Clinical applications of stem cell-derived exosomes

Although stem cell-based therapy has demonstrated considerable potential to manage certain diseases more successfully than conventional surgery,it nevertheless comes with inescapable drawbacks that might limit its clinical translation.Compared to stem cells,stem cell-derived exosomes possess numerous advantages,such as non-immunogenicity,non-infusion toxicity,easy access,effortless preservation,and freedom from tumorigenic potential and ethical issues.Exosomes can inherit similar therapeutic effects from their parental cells such as embryonic stem cells and adult stem cells through vertical delivery of their pluripotency or multipotency.After a thorough search and meticulous dissection of relevant literature from the last five years,we present this comprehensive,up-to-date,specialty-specific and disease-oriented review to highlight the surgical application and potential of stem cell-derived exosomes.Exosomes derived from stem cells(e.g.,embryonic,induced pluripotent,hematopoietic,mesenchymal,neural,and endothelial stem cells)are capable of treating numerous diseases encountered in orthopedic surgery,neurosurgery,plastic surgery,general surgery,cardiothoracic surgery,urology,head and neck surgery,ophthalmology,and obstetrics and gynecology.The diverse therapeutic effects of stem cells-derived exosomes are a hierarchical translation through tissue-specific responses,and cell-specific molecular signaling pathways.In this review,we highlight stem cell-derived exosomes as a viable and potent alternative to stem cell-based therapy in managing various surgical conditions.We recommend that future research combines wisdoms from surgeons,nanomedicine practitioners,and stem cell researchers in this relevant and intriguing research area.

AI-Based Modeling and Data-Driven Evaluation for Smart Manufacturing Processes

Smart manufacturing refers to optimization techniques that are implemented in production operations by utilizing advanced analytics approaches. With the widespread increase in deploying industrial internet of things(IIOT) sensors in manufacturing processes, there is a progressive need for optimal and effective approaches to data management.Embracing machine learning and artificial intelligence to take advantage of manufacturing data can lead to efficient and intelligent automation. In this paper, we conduct a comprehensive analysis based on evolutionary computing and neural network algorithms toward making semiconductor manufacturing smart.We propose a dynamic algorithm for gaining useful insights about semiconductor manufacturing processes and to address various challenges. We elaborate on the utilization of a genetic algorithm and neural network to propose an intelligent feature selection algorithm. Our objective is to provide an advanced solution for controlling manufacturing processes and to gain perspective on various dimensions that enable manufacturers to access effective predictive technologies.