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Record W2981355481 · doi:10.21577/0100-4042.20170430

DETECTION OF THE PEPTIDE P17-1 (HIV) BY SURFACE ENHANCE RAMAN SCATTERING (SERS)

2019· article· en· W2981355481 on OpenAlexafffund
Leandro B. Carneiro, Marli L. Moraes, Sidney J. L. Ribeiro, Alexandre G. Brolo

Bibliographic record

VenueQuímica Nova · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsUniversity of Victoria
FundersBritish Columbia Knowledge Development FundNatural Sciences and Engineering Research Council of CanadaConselho Nacional de Desenvolvimento Científico e TecnológicoCiência sem FronteirasFundação de Amparo à Pesquisa do Estado de São PauloUniversity of Victoria
KeywordsRaman scatteringHuman immunodeficiency virus (HIV)Materials sciencePeptideRaman spectroscopySurface (topology)NanotechnologyScatteringBiophysicsOpticsMedicineVirologyChemistryPhysicsBiologyBiochemistry

Abstract

fetched live from OpenAlex

A bioanalytical method for HIV detection based on surface-enhanced Raman scattering (SERS) is described.The assay targeted the peptide p17-1.This peptide sequence is present in the protein p17, a structural protein essential in the life cycle of the human immunodeficiency retrovirus (HIV).The antibody anti-p17 was immobilized in a planar gold surface using self-assembled techniques.This planar platform was used to capture the target analyte (p17-1 peptide).The peptide detection was carried out using specially designed SERS probes, consisting of gold nanoparticles coated with a Raman reporter molecule (Nile Blue A) and functionalized with anti-p17.This sandwich assay led to the successful detection of the peptide p17-1 at 40.8 ng/mL levels.The sandwich assay described here demonstrated that the SERS technique can rival more common fluorescence methods, such as ELISA, in the detection HIV-type virus.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.006
GPT teacher head0.248
Teacher spread0.242 · 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

Citations2
Published2019
Admission routes2
Has abstractyes

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