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Record W4360946340 · doi:10.1111/bph.16078

Novel therapeutic approaches to target neurodegeneration

2023· review· en· W4360946340 on OpenAlexfundno aff
Alerie Guzman de la Fuente, Silvia Pelucchi, Jérôme Mertens, Mónica Di Luca, Daniela Mauceri, Elena Marcello

Bibliographic record

VenueBritish Journal of Pharmacology · 2023
Typereview
Languageen
FieldMedicine
TopicAlzheimer's disease research and treatments
Canadian institutionsnot available
FundersH2020 European Research CouncilMinistero dell'Università e della RicercaFondazione CariploInstituto de Salud Carlos IIIMichael J. Fox Foundation for Parkinson's ResearchAustrian Science FundHorizon 2020 Framework ProgrammeQueen's University BelfastWellcome TrustChica and Heinz Schaller FoundationNational Institutes of HealthQueen's UniversityEuropean CommissionBrightFocus FoundationNational Institute on AgingAlzheimer's AssociationAlzheimer Forschung Initiative
KeywordsNeurodegenerationNeuroinflammationNeuroscienceAutophagyBiologyMedicineInflammationDiseasePathologyImmunologyGenetics

Abstract

fetched live from OpenAlex

Ageing is the main risk factor common to most primary neurodegenerative disorders. Indeed, age-related brain alterations have been long considered to predispose to neurodegeneration. Although protein misfolding and the accumulation of toxic protein aggregates have been considered as causative events in neurodegeneration, several other biological pathways affected by brain ageing also contribute to pathogenesis. Here, we discuss the evidence showing the involvement of the mechanisms controlling neuronal structure, gene expression, autophagy, cell metabolism and neuroinflammation in the onset and progression of neurodegenerative disorders. Furthermore, we review the therapeutic strategies currently under development or as future approaches designed to normalize these pathways, which may then increase brain resilience to cope with toxic protein species. In addition to therapies targeting the insoluble protein aggregates specifically associated with each neurodegenerative disorder, these novel pharmacological approaches may be part of combined therapies designed to rescue brain function.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0060.003

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 head0.319
GPT teacher head0.425
Teacher spread0.106 · 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 source (direct Gemma or distilled Codex), 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

Citations26
Published2023
Admission routes1
Has abstractyes

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