Nanoformulated Remdesivir with Extremely Low Content of Poly(2-oxazoline)-Based Stabilizer for Aerosol Treatment of COVID-19

The rise of the novel virus SARS-CoV2 which causes the disease known as COVID-19 has led to a global pandemic claiming millions of lives. With no clinically approved treatment for COVID-19, physicians initially struggled to treat the disease, and a need remains for improved antiviral therapies in this area. It is conceived early in the pandemic that an inhalable formulation of the drug remdesivir which directly targets the virus at the site of infection could improve therapeutic outcomes in COVID-19. A set of requirements are developed that would be conducive to rapid drug approval: 1) try to use GRAS reagents 2) minimize excipient concentration and 3) achieve a working concentration of 5 mg/mL remdesivir to obtain a deliverable dose which is 5-10% of the IV dose. In this work, it is discovered that Poly(2-oxazoline) block copolymers can stabilize drug nanocrystal suspensions and provide suitable formulation characteristics for aerosol delivery while maintaining antiviral efficacy. The authors believe POx block copolymers can be used as a semi-ubiquitous stabilizer for the rapid development of nanocrystal formulations for new and existing diseases

PEG-Free Polyion Complex Nanocarriers for Brain-Derived Neurotrophic Factor

Many therapeutic formulations incorporate poly(ethylene glycol) (PEG) as a stealth component to minimize early clearance. However, PEG is immunogenic and susceptible to accelerated clearance after multiple administrations. Here, we present two novel reformulations of a polyion complex (PIC), originally composed of poly(ethylene glycol)113-b-poly(glutamic acid)50 (PEG-PLE) and brain-derived neurotrophic factor (BDNF), termed Nano-BDNF (Nano-BDNF PEG-PLE). We replace the PEG based block copolymer with two new polymers, poly(sarcosine)127-b-poly(glutamic acid)50 (PSR-PLE) and poly(methyl-2-oxazolines)38-b-poly(oxazolepropanoic acid)27-b-poly(methyl-2-oxazoline)38 (PMeOx-PPaOx-PMeOx), which are driven to association with BDNF via electrostatic interactions and hydrogen bonding to form a PIC. Formulation using a microfluidic mixer yields small and narrowly disperse nanoparticles which associate following similar principles. Additionally, we demonstrate that encapsulation does not inhibit access by the receptor kinase, which affects BDNF’s physiologic benefits. Finally, we investigate the formation of nascent nanoparticles through a series of characterization experiments and isothermal titration experiments which show the effects of pH in the context of particle self-assembly. Our findings indicate that thoughtful reformulation of PEG based, therapeutic PICs with non-PEG alternatives can be accomplished without compromising the self-assembly of the PIC

An Integrated Analysis Framework of Convolutional Neural Network for Embedded Edge Devices

Recently, IoT applications using Deep Neural Network (DNN) to embedded edge devices are increasing. Generally, in the case of DNN applications in the IoT system, training is mainly performed in the server and inference operation is performed on the edge device. The embedded edge devices still take a lot of loads in inference operations due to low computing resources, so proper customization of DNN with architectural exploration is required. However, there are few integrated frameworks to facilitate exploration and customization of various DNN models and their operations in embedded edge devices. In this paper, we propose an integrated framework that can explore and customize DNN inference operations of DNN models on embedded edge devices. The framework consists of the GUI interface part, the inference engine part, and the hardware Deep Learning Accelerator (DLA) Virtual Platform (VP) part. Specifically it focuses on Convolutional Neural Network (CNN), and provides integrated interoperability for convolutional neural network models and neural network customization techniques such as quantization and cross-inference functions. In addition, performance estimation is possible by providing hardware DLA VP for embedded edge devices. Those features are provided as web-based GUI interfaces, so users can easily utilize them

An Integrated Analysis Framework of Convolutional Neural Network for Embedded Edge Devices

Recently, IoT applications using Deep Neural Network (DNN) to embedded edge devices are increasing. Generally, in the case of DNN applications in the IoT system, training is mainly performed in the server and inference operation is performed on the edge device. The embedded edge devices still take a lot of loads in inference operations due to low computing resources, so proper customization of DNN with architectural exploration is required. However, there are few integrated frameworks to facilitate exploration and customization of various DNN models and their operations in embedded edge devices. In this paper, we propose an integrated framework that can explore and customize DNN inference operations of DNN models on embedded edge devices. The framework consists of the GUI interface part, the inference engine part, and the hardware Deep Learning Accelerator (DLA) Virtual Platform (VP) part. Specifically it focuses on Convolutional Neural Network (CNN), and provides integrated interoperability for convolutional neural network models and neural network customization techniques such as quantization and cross-inference functions. In addition, performance estimation is possible by providing hardware DLA VP for embedded edge devices. Those features are provided as web-based GUI interfaces, so users can easily utilize them

GuardiaNN: Fast and Secure On-Device Inference in TrustZone Using Embedded SRAM and Cryptographic Hardware

