Complete sequence analysis of human toll-like receptor 3 gene in natural killer cells of multiple sclerosis patients
Bibliographic record
Abstract
BACKGROUND: Multiple sclerosis (MS) is a chronic demyelinating disease of the central nervous system (CNS) where both environmental and genetic risk factors play a role. Among the environmental risk factors, EBV and HSV infections have been suggested as strong candidates contributing to MS pathology/progression. Viral recognition and control is largely tasked to the NK cells via TLR recognition and various cytotoxic and immunoregulatory functions. The present work aimed to characterize NK cells isolated from MS patients for genetic polymorphisms in the gene encoding for TLR3, as TLR3 in NK cells is important in herpesvirus recognition. METHODS: Highly purified NK cells isolated from peripheral blood of MS patients (n = 27) and healthy controls (n = 30) were used to sequence all five exons of the TLR3 gene using sanger sequencing. Alignment of the obtained sequences with the wild-type TLR3 sequence was used to identify genetic polymorphisms within the TLR3 gene. RESULTS: The alignment identified multiple substitution mutations across the five exons of the TLR3 gene (rs116729895, rs3775296, rs377529, rs3775290, rs3775291, rs376735334 and rs73873710). A significant difference was observed in the allele distribution of rs3775291 (Leu412Phe) between MS patients and HC, whereby the minor allele was detected in 38.9% of MS patients versus 11% of HC (Fisher's exact test, p = 0.021). CONCLUSION: There appears to be a possible association between the TLR3 missense mutation rs3775291 and multiple sclerosis, which might be attributed to changes in the TLR3 functional properties.
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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 | 0.000 |
| 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) | 0.002 | 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".