Correlations between peripheral vascular function, inflammation and depression in human subjects
Bibliographic record
Abstract
In addition to traditional risk factors for peripheral vasculopathy, including altered lipid profiles and chronic inflammation, ongoing study has suggested that psychological health, and specifically personal depression, may represent a profound contributor to poor vascular outcomes. The present study interrogated relationships between established risk factors for peripheral vascular disease, indices of personal depression, and vascular/endothelial function in human subjects recruited through a vascular surgery clinic at WVU HSC. During clinic visits, patients completed a depression screener, received baseline evaluations of health, provided venous blood samples and had brachial artery hyperemia evaluated following alleviation of 2 and 5 minute occlusions via ultrasound imaging. Results indicated that reactive hyperemia was reduced in patients in proportion to plasma TNF‐α and IL‐1β, (IL‐1β > TNF‐α). Additionally, depression severity was correlated both inflammation markers, as well as with impairments to reactive hyperemia. However, there was no evidence that self‐reported depression was an exacerbating influence on RH independent from that for inflammation. These results suggest that depression may not represent a causative factor contributing to poor vascular outcome, but may reflect a pathology arising from establishment of vascular dysfunction. (NIH R01 DK64668, AHA EIA 0740129N)
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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 | 0.000 | 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".