MétaCan
Menu
Back to cohort
Record W2559777674 · doi:10.1113/jp270213

New perspectives on amyotrophic lateral sclerosis: the role of glial cells at the neuromuscular junction

2016· review· en· W2559777674 on OpenAlexafffund
Danielle Arbour, Christine Vande Velde, Richard Robitaille

Bibliographic record

VenueThe Journal of Physiology · 2016
Typereview
Languageen
FieldMedicine
TopicAmyotrophic Lateral Sclerosis Research
Canadian institutionsCentre Hospitalier de l’Université de MontréalUniversité de Montréal
FundersCanadian Institutes of Health Research
KeywordsAmyotrophic lateral sclerosisNeuroscienceNeuromuscular junctionDenervationSynapseBiologyMotor neuronSpinal cordDiseaseMedicineAnatomyPathology

Abstract

fetched live from OpenAlex

Amyotrophic lateral sclerosis (ALS) is a disease leading to the death of motor neurons (MNs). It is also recognized as a non-cell autonomous disease where glial cells in the CNS are involved in its pathogenesis and progression. However, although denervation of neuromuscular junctions (NMJs) represents an early and major event in ALS, the importance of glial cells at this synapse receives little attention. An interesting possibility is that altered relationships between glial cells and MNs in the spinal cord in ALS may also take place at the NMJ. Perisynaptic Schwann cells (PSCs), which are glial cells at the NMJ, show great morphological and functional adaptability to ensure NMJ stability, maintenance and repair. More specifically, PSCs change their properties according to the state of innervation. Hence, abnormal changes or lack of changes can have detrimental effects on NMJs in ALS. This review will provide an overview of known and hypothesized interactions between MN nerve terminals and PSCs at NMJs during development, aging and ALS-induced denervation. These neuron-PSC interactions may be crucial to the understanding of how degenerative changes begin and progress at NMJs in ALS, and represent a novel therapeutic target.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.951
Threshold uncertainty score0.850

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.038
GPT teacher head0.307
Teacher spread0.269 · 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 designNot applicable
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

Citations77
Published2016
Admission routes2
Has abstractyes

Explore more

Same venueThe Journal of PhysiologySame topicAmyotrophic Lateral Sclerosis ResearchFrench-language works237,207