Microneedle system for tissue engineering and regenerative medicines: a smart and efficient therapeutic approach
Bibliographic record
Abstract
The global demand for an enhanced quality of life and extended lifespan has driven significant advancements in tissue engineering and regenerative medicine. These fields utilize a range of interdisciplinary theories and techniques to repair structurally impaired or damaged tissues and organs, as well as restore their normal functions. Nevertheless, the clinical efficacy of medications, materials, and potent cells used at the laboratory level is always constrained by technological limitations. A novel platform known as adaptable microneedles has been developed to address the abovementioned issues. These microneedles offer a solution for the localized distribution of various cargos while minimizing invasiveness. Microneedles provide favorable patient compliance in clinical settings due to their effective administration and ability to provide a painless and convenient process. In this review article, we summarized the most recent development of microneedles, and we started by classifying various microneedle systems, advantages, and fundamental properties. Subsequently, it provides a comprehensive overview of different types of microneedles, the material used to fabricate microneedles, the fundamental properties of ideal microneedles, and their applications in tissue engineering and regenerative medicine, primarily focusing on preserving and restoring impaired tissues and organs. The limitations and perspectives have been discussed by concluding their future therapeutic applications in tissue engineering and regenerative medicines.
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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 | 0.001 |
| 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 | 0.001 |
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".