MétaCan
Menu
Back to cohort
Record W7115686939 · doi:10.3389/fnut.2025.1691328

Role of oral hyaluronic acid for joint health: insights from rat models and clinical trials

2025· article· en· W7115686939 on OpenAlexaboutno aff

Bibliographic record

VenueFrontiers in Nutrition · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProteoglycans and glycosaminoglycans research
Canadian institutionsnot available
Fundersnot available
KeywordsHyaluronic acidClinical trialJoint (building)Rat modelOsteoarthritisCartilage

Abstract

fetched live from OpenAlex

Background: Early studies have demonstrated the significant potential of hyaluronic acid (HA) in alleviating osteoarthritis (OA); however, the relationship between different molecular weights (MWs) and efficacy remains unclear. Methods: The rat model was used to evaluate the effects of different MWs of HA on OA and to identify the MW that was most effective in alleviating OA. Based on this, a clinical trial was conducted to verify the selected HA's clinical efficacy. Results: The results showed that HA significantly reduced joint swelling in rats, dramatically increased HA content in the serum and joint synovial fluid, decreased serum and joint synovial fluid levels of pro-inflammatory cytokines, and reduced the expression of matrix metalloproteinases (MMPs), inducible nitric oxide synthase (iNOS), and cyclooxygenase-2 (COX-2) when compared with the OA group, especially high-MW HA. Importantly, these protective roles may be attributed to the immune regulation of HA. Clinical trial results indicated that HA significantly decreased pain, stiffness, and physical function of Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) scores and had no significant impact on blood and urine indices. Conclusion: Our findings suggest that oral supplementation with HA can reduce the progression of arthritis, pain, and cartilage damage, and can be a new strategy to relieve joint discomfort.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.339
Threshold uncertainty score0.413

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.065
GPT teacher head0.385
Teacher spread0.320 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueFrontiers in NutritionSame topicProteoglycans and glycosaminoglycans researchFrench-language works237,207