INFLUENCE OF BETALAIN-RICH EXTRACT ON REDUCTION OF DISCOMFORT ASSOCIATED WITH OSTEOARTHRITIS**
Bibliographic record
Abstract
Summary Introduction. Osteoarthritis (OA) subjects typically experience progressive discomfort related to pain, joint stiffness, and general tiredness. The most common treatment of these conditions includes use of non-steroidal anti-inflammatory drugs (NSAIDS). However, efficacy of NSAID treatment is generally not completely satisfactory. Therefore, further improvements in management of OA-associated discomfort are needed. Aim. The aim was to verify whether a betalain-rich red beet extract at dose range of 35-100 mg twice per day could reduce discomfort associated with osteoarthritis (OA) conditions. Materials and methods. Study participants experiencing OA symptoms were treated with red beet extract (RBE) twice per day for exactly ten days. McGill and Energy Score data were evaluated at days 1, 5 and 10. The serum levels of advanced oxidation protein products (AOPP) were measured using a commercial kit (Cell Biolabs, Inc., #STA318). Sera from volunteers treated with RBE were subjected to a cytokines and chemokines array as offered by Qynsys Inc. Results. Collected data showed that ingestion of RBE for 10 days reduced McGill scores in a time- and dose-dependent manner with maximum 33% reduction as compared to the first day of the treatment. Interestingly, due to the treatment, serum levels of TNF-alpha were reduced in subjects whose serum TNF-alpha was greater than 1 pg/mL prior to initiation of the treatment. It was also found that serum levels of AOPP (proteins oxidized by hypochlorous acid/hypochlorites) were reduced by up to 48% after 10 days of the treatment. Conclusions. This study showed that ingestion of RBE, at dosages greater than 35 mg, had a beneficial effect on pain associated with OA conditions. RBE may act by inhibiting protein oxidation typically induced by hypochlorous acid released from active neutrophils.
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) | 0.003 | 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".