Osteoarthritis improvement effect of <i>Chrysanthemum zawadskii</i> var. <i>latilobum</i> extract in relation to genotype
Bibliographic record
Abstract
Abstract: Objectives: To determine whether SNPs of osteoarthritis (OA)-related genes predict the effect of Chrysanthemum zawadskii var. latilobum (CZ) extract in OA patients with OA. Subjects/methods: To analyze correlations between CZ extract effects in humans and their genotypes, 121 Korean patients with OA were recruited. Patients ingested 600 mg/day of the CZ extract GCWB106 (one tablet daily), including 250-mg CZ, or placebo (one tablet daily) for 12 weeks. Twenty SNPs were genotyped in 11 genes associated with OA pathogenesis, including tumor necrosis factor-alpha (TNF-α) and matrix metalloproteinases (MMPs), and 9 genes involved in OA-related dietary intervention. The Visual Analogue Scale (VAS) and Korean Western Ontario and McMaster Universities (K-WOMAC) were measured as indicators of GCWB106 effect. Statistical comparisons were performed using Kruskal-Wallis tests to identify associations between these scales and genotyped loci in patients with OA. Results: Three SNPs ( PPARG rs3856806, MMP13 rs2252070, and ZIP2 rs2234632) were significantly associated with the degree of change in VAS pain score. Homozygous CC genotype carriers of rs3856806, G allele carriers (GA or GG) of rs2252070, and T allele carriers (GT or TT) of rs2234632 showed lower VAS score (i.e., less severe symptoms) in the GCWB106 group (n=53) than the placebo group (n=57) (p=0.026, p=0.009, and p=0.025, respectively). Gene–gene interaction effects on GCWB106-mediated pain relief were then examined, and it was found that the addition of each genotype resulted in a greater decrease in VAS pain score in the GCWB106 group (p=0.0024) but not the placebo group (p=0.7734). Conclusions: These novel predictive markers for the pain-relieving effects of GCWB106 may be used in the personalized treatment of patients with OA.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".