The correlation of cognitive dysfunction with serum brain-derived neurotrophic factor level in depression patients
Bibliographic record
Abstract
Objective To investigate the characteristics of cognitive dysfunction in patients with depression, and identify the correlation between cognitive dysfunction and serum brain-derived neurotrophic factor(BDNF) level. Methods All participants including 73 depressed patients and 71 healthy controls were received clinical and cognitive assessments at admission, the depression group was divided into two groups by the score of Beijing version of the Montreal Cognitive Assessment (MoCA-BJ), one was depression with cognitive dysfunction group which had 36 cases, the other was depression without cognitive dysfunction group which had 37 cases.Concentration of BDNF was measured by the ELISA method. Results Cognitive impairments were found in numerous cognitive domains of depressed patients, including visuospatial and executive abilities, attention, delayed recall and orientation(P 0.05), and the levels were significantly lower than that in healthy people ((16.55±7.47)ng/ml, P 0.05). Conclusion Depression patients have cognitive dysfunction in numerous cognitive domains, including visuospatial and executive abilities, attention, delayed recall and orientation.Serum BDNF level is closely related with depression, while, it has no obvious relationship with cognition function in depression. Key words: Depression; Cognitive dysfunction; Brain-derived neurotrophic factor
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 | 0.001 | 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".