Novel findings in relation to multiple anti‐atherosclerotic effects of XueZhiKang in humans
Bibliographic record
Abstract
Abstract Background Previous studies have clearly demonstrated that XueZhiKang (XZK), an extract of cholestin, can decrease low‐density lipoprotein cholesterol (LDL‐C) and cardiovascular events. However, the mechanism of the effects of XZK on atherosclerosis (AS) in humans has been reported less frequently. In the present study, we investigated the impact of XZK on lipoprotein subfractions, oxidized LDL (oxLDL), and interleukin‐6 (IL‐6). Methods From October 2015 to July 2016, 40 subjects were enrolled in this study. Of them, 20 subjects with dyslipidemia received XZK 1200 mg/day for 8 weeks (XZK group); 20 additional healthy subjects who did not receive therapy acted as controls. The plasma lipoprotein subfractions, oxLDL, and IL‐6 were examined at baseline and again at 8 weeks. Results Data showed that XZK could significantly decrease not only plasma LDL‐C levels (87.26 ± 24.45 vs . 123.34 ± 23.99, P < 0.001), total cholesterol (4.14 ± 0.87 vs . 5.08 ± 1.03, P < 0.001), triglycerides (0.95 ± 0.38 vs . 1.55 ± 0.61, P < 0.05), and apolipoprotein B (1.70 ± 0.35 vs . 1.81 ± 0.72, P < 0.05), but also oxLDL (36.36 ± 5.31 vs . 49.20 ± 15.01, P < 0.05) and IL‐6 (8.50 ± 7.40 vs . 10.40 ± 9.49, P < 0.05). At the same time, XZK reduced the concentration of small LDL‐C (1.78 ± 2.17 vs . 6.33 ± 7.78, P < 0.05) and the percentage of the small LDL subfraction (1.09 ± 1.12 vs . 3.07 ± 3.09, P < 0.05). Conclusions Treatment with 1200 mg/day XZK for 8 weeks significantly decreased the atherogenic small LDL subfraction and reduced oxidative stress and inflammatory markers, in addition to affecting the lipid profile, suggesting multiple beneficial effects in coronary artery disease.
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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 | 0.001 |
| 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) | 0.000 | 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".