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Record W2323556554 · doi:10.1021/nn102873w

Rapid Screening of Genetic Biomarkers of Infectious Agents Using Quantum Dot Barcodes

2011· article· en· W2323556554 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueACS Nano · 2011
Typearticle
Languageen
FieldEngineering
TopicBiosensors and Analytical Detection
Canadian institutionsUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsBarcodeQuantum dotInfectious disease (medical specialty)MalariaComputer scienceComputational biologyVirologyNanotechnologyBiologyDiseaseMedicineMaterials scienceImmunology

Abstract

fetched live from OpenAlex

The development of a rapid and sensitive infectious disease diagnostic platform would enable one to select proper treatment and to contain the spread of the disease. Here we examined the feasibility of using quantum dot (QD) barcodes to detect genetic biomarkers of the bloodborne pathogens HIV, malaria, hepatitis B and C, and syphilis. The genetic fragments from these pathogens were detected in less than 10 min at a sample volume of 200 μL and with a detection limit in the femtomol range. A next step for the advancement of QD barcode technology to the clinic will require validation of the technology with human samples to assess for matrix effects, head-to-head comparison with existing detection method, development of techniques to automate the assay and detection process, and simplification of analytical device for the read-out of the barcode signal. Our study provides an important intermediate step in the translation of QD barcode technology for screening infectious disease agents in the developed and developing world.

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.

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 score0.107
Threshold uncertainty score0.324

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 head0.038
GPT teacher head0.229
Teacher spread0.191 · 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