Arginine Metabolic Disruption Impairs Hair Regeneration via ROS‐Mediated Inactivation of mTOR Signaling in Androgenetic Alopecia
Bibliographic record
Abstract
Androgenetic alopecia (AGA), a pervasive hair loss disorder, lacks effective therapies due to incomplete pathogenic understanding. Growing evidence suggests a connection between AGA and metabolic disorders. Leveraging unbiased serum metabolomics, a strikingly differentiated metabolic signature in AGA patients compared to healthy controls is identified, with arginine deficiency exhibiting the most pronounced reduction among all amino acids. Concomitant downregulation of the arginine transporter SLC7A1 and upregulation of arginine catabolic enzyme ARG2 in balding HFs are further identified, collectively driving localized arginine scarcity through impaired uptake and accelerated catabolism. This metabolic perturbation triggers pathological reactive oxygen species (ROS) accumulation in hair follicles (HFs), which, in turn, inhibits mTOR signaling and impairs HF regeneration. Conversely, arginine restoration via exogenous supplementation or inhibiting arginine-to-ornithine conversion with ARG2 siRNA rescues hair growth in both murine AGA model and cultured human HFs. Most importantly, a microneedle-based delivery system for targeted dermal arginine replenishment demonstrates robust therapeutic efficacy in humanized AGA models. This work establishes arginine insufficiency as a core pathogenic driver in AGA and validates localized metabolic correction as a promising clinical strategy.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".