Brain-Derived Neurotrophic Factor Profile in Ischemic Heart Disease; a Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Abstract Background Patients with ischemic heart disease (IHD) are at high risk of ischemic heart events, and novel biomarkers that reflect the severity of atherosclerotic disease and the fragility of coronary plaques are warranted. The Brain-Derived Neurotrophic Factor (BDNF) regulates endothelial and macrophage activation, suggesting a role in atherosclerotic plaque behavior. However, the precise involvement of BDNF in cardiovascular events is still unknown. This is the major goal of this study. Method We explored four databases for studies comparing BDNF levels in patients with IHD and controls. The Newcastle-Ottawa scale was used to evaluate the quality of included articles, and statistical analyses were conducted using R version 4.0.4. Results The final analysis comprised nine investigations covering 1,137 IHD patients and 724 controls. The preliminary result revealed statistically insignificant lower levels of BDNF in IHD compared with controls (SMD = -0.57, 95% CI [-1.18; 0.04], p-value = 0.068). After removing outliers, the following statistically significant results were obtained: SMD − 0.41 (95% CI [-0.78; -0.03], p-value = 0.03). In addition, subgroup analysis demonstrated no significant difference between serum and plasma levels (p-value = 0.53). No predictor for the difference in BDNF levels was revealed using meta-regression. Conclusion To conclude, when outlier studies were excluded, serum and plasma BDNF concentrations were considerably lower in patients with IHD than in healthy controls. Further studies of higher quality are required to investigate the function and value of BDNF in IHD.
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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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.022 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".