BAG3 Attenuates Ischemia-Induced Skeletal Muscle Necroptosis in Diabetic Experimental Peripheral Artery Disease

Peripheral artery disease (PAD) is characterized by impaired blood flow to the lower extremities, resulting in ischemic limb injuries. Individuals with diabetes and PAD typically have more severe ischemic limb injuries and limb amputations, but the mechanisms involved are poorly understood. Previously, we identified BAG3 as a gene within a mouse genetic locus termed limb salvage QTL1 on mouse chromosome 7 that determined the extent of limb necrosis following ischemic injury in C57Bl/6 mice. Whether BAG3 deficiency plays a role in the severe ischemic injury observed in diabetic PAD is not known. In vitro, we found simulated ischemia enhanced BAG3 expression in primary human skeletal muscle cells, whereas BAG3 knockdown increased necroptosis markers and decreased cell viability. In vivo, ischemic skeletal muscles from hind limbs of high-fat diet (HFD)-fed mice showed poor BAG3 expression compared to normal chow diet (NCD)-fed mice, and this was associated with increased limb amputations. BAG3 overexpression in ischemic skeletal muscles from hind limbs of HFD mice rescued limb amputation and improved autophagy, necroptosis, skeletal muscle function and regeneration. Therefore, BAG3 deficiency in ischemic skeletal muscles contributes to the severity of ischemic limb injury in diabetic PAD, likely through autophagy and necroptosis pathways.     

Flexible electrospun PVDF/WO3 nanocomposite fibers for piezoelectric energy harvesting applications

The emerging field of energy harvesting depends on the electrically conductive materials that are highly flexible and deformable. The morphological, structural, thermal, mechanical, and piezoelectric output studies of electrospun polyvinylidene fluoride (PVDF) and PVDF/WO₃ nanorods composite nanofibers were investigated for the piezoelectric energy harvesting applications. There is a significant enhancement in the piezoelectric β phase after the addition of the WO₃ nanorods into the PVDF. The elemental composition of the PVDF/WO₃ nanorods composite nanofibers is confirmed by the W, O, F, and C elements. The thermal stability of the WO₃ nanorods added composite nanofibers was increased up to 30°C in reference to TGA responses. Based on the mechanical test, the maximum tensile strength and modulus of elasticity were enhanced around by 220 and 246% for the WO₃-integrated PVDF nanofibers. Furthermore, the piezoelectric coefficient of 18.98 pC/N is achieved for the composite PVDF nanofibers which are mainly due to the improvement of the electroactive β phase. The piezoelectric energy harvesting responses were found an output voltage of 2.1 V based on the microstrain set-up. Thus, these WO₃ nanorods incorporated PVDF nanofibers keep the great potential for the piezoelectric energy harvesting, wearable electronics and biomedical applications.     

Mechanical Characterization of Near-Isotropic Inconel 718 Fabricated by Laser Powder-Bed Fusion

Metallurgical and Materials Transactions A - The characterization of Ni-based superalloy Inconel® 718 fabricated by powder-based laser fusion process was performed to study microstructural...     

Improved Load-Balanced Clustering for Energy-Aware Routing (ILBC-EAR) in WSNs

Sensors are considered as important elements of electronic devices. In many applications and service, Wireless Sensor Networks (WSNs) are involved in significant data sharing that are delivered to the sink node in energy efficient manner using multi-hop communications. But, the major challenge in WSN is the nodes are having limited battery resources, it is important to monitor the consumption rate of energy is very much needed. However, reducing energy consumption can increase the network lifetime in effective manner. For that, clustering methods are widely used for optimizing the rate of energy consumption among the sensor nodes. In that concern, this paper involves in deriving a novel model called Improved Load-Balanced Clustering for Energy-Aware Routing (ILBC-EAR), which mainly concentrates on optimal energy utilization with load-balanced process among cluster heads and member nodes. For providing equal rate of energy consumption among nodes, the dimensions of framed clusters are measured. Moreover, the model develops a Finest Routing Scheme based on Load-Balanced Clustering to transmit the sensed information to the sink or base station. The evaluation results depict that the derived energy aware model attains higher rate of life time than other works and also achieves balanced energy rate among head node. Additionally, the model also provides higher throughput and minimal delay in delivering data packets.     

