MétaCan
Menu
Back to cohort
Record W2131239755 · doi:10.1089/107999000312540

Transforming Growth Factor- <i>beta</i> mRNA and Protein in Hypertrophic Scar Tissues and Fibroblasts: Antagonism by IFN- <i>alpha</i> and IFN- <i>gamma</i> <i>In Vitro</i> and <i>In Vivo</i>

2000· article· en· W2131239755 on OpenAlexaff
Edward E. Tredget, Rijian Wang, Qiong Shen, Paul G. Scott, Aziz Ghahary

Bibliographic record

VenueJournal of Interferon & Cytokine Research · 2000
Typearticle
Languageen
FieldMedicine
TopicWound Healing and Treatments
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsWound healingFibroblastCytokineTransforming growth factorMolecular biologyIn vitroIn vivoHypertrophic scarTransforming growth factor betaChemistryFibrosisHydroxyprolineGrowth factorImmunologyBiologyPathologyEndocrinologyMedicineBiochemistryReceptor

Abstract

fetched live from OpenAlex

Hypertrophic scarring (HSc) following burn injury is a common, disfiguring, and functionally limiting form of dermal fibrosis, compromising recovery. Previously, elevated levels of transforming growth factor-beta1 (TGF-beta1), a fibrogenic cytokine, were found in wounds and serum of severely injured patients, antagonized in part by treatment with systemic interferon-alpha2b (IFN-alpha2b) both in vitro and in vivo. It is hypothesized that in wound healing after injury, platelets are an initial source of TGF-beta, but wound fibroblasts may be capable, after activation, of autoamplification of the initial response to injury by increasing TGF-beta mRNA and protein that may subsequently be responsive to IFN therapy with IFN-alpha or IFN-gamma or both. Using three pairs of site-matched HSc and normal fibroblasts from the same individuals, nonconfluent and near confluent fibroblasts were treated with TGF-beta, and cell proliferation and collagen production were assayed using cell counting and 18O2 isotopic uptake into hydroxyproline before analysis by gas chromatography-mass spectrometry (GC-MS). HSc and normal fibroblasts were assayed for the production of TGF-beta protein secretion using ELISA for TGF-beta1, TGF-beta2, and TGF-beta3 after acidification of medium samples from 96-h cultures. HSc and normal fibroblasts were treated with IFN-alpha2b or IFN-gamma or both for 96 h. Quantitative RT-PCR and Northern analysis were performed using newly synthesized internal standards for human TGF-beta1. TGF-beta stimulates both HSc and normal fibroblast proliferation. Collagen synthesis is greater in HSc than in normal fibroblasts and is maximally stimulated at 75 pM TGF-beta. TGF-beta stimulated collagen metabolism is antagonized by IFN-alpha or IFN-gamma or both in an additive fashion. HSc and normal fibroblasts not only possess the mRNA for TGF-beta1 but also secrete mature TGF-beta protein. Treatment of HSc and normal fibroblasts with IFN-alpha2b or IFN-gamma antagonizes TGF-beta protein production, and additive effects occur. RT-PCR demonstrates that after IFN treatment, downregulation of TGF-beta1 mRNA accounts in part for the reduction in protein secretion in HSc fibroblasts. Elevations of systemic TGF-beta may be due to wound fibroblasts. TGF-beta synthesis and antagonism of fibroblast TGF-beta protein secretion occurs with either IFN-alpha or IFN-gamma, in part by downregulation of TGF-beta1 mRNA levels.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.025
GPT teacher head0.314
Teacher spread0.290 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations110
Published2000
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Interferon & Cytokine ResearchSame topicWound Healing and TreatmentsFrench-language works237,207