Serum Antioxidant Levels Associated with Subcortical Ischemic Vascular Disease
Bibliographic record
Abstract
OBJECTIVE: We aimed to examine changes in serum bilirubin and uric acid (ua) levels in subcortical ischemic vascular disease (Sivd). in addition, we investigated if altered serum bilirubin and ua levels correlate with the subtypes of Sivd as well as the severity of leukoaraiosis (la). METHODS: this cross-sectional study included 1098 consecutive patients with slight symptoms, such as dizziness, vertigo etc. according to magnetic resonance imaging (Mri) appearances, they were divided into either Sivd group or controls (Cn), and the Sivd group was further grouped in lacunar infarction (li) and la subtypes, as well as different grades. Serum bilirubin and ua levels were determined by the vanadate oxidase method and enzymatic method respectively, after at least an eight hour overnight fasting, in all subjects. RESULTS: the bilirubin level was obviously lower while the ua level was significantly higher in the Sivd group when compared with the controls. Moreover, the la subgroup presented more significant changes in bilirubin and ua when compared to the li subgroup in both males and females. the correlation was positive between the ua levels and the la severity (r=0.134, p=0.006). Multivariate regression analysis revealed that the odds ratio (95% Ci) for Sivd in the lowest tertile of total bilirubin (tbil<9.58 μmol/l) and highest tertile of ua (ua>339 μmol/l) were 2.702(1.936-3.770) and 2.135(1.521-2.996) respectively after adjusting for confounding variables. CONCLUSION: Serum bilirubin levels were lower, whereas ua levels were higher in Sivd patients when compared with controls in both males and females, especially in la patients. Moreover, serum ua levels positively correlated to la severity.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".