Bone Marrow Mesenchymal Stromal Cell Treatment in Patients with Osteoarthritis Results in Overall Improvement in Pain and Symptoms and Reduces Synovial Inflammation
Bibliographic record
Abstract
Patients with late-stage Kellgren-Lawrence knee osteoarthritis received a single intra-articular injection of 1, 10, or 50 million bone marrow mesenchymal stromal cells (BM-MSCs) in a phase I/IIa trial to assess safety and efficacy using a broad toolset of analytical methods. Besides safety, outcomes included patient-reported outcome measures (PROMs): Knee Injury and Osteoarthritis Outcome Score (KOOS) and Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC); contrast-enhanced magnetic resonance imaging (MRI) for cartilage morphology (Whole Organ MRI Scores [WORMS]), collagen content (T2 scores), and synovitis; and inflammation and cartilage turnover biomarkers, all over 12 months. BM-MSCs were characterized by a panel of anti-inflammatory markers to predict clinical efficacy. There were no serious adverse events, although four patients had minor, transient adverse events. There were significant overall improvements in KOOS pain, symptoms, quality of life, and WOMAC stiffness relative to baseline; the 50 million dose achieved clinically relevant improvements across most PROMs. WORMS and T2 scores did not change relative to baseline. However, cartilage catabolic biomarkers and MRI synovitis were significantly lower at higher doses. Pro-inflammatory monocytes/macrophages and interleukin 12 levels decreased in the synovial fluid after MSC injection. The panel of BM-MSC anti-inflammatory markers was strongly predictive of PROMs over 12 months. Autologous BM-MSCs are safe and result in significant improvements in PROMs at 12 months. Our analytical tools provide important insights into BM-MSC dosing and BM-MSC reduction of synovial inflammation and cartilage degradation and provide a highly predictive donor selection criterion that will be critical in translating MSC therapy for osteoarthritis. Stem Cells Translational Medicine 2019;8:746&757.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 | 0.001 |
| 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".