<p>Astroglial Mechanisms Underlying Chronic Insomnia Disorder: A Clinical Study</p>
Bibliographic record
Abstract
PURPOSE: The objective of this study was to investigate whether the serum biomarkers S100 calcium binding protein B (S100B), glial fibrillary acidic protein (GFAP), brain-derived neurotrophic factor (BDNF), and glial cell line-derived neurotrophic factor (GDNF) change in patients with chronic insomnia disorder (CID), and if this is the case, whether the altered levels of these serum biomarkers are associated with poor sleep quality and cognitive decline in CID. PATIENTS AND METHODS: Fifty-seven CID outpatients constituted the CID group; thirty healthy controls (HC) were also enrolled. Questionnaires, polysomnography, Chinese-Beijing Version of Montreal Cognitive Assessment (MoCA-C) and Nine Box Maze Test (NBMT) were used to assess their sleep and neuropsychological function. Serum S100B, GFAP, BDNF, and GDNF were evaluated using enzyme-linked immunosorbent assay. RESULTS: The CID group had higher levels of S100B and GFAP and lower levels of BDNF and GDNF than the HC group. Spearman correlation analysis revealed that poor sleep quality, assessed by subjective and objective measures, was positively correlated with S100B level and negatively correlated with BDNF level. GFAP level correlated positively with poor subjective sleep quality. Moreover, S100B and GFAP levels correlated negatively with general cognitive function assessed using MoCA-C. GFAP level correlated positively with poor spatial working memory (SWM) in the NBMT; BDNF level was linked to poor SWM and object recognition memory (ORcM) in the NBMT. However, principal component analysis revealed that serum S100B level was positively linked to the errors in object working memories, BDNF and GDNF concentrations were negatively linked with errors in ORcM, and GFAP concentration was positively correlated with the errors in the SWM and spatial reference memories. CONCLUSION: Serum S100B, GFAP, BDNF, and GDNF levels were altered in patients with CID, indicating astrocyte damage, and were associated with insomnia severity or/and cognitive dysfunction.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".