Jagged1 and Epidermal Growth Factor Promoted Androgen-Suppressed Mouse Hair Growth In Vitro and In Vivo
Bibliographic record
Abstract
Recent studies have reported that T-reg cells are intimately linked with hair follicles in a stage-dependent manner and play an important role in hair follicle cycling and regeneration in murine skin. Further study revealed that T-reg cell’s regulation of hair follicle growth is through its preferential expression of the Notch ligand Jagged‐1 (Jag1), which facilitates hair follicle regeneration. However, the role of Jag1 in androgen-suppressed hair growth is yet to be investigated. In addition, although epidermal growth factor (EGF) is a mitogen for cells including skin cells, whether it works synergistically with Jag1 to enhance hair follicle development is unknown. The current study intended to investigate effects of topical application of Jag1 on androgen-suppressed hair growth, and to determine the potential synergistic effect of EGF and Jag1 in this process in vivo. Fifty mice were depilated at the dorsal back area to achieve synchronized anagen development, and randomly divided into five groups with the following topical treatments control for 14 days; testosterone to induce androgenetic alopecia; Jagged1 (testosterone + Jagged1); EGF (testosterone + EGF); and Jagged1+EGF (testosterone + Jagged1 + EGF). It was found that EGF and Jag1 by itself respectively, did not promote androgen-suppressed hair growth significantly. This stimulating effect was enhanced in the presence of both EGF and Jagged1 (p<0.05). The hair growth promoting effect was accompanied by better follicle growth, which is associated with increased cell proliferation in the hair follicle and altered the expression of genes that are important in hair follicular cell proliferation and differentiation. Our results provide insights into the therapeutic potential of these peptides for androgenetic alopecia.
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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 | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".