MétaCan
Menu
Back to cohort
Record W4320076286 · doi:10.35493/medu.41.20

The Effect of Autophagic Gene Inhibition on Exosomal Tau

2022· article· en· W4320076286 on OpenAlexvenueaboutno aff
Philip S. Yu

Bibliographic record

VenueThe Meducator · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicExtracellular vesicles in disease
Canadian institutionsnot available
Fundersnot available
KeywordsAutophagyMicrovesiclesNeurodegenerationIntracellularExtracellularCell biologyWestern blotExtracellular vesiclesExosomeGeneBiologyChemistrymicroRNABiochemistryMedicineDiseasePathologyApoptosis

Abstract

fetched live from OpenAlex

Philip Yu is an undergraduate student researcher passionate about translational research in various fields. The following is an independent experimental study on genetic control of the cellular processes that regulate protein expression and transport in neurodegeneration. The study was conducted under the supervision of Dr. Derrick Gibbings in the Department of Cellular and Molecular Medicine at the University of Ottawa. Throughout the study, Philip conducted all experiments involved, in addition to collecting and processing the generated data. The misfolding of the protein tau contributes to the development of Alzheimer’s disease (AD). Misfolded tau is thought to propagate through a homeostatic degradation process known as autophagy, resulting in the export of cellular materials to the extracellular space via extracellular vesicles, called exosomes. By inhibiting the ATG7 and p62 genes necessary for autophagy to occur, the effects on the amount of exosomal and intracellular tau can be observed. Following the analysis of western blot and protein assay data, it was determined that the inhibition of the ATG7 and p62 genes results in a 70% and 60% reduction in the concentration of tau found in exosomes, respectively. These results suggest potential therapeutic applications of autophagic gene inhibition for the treatment of AD.

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 score0.008
Threshold uncertainty score0.239

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 head0.004
GPT teacher head0.234
Teacher spread0.230 · 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

Citations0
Published2022
Admission routes2
Has abstractyes

Explore more

Same venueThe MeducatorSame topicExtracellular vesicles in diseaseFrench-language works237,207