The fast and untraceable virus mutations take lives of thousands of people before the immune system can produce the inhibitory antibody. Recent outbreak of novel coronavirus infected and killed thousands of people in the world. Rapid methods in finding peptides or antibody sequences that can inhibit the viral epitopes of COVID-19 will save the life of thousands. In this paper, we devised a machine learning (ML) model to predict the possible inhibitory synthetic antibodies for Corona virus. We collected 1933 virus-antibody sequences and their clinical patient neutralization response and trained an ML model to predict the antibody response. Using graph featurization with variety of ML methods, we screened thousands of hypothetical antibody sequences and found 8 stable antibodies that potentially inhibit COVID-19. We combined bioinformatics, structural biology, and Molecular Dynamics (MD) simulations to verify the stability of the candidate antibodies that can inhibit the Corona virus.

A longstanding challenge for accurate sensing of biomolecules such as proteins concerns specifically detecting a target analyte in a complex sample (e.g., food) without suffering from nonspecific binding or interactions from the target itself or other analytes present in the sample. Every sensor suffers from this fundamental drawback, which limits its sensitivity, specificity, and longevity. Existing efforts to improve signal-to-noise ratio involve introducing additional steps to reduce nonspecific binding, which increases the cost of the sensor. Conducting polymer-based chemiresistive biosensors can be mechanically flexible, are inexpensive, label-free, and capable of detecting specific biomolecules in complex samples without purification steps, making them very versatile. In this paper, a poly (3,4-ethylenedioxyphene) (PEDOT) and poly (3-thiopheneethanol) (3TE) interpenetrating network on polypropylene–cellulose fabric is used as a platform for a chemiresistive biosensor, and the specific and nonspecific binding events are studied using the Biotin/Avidin and Gliadin/G12-specific complementary binding pairs. We observed that specific binding between these pairs results in a negative ΔR with the addition of the analyte and this response increases with increasing analyte concentration. Nonspecific binding was found to have the opposite response, a positive ΔR upon the addition of analyte was seen in nonspecific binding cases. We further demonstrate the ability of the sensor to detect a targeted protein in a dual-protein analyte solution. The machine-learning classifier, random forest, predicted the presence of Biotin with 75% accuracy in dual-analyte solutions. This capability of distinguishing between specific and nonspecific binding can be a step towards solving the problem of false positives or false negatives to which all biosensors are susceptible.

We present W-Net, a novel Convolution Neural Network (CNN) framework that employs raw ultrasound waveforms from each A-scan, typically referred to as ultrasound Radio Frequency (RF) data, in addition to the gray ultrasound image to semantically segment and label tissues. Unlike prior work, we seek to label every pixel in the image, without the use of a background class. To the best of our knowledge, this is also the first deep-learning or CNN approach for segmentation that analyses ultrasound raw RF data along with the gray image. International patent(s) pending [PCT/US20/37519]. We chose subcutaneous tissue (SubQ) segmentation as our initial clinical goal since it has diverse intermixed tissues, is challenging to segment, and is an underrepresented research area. SubQ potential applications include plastic surgery, adipose stem-cell harvesting, lymphatic monitoring, and possibly detection/treatment of certain types of tumors. A custom dataset consisting of hand-labeled images by an expert clinician and trainees are used for the experimentation, currently labeled into the following categories: skin, fat, fat fascia/stroma, muscle and muscle fascia. We compared our results with U-Net and Attention U-Net. Our novel \emph{W-Net}'s RF-Waveform input and architecture increased mIoU accuracy (averaged across all tissue classes) by 4.5\% and 4.9\% compared to regular U-Net and Attention U-Net, respectively. We present analysis as to why the Muscle fascia and Fat fascia/stroma are the most difficult tissues to label. Muscle fascia in particular, the most difficult anatomic class to recognize for both humans and AI algorithms, saw mIoU improvements of 13\% and 16\% from our W-Net vs U-Net and Attention U-Net respectively.