Duo-role Platelet-rich Plasma: temperature-induced fibrin gel and growth factors’ reservoir for microneedles to promote hair regrowth
Bibliographic record
Abstract
INTRODUCTION: Alopecia concerns more than half our adult population. Platelet-rich plasma (PRP) has been applied in skin rejuvenation and hair loss treatment. However, the pain and bleeding during injection and the troublesome for fresh preparation of each action limit PRP's in-depth applying dedication to clinics. OBJECTIVES: We report a temperature-sensitive PRP induced fibrin gel included in a detachable transdermal microneedle (MN) for hair growth. RESULTS: PRP gel interpenetrated with the photocrosslinkable gelatin methacryloyl (GelMA) to realize sustained release of growth factors (GFs) and led to 14% growth in mechanical strength of a single microneedle whose strength reached 1.21 N which is sufficient to penetrate the stratum corneum. PRP-MNs' release of VEGF, PDGF, and TGF-β were characterized and quantitatively around the hair follicles (HFs) for 4-6 days consecutively. PRP-MNs promoted hair regrowth in mice models. From transcriptome sequencing, PRP-MNs induced hair regrowth through angiogenesis and proliferation. The mechanical and TGF-β sensitive gene Ankrd1 was significantly upregulated by PRP-MNs treatment. CONCLUSION: PRP-MNs show convenient, minimally invasive, painless, inexpensive manufacture, storable and sustained effects in boosting hair regeneration.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".