Aprotinin Attenuates Genomic Expression Variability Following Cardiac Surgery
Bibliographic record
Abstract
BACKGROUND: Aprotinin was the subject of recent controversy regarding adverse clinical outcomes following cardiac surgery. We compared the role of Aprotinin and epsilon-aminocaproic acid on clinical outcomes and the attenuation of the postcardiopulmonary bypass (CPB) response at the genomic expression and cytokine (protein) level. METHODS: Thirty-nine low-risk patients undergoing coronary revascularization (CABG) and/or valve procedures using cardiopulmonary CPB were enrolled into a prospective cohort study. Aprotinin or epsilon-aminocaproic acid was administered to patients. Gene expression was assessed from whole blood mRNA samples collected preoperatively (PRE) and 6 hours (6H) postoperatively. Validation of gene expression was performed with SYBR Green real-time polymerase chain reaction. Cytokine values were quantified from serum preoperatively and postoperatively at 6 H and 4 days and analyzed in a blinded fashion. RESULTS: No difference was detected in baseline characteristics. Inflammatory markers measured did not reveal significant difference between patients receiving Aprotinin (APR) and those receiving epsilon-aminocaproic acid (Amicar). Intraoperative parameters and postoperative outcomes were not significantly different. Compared with PRE samples, 6H samples had 264 upregulated and 548 downregulated genes uniquely in the APR group compared to 4826 upregulated and 1114 downregulated genes uniquely in the Amicar group (p < 0.001). Compared to patients in the Amicar group, APR patients had significantly different gene expression pathways involving NF-kappabeta regulation, programmed cell death and cell-cell adhesion. None of the patients developed postoperative stroke, myocardial infarction, or systemic infections. CONCLUSIONS: Aprotinin leads to significantly less genomic expression variability following CPB compared to Amicar and has a differential effect on specific genomic pathways.
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".