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Record W2783904970 · doi:10.1089/jmf.2017.4034

The Combination of Probiotic Complex, Rosavin, and Zinc Improves Pain and Cartilage Destruction in an Osteoarthritis Rat Model

2018· article· en· W2783904970 on OpenAlexaff
Ji Ye Kwon, Seung‐Hoon Lee, JooYeon Jhun, JeongWon Choi, Kyung‐Ah Jung, Keun Hyung Cho, Seok Jung Kim, Chul Woo Yang, Sung‐Hwan Park, Mi‐La Cho

Bibliographic record

VenueJournal of Medicinal Food · 2018
Typearticle
Languageen
FieldMedicine
TopicOsteoarthritis Treatment and Mechanisms
Canadian institutionsImpact
Fundersnot available
KeywordsProinflammatory cytokineOsteoarthritisCartilageProbioticInflammationMedicineAnabolismCatabolismInternal medicinePharmacologyEndocrinologyPathologyBiologyAnatomyMetabolism

Abstract

fetched live from OpenAlex

Osteoarthritis (OA), a degenerative disorder, induces pain, joint inflammation, and destruction of the articular cartilage matrix. Probiotic complex, rosavin, and zinc have been used as dietary supplements that exhibit anti-inflammatory and antioxidant properties. However, there is no evidence demonstrating a synergic effect in OA. This study aims to determine whether combination with probiotic complex, rosavin, and zinc decreases progression of monosodium iodoacetate (MIA)-induced OA rat model. The combination improved pain levels by preventing cartilage damage. The expression of proinflammatory cytokines and catabolic factors was reduced by the combination within the joint tissue. However, the combination increased anti-inflammatory cytokines as well as the anabolic factor production. The gene level of catabolic factors was decreased with treatment of the combination in chondrocytes isolated from OA patients. These results suggest that the combination can improve MIA development through the inhibition of proinflammatory cytokines and cartilage destruction, thus playing a key role as a therapeutic candidate for OA treatment.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.018
GPT teacher head0.260
Teacher spread0.242 · 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 source (direct Gemma or distilled Codex), 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

Citations37
Published2018
Admission routes1
Has abstractyes

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