Protein-Induced DNA Dumbbell Amplification (PINDA) and its applications to food hazards detection
Bibliographic record
Abstract
Quantification of trace amounts of proteins is technically challenging because proteins cannot be directly amplified like nucleic acids. To improve the analytical sensitivity and to complement conventional protein analysis methods, we developed a highly sensitive and homogeneous detection strategy called Protein-Induced DNA Dumbbell Amplification (PINDA). PINDA combines protein recognition with exponential nucleic acid amplification by using protein binding probes made of DNA strands conjugated to protein affinity ligands. When a pair of probes bind to the same target protein, complementary nucleic acid sequences that are conjugated to each probe are brought into close proximity. The increased local concentration of the probes results in the formation of a stable dumbbell structure of the nucleic acids. The DNA dumbbell is readily amplifiable exponentially using techniques such as loop-mediated isothermal amplification. The PINDA assay eliminates the need for washing or separation steps, and is suitable for on-site applications. Detection of the model protein, thrombin, has a linear range of 10 fM-100 pM and detection limit of 10 fM. The PINDA technique is successfully applied to the analysis of dairy samples for the detection of β-lactoglobulin, a common food allergen, and Salmonella enteritidis, a foodborne pathogenic bacterium. The PINDA assay can be easily modified to detect other targets by changing the affinity ligands used to bind to the specific targets.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".