Emerging microfluidics-enabled platforms for osteoarthritis management: from benchtop to bedside
Bibliographic record
Abstract
Osteoarthritis (OA) is a prevalent debilitating age-related joint degenerative disease.It is a leading cause of pain and functional disability in older adults.Unfortunately, there is no cure for OA once the damage is established.Therefore, it promotes an urgent need for early detection and intervention of OA.Theranostics, combining therapy and diagnosis, emerges as a promising approach for OA management.However, OA theranostics is still in its infancy.Three fundamental needs have to be firstly fulfilled: i) a reliable OA model for disease pathogenesis investigation and drug screening, ii) an effective and precise diagnostic platform, and iii) an advanced fabrication approach for drug delivery and therapy.Meanwhile, microfluidics emerges as a versatile technology to address each of the needs and eventually boost the development of OA theranostics.Therefore, this review focuses on the applications of microfluidics, from benchtop to bedside, for OA modelling and drug screening, early diagnosis, and clinical therapy.We first introduce the basic pathophysiology of OA and point out the major unfilled research gaps in current OA management including lack of disease modelling and drug screening platforms, early diagnostic modalities and disease-modifying drugs and delivery approaches.Accordingly, we then summarize the state-of-the-art microfluidics technology for OA management from in vitro modelling and diagnosis to therapy.Given the existing promising results, we further discuss the future development of microfluidic platforms towards clinical translation at the crossroad of engineering and biomedicine.
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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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".