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Enregistrement W2257671647 · doi:10.1149/ma2015-02/45/1800

Towards Development of a Low-Cost and Sensitive Thermal Sensor for User-Independent Interpretation of Results from Lateral Flow Assay (LFA) Devices

2015· article· en· W2257671647 sur OpenAlexaff
Manu Pallapa, Pouya Rezai

Notice bibliographique

RevueECS Meeting Abstracts · 2015
Typearticle
Langueen
DomaineEngineering
ThématiqueBiosensors and Analytical Detection
Établissements canadiensYork University
Organismes subventionnairesnon disponible
Mots-clésAnalyteFluidicsMaterials scienceMembraneSoftware portabilityNanotechnologyMicrofluidicsCapillary actionNanoparticleComputer scienceBiological systemChemistryChromatographyEngineeringComposite material

Résumé

récupéré en direct d'OpenAlex

Lateral Flow Assays (LFA) are used to detect the presence of specific analytes such as hormones, toxic chemicals or pathogenic microorganisms in various fluidic specimens[1]. Typical applications of LFA devices have been demonstrated in medical diagnostics, food and beverage manufacturing and water monitoring[2-4]. The fluidic specimens that are added to the LFA sample pad flow via capillary forces along a nitrocellulose membrane consisting of coloured nanoparticles and capturing agents at specific locations of the membrane. The target analytes bond to the coloured nanoparticles immunologically and get immobilized downstream by capturing agents on these specific locations. This generates visibly noticeable bands due to continuous accumulation of target analytes. Although LFA devices offer important commercial advantages such as portability, ease-of-use and cost effectiveness, the test result is based on visual interpretation of the coloured nanoparticle-analyte conjugation bands. Visual interpretation is often prone to human error and is inaccurate, subjective and non-quantitative. Optical scanning readers have been developed[5, 6] that are based on image acquisition and processing algorithms. They only rely on the reflective signals (i.e. colour intensity) collected from the surface of the devices to quantify the results. A large amount of signal corresponding to the nanoparticles trapped inside the bulk of the membrane is lost leading to unsatisfactory detection limits and sensitivities. In this work we demonstrate a novel and low-cost thermal sensing approach for quantitative measurement of analyte concentration by exploiting the inherent thermal properties of the nanoparticles accumulated in the bulk of the membrane in LFA devices. An infrared (IR) sensor (Melexis-MLX90614) was mounted on a custom-made XY scanning stage (Fig.1a). A 3D printed structure assembled to the stage housed the LFA test strip and a thermoelectric heater (ThorLabs TEC3-6) (Fig.1a inset). The LFA was performed on a commercial 20mIU (mili International Units) human Chorionic Gonadotropin (hCG) test strip using a positive 20mIU hCG control solution that resulted in a dark control band and a visibly lighter test band (Fig.1b). The distance between the sensor and the test strip was determined by the distance to spot ratio (11.45:1) of the IR sensor. The 3D printed structure and the LFA strip were completely covered with aluminum tape excluding two windows that were opened for the test and the control bands. The polished surface of the aluminum tape had a very low emissivity (0.03) and subsequently assisted in acquiring the relevant thermal data only from the test and the control bands. The XY scanning stage and the real time measurement of the temperature were controlled electronically via an Arduino Uno R3. The heater was energized by a Keithley 2410 source meter to three equilibrium actuation temperatures. The sensor was scanned over the strip in the forward and backward direction to measure the thermal signatures of the test and the control bands. The thermal data measured by the IR sensor and acquired in real-time by the Arduino Uno R3 was analyzed. Fig.2 shows the thermal characteristic of the heated LFA strip for one scanning cycle (i.e. forward and backward scan). Two distinct temperature levels in each major peak are observed that represent the test and the control bands. Fig.3 presents the change in the temperature (DT) between the test and control bands at the three tested equilibrium actuation temperatures. The different temperature levels between the control and the test bands at equilibrium temperatures implies a positive correlation between the amount of nanoparticle-analyte conjugation and the emitted thermal energy. From this result it is evident that the test band has a lower temperature level compared to the control band at each equilibrium temperature because of the lower concentration of nanoparticle conjugated target analyte trapped in the bulk of the nitrocellulose membrane. This may be used in the future to extract valuable information from the LFA device such as the exact concentration of the target analyte captured in the bulk of the bands, rather than the surface of the membrane as currently practiced by the optical scanning methods. The demonstrated thermal modality exploiting the nanoparticles in the bulk of the LFA membranes will be suitable for the development of a low-cost quantitative LFA reader with improved detection limit, sensitivity, and dynamic range when compared to the existing commercial optical scanners. References 1.Posthuma-Trumpie, G.A., J. Korf, and A.v. Amerongen, Anal Bioanal Chem, 2009. 393 : p. 569-582 2.Wong, R.C. and H.Y. Tse, . 2009: Springer Science. 3.Liu, L., et al., Biomed Chromatogr, 2007. 21 (8): p. 861-866 4.O’Farrell, B.,. 2009, Springer. P. 1-33 5.Huang, L.H., et al., . IEEE Sensors Journal, 2009. 10 : p. 1185-1191 6.Gui, C., et al., Nanoscale Research Letters, 2014. 9 (57): p. 1-8 Figure 1

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,258
Score d'incertitude au seuil0,447

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,017
Tête enseignante GPT0,234
Écart entre enseignants0,217 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeExpérimental (laboratoire)
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2015
Routes d'admission1
Résumé présentoui

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