Clinical Relevance of Elastin in the Structure and Function of Skin
Bibliographic record
Abstract
Elastin is the main component of elastic fibers, which provide stretch, recoil, and elasticity to the skin. Normal levels of elastic fiber production, organization, and integration with other cutaneous extracellular matrix proteins, proteoglycans, and glycosaminoglycans are integral to maintaining healthy skin structure, function, and youthful appearance. Although elastin has very low turnover, its production decreases after individuals reach maturity and it is susceptible to damage from many factors. With advancing age and exposure to environmental insults, elastic fibers degrade. This degradation contributes to the loss of the skin's structural integrity; combined with subcutaneous fat loss, this results in looser, sagging skin, causing undesirable changes in appearance. The most dramatic changes occur in chronically sun-exposed skin, which displays sharply altered amounts and arrangements of cutaneous elastic fibers, decreased fine elastic fibers in the superficial dermis connecting to the epidermis, and replacement of the normal collagen-rich superficial dermis with abnormal clumps of solar elastosis material. Disruption of elastic fiber networks also leads to undesirable characteristics in wound healing, and the worsening structure and appearance of scars and stretch marks. Identifying ways to replenish elastin and elastic fibers should improve the skin's appearance, texture, resiliency, and wound-healing capabilities. However, few therapies are capable of repairing elastic fibers or substantially reorganizing the elastin/microfibril network. This review describes the clinical relevance of elastin in the context of the structure and function of healthy and aging skin, wound healing, and scars and introduces new approaches being developed to target elastin production and elastic fiber formation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".