Analysis of the Association Between Polymorphisms within PAI-1 and ACE genes and Ischemic Stroke Outcome After rt-PA Therapy
Bibliographic record
Abstract
PURPOSE: Treatment of Ischemic stroke (IS) in acute phase is based on the use of thrombolytic rt-PA therapy. We aimed to determine whether different alleles and genotypes of I/D ACE gene and 4G/5G PAI-1 gene polymorphisms may influence outcome of rt-PA therapy in patients with IS and the occurrence of haemorrhagic transformation (HT). METHODS: Our study included 94 consecutive patients with IS treated with rt-PA. Modified Rankin Scale (mRS) at 3rd month after IS was used to determine the stroke outcome, with scores 0-1 defining the favourable outcome, and scores 2-6 defining poor outcome. Genotypisation of the ACE-1 I/D polymorphism was performed by polymerase chain reaction and of the PAI-1 4G/5G polymorphism by polymerase chain reaction - restriction fragment length analysis. RESULTS: Regarding PAI-I 4G/5G polymorphism, 44 patients (46.8%) were heterozygotes, and the number of 4G/4G and 5G/5G homozygotes was the same - 25 each (26.6%). Number of heterozygotes for the ACE I/D polymorphism was 54 (57.4%), 9 patients (9.6%) had II, and 31 (33%) DD genotypes. A favourable outcome was recorded in 26 (28.0%) and the poor outcome in 67 (72.0%) patients. Favourable and poor outcome groups did not differ significantly in PAI-1 4G/5G and ACE I/D polymorphisms genotype or allele frequencies. There was a statistically significant difference in the occurrence of HT between patients with ACE II and patients with ACE ID or DD genotypes (p=0.035). CONCLUSION: Results of our study suggest that stroke patients with ACE II genotype, treated with rt-PA, may be at risk of HT.
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 | 0.000 | 0.000 |
| 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) | 0.001 | 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".