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Record W7011614112

Modélisation et analyse de la diffusion et du mélange de réactifs dans des circuits microfluidiques capillaires pour le criblage à haut débit

2023· other· fr· W7011614112 on OpenAlexaboutno aff

Bibliographic record

VenuePolyPublie (École Polytechnique de Montréal) · 2023
Typeother
Languagefr
FieldMedicine
TopicPreterm Birth and Chorioamnionitis
Canadian institutionsnot available
Fundersnot available
KeywordsContext (archaeology)Filter (signal processing)Naturally aspirated engine
DOInot available

Abstract

fetched live from OpenAlex

RÉSUMÉ: Le criblage à haut débit (HTS, de l’anglais High Throughput Screening) est de grande importance dans plusieurs domaines dont les avancées récentes en dépendent. La pharmacologie, la biochimie, la chimie catalytique, la chimie organique et la recherche en matériaux de pointes ne sont que quelques exemples parmi tant d’autres de domaines bénéficiant des technologies récentes de HTS. Les techniques classiques de HTS développées dans les années 1980 sont basées sur l’utilisation des plaques microtitres et sont limitées par des défis liés à l’évaporation des échantillons et l’usage d’équipement de pipetage encombrant et dispendieux. Des avancées en microfluidique – une branche de la mécanique des fluides et de l’ingénierie qui s’intéresse à la miniaturisation et l’automatisation de manipulations expérimentales de fluides principalement en biologie et en chimie – permettent de répondre à certains défis du HTS classique. Notamment, les dispositifs à microcompartiments, les micromatrices et la microfluidique par gouttelettes sont des technologies applicables en HTS. Cependant, certaines limites demeurent tel que la conservation et l’intégration de réactifs, la mise en contact d’un échantillon unique avec différentes conditions expérimentales, la dépendance envers de l’équipement spécialisé et la génération de profils de concentration complexes. Une avancée microfluidique récente issue d’une collaboration entre le groupe de recherche du Pr. Thomas Gervais à Polytechnique Montréal et le département de Precision Diagnostics de IBM Research Europe – Zurich permet d’adresser la plupart de ces limites. En effet, les modules à autocoalescence (SCMs) – un dispositif microfluidique permettant de conserver des points de réactifs séchés et de les combiner sans les déplacer avec un échantillon quelconque –peuvent être multiplexés pour obtenir une matrice de SCMs. Cette géométrie permet de conserver les avantages des SCMs quant à la manipulation de réactifs tout en ayant un dispositif avec des capacités de HTS. Les matrices de SCMs répondent ainsi à la majorité des limites des autres systèmes microfluiques de HTS. L’objectif de ce mémoire est de présenter les outils développés pour planifier efficacement des expériences dans les matrices de SCMs. En premier lieu, ce nouveau dispositif microfluidique est présenté. Il permet de combiner un échantillon unique de 2 à 5 µL avec une matrice de 375 (15 x 25) points de réactifs séchés imprimés préalablement pour les reconstituer et générer des conditions expérimentales variées. Un modèle de transport diffusif est ensuite développé pour décrire l’évolution des profils de concentration après la reconstitution des points de réactifs. ABSTRACT: High throughput screening (HTS) is of great relevance in multiple fields which relied on it for recent progress. Pharmacology, biochemistry, catalytic chemistry, organic chemistry, and material sciences are some examples of such fields. Classical HTS methods developed in the years 1980 are based on the microtiter plate. Those methods are limited by challenges such as solvent evaporation, and expensive and cumbersome pipetting equipment. Recent advances in microfluidics have led to systems that can circumvent most of these challenges. Microcompartment devices, microarrays, and droplet microfluidics all have had great success as HTS systems. However, some limitations remain. Mainly, the conservation and integration of reagents, the sample integration, the need for specialized equipment, and the generation of complex concentration profiles are challenges yet to be fully overcome. A recent microfluidic advancement resulting from a collaboration between Pr. Thomas Ger-vais’ research group at Polytechnique Montréal and the Precision Diagnostics department at IBM Research Europe – Zurich can overcome most of these limitations. Self-coalescence modules (SCMs) are microfluidic devices that can preserve dried reagent spots and allow their reconstitution in a sample without any displacement. These modules can be multiplexed to create an SCM array. This geometry retains the reagent handling advantages of SCMs while providing a device with high-throughput screening (HTS) capabilities. As a result, arrays of SCMs overcome the majority of limitations found in other microfluidic HTS systems. The primary objective of this dissertation is to present the toolbox that has been developed to efficiently plan experiments in SCM arrays. Firstly, this novel microfluidic device is presented. SCM arrays can combine a 2 to 5 µL sample with an array of 375 (15 x 25) printed and dried reagent spots. In doing so, the dried reagents are reconstituted, and complex experimental conditions can arise. A diffusive transport model is developed to describe the evolution of concentration profiles after the reconstitution of the reagent spots. This model solves the Fickian diffusion equa-tion (2nd Fick’s law) for various one-dimensional SCM’s possible geometries using Green’s function method, the image method, and well-known solutions of the diffusion equation. The model assumes that the reagent spots, immediately after reconstitution, have been minimally dispersed and can be considered as Dirac distributions centered on the printing position. The model can therefore be used for the prediction of concentration profiles resulting from an initial printing pattern at any subsequent time t.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.012
GPT teacher head0.254
Teacher spread0.242 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

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Citations0
Published2023
Admission routes1
Has abstractyes

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