BDNF and Cognitive Function in Chilean Schizophrenic Patients
Bibliographic record
Abstract
Despite cognitive symptoms being very important in schizophrenia, not every schizophrenic patient has a significant cognitive deficit. The molecular mechanisms underlying the different degrees of cognitive functioning in schizophrenic patients are not sufficiently understood. We studied the relation between brain-derived neurotrophic factor (BDNF) and cognitive functioning in two groups of schizophrenic patients with different cognitive statuses. According to the Montreal Cognitive Assessment (MoCA) results, the schizophrenic patients were classified into two subgroups: normal cognition (26 or more) and cognitive deficit (25 or less). We measured their plasma BDNF levels using ELISAs. The statistical analyses were performed using Spearman’s Rho and Kruskal–Wallis tests. We found a statistically significant positive correlation between the plasma BDNF levels and MoCA score (p = 0.04) in the subgroup of schizophrenic patients with a cognitive deficit (n = 29). However, this correlation was not observed in the patients with normal cognition (n = 11) and was not observed in the total patient group (n = 40). These results support a significant role for BDNF in the cognitive functioning of schizophrenics with some degree of cognitive deficit, but suggest that BDNF may not be crucial in patients with a normal cognitive status. These findings provide information about the molecular basis underlying cognitive deficits in this illness.
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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 | 0.001 | 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".