Correlation Between High-sensitivity C-reactive Protein and Reactive Oxygen Metabolites During A One-year Period Among Asymptomatic Subjects
Bibliographic record
Abstract
BACKGROUND: Inflammation and oxidative stress are associated with human health and the disease status. The present study aimed to investigate the longitudinal correlation between the diacron reactive oxygen metabolites (d-ROMs) level, as an oxidative stress-related marker, and high-sensitivity C-reactive protein (hsCRP), as an inflammatory marker, during a one-year period among asymptomatic subjects. METHODS: The data, including anthropometric and biochemical markers, were collected at baseline and after the one-year period from 71 participants (male/female = 41/30, mean age 50 years). The correlation between the changes of the d-ROMs and hsCRP levels during the study period was examined. RESULTS: A simple correlation analysis showed a significant and positive correlation to exist between the changes of the d-ROMs and hsCRP levels (r = 0.40, P < 0.01). This significant correlation remained independent in a multiple linear regression analysis adjusted for confounding factors. CONCLUSIONS: The present findings suggest that the relationship between the d-ROMs and hsCRP levels could be prospectively followed, and that monitoring both markers may help to better understand the cooperation of inflammation and oxidative stress in association with health and disease. Further studies are necessary to clarify the biological mechanism(s) responsible for the observed relationship. KEYWORDS: Oxidative stress; Oxygen reactive species; Inflammation; CRP.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".