Cytokine loaded layer-by-layer ultrathin matrices to deliver single dermal papilla cells for spot-by-spot hair follicle regeneration
Bibliographic record
Abstract
The dermal papilla cell (DPC) is a type of highly specialized mesenchymal cells located in hair follicles (HF). Due to the primary role in the epithelial-mesenchymal interaction that enables hair-follicle morphogenesis and hair cycling, DPC has become an attractive cell source for hair regeneration to treat alopecia patients. However, DPCs tend to lose their function during in vitro culture. Hence, there exists a clear need to develop a microenvironment that can recapitulate the interactions within the native milieu of DPCs. Since layer-by-layer (LBL) nano-coating with biocompatible materials on the cell surface displays the versatility with tunable loading and release properties, which can provide a remodeled microenvironment for regulating cell function. Here, we developed a LBL self-assembly technique to single DPCs to create a nano-scale ultrathin extracellular matrix (ECM). We studied that the single cell-based LBL-encapsulation would not impact the viability, morphology, proliferation and intrinsic properties of DPCs using Western blot and mRNA expressions of β-catenin, ALP and α-SMA. We then included fibroblast growth factor-2 (FGF-2) into the LBL nano-structure to regulate the DPC function. Finally, we performed in vivo hair reconstitution assays using LBL-encapsulated DPCs combined with freshly isolated epidermal cells (EPCs) and found this strategy can treat hair loss.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 teacher head, 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".