Rapid Screening of Genetic Biomarkers of Infectious Agents Using Quantum Dot Barcodes
Bibliographic record
Abstract
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.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".