Differential Impact of Dietary Vitamin K (phylloquinone) on Coagulation Factor Activities and Clotting Times in Warfarin-Treated Rats
Bibliographic record
Abstract
Investigate the impact of variable vitamin K (VK) intakes on the coagulation activities of four VK-dependent factors and clotting times, in warfarin-treated rats. Male Wistar rats were randomly allocated to a AIN-93 based diet containing low (L: 80 mcg/kg/d), adequate (A: 750 mcg/kg/d) or enriched (E: 2000 mcg/kg/d) phylloquinone (K1) containing diet (n = 24/diet group). After one week, half the animals from each diet group were randomly allocated to receive 0.2 mg warfarin/kg/d through drinking water (W gp) or plain water (C gp), for 10 weeks. Coagulation activity (%) was assessed for factors II, VII, IX and X, and clotting times were based on prothrombin [PT (sec)] and activated thromboplastin times [APTT (sec)]. Measures were obtained at the end of the study and were conducted in the hospital clinical laboratory using standard procedures. Diet effects within C and W groups were investigated using one-way ANOVA and uncorrected Fisher post-hoc tests. Warfarin treatment resulted in significantly higher clotting times (PT and APTT) in all diet groups when compared to corresponding C groups (p < 0.05), the highest increase being observed in the L, followed by A and E groups, each diet being statistically different from each other (p < 0.01). Warfarin treatment also resulted in statistically significant decreases in activities of all coagulation factors although the impact of the diets varied according to factors: FVII and FX, between L and E groups only; FIX, between L and A, and L and E groups; FII, between all diet groups; (p < 0.05 in all cases). Results from this study confirm the impact of dietary VK on coagulation factor activities and resulting clotting times, and suggest that for a given dose of W, this impact will depend on VK intake levels. Currently, individuals undergoing warfarin treatment are advised to aim for stable daily VK intakes. Results from this study provide data supporting this recommendation. This study was funded by CIHR and MHI Foundation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".