(<i>Invited</i>) Translation of Plasmonic Sensors for Point-of-Care Monitoring
Bibliographic record
Abstract
In this presentation, we will focus on the recent use of plasmonic sensors for clinical trials to monitor the presence of antibodies elicited by individuals who have been infected or vaccinated against COVID-19 and to monitor patients before blood donation or transfusion. In this case, we were using a portable SPR platform to screen for the presence of antibodies or proteins in the sera of individuals. In the case of COVID, we have measured the quality of their immune response with affinity and pseudo-neutralization assays in more than 1000 clinical samples. We will present data on the immune response to the original strain and to a number of variants of concern in Canada and elsewhere, and how it correlates with more established biological techniques such as ELISA and microneutralization. The second example will showcase our efforts in monitoring IgA and ferritin in blood in the context of blood drives and transfusions. We will also show a new method to rapidly collect serum from blood to facilitate deployment of the sensors at the point of need.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.040 | 0.017 |
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".