The Effects of Consumption L-Arabinose on Metabolic Syndrome in Humans
Bibliographic record
Abstract
On the basis of results in rat, L-arabinose decreased total cholesterol (TC), triglycerides (TG), fasting glucose, systolic blood pressure, increased high-density lipoprotein cholesterol (HDLC), and enhanced the glucose tolerance. The primary purposes of the present study was to determine the effects of consumption L-arabinose on metabolic syndrome in humans.All volunteers received L-Arabinose by dissolving it in water. The volunteers didn’t change the diet habits and lifestyles during the whole experiment. The trial lasted for 6 months, and experimental indicators were assayed every two months, which including weight, waist circumference, blood pressure, TG, TC, HDLC, low-density lipoprotein cholesterol (LDLC), fasting plasma glucose, erum uric acid, serum creatinine (Scr), bloodurea nitrogen (BUN), alanine aminotransferase (ALT) and aspartate aminotransferase (AST). Our results showed that the L-arabinose decreased waist circumference, TC, fasting glucose, serum uric acid, ALT and slightly increased HDLCand slightly decreased diastolic blood pressure after 6 months. A tendency for decreased waist circumference, TC, fasting glucose, serum uric acid, ALT after 2, 4 and 6 months of treatment with L-arabinose was observed. In addition, L-arabinose decreased TC, LDLC and body weight. No effects on Scr, BUN, AST. In conclusions, L-arabinosewould reduce most the metabolic syndrome risk factors(decreased circumference, TC, fasting glucose, and so on), and treat the metabolic syndrome as a whole.The present study would provides strong evidence that long-term received L-arabinose would be manage metabolic syndrome.
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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 | 0.000 |
| 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) | 0.001 | 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".