Serum and mucosal brain derived neurotrophic factor (BDNF) in severe obstructive sleep apnea syndrome (OSAS). Effects of CPAP treatment on BDNF serum levels
Bibliographic record
Abstract
Background: OSAS is characterized by a chronic intermittent hypoxia (CIH) and sleep fragmentation. PSG is the gold standard for diagnosis. New biomarkers have been proposed. BDNF is a key mediator of cognitive functions. Changes of BDNF serum levels follow CIH. OSAS patients have mucosal upper airways inflammation. BDNF has been identified in various chronic inflammatory airways diseases. Objectives: BDNF serum levels and mucosal expression in severe OSAS patients compared to controls. Relationship of serum BDNF with neurocognitive function. Changes in serum BDNF after CPAP. Methods: Serum BDNF, upper airways mucosal biopsy and Montreal Cognitive Assestment (MoCA) are performed in 12 naïve severe OSAS patients and in 8 controls. In OSAS patients serum BDNF and MoCA were reassessed after 45 days of CPAP treatment. Results: BDNF in OSAS was significantly higher than controls (p=0.01) and showed a positive correlation with ODI (p=0.01, r=0.6). Serum BDNF had a decreasing trend after CPAP treatment. Before the treatment, MoCA scores were lower in OSAS than controls and are positively correlated with serum BDNF levels (p=0.02, r=0.57). MoCA is normalized by CPAP therapy. Immunohistochemistry of mucosal biopsy in OSAS patients showed higher inflammatory infiltrate CD3+ CD79a+ (BDNF positive) than controls. Conclusions: OSAS patients showed higher serum BDNF levels and lower MoCA scores than controls. The positive correlation between BDNF values and MoCA might reveal a peculiar BDNF-mediated neuroprotective mechanism against neuroinflammation induced by hypoxia. OSAS have mucosal upper airway lymphocytic inflammation BDNF positive.
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 | 0.000 |
| 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".