Engineered vitamin E-tethered non-immunogenic facial lipopeptide for developing improved siRNA based combination therapy against metastatic breast cancer.

RNA interference based therapeutic gene silencing is an emerging platform for managing highly metastatic breast cancer. Cytosolic delivery of functional siRNA remains the key obstacle for efficient RNAi therapy. To overcome the challenges of siRNA delivery, we have engineered a vitamin E-tethered, short, optimum protease stabilized facial lipopeptide based non-immunogenic, biocompatible siRNA transporter to facilitate the clinical translation in future. Our designed lipopeptide has an Arginine-Sarcosine-Arginine segment for providing optimum protease-stability, minimizing adjacent arginine-arginine repulsion and reducing intermolecular aggregation and α-tocopherol as the lipidic moiety for facilitating cellular permeabilization. Interestingly, our designed non-immunogenic siRNA transporter has exhibited significantly better long term transfection efficiency than HiPerFect and can transfect hard to transfect primary cell line, HUVEC. Our engineered siRNA therapeutics demonstrated high efficacy in managing metastasis against triple negative breast cancer by disrupting the crosstalk of endothelial cells and MDA-MB-231 and reduced stemness and metastatic markers, as evidenced by downregulating critical oncogenic pathways. Our study aimed at silencing Notch1 signalling to achieve "multi-targeted" therapy with a single putative molecular medicine. We have further developed mechanistically rational combination therapy combining Notch1 silencing with a repurposed drug m-TOR inhibitor, metformin, which demonstrated synergistic interaction and enhanced antitumor efficacy against cancer metastasis.

Harnessing Peptide-Functionalized Multivalent Gold Nanorods for Promoting Enhanced Gene Silencing and Managing Breast Cancer Metastasis.

Small interfering RNA (siRNA) has become the cornerstone against undruggable targets and for managing metastatic breast cancer. However, an effective gene silencing approach is faced with a major challenge due to the delivery problem. In our present study, we have demonstrated efficient siRNA delivery, superior gene silencing, and inhibition of metastasis in triple-negative breast cancer cells (MDA-MB-231) using rod-shaped (aspect ratio: 4) multivalent peptide-functionalized gold nanoparticles and compared them to monovalent free peptide doses. Multivalency is a new concept in biology, and tuning the physical parameters of multivalent nanoparticles can enhance gene silencing and antitumor efficacy. We explored the effect of the multivalency of shape- and size-dependent peptide-functionalized gold nanoparticles in siRNA delivery. Our study demonstrates that peptide functionalization leads to reduced toxicity of the nanoparticles. Such designed peptide-functionalized nanorods also demonstrate antimetastatic efficacy in Notch1-silenced cells by preventing EMT progression in vitro. We have shown siRNA delivery in the hard-to-transfect primary cell line HUVEC and also demonstrated that the Notch1-silenced MDA-MB-231 cell line has failed to form nanobridge-mediated foci with the HUVEC in the co-culture of HUVEC and MDA-MB-231, which promote metastasis. This antimetastatic effect is further checked in a xenotransplant in vivo zebrafish model. In vivo studies also suggest that our designed nanoparticles mediated inhibition of micrometastasis due to silencing of the Notch1 gene. The outcome of our study highlights that the structure-activity relationship of multifunctional nanoparticles can be harnessed to modulate their biological activity.

Modular pulsed power supply for characterization of high-power microwave devices.

Developing a robust and reliable high-voltage (HV) pulse power system (PPS) is essential for the characterization and testing of microwave tubes and industrial applications. This paper presents the design, simulation, and implementation of a modular and versatile high-voltage pulse power supply used in microwave device characterization and testing. A microcontroller-based digitally controlled pulse generation unit is also developed to generate the trigger pulses for high-voltage switching modules to control the turn-ON and turn-OFF of the switching devices. The digitally controlled pulse generation unit provides better resolution of pulse width, pulse repetition frequency, and system protection over the entire range of operations. The pulse generation and control circuit controls the pulse repetition frequency (PRF) and pulse wide (PW). The PRF and PW can be varied from 100 to 1000 Hz and 10 to 100 µs, respectively. The experimental prototype with ten series-connected high-voltage modules is connected to achieve a 25 kV pulsed output voltage. The experimental results of HV-PPS on a resistive dummy load and 5 GHz klystron tube are presented in this work.

Emerging Approaches for Enabling RNAi Therapeutics.

RNA interference (RNAi) is a primitive evolutionary mechanism developed to escape incorporation of foreign genetic material. siRNA has been instrumental in achieving the therapeutic potential of RNAi by theoretically silencing any gene of interest in a reversible and sequence-specific manner. Extrinsically administered siRNA generally needs a delivery vehicle to span across different physiological barriers and load into the RISC complex in the cytoplasm in its functional form to show its efficacy. This review discusses the designing principles and examples of different classes of delivery vehicles that have proved to be efficient in RNAi therapeutics. We also briefly discuss the role of RNAi therapeutics in genetic and rare diseases, epigenetic modifications, immunomodulation and combination modality to inch closer in creating a personalized therapy for metastatic cancer. At the end, we present, strategies and look into the opportunities to develop efficient delivery vehicles for RNAi which can be translated into clinics.

