia Gravis
Bibliographic record
Abstract
BACKGROUND: Myasthenia gravis (MG) is an autoimmune neuromuscular disease characterized by varying degrees of weakness of the skeletal muscles. Specific auto-antibodies against acetylcholine receptor (AChR) are present in the majority of MG patients, although the main cause behind its development still remains unclear. Recently MG development following West Nile virus (WNV) infection has been described in patients without any earlier evidence of MG. It is known that infectious agents trigger immune response and occasionally initiate autoimmune disease. WNV, the causative agent of both benign illness and neuroinvasive disease, has become endemic in many countries in all continents. METHODS: In the present study, 29 patients (15 males and 14 females, 19 - 78 years old) with confirmed diagnosis of MG and elevated levels of AChR autoantibodies were screened for the presence of serum anti-WNV antibodies and compared to a similar population affected by different autoimmune diseases. Indirect immunofluorescent antibody technique was used to evaluate the reaction of patients' sera on cells infected by WNV. RESULTS: Positive fluorescent signals for anti-WNV IgG were obtained in 17% of MG patients, although no clinical manifestations related to WNV infection were reported. These results are in agreement with previous data and appear of great interest in the understanding of the pathogenic autoimmune mechanisms at the bases of MG development. CONCLUSION: As already observed in other human autoimmune diseases, pathogen-triggered autoimmunity could be involved in MG by breaking immunological self-tolerance through possible mechanisms of molecular mimicry between virus proteins and AChR subunits. In predisposed individuals, WNV infection could also represent an additional risk factor to initiate MG.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.001 | 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".