We evaluated the performance of the Abbott BinaxNOW rapid antigen test for coronavirus disease 2019 (Binax-CoV2) to detect virus among persons, regardless of symptoms, at a public plaza site of ongoing community transmission. Titration with cultured severe acute respiratory syndrome coronavirus 2 yielded a human observable threshold between 1.6 × 104-4.3 × 104 viral RNA copies (cycle threshold [Ct], 30.3–28.8). Among 878 subjects tested, 3% (26 of 878) were positive by reverse-transcription polymerase chain reaction, of whom 15 of 26 had a Ct <30, indicating high viral load; of these, 40% (6 of 15) were asymptomatic. Using this Ct threshold (<30) for Binax-CoV2 evaluation, the sensitivity of Binax-CoV2 was 93.3% (95% confidence interval, 68.1%–99.8%) (14 of 15) and the specificity was 99.9% (99.4%–99.9%) (855 of 856).

We evaluated the performance of the Abbott BinaxNOWTM Covid-19 rapid antigen test to detect virus among persons, regardless of symptoms, at a public plaza site of ongoing community transmission. Titration with cultured clinical SARS-CoV-2 yielded a human observable threshold between 1.6x104-4.3x104 viral RNA copies (cycle threshold (Ct) of 30.3-28.8 in this assay). Among 878 subjects tested, 3% (26/878) were positive by RT-PCR, of which 15/26 had a Ct<30, indicating high viral load. 40% (6/15) of Ct<30 were asymptomatic. Using this Ct<30 threshold for Binax-CoV2 evaluation, the sensitivity of the Binax-CoV2 was 93.3% (14/15), 95% CI: 68.1-99.8%, and the specificity was 99.9% (862/863), 95% CI: 99.4-99.9%.

Manual microscopic inspection of fixed and stained blood smears has remained the gold standard for Plasmodium parasitemia analysis for over a century. Unfortunately, smear preparation consumes time and reagents, while manual microscopy is skill-dependent and labor-intensive. Here, we demonstrate that deep learning enables both life stage classification and accurate parasitemia quantification of ordinary brightfield microscopy images of live, unstained red blood cells. We tested our method using both a standard light microscope equipped with visible and near-ultraviolet (UV) illumination, and a custom-built microscope employing deep-UV illumination. While using deep-UV light achieved an overall four-category classification of Plasmodium falciparum blood stages of greater than 99% and a recall of 89.8% for ring-stage parasites, imaging with near-UV light on a standard microscope resulted in 96.8% overall accuracy and over 90% recall for ring-stage parasites. Both imaging systems were tested extrinsically by parasitemia titration, revealing superior performance over manually-scored Giemsa-stained smears, and a limit of detection below 0.1%. Our results establish that label-free parasitemia analysis of live cells is possible in a biomedical laboratory setting without the need for complex optical instrumentation. We anticipate future extensions of this work could enable label-free clinical diagnostic measurements, one day eliminating the need for conventional blood smear analysis.

Luminescence is ubiquitous in biology research and medicine. Conceptually simple, the detection of luminescence nonetheless faces technical challenges because relevant signals can exhibit exceptionally low radiant power densities. Although low light detection is well-established in centralized laboratory settings, the cost, size, and environmental requirements of high-performance benchtop luminometers are not compatible with geographically-distributed global health studies or resource-constrained settings. Here we present the design and application of a ~$700 US handheld, battery-powered luminometer with performance on par with high-end benchtop instruments. By pairing robust and inexpensive Silicon Photomultiplier (SiPM) sensors with a low-profile shutter system, our design compensates for sensor non-idealities and thermal drift, achieving a limit of detection of 1.6E-19 moles of firefly luciferase, or approximately 1fW of radiant optical power. Using these devices, we performed split luciferase serology studies to monitor sars-cov-2 antibodies in a cohort in the United States, as well as a field study in Bangladesh.

Manual microscopic inspection of fixed and stained blood smears has remained the gold standard for Plasmodium parasitemia analysis for over a century. Unfortunately, smear preparation consumes time and reagents, while manual microscopy is skill-dependent and labor-intensive. Here, we demonstrate that label-free microscopy combined with deep learning enables both life stage classification and accurate parasitemia quantification. Using a custom-built microscope, we find that deep-ultraviolet light enhances image contrast and resolution, achieving four-category classification of Plasmodium falciparum blood stages at an overall accuracy greater than 99%. To increase accessibility, we extended our method to a commercial brightfield microscope using near-ultraviolet and visible light. Both systems were tested extrinsically by parasitemia titration, revealing superior performance over manually-scored Giemsa-stained smears, and a limit of detection below 0.1%. Our results suggest that label-free microscopy combined with deep learning could eliminate the need for conventional blood smear analysis.