Evaluation of Efficacy and Safety of NEXT-II®, a Novel Water-Soluble, Undenatured Type II Collagen in Subjects with Potential Risks in the Knee Joint Health from Healthy Population
Bibliographic record
Abstract
Background: Oral administration of a novel water-soluble undenatured type II collagen (NEXT-II®) has been demonstrated to ameliorate the signs and symptoms of rheumatoid arthritis (RA) in animal models. In the present investigation, we conducted a pilot study to examine the efficacy and safety of NEXT-II® in borderline subjects defined as healthy and non-diseased state, but with potential risks in knee joint health. Method: We employed Western Ontario McMaster Index (WOMAC) score and Visual Analog Scale (VAS) scores to assess the extent of improvement in the knee joints in these volunteers following supplementation of 40 mg NEXT-II® (10 mg as undenatured type II collagen) over a period of 12 weeks. Result: The results demonstrated that NEXT-II® treatment significantly reduced WOMAC and VAS scores compared to subjects at baseline. Specifically, in the evaluation using VAS, the borderline subjects at resting, walking, and going up and down the stairs revealed significant improvement when compared to the baseline. Conclusion: The results of the studies demonstrated that NEXT-II® might be an ingredient which is safe and effective in the application of dietary supplement in ameliorating joint pain and symptoms of the borderline subjects without any adverse events.
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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 | 0.001 | 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".