Higher inflammation and cerebral white matter injury associated cognitive deficit in asthmatic patients with depression
Bibliographic record
Abstract
Abstract Background Depression is a common co-morbidity in asthma, worsening asthma control and impairing patient’s quality of life. Previous studies have reported a higher risk of cognitive deficit in depression. However, few research has focused on the cognitive status of asthmatic patients with depression. Evidence showed that inflammation may play an important role in both asthma and depression. Moreover, cerebral white matter injury, which could be induced by inflammation, has been associated with depression. The present study aimed to assess the cognitive function and explore the potential mechanism in patients with asthma, depression and comorbidity. Methods We admitted four groups: Asthma comorbid Depression group (A + D, n = 26), Depression group (D, n = 25), Asthma group (A, n = 33) and Normal controls (N, n = 28). Cognitive function was assessed by Montreal Cognitive Assessment (MoCA). Various inflammatory cytokines were measured, including interleukin-1β (IL-1β), interleukin-6 (IL-6), tumor necrosis factor-α (TNF-α), high-mobility group box 1(HMGB1) and Netrin-1. Cerebral white matter injury was assessed by serum myelin basic protein (MBP) and myelin oligodendrocyte glycoprotein (MOG), and their correlations with cognitive performance were calculated. Results A + D group showed the highest incidence of cognitive deficit with specific affected cognitive domain. Compared to N group, serum levels of IL-6, HMGB1, Netrin-1, MBP and MOG were significantly elevated in A + D group. MOG level was negatively correlated with MoCA score. Conclusion Patients with comorbid condition presented more definite, severe cognitive deficit and higher level of inflammatory cytokines. Cerebral white matter injury may account for the cognitive deficit in these patients and MOG could be a potential biomarker of this process.
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 | 0.001 | 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) | 0.002 | 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".