Influence of interleukin‐1α on androgen receptor expression and cytokine secretion by cultured human dermal papilla cells
Bibliographic record
Abstract
Dermal papilla cells (DPC) control the growth character of the hair follicle through their elaboration of mitogenic factors and extracellular matrix components. Further, the dermal papilla is a primary site of androgen action in the hair follicle. Interleukin-1alpha (IL-1alpha) is prominent in skin wounding and inflammatory responses although regarded as a negative hair growth regulator. We studied the effect of IL-1alpha and the potent androgen 5alpha-dihydrotestosterone (DHT) on the expression of the androgen receptor (AR) and various factors secreted by cultured human temporal scalp DPC. IL-1alpha triggered cellular changes consistent with nuclear factor-kappaB pathway activation as well as reduced AR mRNA and protein expression levels for DHT-stimulated DPC. This cytokine also increased DPC supernatant keratinocyte growth factor (KGF), vascular endothelial growth factor (VEGF), IL-8 and granulocyte-macrophage colony-stimulating factor (GM-CSF) concentrations. IL-1alpha did not influence DPC supernatant levels of transforming growth factor-beta1, a negative hair growth regulator. The stimulatory effect of IL-1alpha on DPC VEGF, GM-CSF, KGF, and IL-8 expression was also evident at the mRNA level for these cytokines. IL-1alpha also increased mRNA transcript levels of protease-nexin-1, a secreted serine protease inhibitor expressed in the dermal papilla of anagen-stage hair follicles. Although DHT did not affect supernatant cytokine concentrations, the androgen altered mRNA transcript levels of several factors for DPC co-stimulated with IL-1alpha. In consideration of its in vitro activity profile, IL-1alpha may be an important modifier of dermal papilla activity as well as potentially influence androgen-regulated gene expression in DPC.
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 | 0.000 |
| 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) | 0.002 | 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".