Advances in organ-on-a-chip systems for modelling joint tissue and osteoarthritic diseases
Bibliographic record
Abstract
Joint-on-a-chip (JOC) models are powerful tools that aid in osteoarthritis (OA) research. These microfluidic devices apply emerging organ-on-a-chip technology to recapitulate a multifaceted joint tissue microenvironment. JOCs address the need for advanced, dynamic in vitro models that can mimic the in vivo tissue environment through joint-relevant biomechanical or fluidic integration, an aspect that existing in vitro OA models lack. There are existing review articles on OA models that focus on animal, tissue explant, and two-dimensional and three-dimensional (3D) culture systems, including microbioreactors and 3D printing technology, but there has been limited discussion of JOC models. The aim of this article is to review recent developments in human JOC technology and identify gaps for future advancements. Specifically, mechanical stimulation systems that mimic articular movement, multi-joint tissue cultures that enable crosstalk, and systems that aim to capture aspects of OA inflammation by incorporating immune cells are covered. The development of an advanced JOC model that captures the dynamic joint microenvironment will improve testing and translation of potential OA therapeutics.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".