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Record W3216500586 · doi:10.3389/fnins.2021.790114

Antibody-Based Therapeutic Interventions for Amyotrophic Lateral Sclerosis: A Systematic Literature Review

2021· review· en· W3216500586 on OpenAlexafffund
Amélie Poulin-Brière, Edris Rezaei, Silvia Pozzi

Bibliographic record

VenueFrontiers in Neuroscience · 2021
Typereview
Languageen
FieldMedicine
TopicAmyotrophic Lateral Sclerosis Research
Canadian institutionsUniversité Laval
FundersInstitute of Neurosciences, Mental Health and AddictionCanadian Institutes of Health ResearchALS Society of Canada
KeywordsAmyotrophic lateral sclerosisMedicineC9orf72DiseaseSOD1NeuroscienceFrontotemporal lobar degenerationPsychological interventionFrontotemporal dementiaPathologyPsychologyDementiaPsychiatry

Abstract

fetched live from OpenAlex

Amyotrophic Lateral Sclerosis (ALS) is a mid-life onset neurodegenerative disease that manifests its symptomatology with motor impairments and cognitive deficits overlapping with Frontotemporal Lobar Degeneration (FTLD). The etiology of ALS remains elusive, with various mechanisms and cellular targets implicated, and no treatment can reverse or stop the progression of the pathology. Therapeutic interventions based on passive immunization are gaining attention for neurodegenerative diseases, and FDA recently approved the first antibody-based approach for Alzheimer's disease. The present systematic review of the literature aims to highlight the efforts made over the past years at developing antibody-based strategies to cure ALS. Thirty-one original research papers have been selected where the therapeutic efficacy of antibodies were investigated and described in patients and animal models of ALS. Antibody-based interventions analyzed, target both extracellular molecules implicated in the pathology and intracellular pathogenic proteins known to drive the disease, such as SOD1, TDP-43 or C9ORF72 repeats expansions. The potentials and limitations of these therapeutic interventions have been described and discussed in the present review.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.074
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0050.002
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.142
GPT teacher head0.422
Teacher spread0.280 · 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 designSystematic review
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

Citations24
Published2021
Admission routes2
Has abstractyes

Explore more

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