PCR-like performance of rapid test with permselective tunable nanotrap

Electro-Optical Multiclassification Platform for Minimizing Occasional Inaccuracy in Point-of-Care Biomarker Detection

On-site diagnostic tests that accurately identify disease biomarkers lay the foundation for self-healthcare applications. However, these tests routinely rely on single-mode signals and suffer from insufficient accuracy, especially for multiplexed point-of-care tests (POCTs) within a few minutes. Here, this work develops a dual-mode multiclassification diagnostic platform that integrates an electrochemiluminescence sensor and a field-effect transistor sensor in a microfluidic chip. The microfluidic channel guides the testing samples to flow across electro-optical sensor units, which produce dual-mode readouts by detecting infectious biomarkers of tuberculosis (TB), human rhinovirus (HRV), and group B streptococcus (GBS). Then, machine-learning classifiers generate three-dimensional (3D) hyperplanes to diagnose different diseases. Dual-mode readouts derived from distinct mechanisms enhance the anti-interference ability physically, and machine-learning-aided diagnosis in high-dimensional space reduces the occasional inaccuracy mathematically. Clinical validation studies with 501 unprocessed samples indicate that the platform has an accuracy approaching 99%, higher than the 77%-93% accuracy of rapid point-of-care testing technologies at 100% statistical power (>150 clinical tests). Moreover, the diagnosis time is 5 min without a trade-off of accuracy. This work solves the occasional inaccuracy issue of rapid on-site diagnosis, endowing POCT systems with the same accuracy as laboratory tests and holding unique prospects for complicated scenes of personalized healthcare.

Enhancing Loop-Mediated Isothermal Amplification through Nonpowered Nanoelectric Preconcentration

The global threat posed by the COVID-19 pandemic has catalyzed the development of point-of-care (POC) molecular diagnostics. While loop-mediated isothermal amplification (LAMP) stands out as a promising technique among FDA-approved methods, it is occasionally susceptible to a high risk of false positives due to nonspecific amplification of a primer dimer. In this work, we report an enhancing LAMP technique in terms of assay sensitivity and reliability through streamlined integration with a nonpowered nanoelectric preconcentration (NPP). The NPP, serving as a sample preparation tool, enriched the virus concentration in samples prior to the subsequent LAMP assay. This enrichment enabled not only to achieve more sensitive assay but also to shorten the assay time for all tested clinical samples by ∼10 min compared to the conventional LAMP. The shortened assay time suppresses the occurrence of nonspecific amplification by not providing the necessary incubation time, effectively suppressing misidentification by false positives. Utilizing this technique, we also developed a prototype of the POC NPP-LAMP kit. This kit offers a streamlined diagnostic process for nontrained individuals, from the sample enrichment, transfer of the enriched sample to LAMP assays, which facilitates on-site/on-demand diagnosis of SARS-CoV-2. This development holds the potential to contribute toward preventing not only the current outbreak but also future occurrences of pandemic viruses.

Biporous silica nanostructure-induced nanovortex in microfluidics for nucleic acid enrichment, isolation, and PCR-free detection

Efficient pathogen enrichment and nucleic acid isolation are critical for accurate and sensitive diagnosis of infectious diseases, especially those with low pathogen levels. Our study introduces a biporous silica nanofilms-embedded sample preparation chip for pathogen and nucleic acid enrichment/isolation. This chip features unique biporous nanostructures comprising large and small pore layers. Computational simulations confirm that these nanostructures enhance the surface area and promote the formation of nanovortex, resulting in improved capture efficiency. Notably, the chip demonstrates a 100-fold lower limit of detection compared to conventional methods used for nucleic acid detection. Clinical validations using patient samples corroborate the superior sensitivity of the chip when combined with the luminescence resonance energy transfer assay. The enhanced sample preparation efficiency of the chip, along with the facile and straightforward synthesis of the biporous nanostructures, offers a promising solution for polymer chain reaction-free detection of nucleic acids.

Advancing diagnostic efficacy using a computer vision-assisted lateral flow assay for influenza and SARS-CoV-2 detection

Lateral flow assays (LFAs) have emerged as indispensable tools for point-of-care testing during the pandemic era. However, the interpretation of results through unassisted visual inspection by untrained individuals poses inherent limitations. In our study, we propose a novel approach that combines computer vision (CV) and lightweight machine learning (ML) to overcome these limitations and significantly enhance the performance of LFAs. By incorporating CV-assisted analysis into the LFA assay, we achieved a remarkable three-fold improvement in analytical sensitivity for detecting Influenza A and for SARS-CoV-2 detection. The obtained R2 values reached approximately 0.95, respectively, demonstrating the effectiveness of our approach. Moreover, the integration of CV techniques with LFAs resulted in a substantial amplification of the colorimetric signal specifically for COVID-19 positive patient samples. Our proposed approach, which incorporates a simple machine learning algorithm, provides substantial enhancements in assay sensitivity, improving diagnostic efficacy and accessibility of point-of-care testing without requiring significant additional resources. Moreover, the simplicity of the machine learning algorithm enables its standalone use on a mobile phone, further enhancing its practicality for point-of-care testing.