MétaCan
Menu
Back to cohort

Role of Tau Protein on Photophysical Properties of Carbon Dots

2021· article· en· W3170739172 on OpenAlexafffund
Musonda Mitti, Sarah Lucas, Rafik Naccache, Sanela Martić

Bibliographic record

VenueThe FASEB Journal · 2021
Typearticle
Languageen
FieldMaterials Science
TopicCarbon and Quantum Dots Applications
Canadian institutionsConcordia UniversityTrent University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTau proteinFluorescenceChemistryProtein aggregationThioflavinBiophysicsQuantum yieldIntrinsically disordered proteinsNanomaterialsNanotechnologyBiochemistryAlzheimer's diseaseMaterials scienceDiseaseBiology

Abstract

fetched live from OpenAlex

Aggregation of intrinsically disordered Tau protein is a key biomarker of the progressive neurodegenerative disorder Alzheimer's Disease [1]. Although the precise mechanism by which tau aggregates is not well understood, abnormal post‐translational modifications of tau have been implicated in the formation of tau aggregates and neurofibrillary tangles. In vitro, Tau aggregation may be detected by the fluorescent Thioflavin T dye, which is limited by non‐specific interactions between the dye and other additives and low fluorescence quantum yield. CDs are a highly modifiable nanomaterials with characteristic optical properties and poorly‐understood protein interactions. Using the novel fluorescent nanoallotrope, Carbon dots (CDs), we examined an alternative method for facile detection of intrinsically disordered Tau protein. Through photophysical studies of tau protein with two varieties of CDs, we determined that Tau protein decreased the fluorescent intensity of dual‐fluorescing CDs to a greater extent than nitrogen‐doped CDs after monitoring fluorescence. Tau protein aggregation was also induced by using heparin and this process was monitored by using ThT as well as CD assays. The CDs exhibited similar properties with heparin‐aggregated tau samples and non‐aggregated tau protein under specific experimental conditions. Data indicate that carbon dots are viable sensors for proteins and could be further optimized for disease biomarker detection and quantification in the development of rapid, sensitive and selective diagnostic assays. References [1] M.D. Weinggarten, A.H. Lockwood, S.Y. Hwo, M.W. Kirschener, Proc. Natl. Acad. Sci., 1975, 72, 1858‐1862.

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 score0.002
Threshold uncertainty score0.145

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.018
GPT teacher head0.230
Teacher spread0.212 · 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

Citations0
Published2021
Admission routes2
Has abstractyes

Explore more

Same venueThe FASEB JournalSame topicCarbon and Quantum Dots ApplicationsFrench-language works237,207