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Record W2802810292 · doi:10.1177/0271678x18773871

MicroRNA-based therapeutics in central nervous system injuries

2018· review· en· W2802810292 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Cerebral Blood Flow & Metabolism · 2018
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicroRNA in disease regulation
Canadian institutionsUniversity of Alberta
FundersNational Institute of Neurological Disorders and Stroke
KeywordsmicroRNACentral nervous systemMedicineNervous systemNeuroscienceComputational biologyBioinformaticsBiologyGeneticsGene

Abstract

fetched live from OpenAlex

Central nervous system (CNS) injuries, such as stroke, traumatic brain injury (TBI) and spinal cord injury (SCI), are important causes of death and long-term disability worldwide. MicroRNA (miRNA), small non-coding RNA molecules that negatively regulate gene expression, can serve as diagnostic biomarkers and are emerging as novel therapeutic targets for CNS injuries. MiRNA-based therapeutics include miRNA mimics and inhibitors (antagomiRs) to respectively decrease and increase the expression of target genes. In this review, we summarize current miRNA-based therapeutic applications in stroke, TBI and SCI. Administration methods, time windows and dosage for effective delivery of miRNA-based drugs into CNS are discussed. The underlying mechanisms of miRNA-based therapeutics are reviewed including oxidative stress, inflammation, apoptosis, blood-brain barrier protection, angiogenesis and neurogenesis. Pharmacological agents that protect against CNS injuries by targeting specific miRNAs are presented along with the challenges and therapeutic potential of miRNA-based therapies.

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.

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 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 score0.977
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
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 head0.017
GPT teacher head0.276
Teacher spread0.260 · 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