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Record W2605549181 · doi:10.11159/icnb17.115

The Effect of Silver Nanoparticles on the Scavenger Receptor-Scara1on Microglia

2017· article· en· W2605549181 on OpenAlexvenueno aff
Katarzyna Sikorska, Grądzka Iwona, Iwona Wasyk

Bibliographic record

VenueProceedings of the World Congress on Recent Advances in Nanotechnology · 2017
Typearticle
Languageen
FieldNeuroscience
TopicNeuroinflammation and Neurodegeneration Mechanisms
Canadian institutionsnot available
Fundersnot available
KeywordsMicrogliaScavenger receptorScavengerSilver nanoparticleNanoparticleChemistryNanotechnologyMaterials scienceMedicineBiochemistryImmunologyRadicalInflammation

Abstract

fetched live from OpenAlex

Alzheimer's disease is one of the most prevalent neurodegenerative diseases in the world. The neuropathological hallmark of Alzheimer's disease is extracellular deposit of amyloid- (A) in the brain. Microglial cells are able to remove of A aggregates by receptor-dependent endocytosis Nanotechnology is one of the fastest developing science discipline and nanoparticles (NPs), due to their strong absorption properties are widely used in industry, and also in medical diagnosis and treatment. It was documented that NPs can prevent the formation of A-aggregates whereby reducing their neurotoxicity and likely can impact on the A-uptake by microglia It is supposed that NPs can increase number of emerging phagocytosis bubbles and the A uptake due to the co-transport phenomenon, or in contrary reduce the number of lipid rafts available and therefore inhibit of A transport by some kind of competition. Moreover, by activating different paths to cell signaling, NPs can probably change the expression of the amyloid -receptors on microglia cell membranes.

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)0.003
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.084
Threshold uncertainty score0.559

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0020.000
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 head0.015
GPT teacher head0.271
Teacher spread0.257 · 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

Citations1
Published2017
Admission routes1
Has abstractyes

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