Scalable Production of Extracellular Vesicles and Its Therapeutic Values: A Review

Extracellular vesicles (EVs) are minute vesicles with lipid bilayer membranes. EVs are secreted by cells for intercellular communication. Recently, EVs have received much attention, as they are rich in biological components such as nucleic acids, lipids, and proteins that play essential roles in tissue regeneration and disease modification. In addition, EVs can be developed as vaccines against cancer and infectious diseases, as the vesicle membrane has an abundance of antigenic determinants and virulent factors. EVs for therapeutic applications are typically collected from conditioned media of cultured cells. However, the number of EVs secreted by the cells is limited. Thus, it is critical to devise new strategies for the large-scale production of EVs. Here, we discussed the strategies utilized by researchers for the scalable production of EVs. Techniques such as bioreactors, mechanical stimulation, electrical stimulation, thermal stimulation, magnetic field stimulation, topographic clue, hypoxia, serum deprivation, pH modification, exposure to small molecules, exposure to nanoparticles, increasing the intracellular calcium concentration, and genetic modification have been used to improve the secretion of EVs by cultured cells. In addition, nitrogen cavitation, porous membrane extrusion, and sonication have been utilized to prepare EV-mimetic nanovesicles that share many characteristics with naturally secreted EVs. Apart from inducing EV production, these upscaling interventions have also been reported to modify the EVs’ cargo and thus their functionality and therapeutic potential. In summary, it is imperative to identify a reliable upscaling technique that can produce large quantities of EVs consistently. Ideally, the produced EVs should also possess cargo with improved therapeutic potential.     

Inhibiting Erastin-Induced Ferroptotic Cell Death by Purine-Based Chelators

Ferroptosis is a cell death event caused by increased lipid peroxidation leading to iron-dependent oxidative stress and is associated with a wide variety of diseases. In recent years, ferroptosis inhibition has emerged as a novel strategy to target different pathologies. Here, we report the synthesis of two purine derivatives, 1 and 2 , for iron chelation strategy and evaluate their potency to inhibit erastin-induced ferroptosis. Both compounds showed efficient iron chelation in solution as well as in cellular environment. The crystal structure of the purine derivatives with iron demonstrated a 2 : 1 (ligand to metal center) stoichiometry for iron and purine derivative complexation. The synthesized compounds also decrease the reactive oxygen species concentration in cell cultures. Compound 2 showed better potency towards the prevention of ferroptotic cell death as compared to commercially available iron chelator in the erastin-induced ferroptosis cell culture model. Such purine analogues are potential functional scaffolds for the development of target molecules for ferroptosis inhibition.     

Strategies for Interference of Insulin Fibrillogenesis: Challenges and Advances

The discovery of insulin came with very high hopes for diabetic patients. In 2021, the world celebrated the 100th anniversary of the discovery of this vital hormone. However, external use of insulin is highly affected by its aggregating tendency that occurs during its manufacturing, transportation, and improper handling which ultimately leads to its pharmaceutically and biologically ineffective form. In this review, we aim to discuss the various approaches used for decelerating insulin aggregation which results in the enhancement of its overall structural stability and usage. The approaches that are discussed are broadly classified as either a measure through excipient additions or by intrinsic modifications in the insulin native structure.     

Metaheuristic Based Clustering with Deep Learning Model for Big Data Classification

Recently, a massive quantity of data is being produced from a distinct number of sources and the size of the daily created on the Internet has crossed two Exabytes. At the same time, clustering is one of the efficient techniques for mining big data to extract the useful and hidden patterns that exist in it. Density-based clustering techniques have gained significant attention owing to the fact that it helps to effectively recognize complex patterns in spatial dataset. Big data clustering is a trivial process owing to the increasing quantity of data which can be solved by the use of Map Reduce tool. With this motivation, this paper presents an efficient Map Reduce based hybrid density based clustering and classification algorithm for big data analytics (MR-HDBCC). The proposed MR-HDBCC technique is executed on Map Reduce tool for handling the big data. In addition, the MR-HDBCC technique involves three distinct processes namely pre-processing, clustering, and classification. The proposed model utilizes the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) technique which is capable of detecting random shapes and diverse clusters with noisy data. For improving the performance of the DBSCAN technique, a hybrid model using cockroach swarm optimization (CSO) algorithm is developed for the exploration of the search space and determine the optimal parameters for density based clustering. Finally, bidirectional gated recurrent neural network (BGRNN) is employed for the classification of big data. The experimental validation of the proposed MR-HDBCC technique takes place using the benchmark dataset and the simulation outcomes demonstrate the promising performance of the proposed model interms of different measures.