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Change of serum interleukin-23 levels and sacroiliac joint magnetic resonance imaging before and after treatment with recombinant human tumor necrosis factor-α receptor II IgG Fc fusion protein for injection in axial spondyloarthritis patients

2015· article· en· W3029326975 on OpenAlexaboutno aff
Peipei Su, Cundong Mi, Cheng Zhao, Zhanrui Chen

Bibliographic record

VenueChin J Rheumatol · 2015
Typearticle
Languageen
FieldMedicine
TopicAutoimmune and Inflammatory Disorders Research
Canadian institutionsnot available
Fundersnot available
KeywordsEtanerceptMedicineRheumatoid arthritisMagnetic resonance imagingPathogenesisTumor necrosis factor alphaGastroenterologyInternal medicineNuclear medicineAnalysis of varianceInterleukinSacroiliac jointAxial spondyloarthritisArthritisUrologyCytokineSurgeryRadiology

Abstract

fetched live from OpenAlex

Objective We investigated interleukin (IL)-23 that might play a role in the pathogenesis of rheumatoid arthritis (RA) and whether it was correlated with disease activity and clinical manifestations in axial spondyloarthritis(SpA). In addition, the Spondyloarthritis Research Consortium of Canada (SPARCC) scores was used to examine whether recombinant human tumor necrosis factor-α receptor Ⅱ IgG Fc fusion protein for injection (rhTNFR: Fc) was effective for the reduction of magnetic resonance imaging (MRI)-proven sacroiliac joint (SIJ) inflammation. Methods The serum IL-23 levels of etanercept and conven-tional treatment groups were measured using enzyme-linked immunosorbent assay kits. At the same time, SIJ MRI SPARCC score levels were assessed by MRI, the change of clinical indicators of patients was observed. ANOVA, repeated measure data of ANOVA and Spearman's correlation analysis were used for statisical analysis. Results ① The basal serum IL-23 levels of the Etanercept group were (34.2±1.8) pg/ml and those of the conventional treatment group were (34.1±1.8) pg/ml (F=1 073.790, P=0.991) , both were significantly higher than the healthy control group (18.1±0.8) pg/ml (P=0.005). After treatment, serum IL-23 levels of the rhTNFR: Fc treatment and conventional treatment group were (24.5±1.7) pg/ml and (25.2±1.7) pg/ml (F=232.488, P=0.242) , (19.2±0.8) pg/ml and (21.6±1.3) pg/ml (F=114.135, P=0.025) , (19.0±0.8) pg/ml and (19.4±0.8) pg/ml (F=23.374, P=0.085) respectively. A significant decrease was observed in the two groups and the serum level of IL-23 of the rhTNFR: Fc group was lower than that of the conventional group at week 12. ② SIJ MRI SPARCC scores of the rhTNFR: Fc group and the conventional drugs group were (20.1± 1.2) scores and (20.7±1.5) scores (F=2003.660, P=0.191) , (12.5±0.8) scores and (15.4±0.9) scores (F=1 680.430, P=0.004) , (8.8±0.9) scores and (12.8± 0.9) scores (F=972.877, P=0.002) , the scores of two group were significantly decreased after treatment. At week 12, 24 of the treatment, the rhTNFR: Fc group scores were lower than the conventional drugs group. ③ The serum IL-23 levels, SIJ MRI SPARCC scores and clinical index (ESR, CRP, PGA, BASDAI, BASFI, BASMI and number of painful joints) were not correlated (P>0.05), the SIJ MRI SPARCC scores and clinical indicators were not correlated (P>0.05). Conclusion ① The serum IL-23 levels of the rhTNFR: Fc are higher than healthy controls. ② rhTNFR: Fc treatment could significantly decrease IL-23 levels and improve the sacroiliac joint inflammation compared to conventional treatment. ③ SIJ MRI is a good assessment method for the detection of SpA sacroiliac joint inflammation. Key words: Arthritis; Interleukins; Sacroiliac joints; Magnetic resonance imaging

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0010.000

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 head0.024
GPT teacher head0.261
Teacher spread0.237 · 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 designObservational
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".

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Published2015
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