A novel RNA detection technique for point-of-care identification of pathogens
Bibliographic record
Abstract
Despite significant progress in recent years to improve capabilities to diagnose infections at point-of-care (POC), there are still technical hurdles that need to be overcome to ensure proper medical interventions. Current microbial POC tests involve polymerase chain reaction (PCR) or sandwich immunoassay (IA) based detection formats. PCR is highly sensitive but requires complex instrumentation, whereas lateral flow (LF) based IA tests are handheld but lack sensitivity. We present here a portable and sensitive technique by integrating an isothermal RNA amplification approach with IA detection format. The technique comprises i) Nucleic Acid Sequence Based isothermal Amplification (NASBA), ii) amplicon tagging with hapten labeled probes, iii) capturing the amplicon and iv) formation of a sandwich complex with an antibody (Ab) that selectively recognizes the DNA-RNA duplex. The results can be extended to develop an automated, portable and highly sensitive diagnostic platform suitable for POC applications.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".