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Record W2985424353 · doi:10.1016/j.coph.2019.10.004

Implementation of pre-clinical methodologies to study fibrosis and test anti-fibrotic therapy

2019· review· en· W2985424353 on OpenAlexfundno aff
Fiona Oakley, Lucy Gee, Neil Sheerin, Lee A. Borthwick

Bibliographic record

VenueCurrent Opinion in Pharmacology · 2019
Typereview
Languageen
FieldMedicine
TopicTissue Engineering and Regenerative Medicine
Canadian institutionsnot available
FundersNational Institute for Health Research Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of OphthalmologyMedical Research Council CanadaNIHR Newcastle Biomedical Research CentreMedical Research CouncilNational Institute for Health and Care Research
KeywordsFibrosisClinical trialMedicinePreclinical testingPulmonary fibrosisIntensive care medicineBioinformaticsPathologyMedical physicsBiology

Abstract

fetched live from OpenAlex

Diseases where fibrosis plays a major role accounts for enormous morbidity and mortality and yet we have very little in our therapeutic arsenal despite decades of research and clinical trials. Our understanding of fibrosis biology is primarily built on data generated in conventional mono-culture or co-culture systems and in vivo model systems. While these approaches have undoubtedly enhanced our understanding of basic mechanisms, they have repeatedly failed to translate to clinical benefit. Recently, there had been a push to generate more physiologically relevant platforms to study fibrosis and identify new therapeutic targets. Here we review the state-of-the-art regarding the development and application of 3D complex cultures, bio-printing and precision cut slices to study pulmonary, hepatic and renal fibrosis.

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)0.001
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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.969
Threshold uncertainty score0.925

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.437
GPT teacher head0.619
Teacher spread0.182 · 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 designOther design
Domainnot available
GenreReview

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

Citations17
Published2019
Admission routes1
Has abstractyes

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