Development of Reliable and High‐Throughput Human Biomimetic Cartilage and Bone Models to Explore Senescence and Personalized Osteoarthritis Treatment Options
Bibliographic record
Abstract
To facilitate effective preclinical testing of senescence treatments for osteoarthritis (OA), we have created reliable biomimetic and high-throughput models using aged human joint tissues. Moreover, concerns regarding scalability led to the concurrent development of a high-throughput human in vitro senescence cartilage organoid model. Osteochondral explants and cells for the cartilage organoid model were isolated from patients undergoing joint replacement surgery due to OA. To induce senescence, explants and organoids were subjected to radiation and/or mechanical loading. Samples were harvested; gene expression of relevant senescent and cartilage genes was measured using RT-qPCR, and protein expression was evaluated using histology. A general senescence phenotype was induced by the perturbations, as shown by senescence-associated β-galactosidase staining. In-depth gene expression analysis revealed that hyperphysiological mechanical loading upregulated gene expression of IL8 and SERPINE1, representing aspects of a senescence-associated secretory phenotype (SASP) profile. Irradiation upregulated CDKN1A, encoding p21, and downregulated LMNB1, representing a cell cycle arrest profile with the absence of a SASP response. Combining the two perturbations showed upregulation of CDKN1A, IL8, and SERPINE and downregulation of LMNB1, representing a complementary senescence model. The high-throughput human in vitro cartilage organoid senescence model showed similar effects to the irradiation explant model. In this study, we present a variety of senescence models of human aged chondrocytes that allows for rapid initial screening of anti-senescence compounds in high-throughput, as well as in-depth, characterization of post-mitotic aged chondrocytes prone to OA pathophysiology. This research advances the development of essential therapeutics for OA.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
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".