COVID-19 has been declared a global pandemic which has brought the world economy and the society to a standstill. The current emphasis of testing is on detection of genetic material of SARS-CoV-2. Such tests are useful for assessing the current state of a subject: Infected or not infected. In addition to such tests, antibody testing is necessary to stratify the population into three groups: never exposed, infected, and immune. Such a stratification is necessary for safely reopening the society and remobilizing the economy. The aim of this review article is to inform the audience of the current diagnostic and surveillance technologies that are being employed for the detection of SARS-CoV-2 antibodies along with their shortcomings, and to highlight microfluidic sensors and devices that show promise of being commercialized for detection and quantification of SARS-CoV-2 antibodies in low-resource and Point-of-Care (POC) settings.

Portable smartphone-based fluorescent microscopes are becoming popular owing to their ability to provide major functionalities offered by regular benchtop microscopes at a fraction of the cost. However, smartphone-based microscopes are still limited to a single fluorophore, fixed magnification, the inability to work with a different smartphones, and limited usability to either glass slides or cover slips. To overcome these challenges, here we present a modular smartphone-based microscopic attachment. The modular design allows the user to easily swap between different sets of filters and lenses, thereby enabling utility of multiple fluorophores and magnification levels. Our microscopic smartphone attachment can also be used with different smartphones and was tested with Nokia Lumia 1020, Samsung Galaxy S9+, and an iPhone XS. Further, we showed imaging results of samples on glass slides, cover slips, and microfluidic devices. A 1951 USAF resolution test target was used to quantify the maximum resolution of the microscope which was found to be 3.9 μm. The performance of the smartphone-based microscope was compared with a benchtop microscope and we found an R² value of 0.99 using polystyrene beads and blood cells isolated from human blood samples collected from Robert Wood Johnson Medical Hospital. Additionally, to count the particles (cells and beads) imaged from the smartphone-based fluorescent microscope, we developed artificial neural networks (ANNs) using multiple training algorithms, and evaluated their performances compared to the control (ImageJ). Finally, we did ANOVA and Tukey's post-hoc analysis and found a p-value of 0.97 which shows that no statistical significant difference exists between the performance of the trained ANN and control (ImageJ).

Leukocyte quantification from whole blood can aid in detecting and managing infections, cardiovascular diseases, and immune system responses. In addition to traditional leukocyte quantification devices such as flow cytometers and benchtop fluorescent microscopes, smartphone-based particle quantifiers are becoming popular because they provide results at a fraction of the cost. One major limitation of these smartphone-based devices is their dependence on desktop computers for data processing, which keeps them from reaching their true translation potential as point of care (POC) devices. In this paper, we present a computer vision and machine learning-enabled methodology to count particles imaged from our 3D printed smartphone-coupled fluorescent microscope. Multiple convolution neural networks (CNN) using different filter sizes were implemented and trained with various learning rates (0.001, 0.0001) and batch sizes (8,16,32). The performance of these trained networks was then tested on green fluorescent microparticles and leukocytes and compared against the ground truth obtained using ImageJ. An R ² value of 0.99 was observed. Next, when cross-validation was done to validate the efficacy of the designed CNN architecture, and the predicted results showed a good correlation (R ² = 0.99) when compared against the ground truth. The performance of the trained model was also evaluated on particles conjugated with a different fluorophore and an R ² value of 0.99 was observed, showcasing its efficacy and versatility. This trained model was then integrated into an Application Programming Interface (API) and is available online for the broader community usage.

In this paper, an Intrusion Detection System (IDS) in the Controller Area Network (CAN) bus of modern vehicles has been proposed. NESLIDS is an anomaly detection algorithm based on the supervised Deep Neural Network (DNN) architecture that is designed to counter three critical attack categories: Denial-of-service (DoS), fuzzy, and impersonation attacks. Our research scope included modifying DNN parameters, e.g. number of hidden layer neurons, batch size, and activation functions according to how well it maximized detection accuracy and minimized the false positive rate (FPR) for these attacks. Our methodology consisted of collecting CAN Bus data from online and in real-time, injecting attack data after data collection, preprocessing in Python, training the DNN, and testing the model with different datasets. Results show that the proposed IDS effectively detects all attack types for both types of datasets. NESLIDS outperforms existing approaches in terms of accuracy, scalability, and low false alarm rates.