Emerging concepts in designing next-generation multifunctional nanomedicine for cancer treatment.

Nanotherapy has emerged as an improved anticancer therapeutic strategy to circumvent the harmful side effects of chemotherapy. It has been proven to be beneficial to offer multiple advantages, including their capacity to carry different therapeutic agents, longer circulation time and increased therapeutic index with reduced toxicity. Over time, nanotherapy evolved in terms of their designing strategies like geometry, size, composition or chemistry to circumvent the biological barriers. Multifunctional nanoscale materials are widely used as molecular transporter for delivering therapeutics and imaging agents. Nanomedicine involving multi-component chemotherapeutic drug-based combination therapy has been found to be an improved promising approach to increase the efficacy of cancer treatment. Next-generation nanomedicine has also utilized and combined immunotherapy to increase its therapeutic efficacy. It helps in targeting tumor immune response sparing the healthy systemic immune function. In this review, we have summarized the progress of nanotechnology in terms of nanoparticle designing and targeting cancer. We have also discussed its further applications in combination therapy and cancer immunotherapy. Integrating patient-specific proteomics and biomarker based information and harnessing clinically safe nanotechnology, the development of precision nanomedicine could revolutionize the effective cancer therapy.

Fuzzy reinforcement learning based intelligent classifier for power transformer faults.

In this work a fuzzy reinforcement learning (RL) based intelligent classifier for power transformer incipient faults is proposed. Fault classifiers proposed till date have low identification accuracy and do not identify all types of transformer faults. Herein, an attempt has been made to design an adaptive, intelligent transformer fault classifier that progressively learns to identify faults on-line with high accuracy for all fault types. In the proposed approach, dissolved gas analysis (DGA) data of oil samples collected from real power transformers (and from credible sources) has been used, which serves as input to a fuzzy RL based classifier. Typically, classification accuracy is heavily dependent on the number of input variables chosen. This has been resolved by using the J48 algorithm to select 8 most appropriate input variables from the 24 variables obtained using DGA. Proposed fuzzy RL approach achieves a fault identification accuracy of 99.7%, which is significantly higher than other contemporary soft computing based identifiers. Experimental results and comparison with other state-of-the-art approaches, highlights superiority and efficacy of the proposed fuzzy RL technique for transformer fault classification.

Maintaining Security and Privacy in Health Care System Using Learning Based Deep-Q-Networks.

In the recent past, Internet of Things (IoT) plays a significant role in different applications such as health care, industrial sector, defense and research etc.… It provides effective framework in maintaining the security, privacy and reliability of the information in internet environment. Among various applications as mentioned health care place a major role, because security, privacy and reliability of the medical information is maintained in an effective way. Even though, IoT provides the effective protocols for maintaining the information, several intermediate attacks and intruders trying to access the health information which in turn reduce the privacy, security and reliability of the entire health care system in internet environment. As a result and to solve the issues, in this research Learning based Deep-Q-Networks has been introduced for reducing the malware attacks while managing the health information. This method examines the medical information in different layers according to the Q-learning concept which helps to minimize the intermediate attacks with less complexity. The efficiency of the system has been evaluated with the help of experimental results and discussions.

Coordinated single-phase control scheme for voltage unbalance reduction in low voltage network.

Low voltage (LV) distribution systems are typically unbalanced in nature due to unbalanced loading and unsymmetrical line configuration. This situation is further aggravated by single-phase power injections. A coordinated control scheme is proposed for single-phase sources, to reduce voltage unbalance. A consensus-based coordination is achieved using a multi-agent system, where each agent estimates the averaged global voltage and current magnitudes of individual phases in the LV network. These estimated values are used to modify the reference power of individual single-phase sources, to ensure system-wide balanced voltages and proper power sharing among sources connected to the same phase. Further, the high X/R ratio of the filter, used in the inverter of the single-phase source, enables control of reactive power, to minimize voltage unbalance locally. The proposed scheme is validated by simulating a LV distribution network with multiple single-phase sources subjected to various perturbations.This article is part of the themed issue 'Energy management: flexibility, risk and optimization'.

Neural-network-based adaptive UPFC for improving transient stability performance of power system.

This paper uses the recently proposed H(infinity)-learning method, for updating the parameter of the radial basis function neural network (RBFNN) used as a control scheme for the unified power flow controller (UPFC) to improve the transient stability performance of a multimachine power system. The RBFNN uses a single neuron architecture whose input is proportional to the difference in error and the updating of its parameters is carried via a proportional value of the error. Also, the coefficients of the difference of error, error, and auxiliary signal used for improving damping performance are depicted by a genetic algorithm. The performance of the newly designed controller is evaluated in a four-machine power system subjected to different types of disturbances. The newly designed single-neuron RBFNN-based UPFC exhibits better damping performance compared to the conventional PID as well as the extended Kalman filter (EKF) updating-based RBFNN scheme, making the unstable cases stable. Its simple architecture reduces the computational burden, thereby making it attractive for real-time implementation. Also, all the machines are being equipped with the conventional power system stabilizer (PSS) to study the coordinated effect of UPFC and PSS in the system.