Elevated C-reactive protein and depression score among post-COVID-19 patients - A cross-sectional study
Bibliographic record
Abstract
Abstract Immune dysfunction after SARS-CoV-2 infection leads to long COVID neurological sequels in COVID-19 survivors. Despite initial respiratory symptoms, neuropsychiatric manifestations have been reported in many COVID-19 patients. We aimed to investigate the psychological symptoms, gut microbiome status, and C-reactive protein levels (CRP) in post-COVID individuals. This cross-sectional study included age and sex-matched individuals with post-COVID (n = 114) or controls (n = 236). We used Mini International Neuropsychiatric Interview (MINI) to diagnose psychiatric disorders. The depressive and anxiety symptoms were assessed by the Hamilton Rating Scale and the stress level by an inventory of stress symptoms and circulating CRP levels measured. In a small cohort (post-COVID = 18 and controls = 46), the gut microbiome was evaluated by 16S rRNA sequencing. Post-COVID individuals exhibited greater severity of depressive symptoms (p = 0.034), higher levels of stress (p = 0.020), and CRP (p = 0.014) as compared to controls. There was no difference in α-diversity but β-diversity (p = 0.001) was significantly different between control and post-COVID groups. Interestingly, post-COVID individuals with depression had greater CRP levels than those with COVID-19 without current major depression disorder (p = 0.023). Post-COVID-19 individuals have demonstrated severe psychological symptoms and altered gut microbiome levels. Although longitudinal studies are needed to study the mental health trajectory of COVID-19 individuals, the levels of CRP may serve as a promising biomarker for the early detection of post-COVID depression.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".