Enhancing metabolic activity and differentiation potential in adipose mesenchymal stem cells via high-resolution surface-acoustic-wave contactless patterning
Bibliographic record
Abstract
Acoustofluidics has shown great potential for label-free bioparticle patterning with excellent biocompatibility. Acoustofluidic patterning enables the induction of cell-cell interactions, which play fundamental roles in organogenesis and tissue development. One of the current challenges in tissue engineering is not only the control of the spatial arrangement of cells but also the preservation of cell patterns over time. In this work, we developed a standing surface acoustic wave-based platform and demonstrated its capability for the well-controlled and rapid cell patterning of adipose-derived mesenchymal stem cells in a high-density homogenous collagen hydrogel. This biocompatible hydrogel is easily UV crosslinked and can be retrieved within 3 min. Acoustic waves successfully guided the cells toward pressure nodal lines, creating a contactless alignment of cells in <5 s in culture media and <1 min in the hydrogel. The acoustically patterned cells in the hydrogel did not show a decrease in cell viability (>90%) 48 h after acoustic induction. Moreover, 45.53% and 30.85% increases in metabolic activity were observed in growth and differentiation media, respectively, on Day 7. On Day 14, a 32.03% change in metabolic activity was observed using growth media, and no significant difference was observed using differentiation media. The alkaline phosphatase activity showed an increase of 80.89% and 24.90% on Days 7 and 14, respectively, for the acoustically patterned cells in the hydrogel. These results confirm the preservation of cellular viability and improved cellular functionality using the proposed high-resolution acoustic patterning technique and introduce unique opportunities for the application of stem cell regenerative patches for the emerging field of tissue engineering.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".