The role of positional information in determining dermal fibroblast diversity
Bibliographic record
Abstract
The largest mammalian organ, skin, consisting of a dermal connective tissue layer that underlies and supports the epidermis, acts as a protective barrier that excludes external pathogens and disseminates sensory signals emanating from the local microenvironment. Dermal connective tissue is comprised of a collagen-rich extracellular matrix (ECM) that is produced by connective tissue fibroblasts resident within the dermis. When wounded, a tissue repair program is induced whereby fibroblasts, in response to alterations in the microenvironment, produce new ECM components, resulting in the formation of a scar. Failure to terminate the normal tissue repair program causes fibrotic conditions including: hypertrophic scars, keloids, and the systemic autoimmune connective tissue disease scleroderma (systemic sclerosis, SSc). Histological and single-cell RNA sequencing (scRNAseq) studies have revealed that fibroblasts are heterogeneous and highly plastic. Understanding how this diversity contributes to dermal homeostasis, wounding, fibrosis, and cancer may ultimately result in novel anti-fibrotic therapies and personalized medicine. This review summarizes studies supporting this concept.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".