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Record W1913086588 · doi:10.1016/j.celrep.2015.06.019

The Regulatory Machinery of Neurodegeneration in In Vitro Models of Amyotrophic Lateral Sclerosis

2015· article· en· W1913086588 on OpenAlexfundno aff
Burcin Ikiz, Mariano J. Alvarez, Diane B. Ré, Virginia Le Verche, Kristin A. Politi, Francesco Lotti, Sudarshan Phani, Radhika Pradhan, Changhao Yu, Gist F. Croft, Arnaud Jacquier, Christopher E. Henderson, Andrea Califano, Serge Przedborski

Bibliographic record

VenueCell Reports · 2015
Typearticle
Languageen
FieldMedicine
TopicAmyotrophic Lateral Sclerosis Research
Canadian institutionsnot available
FundersUniversity of OttawaNational Institute of Neurological Disorders and StrokeNational Cancer InstituteNational Institutes of HealthNational Institute of Environmental Health SciencesPhilippe Foundation
KeywordsAmyotrophic lateral sclerosisNeurodegenerationNeuroscienceIn vitroBiologyMedicineDiseaseGeneticsPathology

Abstract

fetched live from OpenAlex

Neurodegenerative phenotypes reflect complex, time-dependent molecular processes whose elucidation may reveal neuronal class-specific therapeutic targets. The current focus in neurodegeneration has been on individual genes and pathways. In contrast, we assembled a genome-wide regulatory model (henceforth, "interactome"), whose unbiased interrogation revealed 23 candidate causal master regulators of neurodegeneration in an in vitro model of amyotrophic lateral sclerosis (ALS), characterized by a loss of spinal motor neurons (MNs). Of these, eight were confirmed as specific MN death drivers in our model of familial ALS, including NF-κB, which has long been considered a pro-survival factor. Through an extensive array of molecular, pharmacological, and biochemical approaches, we have confirmed that neuronal NF-κB drives the degeneration of MNs in both familial and sporadic models of ALS, thus providing proof of principle that regulatory network analysis is a valuable tool for studying cell-specific mechanisms of neurodegeneration.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.563
Threshold uncertainty score0.354

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.052
GPT teacher head0.266
Teacher spread0.214 · 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

Citations54
Published2015
Admission routes1
Has abstractyes

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