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Record W2076753211 · doi:10.3109/21678421.2014.881377

RNA metabolism in ALS: When normal processes become pathological

2014· review· en· W2076753211 on OpenAlexafffund
Cristian A. Droppelmann, Danae Campos‐Melo, Muhammad Ishtiaq, Kathryn Volkening, Michael J. Strong

Bibliographic record

VenueAmyotrophic Lateral Sclerosis and Frontotemporal Degeneration · 2014
Typereview
Languageen
FieldMedicine
TopicAmyotrophic Lateral Sclerosis Research
Canadian institutionsWestern University
FundersCanadian Institutes of Health Research
KeywordsStress granuleRNABiologyRNA-binding proteinAmyotrophic lateral sclerosisCell biologyBiogenesisNeurodegenerationNeuroscienceDiseaseGeneticsMessenger RNAGeneTranslation (biology)MedicinePathology

Abstract

fetched live from OpenAlex

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease caused by the death of motor neurons. While the exact molecular and cellular basis for motor neuron death is not yet fully understood, the current conceptualization is that multiple aberrant biological processes contribute. Among these, one of the most compelling is based on alterations of RNA metabolism. In this review, we examine how the normal process of cellular response to stress leading to RNA stress granule formation might become pathological, resulting in the formation of stable protein aggregates. We discuss the emerging roles of post-translational modifications of RNA binding proteins in the genesis of these aggregates. We also review the contemporary literature regarding the potential role for more widespread alterations in RNA metabolism in ALS, including alterations in miRNA biogenesis, spliceosome integrity and RNA editing. A hypothesis is presented in which aberrant RNA processing, modulated through pathological stress granule formation as a reflection of either mutations within intrinsically disordered or prion-like domains of critical RNA binding proteins, or the post-translational modification of RNA binding proteins, contributes directly to motor neuron death.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.975
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.001
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 head0.087
GPT teacher head0.323
Teacher spread0.236 · 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.

Study designOther design
Domainnot available
GenreReview

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

Citations63
Published2014
Admission routes2
Has abstractyes

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