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Record W7037988896

Expression and subcellular localization of circRNAs dysregulated ALS that are encoded in cytoskeletal protein genes

2021· article· en· W7037988896 on OpenAlexaff

Bibliographic record

VenueScholarship@Western (Western University) · 2021
Typearticle
Languageen
FieldComputer Science
TopicMusic Technology and Sound Studies
Canadian institutionsWestern University
FundersStrong
KeywordsSubcellular localizationCytoskeletonAmyotrophic lateral sclerosisPathogenesisImmunofluorescenceHEK 293 cellsGene expressionGeneProtein subcellular localization prediction
DOInot available

Abstract

fetched live from OpenAlex

Amyotrophic lateral sclerosis (ALS) is a fatal and progressive neurodegenerative disorder that causes the death of motor neurons. While alterations in the metabolism of RNA, including RNA-binding proteins have been linked to the pathogenesis of ALS, our understanding of the role of non-coding RNAs including circRNAs is less well developed. In this study, using a combination of fluorescence in situ hybridization (FISH) and immunofluorescence (IF) with markers of RNP granules, I investigated the effect of osmotic stress on the localization and expression of a selection of circRNAs whose expression is dysregulated in ALS. Alteration in the number of granules for two circRNAs was observed in HEK293T cells under osmotic stress. I also have observed that circRNAs are present in RNP granules under a stress condition. This finding is consistent with the hypothesis that circRNAs might participate in the biology of ribonucleprotein (RNP) granules under pathological conditions such as those observed in ALS.

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 score0.000
Threshold uncertainty score0.001

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)0.0000.000

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 head0.062
GPT teacher head0.270
Teacher spread0.208 · 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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