The Relationship Between Bridging Integrator 1 Gene Polymorphism and Susceptibility to Alzheimer’s Disease
Bibliographic record
Abstract
Background: Alzheimer’s Disease (AD) is the most common type of dementia. The role of genetic factors in AD development remains non-demonstrated. Objectives: In this study, we aimed to investigate the association between one of the BIN1 gene’s single-nucleotide polymorphisms (SNP) rs744373 and Late-Onset Alzheimer’s Disease (LOAD) in an Iranian population in Guilan Province. Materials & Methods: In this case-control study, 110 patients with LOAD and 110 unrelated healthy controls were recruited. Polymerase chain reaction-restriction length polymorphism (PCRRFLP) was performed for genotyping the BIN1 gene’s SNP rs744373. Electrophoresis was thereafter conducted using agarose gel and DNA-safe stain, and the gels were visualized under an Ultraviolet (UV) trans-illuminator. The allelic and genotypic frequencies were determined. Results: The frequency of allele T (Wild-type allele) in the control and the LOAD groups was 70.9% (n=159) and 58.6% (n=129), respectively (P=0.007). The frequency of allele C in the LOAD group (41.4%) (n=91) was significantly higher than that of the control group (29.1%) (n=64) (P=0.007). BIN’s homozygous genotype (CC) frequency was significantly higher in the LOAD group than in the control group (P=0.043). Conclusion: The rs744373 SNP of the BIN1 gene is significantly associated with the risk of developing AD in the studied population.
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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 | 0.000 | 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".