Liquid Biopsy Using The Nanotube-CTC- Chip

In this work, we describe the development of the nanotube-CTC-chip for capture and enumeration of circulating tumor cells (CTC) in breast cancer patients. The nanotube-CTC-chip is a new 76-element microarray technology that combines carbon nanotube surfaces with microarray batch manufacturing techniques for capture and isolation of tumor-derived epithelial cells.\n\nUsing a combination of red blood cell (RBC) lysis and preferential adherence, we demonstrate the capture and enrichment of CTCs with 5-log reduction of contaminating WBCs. EpCAM-MDA-MB-231/Luciferase-2A-Green Fluorescent Protein (GFP) cells were spiked in the blood of wild mice and enriched using an RBC lysis protocol. The enriched samples were then processed using the nanotube-CTC-chip for preferential CTC adherence on the nanosurface and counting the GFP cells enabled anywhere from 89-100% capture from the droplets.\n\nElectron microscopy (EM) studies revealed strong focal adhesion with filaments from the cell body to the nanotube surface. The preferential adherence strategy of CTC capture compared to collagen adhesion matrix (CAM) scaffolding method, which in the past reported as a viable strategy for CTC capture in patients. The CAM scaffolding on the device surface yielded 50% adherence with 100% tracking of cancer cells (adhered and non-adhered) versus carbon nanotubes with >90% adherence and 100% tracking for the same protocol.\n\nThe nanotube-CTC-chip successfully captured CTCs in the peripheral blood of breast cancer patients (Stage 1-4) with a range of 4–238 CTCs per 8.5 ml blood or 0.5-28 CTCs per ml. CTCs (based on CK8/18, Her2, EGFR) successfully identified in 7/7 breast cancer patients, and no CTCs captured in healthy controls (n=2). CTC enumeration based on different markers using the nanotube-CTC-chip enables dynamic views of metastatic progression

A Thermoacoustic Model for High Aspect Ratio Nanostructures

In this paper, we have developed a new thermoacoustic model for predicting the resonance frequency and quality factors of one-dimensional (1D) nanoresonators. Considering a nanoresonator as a fix-free Bernoulli-Euler cantilever, an analytical model has been developed to show the influence of material and geometrical properties of 1D nanoresonators on their mechanical response without any damping. Diameter and elastic modulus have a direct relationship and length has an inverse relationship on the strain energy and stress at the clamp end of the nanoresonator. A thermoacoustic multiphysics COMSOL model has been elaborated to simulate the frequency response of vibrating 1D nanoresonators in air. The results are an excellent match with experimental data from independently published literature reports, and the results of this model are consistent with the analytical model. Considering the air and thermal damping in the thermoacoustic model, the quality factor of a nanowire has been estimated and the results show that zinc oxide (ZnO) and silver-gallium (Ag2Ga) nanoresonators are potential candidates as nanoresonators, nanoactuators, and for scanning probe microscopy applications

Dynamic detection and reversal of myocardial ischemia using an artificially intelligent bioelectronic medicine

Potentially damaging “heart stress” is reversed using reactive nerve stimulation controlled by artificial intelligence. Myocardial ischemia is spontaneous, frequently asymptomatic, and contributes to fatal cardiovascular consequences. Importantly, myocardial sensory networks cannot reliably detect and correct myocardial ischemia on their own. Here, we demonstrate an artificially intelligent and responsive bioelectronic medicine, where an artificial neural network (ANN) supplements myocardial sensory networks, enabling reliable detection and correction of myocardial ischemia. ANNs were first trained to decode spontaneous cardiovascular stress and myocardial ischemia with an overall accuracy of ~92%. ANN-controlled vagus nerve stimulation (VNS) significantly mitigated major physiological features of myocardial ischemia, including ST depression and arrhythmias. In contrast, open-loop VNS or ANN-controlled VNS following a caudal vagotomy essentially failed to reverse cardiovascular pathophysiology. Last, variants of ANNs were used to meet clinically relevant needs, including interpretable visualizations and unsupervised detection of emerging cardiovascular stress. Overall, these preclinical results suggest that ANNs can potentially supplement deficient myocardial sensory networks via an artificially intelligent bioelectronic medicine system