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Record W1967123657 · doi:10.4061/2011/286536

A<i>β</i> Behavior on Neuronal Membranes: Aggregation and Toxicities

2011· article· en· W1967123657 on OpenAlexaff
Katsuhiko Yanagisawa, Jacques Fantini, Avijit Chakrabartty, Anne Eckert

Bibliographic record

VenueInternational Journal of Alzheimer s Disease · 2011
Typearticle
Languageen
FieldMedicine
TopicAlzheimer's disease research and treatments
Canadian institutionsUniversity of TorontoOntario Institute for Cancer Research
Fundersnot available
KeywordsLipid raftProtein aggregationNeuroscienceMembraneSphingolipidCell biologyChemistryBiologyBiochemistry

Abstract

fetched live from OpenAlex

A growing body of evidence suggests that the aggregation and toxic potentials of amyloidogenic proteins, including amyloid β-protein (Aβ), α-synuclein, and prion protein, emerge through the interaction of these proteins with neuronal and/or glial membranes. The aggregation and deposition of Aβ are the initial events of Alzheimer's disease (AD), and the toxicity of aggregated Aβ is the basis for the neuronal loss in AD brains. Thus, the Aβ behavior on neuronal membranes should be one of the critical issues to be clarified for our further understanding of the pathogenesis of AD and to develop therapeutic strategies. To accelerate studies in this field, we have invited original research articles as well as review articles that will provide novel information for our special issue. The first three papers of this special issue describe the crucial involvement of lipid rafts, which are specific membrane microdomains on the cell surface that are rich in sphingolipids and cholesterol, in the production, aggregation, and toxicities of Aβ. The subsequent three papers focus on the gangliosides, which are the major constituent of lipid rafts, particularly in terms of their role in the induction of conformational changes of Aβ, leading to their aggregation and emerging toxicities. The next two articles address how Aβ causes neuronal injury by showing the possibility of formation of amyloid channels in the neuronal membranes, resulting in the disruption of calcium homeostasis that is critical for the function and survival of neurons, and the possibility of generation of radicals. In regard to the Aβ toxicities, much attention has been paid to the argument that the accumulation of Aβ inside neurons may be the critical step. In this context, the next two papers propose a mechanism by which Aβ enters the neurons, which are followed by another two papers showing how the internalized Aβ acts pathologically inside neurons, emphasizing the possibility that the mitochondria may be a target of intraneuronal Aβ. A further argument for the possible interaction between Aβ and neuronal membranes is presented in the next four papers. In these papers, it is presented how Aβ affects the properties of neuronal membranes or, conversely, how the alteration of membrane properties affects the processing of amyloid precursor protein (APP) leading to Aβ generation. Note that the metabolism of neuronal lipids, particularly sphingolipids and ceramide, can be regulated in association with APP processing. The final paper of this special issue describes a foresighted aspect of science and technology of nanochemistry with respect to the pathological protein aggregation, which is likely based on the catalysts of membrane lipids, suggesting an opportunity for developing novel nanomedicines and nanodiagnostics for various amyloidoses. We all look forward to seeing further expansion of studies in this field in the near future. Katsuhiko Yanagisawa Jacques Fantini Avijit Chakrabartty Anne Eckert

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.056
GPT teacher head0.326
Teacher spread0.270 · 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 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

Citations9
Published2011
Admission routes1
Has abstractyes

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