Development of a Three-Dimensional Bioartificial Shoulder Joint Implant Mimetic of Periprosthetic Joint Infection
Bibliographic record
Abstract
Postsurgical infections of the shoulder joint involving Cutibacterium acnes are difficult to diagnose and manage. Despite the devastating clinical complications and costly health care burden of joint infections, the scarcity of joint infection models was identified as an unmet need by the 2019 International Consensus on Orthopedic Infections. In this study, we have developed a novel 3D shoulder joint implant mimetic (S-JIM) that includes a surgical metal surface and supports a co-culture of C. acnes and patient-derived shoulder capsule fibroblasts. Our findings indicate the S-JIM can generate a near anaerobic interior environment that allows for C. acnes proliferation and elicits fibroblast cell lysis responses that are consistent with clinical reports of tissue necrosis. Using the S-JIM, we have provided proof-of-concept for the use of mass spectrometry in real-time detection of C. acnes joint infections during surgery. The S-JIM is the first in vitro cell culture-based biomimetic of periprosthetic joint infection (PJI) that provides a preclinical method for the rapid and reliable testing of novel anti-PJI interventions. Impact statement We have developed the first 3D laboratory biomimetic of the postsurgical human shoulder joint to study periprosthetic joint infections.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".