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Record W4220722511 · doi:10.1039/d2cc00792d

Dual-amplified CRISPR-Cas12a bioassay for HIV-related nucleic acids

2022· article· en· W4220722511 on OpenAlexaff
Jing Zhou, Jianyu Hu, Rui Liu, Chaoqun Wang, Yi Lv

Bibliographic record

VenueChemical Communications · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCRISPR and Genetic Engineering
Canadian institutionsUniversity of Alberta
FundersFundamental Research Fund of Shandong UniversitySichuan UniversityDepartment of Science and Technology of Sichuan ProvinceNational Natural Science Foundation of China
KeywordsNucleic acidCRISPRBioassayDNANucleic Acid Amplification TestsHuman immunodeficiency virus (HIV)Computational biologyChemistryCombinatorial chemistryBiologyMolecular biologyNanotechnologyChromatographyBiochemistryGeneticsGeneMaterials scienceVirology

Abstract

fetched live from OpenAlex

Nucleic acid amplification strategies have successfully dominated ultrasensitive bioassays, but they sometimes bring high time-consumption, multi-step operation, increased contamination risk, and mismatch-related inaccuracy. We proposed a nucleic acid amplification-free method called the AuNPs-tagging based CRISPR-Cas12a bioassay platform. The signal amplification was realized by integrating the self-amplification effect of CRISPR-Cas12a with the enhancement effect of the large number of detectable atoms inside each gold nanoparticle. The proposed method achieved a low LOD of 1.05 amol in 40 min for HIV-related DNA.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.562
Threshold uncertainty score0.580

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 science0.0010.001
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 head0.019
GPT teacher head0.318
Teacher spread0.298 · 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

Citations29
Published2022
Admission routes1
Has abstractyes

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