Cytotoxicities of Tumor-Seeking Dyes: Impact on Future Clinical Trials

Heptamethine (Cy7) dyes with meso-Cl substituents injected intravenously (iv) into mice accumulate in tumors and persist there over several days. We believe this occurs via meso-Cl displacement by the only free cysteine residues of albumin; therefore, conjugating tumor-seeking dyes with fragments can increase selective therapeutic delivery to tumors and drug residence. This strategy has elevated significance recently because the first tumor-seeking dye-drug conjugate has moved into clinical trials. Options for further clinical research include modifying the dye, and use of preformed albumin adducts instead of dyes alone. Herein we show correlations of cytotoxicities, lipophilicities, organelle localization, apoptosis, cell-cycle arrest, wound healing/migration assays, and reactivities/affinities with human serum albumin are difficult to observe. However, our studies arrived at an important conclusion: preformed dye-drug-HSA adducts are less cytotoxic, and therefore preferable for subsequent clinical work, relative to direct injection of meso-Cl-containing forms.     

Genetics Based Compact Fuzzy System for Visual Sensor Network

As a component of Wireless Sensor Network (WSN), Visual-WSN (VWSN) utilizes cameras to obtain relevant data including visual recordings and static images. Data from the camera is sent to energy efficient sink to extract key-information out of it. VWSN applications range from health care monitoring to military surveillance. In a network with VWSN, there are multiple challenges to move high volume data from a source location to a target and the key challenges include energy, memory and I/O resources. In this case, Mobile Sinks(MS) can be employed for data collection which not only collects information from particular chosen nodes called Cluster Head (CH), it also collects data from nearby nodes as well. The innovation of our work is to intelligently decide on a particular node as CH whose selection criteria would directly have an impact on QoS parameters of the system. However, making an appropriate choice during CH selection is a daunting task as the dynamic and mobile nature of MSs has to be taken into account. We propose Genetic Machine Learning based Fuzzy system for clustering which has the potential to simulate human cognitive behavior to observe, learn and understand things from manual perspective. Proposed architecture is designed based on Mamdani’s fuzzy model. Following parameters are derived based on the model residual energy, node centrality, distance between the sink and current position, node centrality, node density, node history, and mobility of sink as input variables for decision making in CH selection. The inputs received have a direct impact on the Fuzzy logic rules mechanism which in turn affects the accuracy of VWSN. The proposed work creates a mechanism to learn the fuzzy rules using Genetic Algorithm (GA) and to optimize the fuzzy rules base in order to eliminate irrelevant and repetitive rules. Genetic algorithm-based machine learning optimizes the interpretability aspect of fuzzy system. Simulation results are obtained using MATLAB. The result shows that the classification accuracy increase along with minimizing fuzzy rules count and thus it can be inferred that the suggested methodology has a better protracted lifetime in contrast with Low Energy Adaptive Clustering Hierarchy (LEACH) and LEACH-Expected Residual Energy (LEACH-ERE).     

Fracture Toughness Characteristics of Stacked Bilayer Graphene via Far-Field Displacement Measurements

Mechanical properties of graphene, e.g., strength, modulus, and fracture toughness are extremely sensitive to flaws. Here the fracture properties of stacked bilayer graphene sheets (SBLG) are reported, obtained via stacking two individually grown graphene sheets. The SBLG is presented here as a building block for flaw-resilient nanomaterials. The fracture properties of freestanding SBLG sheets, suspended on transmission electron microscope (TEM) grids, are characterized by stretching the TEM grid inside an scanning electron microscope (SEM) chamber and monitoring the local displacements in real-time. The fracture toughness is measured and expressed as a function of the critical displacement required to propagate existing cracks in the experiment via computational models. This approach decouples force and displacements measurements, and utilizes the known elastic modulus along with the known displacement boundary conditions at the onset of crack growth to estimate the far field force and stress. This strategy represents a breakthrough in nanoscale fracture mechanics for statistical analysis and high throughput experimens on multiple samples at a time. Results demonstrate that the SBLG is markedly tougher than as-grown single or multilayer graphene, with a mode I fracture toughness of ≈28.06 ± 7.5 MPa$\sqrt{m}$. The mechanisms leading to a higher toughness of SBLG are also analyzed and discussed.     

Deep Learning ResNet101 Deep Features of Portable Chest X-Ray Accurately Classify COVID-19 Lung Infection

This study is designed to develop Artificial Intelligence (AI) based analysis tool that could accurately detect COVID-19 lung infections based on portable chest x-rays (CXRs). The frontline physicians and radiologists suffer from grand challenges for COVID-19 pandemic due to the suboptimal image quality and the large volume of CXRs. In this study, AI-based analysis tools were developed that can precisely classify COVID-19 lung infection. Publicly available datasets of COVID-19 (N = 1525), non-COVID-19 normal (N = 1525), viral pneumonia (N = 1342) and bacterial pneumonia (N = 2521) from the Italian Society of Medical and Interventional Radiology (SIRM), Radiopaedia, The Cancer Imaging Archive (TCIA) and Kaggle repositories were taken. A multi-approach utilizing deep learning ResNet101 with and without hyperparameters optimization was employed. Additionally, the features extracted from the average pooling layer of ResNet101 were used as input to machine learning (ML) algorithms, which twice trained the learning algorithms. The ResNet101 with optimized parameters yielded improved performance to default parameters. The extracted features from ResNet101 are fed to the k-nearest neighbor (KNN) and support vector machine (SVM) yielded the highest 3-class classification performance of 99.86% and 99.46%, respectively. The results indicate that the proposed approach can be better utilized for improving the accuracy and diagnostic efficiency of CXRs. The proposed deep learning model has the potential to improve further the efficiency of the healthcare systems for proper diagnosis and prognosis of COVID-19 lung infection.     

Pathways to improve nephrologist comfort in managing patients on in-center or home self-care dialysis

In end-stage kidney disease (ESKD), patient engagement and empowerment are associated with improved survival and complications. However, patients lack education and confidence to participate in self-care. The development of in center self-care hemodialysis can enable motivated patients to allocate autonomy, increase satisfaction and engagement, reduce human resource intensiveness, and cultivate a curiosity about home dialysis. In this review, we emphasize the role of education to overcome barriers to home dialysis, strategies of improving home dialysis utilization in the COVID 19 era, the significance of in-center self-care dialysis (e.g., cost containment and empowering patients), and implementation of an in-center self-care dialysis as a bridge to home hemodialysis (HHD).