Excessive fluoride intake for a long time has been demonstrated to provoke hepatic oxidative stress in adults. However, the response to fluoride toxicity of liver in newborns exposed to fluoride during embryonic and suckling stages remains unclear. In this study, female Kunming mice were administrated with 25, 50, and 100 mg/L sodium fluoride (NaF) from prenatal day 0 to day 21 after delivery, and the antioxidative status in the liver of their pups at postnatal day 21 was evaluated. The results showed that compared with the control group, NaF significantly increased malondialdehyde (MDA) level and reduced catalase (CAT) activity, while no statistical difference was observed in activities and mRNA expressions of superoxide dismutase (SOD), glutathione peroxidase (GPx), and glutathione reductase (GR). Notably, with comparison to the controls, the protein level of CAT was significantly reduced in medium- and high-fluoride groups, while its relative mRNA abundance was enhanced which could result from the encouragement of the lowered CAT protein expression. These findings suggested that CAT was more susceptible to low-fluoride exposure in early life.
Covid-19 pandemic has ushered in a new school and academic year for students in a distance learning regime. This new daily routine was unprecedented and undoubtedly unusual, especially for the younger ones. At this point and at these ages, the risk of cyber fraud is even greater. The transition from the physical environment to the Internet took place quickly without the appropriate time to control potential risks and the proper information and training of teachers and students. Some common threats that need to be addressed to protect learners and their data when using e-learning methods are malicious remote access, malware, phishing, cyber fraud, etc. Considering the above situation, this work presents an innovative cyber risk recommendation system for digital education management platforms. The system in question is a distributed two-stage algorithm based on game theory and machine learning, which is trained by the constant change in the choice of recommendations by users to maximize security. We examine the algorithm’s ability to simulate a user system in which everyone independently selects a user recommendation, assesses the environment and the implications of this choice, and then concludes whether it will continue to have that recommendation fixed. The methodology with which we have represented the digital e-learning system has been done with an approach that directly corresponds with their general view as a cyber-physical-social system. We consider the digital school as an environment that brings limitations, leading us to a pretty demanding personalization problem. Users coexist in this environment, in which everyone acts voluntarily but influences and is influenced by the surrounding environment. Our results lead us to conclude that this algorithm responds in a fully effective, flexible, and efficient way to the needs of protection and risk assessment of e-learning education systems.
Considering the priority for personalized and fully customized learning systems, the innovative computational intelligent systems for personalized educational technologies are the timeliest research area. Since the machine learning models reflect the data over which they were trained, data that have privacy and other sensitivities associated with the education abilities of learners, which can be vulnerable. This work proposes a recommendation system for privacy-preserving education technologies that uses machine learning and differential privacy to overcome this issue. Specifically, each student is automatically classified on their skills in a category using a directed acyclic graph method. In the next step, the model uses differential privacy which is the technology that enables a facility for the purpose of obtaining useful information from databases containing individuals’ personal information without divulging sensitive identification about each individual. In addition, an intelligent recommendation mechanism based on collaborative filtering offers personalized real-time data for the users’ privacy.
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