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Record W2997545544 · doi:10.22215/etd/2019-13741

Neuroinflammation and Parkinson’s Disease: Understanding the Inflammatory Process Induced by Chronic Peripheral Injection of LPS in LRRK2 G2019S KI Mice in Correlation with Age

2019· dissertation· en· W2997545544 on OpenAlexaff
Anuroopa Dinesh

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldMedicine
TopicParkinson's Disease Mechanisms and Treatments
Canadian institutionsCarleton University
Fundersnot available
KeywordsNeuroinflammationGenotypeSalineParkinson's diseaseDiseaseMedicinePeripheralInternal medicineEndocrinologyPhysiologyBiologyGenetics

Abstract

fetched live from OpenAlex

Parkinson' disease is a progressive neurodegenerative disease arising from a collective effect of advancing age, genetic vulnerabilities and environmental toxins.The objective of the current study was to elucidate a synergistic effect of the advanced age, G2019S mutation and immunological stress (LPS) on neuroinflammation.In the present study, male mice were given five intraperitoneal injections of 250mg/kg of LPS (or saline) every alternate day across the two levels of the age and genotype (age; old vs young, genotype; WT vs G2019S).In line with our expectation, there was a significant loss of TH+ cells in old-G2019S mice that received LPS compared to the young-WT that received saline.There was a significant effect of age and genotype on baseline locomotor activity of these animals.Age and genotype predominantly affected other aspects like increased CX3CR1 expression and increased SiRT3 expression in SNc.

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 score0.005

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 integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.014
GPT teacher head0.256
Teacher spread0.242 · 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
Published2019
Admission routes1
Has abstractyes

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