Association of plasma growth arrest-specific protein 6 (Gas6) concentrations with albuminuria in patients with type 2 diabetes
Bibliographic record
Abstract
AIMS: New biomarkers are required to detect diabetic nephropathy earlier in persons with type 2 diabetes mellitus. Recent experimental studies indicate that growth arrest-specific protein 6 (Gas6) may have a role in pathogenesis of complications associated with diabetes. The objective of the current study is to examine whether plasma Gas6 concentrations are associated with albuminuria in persons with type 2 diabetes mellitus. METHODS: About 32 patients with diabetes which have micro or macroalbuminuria, 37 patients with diabetes and normoalbuminuria, and 30 healthy volunteers were recruited. Plasma Gas6 levels were measured by ELISA. Hemoglobin A1c (HbA1c), serum C reactive protein, fibrinogen and 24-h urine samples for microalbuminuria were analyzed by Primus PDQ, Beckman Coulter Immage 800, STA Compact and Roche Cobas Integra 800 analyzer, respectively. Statistical analysis was performed using SPSS (Statistical Package for Social Sciences) for Windows 11.5. RESULTS: There was a noteworthy difference among the three groups for Gas6 according to the Kruskal-Wallis test (p < 0.01). Plasma Gas6 concentrations were higher in patients with micro or macroalbuminuria [20.9 ng/mL (16.7-27.0); median (25-75% percentile)] compared to patients with normoalbuminuria [16.5 ng/mL (13.1-22.9)], and healthy controls [15.3 ng/mL (8.3-33.6)]. CONCLUSIONS: In conclusion, this is the first study indicating that plasma Gas6 levels are associated with albuminuria in patients with type 2 diabetes. This study could be considered a starting point to focus on the association between Gas6 and diabetic nephropathy.
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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 | 0.000 | 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".