MicroRNA/mRNA regulatory networks in the control of skin development and regeneration
Bibliographic record
Abstract
Skin development, postnatal growth and regeneration are governed by complex and well-balanced programs of gene activation and silencing. The crosstalk between small non-coding microRNAs (miRNAs) and mRNAs is highly important for steadiness of signal transduction and transcriptional activities as well as for maintenance of homeostasis in many organs, including the skin. Recent data demonstrated that the expression of many genes, including cell type-specific master transcription regulators implicated in the control of skin development and homeostasis, is regulated by miRNAs. In addition, individual miRNAs could mediate the effects of these signaling pathways through being their downstream components. In turn, the expression of a major constituent of the miRNA processing machinery, Dicer, can be controlled by cell type-specific transcription factors, which form negative feedback loop mechanisms essential for the proper execution of cell differentiation- associated gene expression programs and cell-cell communications during normal skin development and regeneration. This review summarizes the available data on how miRNA/mRNA regulatory networks are involved in the control of skin development, epidermal homeostasis, hair cycle-associated tissue remodeling and pigmentation. Understanding of the fundamental mechanisms that govern skin development and regeneration will contribute to the development of new therapeutic approaches for many pathological skin conditions by using miRNA-based interventions.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".