Toll‐like receptor 4 polymorphisms in Saudi population with cardiovascular diseases
Bibliographic record
Abstract
BACKGROUND: Toll-like receptors play a substantial role in innate immunity and the effects of TLR4 genetic variants on cardiovascular diseases are still largely unknown. Therefore, we aimed to investigate the effects of TLR4 polymorphisms on cardiovascular diseases risk in the Saudi population. METHODS: Three tag single-nucleotide polymorphisms (rs2770150, rs10759931, and rs4986790) in TLR4 were studied on 222 unrelated patients with cardiovascular diseases and 190 healthy volunteers. RESULTS: We found that, in patients over 60 years old, the frequency of the TT genotype in rs2770150 and the variant allele G in rs10759931 were higher compared to the control group. Based on gender, the genotype frequency of rs2770150 increases the risk for cardiovascular diseases in female patients by 3.6-fold. The allele frequency for the G allele of rs10759931 increased the risk for CVDs in male patients by more than 1.5-fold. Furthermore, the genotype frequency of rs2770150 had a significant association with cardiovascular diseases in patients without hypertension and G allele of rs10759931 significantly increased the risk of cardiovascular diseases in patients that smoked. After Bonferroni correction only patients without hypertension showed significant risk of CVD with rs2770150. CONCLUSION: A deeper understanding of the genetic variability of TLR4 will enable us to better identification of biomarkers for early detection and prognosis, and also enhance the decision-making process of treatments for cardiovascular diseases.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
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".