Rapid Quantification of Sars-Cov-2 Antibodies with a Portable Surface Plasmon Resonance Biosensor
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».