Smartphone fluorescent microscopes (SFM) offer many functional characteristics similar to their benchtop counterparts at a fraction of the cost and have been shown to work for biomarker detection in many biomedical applications. However, imaging and quantification of bioparticles in the sub-micron and nanometer range remains challenging as it requires aggressive robustness and high-performance metrics of the building blocks of SFM. Here, we explored multiple excitation modalities and their performance on the imaging capability of an SFM. Employing spatial positional variations of the excitation source with respect to the imaging sample plane (i.e., parallel, perpendicular, oblique), we developed three distinct SFM variants. These SFM variants were tested using green-fluorescent beads of four different sizes (8.3, 2, 1, 0.8 μm). Optimal excitation voltage range was determined by imaging these beads at multiple excitation voltages to optimize for no data loss and acceptable noise levels for each SFM variant. The SFM with parallel excitation was able to only image 8.3 μm beads while the SFM variants with perpendicular and oblique excitation were able to image all four bead sizes. Relative performance of the SFM variants was quantified by calculating signal difference to noise ratio (SDNR) and contrast to noise ratio (CNR) from the captured images. SFM with oblique excitation generated the highest SDNR and CNR values, whereas, for power consumption, SFM with perpendicular excitation generated the best results. This study sheds light on significant findings related to performance of SFM systems and their potential utility in biomedical applications involving sub-micron imaging. Similarly, findings of this study are translatable to benchtop microscopy instruments as well as to enhance their imaging performance metrics.

The ability to kill infecting microbes is an essential facet of our immune response to an infection. However, phagocytic ability is often overlooked as a part of immunological profile in infected patients' diagnosis, as the understanding of phagocytic capabilities in disease states is incomplete. In this work, we have evaluated for the first time the relationship between blood lactate level and the neutrophil phagocytic activity at a single-cell level. Blood samples (N = 19) were grouped on the basis of their blood lactate levels i.e., below (control) or above 2 mmol L⁻¹ (high-risk) (i.e., 2 mmol L⁻¹ is a common clinical lactate threshold used for patients' triage). Neutrophils were isolated from whole blood and then incubated with fluorescent IgG coated beads for 40 minutes, and the ability of each neutrophil to internalize beads was quantified. Single-cell phagocytic activity analysis has shown interesting findings such as: (i) a single neutrophil was able to internalize up to 7 beads, (ii) for a control group, 39.76% cells didn't internalize any beads, while for a high-risk group, 30.65% cells didn't show any phagocytic activity, (iii) similarly, 30.46% cells internalize only 1 bead in a control group, while for a high-risk group the activity is slightly higher with only 31.73% cells showing single bead internalization, and (iv) 7 bead internalization activity was much higher for samples in a high-risk group (0.6% cells) compared to a control group (0.17% cells). We used multiple statistical tests to compare these differences. For a two-tailed T-test, we used the mean phagocytic activity of the cells (i.e., the average number of beads internalized by cells) isolated from the blood samples in the two groups (1.14 vs. 1.35) and found the p-value to be 0.08. We also used principal component analysis (PCA) on this high dimensional phagocytic activity distribution data and performed dimension reduction. However, the first 3 principal components didn't show a clear distinction between groups. Next, we developed machine learning models using artificial neural networks (ANNs) to differentiate between the distribution of phagocytic activity in neutrophil populations of the two groups. Our models yielded area under curve (AUC) values below 0.7 for receiver operator characteristic curves. Although our study highlighted interesting phagocytic activity findings at a single cell level, it further highlights the need for integration of an individual patient's medical record to get more personalized insights into individual phagocytic activity in the future.