Test de génotypage plaquettaire in vitro à base de sandwichs de microparticules biofonctionnalisées : détection par capteur de fluorescence à ondes évanescentes, imagerie de fluorescence et cytométrie en flux
Bibliographic record
Abstract
This thesis is about the development of a new assay to capture DNA. This assay is based on the formation of sandwiches between biofunctionnalized with oligonucleotides carboxylatex microparticles combined with fluorescence detection. It should be able to discriminate single nucleotide polymorphism (SNP). This assay is designed to be applied to platelet genotyping for the research of the gene HPA-1. The main goal of this work was to improve an assay previously developed (Trévisan, 2011) by INL and EFS Rhône-Alpes. The objectives are to reduce the number of steps and to simplify the test. To do so, the molecular interactions used in order to capture target DNA are modified and fluorescent microparticles are used for the detection. In the presence of target DNA, sandwiches are formed between both biofunctionnalized fluorescent and magnetic particles. Those sandwiches are purified through magnetic separation. Then, fluorescence is detected by three methods: flow cytometry, fluorescence imaging and Evareader (detection with an evanescent wave). First, chemical and biological parameters for the functionalization of the different particles (magnetic and fluorescent) are determined. The conditions for the capture of target DNA were optimized. Then, the formation and the detection of the sandwiches were estimated by measuring the fluorescence using three different methods: flow cytometry, fluorescence imaging and Evareader. The results obtained with the three methods are consistent. They show that the new system enables to capture synthetic target (oligonucleotide) HPA-1 with a reduction of total time analysis of 45 min. In our conditions, SNP can be discriminated for HPA-1 gene. For this discrimination, the fluorescence signal ratio about 2.5 to 3. This ratio should be improved by optimizing the conditions of sandwiches formation. Next step will consist in the optimization of the system developed to capture DNA in order to gain specificity and to determine the limit of detection. This test should also be validated with biological samples. In the long term, fluorescence could be detected by a miniaturized photodetector developed in the University of Sherbrook. Preliminary studies presented in this manuscript show the potentialities of this new transducer.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".