Duchenne muscular dystrophy (DMD) is characterized by muscle degeneration and progressive weakness. There is considerable inter-patient variability in disease onset and progression, which can confound the results of clinical trials. Here we show that a common null polymorphism (R577X) in ACTN3 results in significantly reduced muscle strength and a longer 10 m walk test time in young, ambulant patients with DMD; both of which are primary outcome measures in clinical trials. We have developed a double knockout mouse model, which also shows reduced muscle strength, but is protected from stretch-induced eccentric damage with age. This suggests that α-actinin-3 deficiency reduces muscle performance at baseline, but ameliorates the progression of dystrophic pathology. Mechanistically, we show that α-actinin-3 deficiency triggers an increase in oxidative muscle metabolism through activation of calcineurin, which likely confers the protective effect. Our studies suggest that ACTN3 R577X genotype is a modifier of clinical phenotype in DMD patients.
There has been an increase in credit card fraud as e-commerce has become more widespread. Financial transactions are essential to our economy, so detecting bank fraud is essential. Experiments on automated and real-time fraud detection are needed here. There are numerous machine learning techniques for identifying credit card fraud, and the most prevalent are support vector machine (SVM), logic regression, and random forest. When models penalise all errors equally during training, the quality of these detection approaches becomes crucial. This paper uses an innovative sensing method to judge the classification algorithm by considering the misclassification cost and at the same time by employing SVM hyperparameter optimization using grid search cross-validation and separating the hyperplane using the theory of reproducing kernels like linear, Gaussian, and polynomial, and the robustness is maintained. Because of this, credit card fraud has been identified significantly more successful than in the past.
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