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Record W2765944252 · doi:10.1039/c7an00744b

Tip-enhanced Raman spectroscopy of amyloid β at neuronal spines

2017· article· en· W2765944252 on OpenAlexafffund
Mohammadali Tabatabaei, Fabiana A. Caetano, Farshid Pashee, Stephen S. G. Ferguson, François Lagugné‐Labarthet

Bibliographic record

VenueThe Analyst · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSpectroscopy Techniques in Biomedical and Chemical Research
Canadian institutionsUniversity of OttawaWestern University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsSynapseContext (archaeology)PathogenesisRaman spectroscopyNeuroscienceChemistryBiophysicsAmyloid (mycology)Glutamate receptorBiologyBiochemistryPhysicsReceptorOpticsImmunology

Abstract

fetched live from OpenAlex

The early stages of Alzheimer's disease pathogenesis are thought to occur at the synapse level, since synapse loss can be directly correlated with memory dysfunction. Considerable evidence has suggested that amyloid beta (Aβ), a secreted proteolytic derivative of amyloid precursor protein, appears to be a critical factor in the early 'synaptic failure' that is observed in Alzheimer's disease pathogenesis. The identification of Aβ at neuronal spines with high spatial resolution and high surface specificity would facilitate unraveling the intricate effect of Aβ on synapse loss and its effect on neighboring neuronal connections. Here, tip-enhanced Raman spectroscopy was used to map the presence of Aβ aggregations in the vicinity of the spines exposed to Aβ preformed in vitro. Exposure to Aβ was of 1 and 6 hours. The intensity variation of selected vibrational modes of Aβ was mapped by TERS for different exposure times to Aβ. Of interest, we discuss the distinct contributions of the amide modes from Aβ that are enhanced by the TERS process and in particular the suppression of the amide I mode in the context of recently reported observations in the literature.

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.284

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.014
GPT teacher head0.336
Teacher spread0.322 · 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

Citations38
Published2017
Admission routes2
Has abstractyes

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