Smartphone-Based Fully Automated Optofluidic Device with Laser Irradiation-Induced Image Whitening

Immunity testing against COVID-19 from blood by an IoT-enabled and AI-controlled multiplexed microfluidic platform

Developing herd immunity against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is pivotal for changing the course of the coronavirus disease 2019 (COVID-19) pandemic. However, the uncertainty of vaccine-induced immunity development and inequitable distribution of vaccines hinders the global vaccination effort. Therefore, routine serodiagnosis and ensuring effective vaccination on a time-to-time basis are essential for developing sustainable immunity against SARS-CoV-2. Herein, an AI-driven multiplexed point-of-care testing (POCT) platform capable of utilizing a microfluidic lab-on-a-chip (LOC) device has been proposed for analyzing bodily fluid response against SARS-CoV-2. The developed platform has been successfully utilized for the quantification of SARS-CoV-2 S-protein, N-protein, IgM, and IgG from human blood samples with limits of detection (LODs) as low as 0.01, 0.02, 0.69, and 0.61 ng/mL respectively. Finally, a data-receptive web-based dashboard system has been developed and demonstrated to provide real-time, territory-specific analysis of herd immunity progress from the test results. Thus, the proposed platform could be an imperative tool for healthcare authorities to analyze and restrain ongoing COVID-19 outbreaks or similar pandemics in the future by ensuring effective immunization.

A miniaturized analytical system with packed epoxy-functionalized mesoporous organosilica for copper determination using a customized Android-based software

A smartphone-assisted determination of copper ions is introduced by using a down-scaled microfluidic mixer. The system was coupled with a micro-column packed with a periodic mesoporous organosilica (PMO) material for preconcentration of copper ions. Copper ions were reduced to Cu(I) on-chip to selectively form an orange-colored complex with neocuproine. A novel Android-based software was made to determine the color change of the adsorbent by analyzing red-green-blue (RGB) components of images from the packed PMO material. Four porous framework materials with high porosity and chemical stability were synthesized and compared for the extraction of the Cu-neocuproine complex. The main parameters influencing the complex extraction efficiency were optimized. The analytical performance of the method showed limit of detection and quantification of 0.2 μg L^-1 and 0.5 μg L^-1, respectively. The accuracy and precision of the method were determined as recovery > 92% and relative standard deviations < 5.2% at medium concentration level (n = 5). Due to accumulation of the retained analyte in a single point and elimination of the stripping step, the RGB-based method showed sensitivity and precision higher than inductively coupled plasma-atomic emission spectrometry (ICP-AES) for determination of copper ions. To investigate the applicability of the method, six different water samples were analyzed. The t-test on the data showed that the method has no significant difference when compared with ICP-AES determination.

An Internet-of-Disease System for COVID-19 Testing Using Saliva by an AI-Controlled Microfluidic ELISA Device

Throughout coronavirus disease (COVID-19) outbreaks, the centers for disease control and prevention (CDCP) of a country require monitoring of particular territories to provide public health guidance. In this work, the Internet of Diseases (IoD) is suggested for continuous real-time monitoring of infectious diseases for public health. Because converging information and communication technologies (ICTs) with point-of-care (POC) devices to enable the IoD for continuous real-time health monitoring and processing of clinical records are crucial, an IoD platform associating a lab-on-a-chip (LOC) device to diagnose severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) from oropharyngeal saliva samples have been developed and uploaded the resulted diagnostic data into a cloud-based system to be connected with CDCP. Moreover, a choropleth IoD map to visualize provincial infection rate is proposed along with the IoD platform. The developed platform is applied for the quantification of SARS-CoV-2 N-protein antigen with a LOD as low as 0.013 ng mL^-1 and the infection rate of various provinces is projected with the IoD map successfully. Thus, the proposed IoD system has the potential to become an imperative tool for the disease control and prevention centers to restrain COVID-19 outbreaks by identifying the severity of particular regions.

Smartphone-Based Platforms for Clinical Detections in Lung-Cancer-Related Exhaled Breath Biomarkers: A Review

Lung cancer has been studied for decades because of its high morbidity and high mortality. Traditional methods involving bronchoscopy and needle biopsy are invasive and expensive, which makes patients suffer more risks and costs. Various noninvasive lung cancer markers, such as medical imaging indices, volatile organic compounds (VOCs), and exhaled breath condensates (EBCs), have been discovered for application in screening, diagnosis, and prognosis. However, the detection of markers still relies on bulky and professional instruments, which are limited to training personnel or laboratories. This seriously hinders population screening for early diagnosis of lung cancer. Advanced smartphones integrated with powerful applications can provide easy operation and real-time monitoring for healthcare, which demonstrates tremendous application scenarios in the biomedical analysis region from medical institutions or laboratories to personalized medicine. In this review, we propose an overview of lung-cancer-related noninvasive markers from exhaled breath, focusing on the novel development of smartphone-based platforms for the detection of these biomarkers. Lastly, we discuss the current limitations and potential solutions.

Artificial Intelligence-Controlled Microfluidic Device for Fluid Automation and Bubble Removal of Immunoassay Operated by a Smartphone

There have been tremendous innovations in microfluidic clinical diagnostics to facilitate novel point-of-care testing (POCT) over the past decades. However, the automatic operation of microfluidic devices that minimize user intervention still lacks reliability and repeatability because microfluidic errors such as bubbles and incomplete filling pose a major bottleneck in commercializing the microfluidic devices for clinical testing. In this work, for the first time, various states of microfluid were recognized to control immunodiagnostics by artificial intelligence (AI) technology. The developed AI-controlled microfluidic platform was operated via an Android smartphone, along with a low-cost polymer device to effectuate enzyme-linked immunosorbent assay (ELISA). To overcome the limited machine-learning capability of smartphones, the region-of-interest (ROI) cascading and conditional activation algorithms were utilized herein. The developed microfluidic chip was incorporated with a bubble trap to remove any bubbles detected by AI, which helps in preventing false signals during immunoassay, as well as controlling the reagents' movement with an on-chip micropump and valve. Subsequently, the developed immunosensing platform was tested for conducting real ELISA using a single microplate from the 96-well to detect the Human Cardiac Troponin I (cTnI) biomarker, with a detection limit as low as 0.98 pg/mL. As a result, the developed platform can be envisaged as an AI-based revolution in microfluidics for point-of-care clinical diagnosis.