DNA Modulates the Interaction of Genetically Engineered DNA-Binding Proteins and Gold Nanoparticles: Diagnosis of High-Risk HPV Infection
Bibliographic record
Abstract
Gene detection has an important role in diagnosing several serious diseases and genetic defects in modern clinical medicine. Herein, we report a fast and convenient gene detection method based on the modulation of the interaction between a heat-resistant double-stranded DNA (dsDNA)-binding protein (Sso7d) and gold nanoparticles (Au NPs). We prepared a recombinant Cys-Sso7d, which is Sso7d with an extra cysteine (Cys) residue in the N-terminus, through protein engineering to control the interaction between Sso7d and Au NPs. Cys-Sso7d exhibited a stronger affinity for Au NPs and more easily induced the aggregation of Au NPs than Sso7d. In addition, Cys-Sso7d retained its ability to bind with dsDNA. The aggregation of Au NPs induced by Cys-Sso7d was diminished in the presence of dsDNA, which could be utilized as a transduction mechanism for the detection of the polymerase chain reaction (PCR) products of human papillomavirus (HPV) gene fragments (HPV types 16 and 18). The Cys-Sso7d/Au NP probe could detect as few as 1 copy of the HPV gene. The sensitivity and specificity of the Cys-Sso7d/Au NP probe for Pap smear clinical specimens (n = 52) for HPV 16 and HPV 18 detection were 85.7%/100.0% and 85.7%/91.7%, respectively. Our results demonstrate that the Cys-Sso7d/Au NP probe can be used to diagnose high-risk HPV types in Pap smear samples with high sensitivity, specificity, and accuracy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".