Rapid Quantification of Sars-Cov-2 Antibodies with a Portable Surface Plasmon Resonance Biosensor
Bibliographic record
Abstract
The development of rapid, accurate and affordable diagnostic tests is essential for screening the population in order to properly manage and control the spread of the COVID-19 pandemic caused by the novel coronavirus (SARS-CoV-2). Current diagnostic techniques rely on tests which are time-consuming, must be performed in a specialized laboratory, and are limited in terms of sensitivity and specificity to active infection. Therefore, they don’t provide any information on previous exposure to the virus. Optical biosensors have the potential to overcome the current limitations in diagnostic techniques for COVID-19. My research project aims to develop a new rapid and precise analytical method using surface plasmon resonance (SPR) to detect anti-SARS-CoV-2 antibodies directly in crude sample. The SPR biosensor used in this project is based on the Kretschmann configuration. When the polarized light travels from the higher refractive index medium to the lower refractive index medium, the total internal reflection occurs. This leads to the formation of evanescent waves which are responsible for the excitation of the surface plasmons. Changes in the refractive index of the sample solution vary the resonance wavelength. Therefore, the adsorption-desorption activities on the surface of the sensor can be analyzed by tracking the wavelength location of the SPR band over time. The SPR technique allows real-time and label-free detection of molecular interactions. It is also very sensitive, can reduce test time to minutes, and can be used at the point of care for infectious diseases. Serological test involves the direct quantification of antibodies by exposing them to the virus protein immobilized on the surface of the sensor. Antibodies to SARS-CoV-2 are produced by the immune system within days or even weeks after viral infection and remain at an elevated level for months. Many research works in the development and evaluation of coronavirus vaccines require antibody testing for characterization of the affinity and kinetics of the immune response during vaccine development. Therefore, the development of a robust method by using SPR for performing serological tests is necessary. In this project, the various analytical parameters are optimized in order to increase the specific interactions between SARS-CoV-2 proteins and human antibodies and to decrease the non-specific adsorptions of other proteins to ensure a sensitive and specific test. Different COVID-19 proteins, such as the Nucleocapsid, the S1 domain of the Spike, the RBD domain of the Spike and the complete Spike were immobilized on the gold surface functionalized with the peptides 3-MPA-LHDLHD-OH to detect animal antibodies raised against each protein in the buffer and human serum. In each case, the immobilization conditions, such as concentration, chemistry and pH were optimized and detection limits were calculated for each antibody of animal origin. Although the direct detection of antibodies worked well, the use of a secondary antibody improved the results significantly in human serum. The validation of tests with animal antibodies made it possible to move on to the next step, which consists in using this detection method with a first cohort of clinical samples provided by the Centre hospitalier de l’Université de Laval (CHUL) to validate my research work in a clinical setting.
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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".