Bibliographic record
Abstract
In recent years, the field of iron studies has expanded into sub-domains that investigate the regulation of this metal in various tissues including the heart, mucosal surfaces, tumours, and the skin. Iron homeostasis in the skin and the role of other non-hepatic cells in the regulation of iron are currently incompletely understood. This paper summarizes the role of iron in wound healing, highlights the importance of maintaining iron concentrations within an intermediate range to avoid toxicity or defects; and integrates the antimicrobial role, interactions, and regulation of various cell types. Notably, the autoregulation of hepcidin secretion by keratinocytes and recruited myeloid cells is described. Additionally, the potential therapeutic role of iron chelators in infection control and their mechanisms of action are explored. This paper aims to elucidate the relevance of local iron control in epidermal infections. Although some of the molecular details underlying this condition remain unclear, published data suggest that iron-regulating therapies are a promising treatment for the eradication of skin infections due to their wound-healing and immune-modulating potential.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".