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Record W4205974951 · doi:10.22215/etd/2021-14640

Metabotropic Glutamate Receptor 5 Negative Allosteric Modulation Effects on Microglia Phenotype

2021· dissertation· en· W4205974951 on OpenAlexaff
Natasha Curtis

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldNeuroscience
TopicNeuroinflammation and Neurodegeneration Mechanisms
Canadian institutionsCarleton University
Fundersnot available
KeywordsMetabotropic glutamate receptor 5MicrogliaNeuroscienceContext (archaeology)Metabotropic glutamate receptorMetabotropic glutamate receptor 1Glutamate receptorCell biologyBiologyChemistryReceptorInflammationBiochemistryImmunology

Abstract

fetched live from OpenAlex

Microglia are the resident immunocompetent cells of the central nervous system (CNS).They are extremely plastic and critical for neurogenesis, circuit structure and function, and play a crucial role in early synapse formation.It is thought that metabotropic glutamate receptor 5 (mGluR5) plays a role in the modulation of microglial phenotype.In this thesis, we assessed the impact of the mGluR5 acting negative allosteric drug, (2-chloro-4-[2[2,5-dimethyl-1-[4-(trifluoromethoxy) phenyl] imidazol-4-yl] ethynyl] pyridine (CTEP), alone and the context of the inflammatory endotoxin, lipopolysaccharide (LPS) upon BV2 cells.This was done to assess if mGluR5 plays a role in microglia phenotype modulation.It was found that LPS has an effect on the expression of mGluR5, but otherwise did not have a significant impact upon other markers.This thesis is a starting point for future studies that seek to determine the role of mGluR5 signalling on microglial phenotype.As a disclaimer, the principal investigator notes that this thesis is a work in progress and all data described herein, should be considered preliminary and not final.In part, the thesis was adversely impacted by infrastructure building issues at Carleton University that restricted proper access for the student.include a special thank you to Teresa Fortin whose advice and support allowed for such a prompt completion of this thesis.Also, my gratitude goes to my committee members for their valuable feedback and advice.Finally, I would like to express a special thank you to my supervisor Dr Shawn Hayley.Due to issues arising as a result of the COVID-19 pandemic and problems with access to the recently created Health Sciences Building at Carleton this has been a very valuable learning experience for me.I have learnt so much resilience and steadfastness.I would like to thank everyone who helped me stay committed to the continuing of my education through this difficult time.

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 score0.006
Threshold uncertainty score0.021

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)0.0060.001

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 head0.019
GPT teacher head0.261
Teacher spread0.241 · 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
Published2021
Admission routes1
Has abstractyes

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