LncRNA MSTRG.14227.1 regulates the morphogenesis of secondary hair follicles in Inner Mongolia cashmere goats via targeting ADAMTS3 by sponging chi-miR-433
Bibliographic record
Abstract
The cashmere goat is a type of livestock primarily known for its cashmere. Cashmere has a soft hand feel and good luster. It is a vital raw material in the textile industry, possessing significant economic value. Improving the yield and quality of cashmere can accelerate the development of the cashmere industry and increase the incomes of farmers and herdsmen. The embryonic stage is the main stage of the formation of hair follicle structure, which directly affects the yield and quality of cashmere. With the rapid advancements in modern molecular technology and high-throughput sequencing, many signaling molecules have been identified as playing critical roles in hair follicle development. Long non-coding RNA (lncRNA), which lacks protein-coding ability and exceeds 200 nucleotides in length, has been discovered to play a role in hair follicle development. In this study, the lncRNA MSTRG.14227.1, which is associated with the morphogenesis of secondary hair follicles, was screened and identified based on previously established lncRNA expression profiles derived from skin tissues of cashmere goats at different embryonic stages. This lncRNA has been shown to inhibit the proliferation and migration of dermal fibroblasts. Furthermore, we confirmed through bioinformatics analysis and dual-luciferase reporter assays that lncRNA MSTRG.14227.1 can function as a sponge for chi-miR-433, thereby alleviating the inhibitory effect of chi-miR-433 on its target gene ADAMTS3. In conclusion, the results of this study suggest that lncRNA MSTRG.14227.1 can inhibit the morphogenesis of secondary hair follicles through the chi-miR-433/ADAMTS3 signaling axis.
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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".