Genetic polymorphisms influencing total and γ′ fibrinogen levels and fibrin clot properties in <scp>A</scp>fricans
Bibliographic record
Abstract
Inter-ethnic variation in fibrinogen levels is hypothesized to be the result of differences in genetic background. No information is available regarding the contribution of genetics to fibrinogen γ' in Africans. Only limited information is available regarding the interaction between genotypes and total and γ' fibrinogen concentration in determining fibrin clot properties. Our aim was to investigate the effect of polymorphisms in the fibrinogen and Factor XIII genes on total and γ' fibrinogen and clot properties (turbidimetry) in 2010 black Africans as well as to determine their interactions. Significant associations were observed between rs1049636 (FGG gene), with total fibrinogen levels and between rs2070011 (FGA promoter area) and fibrinogen γ' levels. Significant associations were observed between single nucleotide polymorphisms (SNPs) in the FGA (rs2070011), FGB (rs1800787) and FGG (rs1049636) genes and fibre size. Significant interactions were found between total and/or γ' fibrinogen levels and SNPs in the FGA (rs2070011), FGB (rs2227385, rs1800787, rs1800788, rs4220) and F13A1 genes (rs5985) in determining clot properties. The different SNPs influenced the relationships between total and γ' fibrinogen levels with clot properties in opposing directions. Genetic influences may be ethnic-specific and should not only focus on fibrinogen concentration, but also on functionality in determining its role in CVD.
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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 | 0.001 | 0.001 |
| 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) | 0.003 | 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".