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E045 Role of Thrombospondin-4 in pain sensitisation of knee osteoarthritis in relation to semi-automated joint space measures in routine radiographs

2025· article· en· W4409899228 on OpenAlexaboutno aff
Soraya Koushesh, Kuti Seyi-Taylor, Andisheh Niakan, Richard Ljuhar, Nidhi Sofat, Franklyn A. Howe

Bibliographic record

VenueLara D. Veeken · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAngiogenesis and VEGF in Cancer
Canadian institutionsnot available
Fundersnot available
KeywordsOsteoarthritisRadiographyMedicineKnee JointJoint (building)OrthodonticsRelation (database)RadiologySurgeryComputer sciencePathologyData miningEngineering

Abstract

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Abstract Background/Aims Pain and stiffness in knee osteoarthritis (OA) is linked to inflammation and structural damage. The source of OA pain is not fully established and a better understanding is required for individually optimised treatments. Many patients cycle through treatments including analgesics and steroid injections but those with pain sensitisation may be less responsive to conventional therapies. Thrombospondin-4 (TSP-4) is a glycoprotein found in the extra-cellular matrix and neuromuscular junction. Its increased expression promotes neuropathic pain and central sensitisation in rodents, and is found elevated in cartilage and bone marrow lesions (BMLs) in humans with knee OA. We hypothesise elevated TSP-4 results in pain sensitisation and we investigate how variation in pain relates to serum TSP-4, clinical variables and joint space (JS) measures. Methods TSP-4 was measured in serum samples from participants with knee OA patients using ELISA (ELH-TSP-4, RayBiotech, Tebubio, UK ) with full informed consent, along with age, sex, body mass index (BMI), Hospital Anxiety and Depression Scale (HADS), Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) subscales of pain and stiffness, and PainDETECT scores. Kellgren-Lawrence grade (KLG) and JS were measured semi-automatically from medial and lateral regions using plain OA knee radiographs. A general linear model (GLM) with age, BMI and HADS as base covariates, was used to investigate which of sex, TSP-4 and JS parameters contributed significantly to variability in WOMAC and painDETECT scores. Results Of 93 patients studied, 25 were male, 68 female, with mean [range] scores: WOMAC pain 54.9 [2, 98]; WOMAC Stiffness 59.3 [0, 99.5]; painDETECT 12.7 [0, 28)]. Table 1 shows parameter ranges for other variables used in the GLM and those significantly contributing to variability in WOMAC and painDETECT scores. Conclusion TSP-4 was a highly significant (p < 0.001) contributor to variability in pain and sensitisation, and to stiffness. Sex and JS were additional factors affecting stiffness. TSP-4 is a potential biomarker for objectively quantifying pain along with structural measures from imaging. Our work suggests that people with OA and pain sensitisation can be stratified using serum TSP-4, which could aid in better disease management and clinical decision-making for future OA treatments. Disclosure A. Law: None. S. Koushesh: None. A. Harrison: None. K. Seyi-Taylor: None. A. Niakan: None. R. Ljuhar: Corporate appointments; CEO ImageBiopsy Lab. Shareholder/stock ownership; Shareholder of ImageBiopsy Lab. N. Sofat: None. F.A. Howe: None.

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)0.004
metaresearch head score (Gemma)0.008
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.008
GPT teacher head0.238
Teacher spread0.230 · 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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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