Real time label-free monitoring of plasmonic polymerase chain reaction products
Bibliographic record
Abstract
The high mortality rate in developing countries stemming from poverty and diseases, and the pressure on healthcare budgets in developed countries have evoked a major concern in healthcare delivery. The need for less costly and patient-centered healthcare delivery brings point-of-care testing (PoCT) to the fore. PoCT devices help to eliminate the overheads associated with centralized bench-top laboratory instruments. Although, handheld devices such as Glucose biosensor strip exist, small handheld PoCT devices for molecular techniques such as Polymerase Chain Reaction (PCR) used to provide infectious disease testing are new and emerging.Polymerase Chain Reaction (PCR) is a biological technique used to amplify DNA. PCR makes it possible to replicate DNA and generate millions of copies from a single strand of DNA. This finds applications in the medical field to identify and detect infectious diseases. PCR is also a very important component of every laboratory involved in molecular biology experimentation. Conventional PCR equipment is expensive and require a significant amount of personnel time and space to setup and run in the laboratory. Another critical aspect of PCR systems is the need to detect amplified products, but this ability is lacking in most conventional PCR systems. Given this background, the aim of this work is to demonstrate a simple, cheap, effective and patient-centered PCR systems to mediate the shortcomings of conventional PCR machines especially as it concerns the detection of amplified PCR products.Different methods for the detection of PCR products are described. Some of them are relatively insensitive and nonspecific while others are very sensitive and highly specific. The merits and demerits of each method are also outlined.In this work, I have exploited the phase shift between the temperature and transmission output during PCR cycle to demonstrate a low cost and easy label-free plasmonic photodetection of PCR products using a simple probe laser. This method makes it possible to distinguish between negative and positive PCRs, and it can detect PCR product with starting copy number as low as 10,000 genome copies per microliter.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 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 teacher head, 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".