Teresa Chung

Background/Objectives: We have previously reported the engineering of a point-of-care (POC) system that fully automates the procedures for nucleic acid extraction and multiplexed real-time RT-PCR, with a major advantage of high-level multiplexing. In this study, we applied and validated the system in a respiratory tract infection setting. Methods: An automatic nested real-time RT-PCR assay was developed (POCm). It was a 40-plex assay that simultaneously detected 39 epidemiologically important respiratory pathogens in 1.5 h in the POC system. The analytical and clinical performance was evaluated. Results: The analytical sensitivities of the POCm assay were comparable to those of its single-plex counterparts performed manually on a bench-top. The minimum detectable concentrations ranged from 53 copies/mL to 5.3 × 10³ copies/mL for all pathogen targets except hCoV-NL63 (5.3 × 10⁴ copies/mL). The quantitative performance was demonstrated by the linear correlations between Ct values and input concentrations for all pathogen targets, with 24 of them demonstrating coefficients of correlation (r) greater than 0.9. The POCm assay was subsequently evaluated in 283 clinical samples. A high level of agreement (98.2–100%) was achieved for pathogen detection results between POCm and standard diagnostic methods. The POCm result was also fully concordant with the result of another commercial POC multiplex platform. For positive clinical samples, pairwise Ct values measured by POCm closely correlated with those of the bench-top reference method (r = 0.70). The feasibility of mutation genotyping of the viral subtype was further demonstrated. Conclusions: This study demonstrated the practicality of POCm for routine testing in clinical laboratories. Further clinical trials are being conducted to evaluate the clinical performance of the system.

Objective: Infectious diseases are global health challenge, impacted the communities worldwide particularly in the midst of COVID-19 pandemic. The need of rapid and accurate automated systems for detecting pathogens of concern has always been critical. Ideally, such systems shall detect a large panel of pathogens simultaneously regardless of well-equipped facilities and highly trained operators, thus realizing on-site diagnosis for frontline healthcare providers and in critical locations such as borders and airports. Methods & Results: Avalon Automated Multiplex System, AAMST, is developed to automate a series of biochemistry protocols to detect nucleic acid sequences from multiple pathogens in one test. Automated processes include isolation of nucleic acids from unprocessed samples, reverse transcription and two rounds of amplifications. All procedures are carried out in a microfluidic cartridge performed by a desktop analyzer. The system was validated with reference controls and showed good agreement with their laboratory counterparts. In total 63 clinical samples, 13 positives including those from COVID-19 patients and 50 negative cases were detected, consistent with clinical diagnosis using conventional laboratory methods. Conclusions: The proposed system has demonstrated promising utility. It would benefit the screening and diagnosis of COVID-19 and other infectious diseases in a simple, rapid and accurate fashion.

Recurrent incidents of economically motivated adulteration have long-lasting and devastating effects on public health, economy, and society. With the current food authentication methods being target-oriented, the lack of an effective methodology to detect unencountered adulterants can lead to the next melamine-like outbreak. In this study, an ensemble machine-learning model that can help detect unprecedented adulteration without looking for specific substances, that is, in a non-targeted approach, is proposed. Using raw milk as an example, the proposed model achieved an accuracy and F1 score of 0.9924 and 0. 0.9913, respectively, when the same type of adulterants was presented in the training data. Cross-validation with spiked contaminants not routinely tested in the food industry and blinded from the training data provided an F1 score of 0.8657. This is the first study that demonstrates the feasibility of non-targeted detection with no a priori knowledge of the presence of certain adulterants using data from standard industrial testing as input. By uncovering discriminative profiling patterns, the ensemble machine-learning model can monitor and flag suspicious samples; this technique can potentially be extended to other food commodities and thus become an important contributor to public food safety.