Bottom‐up Nanoencapsulation from Single Cells to Tunable and Scalable Cellular Spheroids for Hair Follicle Regeneration
Bibliographic record
Abstract
Cell surface engineering technology advances cell therapeutics and tissue engineering by accurate micro/nanoscale control in cell-biomaterial ensembles and cell spheroids formation. By tailoring cell surface, microgels can encapsulate cells for versatile uses. However, microgels are coated in a thick layer to house multiple cells together but not a single cell based. Besides, excessive deposition on cell surface is detrimental to cellular functions. Herein, layer-by-layer (LbL) self-assembly to encapsulate single cell using nanogel is reported, owing to its security and tunable thickness at nanoscale, and further forms cell spheroids by physical cross-linking on nanogel-coated cells for delivery. A hair follicle (HF) regeneration model where the dermal papilla cells (DPCs) are given a 3D installation to maintain its ability of HF induction during in vitro culture is studied. Dermal papilla (DP) spheroids are optimized and that LbL-DPCs aggregation is akin to primary DP is demonstrated. The markers ALP, Versican, and NCAM are examined to investigate that high-passaged (P8) DP spheroids can restore the hair induction potential, which are lost in 2D culture. New HFs are regenerated successfully by implantation of DP spheroids in vivo.
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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) | 0.001 | 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".