Development of Integrated Systems for On-Site Infection Detection.

The modern healthcare system faces an unrelenting threat from microorganisms, as evidenced by global outbreaks of new viral diseases, emerging antimicrobial resistance, and the rising incidence of healthcare-associated infections (HAIs). An effective response to these threats requires rapid and accurate diagnostic tests that can identify causative pathogens at the point of care (POC). Such tests could eliminate diagnostic uncertainties, facilitating patient triaging, minimizing the empiric use of antimicrobial drugs, and enabling targeted treatments. Current standard methods, however, often fail to meet the needs of rapid diagnosis in POC settings. Culture-based assays entail long processing times and require specialized laboratory infrastructure; nucleic acid (NA) tests are often limited to centralized hospitals due to assay complexity and high costs. Here we discuss two new POC tests developed in our groups to enable the rapid diagnosis of infection. The first is nanoPCR that takes advantages of core-shell magnetoplasmonic nanoparticles (MPNs): (i) Au shell significantly accelerates thermocycling via volumetric, plasmonic light-to-heat conversion and (ii) a magnetic core enables sensitive in situ fluorescent detection via magnetic clearing. By adopting a Ferris wheel module, the system expedites multisamples in parallel with a minimal setup. When applied to COVID-19 diagnosis, nanoPCR detected SARS-CoV-2 RNA down to 3.2 copy/µL within 17 min. In particular, nanoPCR diagnostics accurately identified COVID-19 cases in clinical samples (n = 150), validating its clinical applicability. The second is a polarization anisotropy diagnostic (PAD) system that exploits the principle of fluorescence polarization (FP) as a detection modality. Fluorescent probes were designed to alter their molecular weight upon recognizing target NAs. This event modulates the probes' tumbling rate (Brownian motion), which leads to changes in FP. The approach is robust against environmental noise and benefits from the ratiometric nature of the signal readout. We applied PAD to detect clinically relevant HAI bacteria (Escherichia coli, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Staphylococcus aureus). The PAD assay demonstrated detection sensitivity down to the single bacterium level and determined both drug resistance and virulence status. In summary, these new tests have the potential to become powerful tools for rapid diagnosis in the infectious disease space. They do not require highly skilled personnel or labor-intensive analyses, and the assays are quick and cost-effective. These attributes will make nanoPCR and PAD well-aligned with a POC workflow to aid physicians to initiate prompt and informed patient treatment.

Fluorescence polarization system for rapid COVID-19 diagnosis.

Prompt diagnosis, patient isolation, and contact tracing are key measures to contain the coronavirus disease 2019 (COVID-19). Molecular tests are the current gold standard for COVID-19 detection, but are carried out at central laboratories, delaying treatment and control decisions. Here we describe a portable assay system for rapid, onsite COVID-19 diagnosis. Termed CODA (CRISPR Optical Detection of Anisotropy), the method combined isothermal nucleic acid amplification, activation of CRISPR/Cas12a, and signal generation in a single assay, eliminating extra manual steps. Importantly, signal detection was based on the ratiometric measurement of fluorescent anisotropy, which allowed CODA to achieve a high signal-to-noise ratio. For point-of-care operation, we built a compact, standalone CODA device integrating optoelectronics, an embedded heater, and a microcontroller for data processing. The developed system completed SARS-CoV-2 RNA detection within 20 min of sample loading; the limit of detection reached 3 copy/µL. When applied to clinical samples (10 confirmed COVID-19 patients; 10 controls), the rapid CODA test accurately classified COVID-19 status, in concordance with gold-standard clinical diagnostics.

Point-of-care cervical cancer screening using deep learning-based microholography.

Most deaths (80%) from cervical cancer occur in regions lacking adequate screening infrastructures or ready access to them. In contrast, most developed countries now embrace human papillomavirus (HPV) analyses as standalone screening; this transition threatens to further widen the resource gap. Methods: We describe the development of a DNA-focused digital microholography platform for point-of-care HPV screening, with automated readouts driven by customized deep-learning algorithms. In the presence of high-risk HPV 16 or 18 DNA, microbeads were designed to bind the DNA targets and form microbead dimers. The resulting holographic signature of the microbeads was recorded and analyzed. Results: The HPV DNA assay showed excellent sensitivity (down to a single cell) and specificity (100% concordance) in detecting HPV 16 and 18 DNA from cell lines. Our deep learning approach was 120-folder faster than the traditional reconstruction method and completed the analysis in < 2 min using a single CPU. In a blinded clinical study using patient cervical brushings, we successfully benchmarked our platform's performance to an FDA-approved HPV assay. Conclusions: Reliable and decentralized HPV testing will facilitate cataloguing the high-risk HPV landscape in underserved populations, revealing HPV coverage gaps in existing vaccination strategies and informing future iterations.

Design and clinical validation of a point-of-care device for the diagnosis of lymphoma via contrast-enhanced microholography and machine learning.

The identification of patients with aggressive cancer who require immediate therapy is a health challenge in low-income and middle-income countries. Limited pathology resources, high healthcare costs and large-case loads call for the development of advanced standalone diagnostics. Here, we report and validate an automated, low-cost point-of-care device for the molecular diagnosis of aggressive lymphomas. The device uses contrast-enhanced microholography and a deep-learning algorithm to directly analyse percutaneously obtained fine-needle aspirates. We show the feasibility and high accuracy of the device in cells, as well as the prospective validation of the results in 40 patients clinically referred for image-guided aspiration of nodal mass lesions suspicious for lymphoma. Automated analysis of human samples with the portable device should allow for the accurate classification of patients with benign and malignant adenopathy.

Computational Optics Enables Breast Cancer Profiling in Point-of-Care Settings.

The global burden of cancer, severe diagnostic bottlenecks in underserved regions, and underfunded health care systems are fueling the need for inexpensive, rapid, and treatment-informative diagnostics. On the basis of advances in computational optics and deep learning, we have developed a low-cost digital system, termed AIDA (artificial intelligence diffraction analysis), for breast cancer diagnosis of fine needle aspirates. Here, we show high accuracy (>90%) in (i) recognizing cells directly from diffraction patterns and (ii) classifying breast cancer types using deep-learning-based analysis of sample aspirates. The image algorithm is fast, enabling cellular analyses at high throughput (∼3 s per 1000 cells), and the unsupervised processing allows use by lower skill health care workers. AIDA can perform quantitative molecular profiling on individual cells, revealing intratumor molecular heterogeneity, and has the potential to improve cancer diagnosis and treatment. The system could be further developed for other cancers and thus find widespread use in global health.