A Review on Biogenic Synthesis of Selenium Nanoparticles and Its Biological Applications

In recent decades, aquaculture and environment plays a noteworthy role in rewarding the massive stipulate in all industries. Environmental damage and disease domination are seen as essential issues in the region. In addition to these, nanotechnology as a fresh and imaginative instruments were extremely feasible in aquaculture and environmental applications. Next-generation biological applications of these nanomaterials might lead to an explosion in the bio industries. In order to utilizing the nanoparticles of biogenic expansion, selenium has plays major role in the biological progresses. Selenium (Se) is a multifunctional trace element. The present review analytically intends to the potential biological applications of biosynthesized selenium nanoparticles (SeNPs). Synthesis of SeNPs physical, chemical and biological methods has been used. Physical and chemical methods of SeNPs have high cost, non ecofriendly, highly time consuming. Therefore, there is a growing concern to develop eco friendly and sustainable methods for biosynthesis. Biosynthesis method has ecofriendly, low cost, nontoxic and zero contamination. Biosynthesis of selenium nanoparticles by plant extracts, bacteria, protein, biopolymers, seaweed extracts, fungi and yeasts have used for capping or stabilizing agents. Therefore this review represented original evidence for antibacterial, antifungal, antibiofilm, antioxidant, anticancer, antidiabetic, antimosquito larvicidal and aquaculture applications of prospective biogenic SeNPs were provided in turn in this regard of literatures. Bio synthesis of SeNPs and it is used for many applications like medical, environmental and aquaculture applications. In this review study, the importance of selenium nanoparticles as a competitive element for sustainable aquaculture and environmental applications is also examined in detail.

     

A Review of Tunnel Field-Effect Transistors for Improved ON-State Behaviour

Silicon - Tunnel Field-effect transistor (TFET) is regarded as the most promising candidate which can possibly replace the traditional MOSFET from current IC technology. It has gained much...     

A systematic review on selection characterization and implementation of probiotics in human health

Food Science and Biotechnology - Probiotics are live bacteria found in food that assist the body's defence mechanisms against pathogens by reconciling the gut microbiota. Probiotics are...     

Improved Soil Quality Prediction Model Using Deep Learning for Smart Agriculture Systems

Soil is the major source of infinite lives on Earth and the quality of soil plays significant role on Agriculture practices all around. Hence, the evaluation of soil quality is very important for determining the amount of nutrients that the soil require for proper yield. In present decade, the application of deep learning models in many fields of research has created greater impact. The increasing soil data availability of soil data there is a greater demand for the remotely avail open source model, leads to the incorporation of deep learning method to predict the soil quality. With that concern, this paper proposes a novel model called Improved Soil Quality Prediction Model using Deep Learning (ISQP-DL). The work considers the chemical, physical and biological factors of soil in particular area to estimate the soil quality. Firstly, pH rating of soil samples has been collected from the soil testing laboratory from which the acidic range has been categorized through soil test and the same data has been taken as input to the Deep Neural Network Regression (DNNR) model. Secondly, soil nutrient data has been given as second input to the DNNR model. By utilizing this data set, the DNNR method is used to evaluate the fertility rate by which the soil quality has been estimated. For training and testing, the model uses Deep Neural Network Regression (DNNR), by utilizing the dataset. The results show that the proposed model is effective for SQP (Soil Quality Prediction Model) with efficient good fitting and generality is enhanced with input features with higher rate of classification accuracy. The results show that the proposed model achieves 96.7% of accuracy rate compared with existing models.