As more and more mobile/embedded applications employ Deep Neural Networks (DNNs) involving sensitive user data, mobile/embedded devices must provide a highly secure DNN execution environment to prevent privacy leaks. Aimed at securing DNN data, recent studies execute part of a DNN in a trusted execution environment (e.g., TrustZone) to isolate DNN execution from the other processes; however, as the trusted execution environments for mobile/embedded devices provide limited memory protection, DNN data remain unencrypted in DRAM and become vulnerable to physical attacks. The devices can prevent the physical attacks by keeping DNN data encrypted in DRAM; when DNN data get referenced during DNN execution, they get loaded to the SRAM and get decrypted by a CPU core. Unfortunately, using the SRAM with demand paging greatly increases DNN execution time due to the inefficient use of the SRAM and the high CPU consumption of data encryption/decryption. In this paper, we present GuardiaNN, a fast and secure DNN framework which greatly accelerates DNN execution without sacrificing security guarantees. To accelerate secure DNN execution, GuardiaNN first reduces slow DRAM accesses with direct convolutions and maximizes the reuse of SRAM-stored data with DNN-friendly SRAM management. Then, aimed at dedicating the limited CPU resources to DNN execution, GuardiaNN offloads DNN data encryption/decryption onto secure cryptographic hardware and employs pipelining to overlap DNN execution with the encryption/decryption. For eight DNNs chosen from five representative mobile/embedded application domains, our implementation of GuardiaNN on STM32MP157C-DK2 development board achieves a geomean speedup of 15.3x and a geomean energy efficiency improvement of 15.2x over a baseline secure DNN framework which employs demand-paged SRAM to secure sensitive data.N

Optimization of Protoplast Isolation from Leaf Mesophylls of Chinese Cabbage (Brassica rapa ssp. pekinensis) and Subsequent Transfection with a Binary Vector

Chinese cabbage is an important dietary source of numerous phytochemicals, including glucosinolates and anthocyanins. The selection and development of elite Chinese cabbage cultivars with favorable traits is hindered by a long breeding cycle, a complex genome structure, and the lack of an efficient plant transformation protocol. Thus, a protoplast transfection-based transformation method may be useful for cell-based breeding and functional studies involving Chinese cabbage plants. In this study, we established an effective method for isolating Chinese cabbage protoplasts, which were then transfected with the pCAMBIA1303 binary vector according to an optimized PEG-based method. More specifically, protoplasts were isolated following a 4 h incubation in a solution comprising 1.5% (v/v) cellulase, 0.25% (v/v) macerozyme, 0.25% (v/v) pectinase, 0.5 M mannitol, 15 mM CaCl2, 25 mM KCl, 0.1% BSA, and 20 mM MES buffer, pH 5.7. This method generated 7.1 × 106 protoplasts, 78% of which were viable. The gfp reporter gene in pCAMBIA1303 was used to determine the transfection efficiency. The Chinese cabbage protoplast transfection rate was highest (68%) when protoplasts were transfected with the 40 μg binary vector for 30 min in a solution containing 40% PEG. The presence of gusA and hptII in the protoplasts was confirmed by PCR. The methods developed in this study would be useful for DNA-free genome editing as well as functional and molecular investigations of Chinese cabbage

Automated Data Check and Calibration System using Matrix Switch Prototype With Small Change of Line-Resistance

In this paper, the hardware performance of a matrix switch prototype with small resistance deviation of each route and an automated data check and calibration system are described. The matrix consists of 12 rows and 3 columns, and the key switch connecting the rows and columns has a small on-resistance change even when the input voltage changes, so the matrix can keep the resistance deviation of each route small. Data check and calibration of circuits that are sensitive to errors due to lead-wires can also be automated with this matrix system to save time and reduce human error.International Foundation for TelemeteringProceedings from the International Telemetering Conference are made available by the International Foundation for Telemetering and the University of Arizona Libraries. Visit https://telemetry.org/contact-us/ if you have questions about items in this collection

A Mitochondrial-Targeted Nitroxide Is a Potent Inhibitor of Ferroptosis

Discovering compounds and mechanisms for inhibiting ferroptosis, a form of regulated, nonapoptotic cell death, has been of great interest in recent years. In this study, we demonstrate the ability of XJB-5-131, JP4-039, and other nitroxide-based lipid peroxidation mitigators to prevent ferroptotic cell death in HT-1080, BJeLR, and panc-1 cells. Several analogues of the reactive oxygen species (ROS) scavengers XJB-5-131 and JP4-039 were synthesized to probe structure–activity relationships and the influence of subcellular localization on the potency of these novel ferroptosis suppressors. Their biological activity correlated well over several orders of magnitude with their structure, relative lipophilicity, and respective enrichment in mitochondria, revealing a critical role of intramitochondrial lipid peroxidation in ferroptosis. These results also suggest that preventing mitochondrial lipid oxidation might offer a viable therapeutic opportunity in ischemia/reperfusion-induced tissue injury, acute kidney injury, and other pathologies that involve ferroptotic cell death pathways