The Application of Multiple Biophysical Cues to Engineer Functional Neocartilage for Treatment of Osteoarthritis. Part I: Cellular Response
Bibliographic record
Abstract
Osteoarthritis (OA) is a complex disease of the joint for which current treatments are unsatisfactory, thus motivating development of tissue engineering (TE)-based therapies. To date, TE strategies have had some success, developing replacement tissue constructs with biochemical properties approaching that of native cartilage. However, poor biomechanical properties and limited postimplantation integration with surrounding tissue are major shortcomings that need to be addressed. Functional tissue engineering strategies that apply physiologically relevant biophysical cues provide a platform to improve TE constructs before implantation. In the previous decade, new experimental and theoretical findings in cartilage biomechanics and electromechanics have emerged, resulting in an increased understanding of the complex interplay of multiple biophysical cues in the extracellular matrix of the tissue. The effect of biophysical stimulation on cartilage, and the resulting chondrocyte-mediated biosynthesis, remodeling, degradation, and repair, has, therefore, been extensively explored by the TE community. This article compares and contrasts the cellular response of chondrocytes to multiple biophysical stimuli, and may be read in conjunction with its companion paper that compares and contrasts the subsequent intracellular signal transduction cascades. Mechanical, magnetic, and electrical stimuli promote proliferation, differentiation, and maturation of chondrocytes within established dose parameters or "biological windows." This knowledge will provide a framework for ongoing studies incorporating multiple biophysical cues in TE functional neocartilage for treatment of 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".