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Record W2807298850 · doi:10.1038/s41598-018-25767-0

TDP-43 regulation of stress granule dynamics in neurodegenerative disease-relevant cell types

2018· article· en· W2807298850 on OpenAlexafffund
Yousra Khalfallah, Rachel Kuta, Camille Grasmuck, Alexandre Prat, Heather D. Durham, Christine Vande Velde

Bibliographic record

VenueScientific Reports · 2018
Typearticle
Languageen
FieldMedicine
TopicAmyotrophic Lateral Sclerosis Research
Canadian institutionsMontreal Neurological Institute and HospitalUniversité de MontréalMcGill UniversityCentre Hospitalier de l’Université de Montréal
FundersCanadian Institutes of Health ResearchFondation Brain CanadaALS Society of Canada
KeywordsNeuroscienceStress granuleBiologyNeurodegenerationCell typeCellDiseaseCell biologyMedicinePathologyGeneGenetics

Abstract

fetched live from OpenAlex

Stress granules (SGs) are cytoplasmic foci that form in response to various external stimuli and are essential to cell survival following stress. SGs are studied in several diseases, including ALS and FTD, which involve the degeneration of motor and cortical neurons, respectively, and are now realized to be linked pathogenically by TDP-43, originally discovered as a component of ubiquitin-positive aggregates within patients' neurons and some glial cells. So far, studies to undercover the role of TDP-43 in SGs have used primarily transformed cell lines, and thus rely on the extrapolation of the mechanisms to cell types affected in ALS/FTD, potentially masking cell specific effects. Here, we investigate SG dynamics in primary motor and cortical neurons as well as astrocytes. Our data suggest a cell and stress specificity and demonstrate a requirement for TDP-43 for efficient SG dynamics. In addition, based on our in vitro approach, our data suggest that aging may be an important modifier of SG dynamics which could have relevance to the initiation and/or progression of age-related neurodegenerative diseases.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.413
Threshold uncertainty score0.348

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.018
GPT teacher head0.277
Teacher spread0.259 · 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 designObservational
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

Citations164
Published2018
Admission routes2
Has abstractyes

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