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Record W4416221382 · doi:10.1302/1358-992x.2025.13.047

FUNCTIONAL PET/MRI OF KNEE SYNOVIAL MACROPHAGE ACTIVITY BEFORE AND AFTER TOTAL KNEE ARTHROPLASTY

2025· article· en· W4416221382 on OpenAlexaff
Zachary J. Koudys, Matthew G. Teeter, Brent A. Lanting, Jake J. Thiessen, C. Thomas Appleton

Bibliographic record

VenueOrthopaedic Proceedings · 2025
Typearticle
Languageen
FieldMedicine
TopicOsteoarthritis Treatment and Mechanisms
Canadian institutionsWestern University
Fundersnot available
KeywordsOsteoarthritisInflammationCD68Magnetic resonance imagingMacrophageArthroplastyTotal knee arthroplastyKnee JointSynovial joint

Abstract

fetched live from OpenAlex

Total knee arthroplasty (TKA) is a surgery with high success rates and good patient outcomes. However, 6.9% of knee replacement surgeries are revisions of old implants due to ongoing pain, stiffness, and loosening. Revision TKA is more expensive and has worse patient outcomes. Recent studies have begun to explore the role of the inflammatory response in poor TKA outcomes. Inflammation is an important predictor of pain in osteoarthritis (OA) and synovial inflammation may play a role in knee stiffness after TKA. Macrophages are the dominant immune cells of the synovium and regulate knee inflammation, and when activated, they upregulate mitochondrial translocator protein (TSPO) expression. Current standard of care features Magnetic Resonance Imaging (MRI) to assess structural changes in the joint. Positron Emission Tomography (PET) can be used to image important biological processes at the cellular level. [18F]FEPPA is a PET tracer that targets TSPO with high specificity. The goal of this work was to validate the use of [18F]FEPPA PET/MRI in the assessment of macrophage activation in knee synovial tissue. This method may allow non-invasive imaging of important inflammatory processes and understand the role of activated macrophages in ongoing pain, stiffness, and loosening after TKA . To validate the use of [18F]FEPPA in imaging activated macrophages in vivo, synovial tissue was gathered from 12 participants with end stage OA who underwent primary TKA. Knee synovial tissue was sectioned and embedded on slides. Tissue sections were incubated in [18F]FEPPA and imaged by autoradiography for 6 hours. Adjacent tissue sections were incubated with DAPI, CD68 antibody, and TSPO antibody for immunofluorescent analysis of the actual macrophage activation. 6 of the full cohort were imaged in a 3T hybrid PET/MRI after injection with [18F]FEPPA and a 45-minute uptake period. MR of both knee joints was performed with sequences including 3D Dual Echo Steady State (DESS), Bilateral T1 weighted and Fast Spin Echo (FSE) with metal artifact reduction if contralateral TKA was present. Standardized Uptake Values (SUV) were calculated from the measured PET signal, injected dose, and patient mass. [18F]FEPPA tracer uptake calculated from autoradiography correlated to the true macrophage activation measured through TSPO+ immunofluorescence with r = 0.85 and p = 0.0029. SUV calculated from PET/MRI was correlated to true macrophage activation found through immunofluorescence with r = 0.90 and p = 0.083. Attenuation correction maps corrected for metal artefacts enabled visualization and measurement of tracer uptake surrounding the femoral and tibial components. Understanding the role of knee joint inflammation may be important in managing pain and stiffness after TKA. The underlying cause of poor TKA outcomes are often unclear.[18F]FEPPA PET/MRI uptake in knee synovial tissue was validated to correlate to the true macrophage activity. This tool may be able to diagnosis a cellular response as the cause of pain or dissatisfaction after TKA. Future work using [18F]FEPPA PET/MRI could assess different reaction types to metal or plastic implant debris, joint stiffness and fibrosis, and periprosthetic infection.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.318
Threshold uncertainty score0.895

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.005
GPT teacher head0.218
Teacher spread0.213 · 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 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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Citations0
Published2025
Admission routes1
Has abstractyes

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