A-410 A laser scanning smartphone-based imaging platform toward point-of-care diagnostic testing
Notice bibliographique
Résumé
Abstract Background Smartphones are being explored for point-of-care testing (POCT) due to their global ubiquity and on-board technologies. When smartphones are paired with advanced luminescent materials like quantum dots (QDs) and polymer dots (Pdots), they have sufficient sensitivity to become a viable alternative to sophisticated laboratory instruments for the quantitative detection of biomarkers. To this end, we have developed and benchmarked a laser-scanning smartphone imaging platform (LS-SIP). Such a device is potentially ideal for readout of fluorescence-based assays, such as those executed with microwell plates, lateral flow test strips, and microfluidic or lab-on-a-chip systems. As a flexible multi-purpose readout device, the LS-SIP will help avoid the possible problem of health care professionals in non-laboratory settings accumulating a myriad of different devices for POCT, each specific to a different assay. Methods The LS-SIP was built from 3D-printed parts, simple optics, a DC motor, and a low-cost, low-power laser diode. A line-shaped laser beam is scanned over an imaging platform. During the scan, the smartphone acquires a movie that is flattened into a complete fluorescence image. A predetermined correction matrix was used to improve precision and correct for spatial non-uniformities in illumination intensity across the field of view. The analytical performance of the device, including benchmarking against a commercial plate reader, was evaluated using multiple fluorescent materials—QDs, dyes, and Pdots—in a custom-designed 50 microwell chip. Proof-of-concept lateral flow binding assays were also done using dextran-coated QDs (Dex-QDs), and advanced made-for-purpose materials like supra-QD and super-QD assemblies. Results For each material, the trend in the smartphone-measured PL intensity versus concentration was approximately sigmoidal (consistent with the gamma correction built into the smartphone video acquisition) with a dynamic range of at least one order of magnitude. For 70 µL aliquots of solution within the microwell chip, limits of detection (LODs) were between 20 pM–2 nM for different colors of Dex-QDs, 3 nM for fluorescein dye, and 70–500 fM for different colors of Pdots. When comparing laser scanning to conventional epi-illumination, the laser scanning had 1-2 orders of magnitude lower LODs. For the lateral flow binding assays, the super-QDs provided the lowest LOD (10 pM) whereas Dex-QDs had the highest LOD (10 nM). Conclusion This research is a step toward improving molecular diagnostic capability and accessibility in resource-limited settings, such as rural and remote communities in Canada and worldwide. In addressing the capability gap between these communities and urban centers, this technology supports greater equity and inclusion in modern health care.
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 enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,047 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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.
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 tête enseignante, 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 ».