Introduction, Tuberous sclerosis complex (TSC) is an autosomal-dominant disorder caused by mutations inactivating TSC1 or TSC2 genes and characterized by the presence of tumors involving many organs, including the brain, heart, kidneys, and skin. Subependymal giant cell astrocytoma (SEGA) is a slow-growing brain tumor almost exclusively associated with TSC. State of the Art: Despite the fact that SEGAs are benign, they require well-considered decisions regarding the timing and modality of pharmacological or surgical treatment. In TSC children and adolescents, SEGA is the major cause of mortality and morbidity. Clinical Implications: Until recently, surgical resection has been the standard therapy for SEGAs but the discovery of the role of the mTOR pathway and the introduction of mTOR inhibitors to clinical practice changed the therapeutic landscape of these tumors. In the current paper, we discuss the pros and cons of mTOR inhibitors and surgical approaches in SEGA treatment. Future Directions: In 2021, the International Tuberous Sclerosis Complex Consensus Group proposed a new integrative strategy for SEGA management. In the following review, we discuss the proposed recommendations and report the results of the literature search for the latest treatment directions.

Acquiring electrocardiographic (ECG) signals and performing arrhythmia classification in mobile device scenarios have the advantages of short response time, almost no network bandwidth consumption, and human resource savings. In recent years, deep neural networks have become a popular method to efficiently and accurately simulate nonlinear patterns of ECG data in a data-driven manner but require more resources. Therefore, it is crucial to design deep learning (DL) algorithms that are more suitable for resource-constrained mobile devices. In this paper, KecNet, a lightweight neural network construction scheme based on domain knowledge, is proposed to model ECG data by effectively leveraging signal analysis and medical knowledge. To evaluate the performance of KecNet, we use the Association for the Advancement of Medical Instrumentation (AAMI) protocol and the MIT-BIH arrhythmia database to classify five arrhythmia categories. The result shows that the ACC, SEN, and PRE achieve 99.31%, 99.45%, and 98.78%, respectively. In addition, it also possesses high robustness to noisy environments, low memory usage, and physical interpretability advantages. Benefiting from these advantages, KecNet can be applied in practice, especially wearable and lightweight mobile devices for arrhythmia classification.

Cerebral palsy (CP) is a disease with a high disability rate and morbidity. The clinical symptoms of cerebral palsy are motor dysfunction and abnormal posture development, often accompanied by cognitive impairment. Massage, a traditional Chinese Medicine therapy, can coordinate Zang and Fu, regulate Qi and blood, make the viscera work more smoothly, and calm Yin and Yang. Furthermore, it has been an effective method for CP in clinical. This paper summarizes a set of simple and standardized manipulations of massage for young rats with CP, which is easy to follow. The procedure follows: first, massage of four limb acupoints, including Quchi (LI11) and Zusanli (ST36); second, massage of the abdomen acupoints Zhongwan (RN12), Tianshu (ST25), Guanyuan (CV4), and Qihai (CV6); and finally, massage of the abdomen of the rats. This set of massage methods considerably improved the motor function of young rats with CP and is simple, standardized, and easy to follow. We adapted this set of manipulation methods in animal models to promote the internationalization and standardization of massage.