Rapid, sensitive, and selective detection of nucleic acid biomarkers for health diagnostic applications becomes feasible for point of care scenarios when the detection instrument is inexpensive, simple, and robust. Here, we report the design, implementation, and characterization of a point of care instrument for photonic resonator absorption microscopy (PRAM) that takes advantage of resonant optical coupling between plasmonic gold nanoparticle tags and a photonic crystal (PC) surface. Matching the PC resonant wavelength to the gold nanoparticle’s surface plasmon wavelength generates localized and efficient quenching of the PC resonant reflection intensity, resulting in the ability to clearly detect and count individual gold nanoparticles when they are captured on the PC surface. Surface-captured nanoparticles are observed by illuminating the PC at normal incidence with polarized light from a low-intensity red LED, and recording of PC reflected intensity on an inexpensive CMOS image sensor. A contrast limited adaptive histogram equalization (CLAHE) image processing algorithm was applied to derive counts of captured nanoparticles. The instrument is utilized in the context of an activate capture + digital counting (AC + DC) assay for a specific miRNA sequence, using nucleic acid toehold probes applied to gold nano-urchin (AuNU) nanoparticles to achieve 160 aM detection limits in a 30 min. assay.

Objectives: To propose a point-of-care image recognition system for kidney stone composition classification using smartphone microscopy and deep convolutional neural networks. Materials and methods:A total of 37 surgically extracted human kidney stones consisting of calcium oxalate (CaOx), cystine, uric acid (UA) and struvite stones were included in the study. All of the stones were fragmented from percutaneous nephrolithotomy (PCNL). The stones were classified using Fourier transform infrared spectroscopy (FTIR) analysis before obtaining smartphone microscope images. The size of the stones ranged from 5 to 10 mm in diameter. Nurugo 400Â smartphone microscope (Nurugo, Seoul, Republic of Korea) was functionalized to acquire microscopic images (magnification = 25Â) of dry kidney stones using iPhone 6s+ (Apple, Cupertino, CA, USA). Each kidney stone was imaged in six different locations. In total, 222 images were captured from 37 stones. A novel convolutional neural network architecture was built for classification, and the model was assessed using accuracy, positive predictive value, sensitivity and F1 scores. Results:We achieved an overall and weighted accuracy of 88% and 87%, respectively, with an average F1 score of 0.84. The positive predictive value, sensitivity and F1 score for each stone type were respectively reported as follows: CaOx (0.82, 0.83, 0.82), cystine (0.80, 0.88, 0.84), UA (0.92, 0.77, 0.85) and struvite (0.86, 0.84, 0.85). Conclusion:We demonstrate a rapid and accurate point of care diagnostics method for classifying the four types of kidney stones. In the future, diagnostic tools that combine smartphone microscopy with artificial intelligence (AI) can provide accessible health care that can support physicians in their decision-making process.

Introduction:Health equity for all patients is an important characteristic of an effective healthcare system. Bias has the potential to create inequities. In this study, we examine emergency department (ED) throughput and care measures for sex-based differences, including metrics such as door-to-room (DTR) and door-to-healthcare practitioner (DTP) times to look for potential signs of systemic bias.

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