FAS promoter polymorphisms and serum sFas level are associated with increased risk of nerve damage in Bangladeshi patients with Guillain-Barré syndrome
Bibliographic record
Abstract
Guillain-Barré syndrome (GBS) is an autoimmune disorder of the peripheral nervous system triggered by molecular mimicry between pathogen lipopolysaccharides and host nerve gangliosides. Polymorphisms in the Fas receptor (FAS) and Fas ligand (FASL) genes may potentially alter the elimination of autoreactive immune cells and affect disease susceptibility or disease severity in GBS. We detected single nucleotide polymorphisms (SNPs) in FAS (-1377G/A and -670A/G) and FASL (-843C/T) in a prospective cohort of 300 patients with GBS and 300 healthy controls from the Bangladeshi population. Genotype distributions were not significantly different between patients with GBS and healthy controls. The FAS -670 AG heterozygous (P = 0.0005, OR = 2.5, 95% CI = 1.5-4.2) and GG homozygous (P = 0.0048, OR = 2.6, 95% CI = 1.3-5.0) genotypes were more common in patients with anti-GM1 antibodies than patients without anti-GM1 antibodies. The FAS -670 G allele was more prevalent in anti-GM1 antibody-positive than -negative patients (P = 0.0002, OR = 1.9, 95% CI = 1.4-2.7) and also in patients with the axonal subtype than demyelinating subtype (P < 0.0001, OR = 4.8, 95% CI = 2.3-10.1). The 1377G/-670G GG haplotype was significantly associated with the axonal subtype (P < 0.0001) and anti-ganglioside antibody-positivity (P = 0.0008) in GBS. Serum sFas (237.5 pg/mL vs. 159.5 pg/mL; P < 0.0001) and sFasL (225.1 pg/mL vs. 183.4 pg/mL; P = 0.0069) were elevated in patients with GBS compared to healthy controls, and among patients with high serum sFas was associated with severe GBS (P = 0.0406). In conclusion, this study indicates FAS-FASL promoter SNPs may promote the production of cross-reactive anti-ganglioside antibodies in GBS.
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 | 0.001 |
| 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) | 0.003 | 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".