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Record W3034016271 · doi:10.22215/etd/2015-11099

Tissue Plasminogen Activator (tPA) Promotes Postnatal Cortical Neuron Survival in Vitro Via JAK2- and mTOR-Dependent Mechanisms

2015· dissertation· en· W3034016271 on OpenAlexaffabout
Julia A. Grummisch

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtease and Inhibitor Mechanisms
Canadian institutionsCarleton University
Fundersnot available
KeywordsNeuroprotectionTissue plasminogen activatorPI3K/AKT/mTOR pathwayActivator (genetics)PharmacologyMedicineViability assayNeuronPlasminogen activatorNeuroscienceIn vitroSignal transductionChemistryCell biologyBiologyInternal medicineReceptorBiochemistry

Abstract

fetched live from OpenAlex

Tissue plasminogen activator (tPA) is the only approved drug for ischemic stroke in Canada but is limited in its clinical efficacy due to its short therapeutic window.This study sought to determine the effect of tPA on postnatal primary cortical neuron viability and aimed to identify the relevant cellular signalling mechanisms underlying the tPA-mediated effects in vitro.The data revealed that tPA significantly increased the propensity for cell survival within a time latency window of up to 3 hours.tPA-induced neuroprotective effects were significantly dependent upon the mTOR and JAK/STAT signalling pathways, while the MEK and PKA signalling pathways were found to play a less critical role.Immunocytochemical staining showed a marked increase in p-S6 expression following treatment with tPA, substantiating the vital role of mTOR activation in tPA-mediated neuroprotection.These results suggest the possibility of targeting the defined mechanisms to expand the therapeutic window of tPA in stroke recovery.tPA PROMOTES POSTNATAL CORTICAL NEURON SURVIVAL iii

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

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)0.0020.001

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 head0.010
GPT teacher head0.257
Teacher spread0.247 · 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 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

Citations0
Published2015
Admission routes2
Has abstractyes

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