MétaCan
Menu
Back to cohort
Record W3127372579 · doi:10.1139/cjpp-2020-0413

Role of prostaglandin E2 in allogeneic mesenchymal stem cell therapy for cardiac repair

2021· review· en· W3127372579 on OpenAlexaffvenue
Niketa Sareen, Abhay Srivastava, Sanjiv Dhingra

Bibliographic record

VenueCanadian Journal of Physiology and Pharmacology · 2021
Typereview
Languageen
FieldMedicine
TopicMesenchymal stem cell research
Canadian institutionsUniversity of ManitobaSt. Boniface Hospital
Fundersnot available
KeywordsMesenchymal stem cellMedicineCell therapyStem-cell therapyStem cellProstaglandin E2Regeneration (biology)BioinformaticsDiseaseRegenerative medicineImmunologyCardiologyIntensive care medicineInternal medicinePathologyCell biologyBiology

Abstract

fetched live from OpenAlex

Ischemic heart disease is among the primary causes of cardiovascular-related deaths worldwide. Conventional treatments including surgical interventions and medical therapies aid in preventing further damage to heart muscle but are unable to provide a permanent solution. In recent years, stem cell therapy has emerged as an attractive alternative to restore damaged myocardium after myocardial injury. Allogeneic (donor-derived) mesenchymal stem cells (MSCs) have shown great promise in preclinical and clinical studies, making them the most widely accepted candidates for cardiac cell therapy. MSCs promote cardiac repair by modulating host immune system and secreting various soluble factors, of which prostaglandin E2 (PGE2) is an important one. PGE2 plays a significant role in regulating cardiac remodeling following myocardial injury. In this review, we provide an overview of allogeneic MSCs as candidates for myocardial regeneration with a focus on the role of the PGE2/cyclooxygenase-2 (COX2) pathway in mediating these effects.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.002

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 head0.054
GPT teacher head0.368
Teacher spread0.314 · 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 designNot applicable
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

Citations3
Published2021
Admission routes2
Has abstractyes

Explore more

Same venueCanadian Journal of Physiology and PharmacologySame topicMesenchymal stem cell researchFrench-language works237,207