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Record W2093479877 · doi:10.1093/nar/gkq1214

A zinc finger protein array for the visual detection of specific DNA sequences for diagnostic applications

2010· article· en· W2093479877 on OpenAlexaff
Moon-Soo Kim, Gulnaz Stybayeva, Ji Youn Lee, Alexander Revzin, David J. Segal

Bibliographic record

VenueNucleic Acids Research · 2010
Typearticle
Languageen
FieldEngineering
TopicBiosensors and Analytical Detection
Canadian institutionsGenome Canada
FundersNational Institute of Biomedical Imaging and BioengineeringNational Institute of General Medical Sciences
KeywordsZinc fingerBiologyDNAComputational biologyDNA sequencingDNA-binding proteinSequencing by hybridizationMolecular biologyGeneticsDNA sequencerGene

Abstract

fetched live from OpenAlex

The visual detection of specific double-stranded DNA sequences possesses great potential for the development of diagnostics. Zinc finger domains provide a powerful scaffold for creating custom DNA-binding proteins that recognize specific DNA sequences. We previously demonstrated sequence-enabled reassembly of TEM-1 β-lactamase (SEER-LAC), a system consisting of two inactive fragments of β-lactamase each linked to engineered zinc finger proteins (ZFPs). Here the SEER-LAC system was applied to develop ZFP arrays that function as simple devices to identify bacterial double-stranded DNA sequences. The ZFP arrays provided a quantitative assay with a detection limit of 50 fmol of target DNA. The method could distinguish target DNA from non-target DNA within 5 min. The ZFP arrays provided sufficient sensitivity and high specificity to recognize specific DNA sequences. These results suggest that ZFP arrays have the potential to be developed into a simple and rapid point-of-care (POC) diagnostic for the multiplexed detection of pathogens.

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)0.001
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 score0.361
Threshold uncertainty score0.242

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.033
GPT teacher head0.317
Teacher spread0.284 · 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

Citations33
Published2010
Admission routes1
Has abstractyes

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