Large-scale validation of a plasmonic sensor for SARS-CoV-2 pseudo-neutralization with a cohort of food and retail workers
Bibliographic record
Abstract
Plasmonic sensors are candidates for numerous clinical applications, but few examples demonstrate their performance on large sample cohorts, a necessary step for clinical translation. The COVID-19 pandemic provided an unprecedented opportunity to validate a surface plasmon resonance (SPR) sensor for SARS-CoV-2 inhibition with a cohort of over 1,000 clinical samples from the longitudinal study of a food and retail worker population. The SPR sensor provided an in vitro model to assess the level of neutralizing antibodies by measuring the inhibition of the SARS-CoV-2 spike protein interaction with ACE-2 following exposure of the spike protein to naive and immune sera (from vaccination and/or infection). In conjunction with population data on vaccination and infection, and epidemiological data from the local jurisdiction of the study cohort, it is shown that the SPR sensor performed well in assessing the level of “pseudo-neutralization” of participant sera and that the response of the SPR sensor correlates (r = 0.74) with a live virus microneutralization assay as well as with metadata of relevant events (vaccination, waves of infection, etc.) that occurred during the study period. Using these data, the article details the challenges and opportunities of using plasmonic sensors in clinical practice.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".