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Bone marrow-derived mesenchymal stem cell and simvastatin treatment leads to improved functional recovery and modified c-Fos expression levels in the brain following ischemic stroke.

2018· article· en· W2888338775 on OpenAlexaff
Gila Pirzad Jahromi, Alireza P. Shabanzadeh, Mina Mokhtari Hashtjini, Seyed Shahabeddin Sadr, Javad Rasouli Vani, Javad Raouf Sarshoori, Jason Charish

Bibliographic record

VenuePubMed · 2018
Typearticle
Languageen
FieldNeuroscience
TopicNeurological Disorders and Treatments
Canadian institutionsKrembil Foundation
Fundersnot available
KeywordsMedicineSimvastatinMesenchymal stem cellStroke (engine)AstrocyteIschemiaNeurogenesisPharmacologyBone marrowInflammationNeuroprotectionInternal medicinePathologyNeuroscienceCentral nervous systemBiology

Abstract

fetched live from OpenAlex

OBJECTIVES: The beneficial outcomes of bone marrow-derived mesenchymal stem cell (BMSC) treatment on functional recovery following stroke has been well established. Furthermore, 5-hydroxy-3-methylglutaryl-coenzyme A (HMG-CoA) reductase inhibitors have also been shown to increase neuronal survival and promote the movement of BMSCs towards the sites of inflammation. However, the precise mechanisms mediating the improved neurological functional recovery in stoke models following a combination treatment of Simvastatin and BMSCs still remained poorly understood. MATERIALS AND METHODS: Here, an embolic stroke model was used to experimentally induce a focal ischemic brain injury by inserting a preformed clot into the middle cerebral artery (MCA). Following stroke, animals were treated either with an intraperitoneal injection of Simvastatin, an intravenous injection of 3 ×106 BMSCs, or a combination of these two treatments. RESULTS: <0.001) in the combination treatment group. CONCLUSION: These results further demonstrate the synergistic benefits of a combination treatment and help to improve our understanding of the underlying mechanisms mediating this beneficial effect.

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 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 score0.269
Threshold uncertainty score0.545

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.054
GPT teacher head0.242
Teacher spread0.188 · 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 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

Citations13
Published2018
Admission routes1
Has abstractyes

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