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Record W4392433383 · doi:10.1016/j.matbio.2024.02.009

The role of positional information in determining dermal fibroblast diversity

2024· article· en· W4392433383 on OpenAlexafffund
Pratyusha Chitturi, Andrew Leask

Bibliographic record

VenueMatrix Biology · 2024
Typearticle
Languageen
FieldMedicine
TopicDermatologic Treatments and Research
Canadian institutionsUniversity of Saskatchewan
FundersCanadian Institutes of Health Research
KeywordsDermisConnective tissueExtracellular matrixScarsFibrosisFibroblastPathologyDermal fibroblastConnective tissue diseaseMyofibroblastScleroderma (fungus)Epidermis (zoology)Cell biologyBiologyMedicineAnatomyDiseaseAutoimmune diseaseCell culture

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.179
Threshold uncertainty score0.080

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.009
GPT teacher head0.306
Teacher spread0.297 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations12
Published2024
Admission routes2
Has abstractyes

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