The Democratization of Diagnosis: Bringing the Power of Medical Diagnosis to the Masses

New Virus Diagnostic Approaches to Ensuring the Ongoing Plant Biosecurity of Aotearoa New Zealand

To protect New Zealand's unique ecosystems and primary industries, imported plant materials must be constantly monitored at the border for high-threat pathogens. Techniques adopted for this purpose must be robust, accurate, rapid, and sufficiently agile to respond to new and emerging threats. Polymerase chain reaction (PCR), especially real-time PCR, remains an essential diagnostic tool but it is now being complemented by high-throughput sequencing using both Oxford Nanopore and Illumina technologies, allowing unbiased screening of whole populations. The demand for and value of Point-of-Use (PoU) technologies, which allow for in situ screening, are also increasing. Isothermal PoU molecular diagnostics based on recombinase polymerase amplification (RPA) and loop-mediated amplification (LAMP) do not require expensive equipment and can reach PCR-comparable levels of sensitivity. Recent advances in PoU technologies offer opportunities for increased specificity, accuracy, and sensitivities which makes them suitable for wider utilization by frontline or border staff. National and international activities and initiatives are adopted to improve both the plant virus biosecurity infrastructure and the integration, development, and harmonization of new virus diagnostic technologies.

Accuracy of smartphone camera urine photo colorimetry as indicators of dehydration

Objective

Direct urine color assessment has been shown to correlate with hydration status. However, this method is subject to inter- and intra-observer variability. Digital image colorimetry provides a more objective method. This study evaluated the diagnostic accuracy of urine photo colorimetry using different smartphones under different lighting conditions, and determined the optimal cut-off value to predict clinical dehydration.

Methods

The urine samples were photographed in a customized photo box, under five simulated lighting conditions, using five smartphones. The images were analyzed using Adobe Photoshop to obtain Red, Green, and Blue (RGB) values. The correlation between RGB values and urine laboratory parameters were determined. The optimal cut-off value to predict dehydration was determined using area under the receiver operating characteristic curve.

Results

A total of 56 patients were included in the data analysis. Images captured using five different smartphones under five lighting conditions produced a dataset of 1400 images. The study found a statistically significant correlation between Blue and Green values with urine osmolality, sodium, urine specific gravity, protein, and ketones. The diagnostic accuracy of the Blue value for predicting dehydration were "good" to "excellent" across all phones under all lighting conditions with sensitivity >90% at cut-off Blue value of 170.

Conclusions

Smartphone-based urine colorimetry is a highly sensitive tool in predicting dehydration.