Use of polypeptide neuroprotection for prevention of postoperative cognitive dysfunction after cardiac surgery: a pilot prospective randomized placebo-controlled clinical study
Bibliographic record
Abstract
Aim. The study was designed to evaluate the efficacy of polypeptide neuroprotection using brain protein “Cellex” for prevention of postoperative cognitive dysfunction after cardiac surgery.Methods. Our study included 60 patients undergoing elective cardiosurgical operations, who were randomized into two groups. In the “Cellex” group, the patients received 1.0 ml of “Cellex” subcutaneously daily during 8 days, beginning from the preoperative day, while the control group patients were given a saline placebo. The cognitive function was assessed using a Montreal cognitive assessment (MoCA) test on the day before surgery and also at Days 3 and 7 postoperatively. The plasma concentrations of S100b protein were measured before surgery, and at Days 3 and 7. The patients’ gas exchange, hemodynamics and cerebral oxygenation were monitored. In addition, the efficacy of “Cellex” and the severity of cognitive dysfunction were evaluated intraoperatively under cardiopulmonary bypass.Results. The duration of intervention and mechanical ventilation, as well as hemodynamics and cerebral oximetry data did not differ significantly between the groups. There was a transient decline of cognitive functions and an increase in plasma concentration of S100b at Day 3 after surgery in both groups (p<0.05). At Day 7, the MoCA score was still decreased in the control group (p<0.003), but returned to the baseline in the “Cellex” group. These effects became more pronounced after cardiopulmonary bypass. The intraoperative PaCO2 correlated with cerebral oxygenation surgery by the beginning and at the end of surgery (rho = 0.305, p = 0.033 and rho = 0.533; p<0.001). Conclusion. The perioperative use of “Cellex” can attenuate cognitive dysfunction after cardiac surgery, especially when following interventions under cardiopulmonary bypass.Received 25 May 2017. Revised 9 November 2017. Accepted 13 November 2017.Funding: The study was carried out with support of the “Farm-Sintez” company’s grant. The money was spent for purchase of the preparation, the authors’ honoraria and purchase of a kit for determining S100b protein concentration. The sponsors’ support had no impact on the study design and data acquisition, analysis and interpretation. The “Farm-Sintez” company’s representatives were not participating in the preparation and publication of the article.Conflict of interest: The study was carried out with support of the “Farm-Sintez” company’s grant.Author contributionsConception and study design: M.A. Putanov, M.M. Sokolova, P.I. Lenkin, M.Yu. Kirov Data collection and analysis: M.A. Putanov, M.M. Sokolova, P.I. Lenkin, I.G. Baskakova, A.N. Kiriluk, D.N. Kazarinov, K.M. Checkaya, T.S. Isakova, M.A. Rumyanceva, V.Yu. Slastilin Statistical data analysis: M.M. Sokolova Drafting the article: M.A. Putanov, M.M. Sokolova Critical revision of the article: M.Yu. Kirov Final approval of the version to be published: M.A. Putanov, M.M. Sokolova, P.I. Lenkin, V.Yu. Slastilin, I.G. Baskakova, A.N. Kiriluk, D.N. Kazarinov, K.M. Checkaya, T.S. Isakova, M.A. Rumyanceva, M.Yu. Kirov
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| 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".