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Record W2246863272 · doi:10.2174/15701611113119990010

Therapeutic Potential of microRNA Modulation in Pulmonary Arterial Hypertension

2015· review· en· W2246863272 on OpenAlexafffund
Jolyane Meloche, Roxane Paulin, Steeve Provencher, Sébastien Bonnet

Bibliographic record

VenueCurrent Vascular Pharmacology · 2015
Typereview
Languageen
FieldMedicine
TopicPulmonary Hypertension Research and Treatments
Canadian institutionsInstitut universitaire de cardiologie et de pneumologie de Québec
FundersCanadian Institutes of Health Research
KeywordsmicroRNAMedicineBMPR2DiseasePulmonary arteryBioinformaticsHypoxia (environmental)Cancer researchPulmonary hypertensionCardiologyInternal medicineGeneBiologyGenetics

Abstract

fetched live from OpenAlex

MicroRNAs have emerged as key players of gene regulation during development and disease states like cancer and cardiovascular diseases. Pulmonary arterial hypertension (PAH), a vascular disease characterized by pulmonary resistance and vessel occlusion, is not spared by microRNA implication. This is not surprising since PAH shares common aberrantly activated pathways with cancers that lead to proliferation and survival of pulmonary arterial smooth muscle cells, among others, within the artery wall and narrowing the lumen. Recent studies demonstrated the role of miR-204 and miR- 206 in pulmonary artery smooth muscle cell (PASMC) proliferation. Other microRNAs, such as miR-145, miR-21 and the miR17/92 cluster, have been associated with the disrupted BMPR2 pathway. During the last couple of years, the number of studies on the role of microRNA in PAH has broadened, defining it clearly as a HOT TOPIC. This current review presents an overview of the most recent knowledge as well as future possibilities. The use of microRNA therapies is still uncertain and poorly applied in the clinical setting yet. It is still critical to increase the knowledge and the translational potential of this HOT TOPIC to make it become a HOPE TOPIC.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.984
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.000
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 head0.109
GPT teacher head0.402
Teacher spread0.293 · 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.

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

Citations38
Published2015
Admission routes2
Has abstractyes

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