Development of a rapid lateral flow assay for detection of anti-coccidioidal antibodies.

Coccidioides spp. are dimorphic fungi that are capable of infecting human and non-human mammals and can cause diverse manifestations of coccidioidomycosis or Valley fever (VF). In combination with clinical symptoms and radiographic findings, antibody-based diagnostic tests are often used to diagnose and monitor patients with VF. Chitinase 1 (CTS1) has previously been identified as the seroreactive antigen used in these diagnostic assays to detect anticoccidial IgG. Here, an indirect enzyme-linked immunosorbent assay to detect IgG to CTS1 demonstrated 165 of 178 (92.7%) patients with a positive result by immunodiffusion (ID) and/or complement fixation (CF) had antibodies to the single antigen CTS1. We then developed a rapid antibody lateral flow assay (LFA) to detect anti-CTS1 antibodies. Out of 143 samples tested, the LFA showed 92.9% positive percent agreement [95% confidence interval (CI), 84.3%-96.9%] and 97.7% negative percent agreement (95% CI, 87.9%-99.6%) with ID and CF assays. Serum or plasma from canines, macaques, and dolphins was also tested by the CTS1 LFA. Test line densities of the CTS1 LFA correlated in a linear manner with the reported CF and ID titers for human and non-human samples, respectively. This 10-min point-of-care test for the rapid detection of anti-coccidioidal antibodies could help to inform healthcare providers in real-time, potentially improving the efficiency of healthcare delivery.

Rapidly adaptable automated interpretation of point-of-care COVID-19 diagnostics.

BACKGROUND: Point-of-care diagnostic devices, such as lateral-flow assays, are becoming widely used by the public. However, efforts to ensure correct assay operation and result interpretation rely on hardware that cannot be easily scaled or image processing approaches requiring large training datasets, necessitating large numbers of tests and expert labeling with validated specimens for every new test kit format. METHODS: We developed a software architecture called AutoAdapt POC that integrates automated membrane extraction, self-supervised learning, and few-shot learning to automate the interpretation of POC diagnostic tests using smartphone cameras in a scalable manner. A base model pre-trained on a single LFA kit is adapted to five different COVID-19 tests (three antigen, two antibody) using just 20 labeled images. RESULTS: Here we show AutoAdapt POC to yield 99% to 100% accuracy over 726 tests (350 positive, 376 negative). In a COVID-19 drive-through study with 74 untrained users self-testing, 98% found image collection easy, and the rapidly adapted models achieved classification accuracies of 100% on both COVID-19 antigen and antibody test kits. Compared with traditional visual interpretation on 105 test kit results, the algorithm correctly identified 100% of images; without a false negative as interpreted by experts. Finally, compared to a traditional convolutional neural network trained on an HIV test kit, the algorithm showed high accuracy while requiring only 1/50th of the training images. CONCLUSIONS: The study demonstrates how rapid domain adaptation in machine learning can provide quality assurance, linkage to care, and public health tracking for untrained users across diverse POC diagnostic tests.

It can be difficult to correctly interpret the results of rapid diagnostic tests that give a visual readout, such as COVID rapid tests. We developed a computational algorithm to interpret rapid test results using an image taken by a smartphone camera. This algorithm can easily be adapted for use on results from different test kits. The algorithm was accurate at interpreting results obtained by members of the public using various COVID rapid tests and diagnostic tests with similar outputs used for other infections. The use of this algorithm should enable accurate interpretation of rapid diagnostic tests by members of the public and hence enable improved medical care.

Rapid Detection of Urinary Tract Infection in 10 min by Tracking Multiple Phenotypic Features in a 30 s Large-Volume Scattering Video of Urine Microscopy.

Rapid point-of-care (POC) diagnosis of bacterial infection diseases provides clinical benefits of prompt initiation of antimicrobial therapy and reduction of the overuse/misuse of unnecessary antibiotics for nonbacterial infections. We present here a POC compatible method for rapid bacterial infection detection in 10 min. We use a large-volume solution scattering imaging (LVSi) system with low magnifications (1-2×) to visualize bacteria in clinical samples, thus eliminating the need for culture-based isolation and enrichment. We tracked multiple intrinsic phenotypic features of individual cells in a short video. By clustering these features with a simple machine learning algorithm, we can differentiate Escherichia coli from similar-sized polystyrene beads, distinguish bacteria with different shapes, and distinguish E. coli from urine particles. We applied the method to detect urinary tract infections in 104 patient urine samples with a 30 s LVSi video, and the results showed 92.3% accuracy compared with the clinical culture results. This technology provides opportunities for rapid bacterial infection diagnosis at POC settings.