Efficacy of high-frequency repetitive transcranial magnetic stimulation in a family with spinocerebellar ataxia type 3: A case report.

Introduction: Spinocerebellar ataxia type 3 (SCA3) is a common autosomal dominant hereditary ataxia, which is caused by a cytosine-adenine-guanine (CAG) repeat expansion on the causative gene ATXN3, usually with lower extremity ataxia as the first symptom, and effective treatment is scarce. Repetitive transcranial magnetic stimulation (rTMS) is a non-invasive technique that regulates the cerebellum and the neural network connected to it. Methods: Herein, we report familial cases of SCA3 in two nephews and their aunt, each of whom was treated with high-frequency (5 Hz) rTMS. The rTMS treatment lasted 2 weeks, once daily for 5 consecutive days a week, about 20 minutes each session. The Scale for the Assessment and Rating of Ataxia (SARA), the International Cooperative Ataxia Rating Scale (ICARS), and proton magnetic resonance spectroscopy (1H-MRS) examination were evaluated before and after rTMS treatment. Results: We found that the ICARS scores improved significantly (p = 0.04), and the NAA/Cr values were elevated in vermis and both cerebellar hemispheres after rTMS treatment. Conclusion: Our study suggested that high-frequency rTMS therapy can contribute to the improvement of cerebellar NAA/Cr value of SCA3 patients, and improve posture and gait as well as limb kinetic function in SCA3 patients.

Using the Unified Theory of Acceptance and Use of Technology (UTAUT) and e-health literacy(e-HL) to investigate the tobacco control intentions and behaviors of non-smoking college students in China: a cross-sectional investigation.

BACKGROUND: Non-smoking college students are starting to smoke in increasing numbers, which shows that their tobacco control situation seems not optimistic. The UTAUT and e-HL are commonly used models and theories to predict health behaviors, while there are few studies on tobacco control. This paper aims to study the influencing factors of tobacco control intention and behavior of non-smoking college students in China by combining the UTAUT and e-HL. METHODS: Based on the stratified sampling method, 625 college students from 12 universities were selected. Data were collected using a self-made questionnaire designed based on the UTAUT and e-health literacy scales. Data were analyzed by SPSS 22 and AMOS 26, including descriptive statistics, one-way variance analysis and structural equation model analysis. RESULTS: The results of one-way variance analysis showed that there were significant differences in the score of non-smoking college students' tobacco control intention or behavior by hometowns, monthly living expenses, and parents' smoking history. Performance expectancy, effort expectancy, social influence had direct positive effects on behavioral intention. Facilitating condition, behavioral intention had direct positive impacts on use behavior and e-HL had an indirect positive impact on use behavior. CONCLUSIONS: The combination of the UTAUT and e-HL can be used as an appropriate framework to predict the influencing factors of non-smoking college students' intention and behavior of tobacco control. Improving performance expectancy, effort expectancy, and e-HL among non-smoking college students, creating positive social environments, and providing facilitating condition are key aspects of increasing their tobacco control intention and behavior. It is also beneficial to promote the implementation of smoke-free campus and smoke-free family projects.

Accurate parameterization of the kinetic energy functional for calculations using exact-exchange.

Electronic structure calculations based on Kohn-Sham density functional theory (KSDFT) that incorporate exact-exchange or hybrid functionals are associated with a large computational expense, a consequence of the inherent cubic scaling bottleneck and large associated prefactor, which limits the length and time scales that can be accessed. Although orbital-free density functional theory (OFDFT) calculations scale linearly with system size and are associated with a significantly smaller prefactor, they are limited by the absence of accurate density-dependent kinetic energy functionals. Therefore, the development of accurate density-dependent kinetic energy functionals is important for OFDFT calculations of large realistic systems. To this end, we propose a method to train kinetic energy functional models at the exact-exchange level of theory by using a dictionary of physically relevant terms that have been proposed in the literature in conjunction with linear or nonlinear regression methods to obtain the fitting coefficients. For our dictionary, we use a gradient expansion of the kinetic energy nonlocal models proposed in the literature and their nonlinear combinations, such as a model that incorporates spatial correlations between higher order derivatives of electron density at two points. The predictive capabilities of these models are assessed by using a variety of model one-dimensional (1D) systems that exhibit diverse bonding characteristics, such as a chain of eight hydrogens, LiF, LiH, C4H2, C4N2, and C3O2. We show that by using the data from model 1D KSDFT calculations performed using the exact-exchange functional for only a few neutral structures, it is possible to generate models with high accuracy for charged systems and electron and kinetic energy densities during self-consistent field iterations. In addition, we show that it is possible to learn both the orbital dependent terms, i.e., the kinetic energy and the exact-exchange energy, and models that incorporate additional nonlinearities in spatial correlations, such as a quadratic model, are needed to capture subtle features of the kinetic energy density that are present in exact-exchange-based KSDFT calculations.

Accurate parameterization of the kinetic energy functional.

The absence of a reliable formulation of the kinetic energy density functional has hindered the development of orbital free density functional theory. Using the data-aided learning paradigm, we propose a simple prescription to accurately model the kinetic energy density of any system. Our method relies on a dictionary of functional forms for local and nonlocal contributions, which have been proposed in the literature, and the appropriate coefficients are calculated via a linear regression framework. To model the nonlocal contributions, we explore two new nonlocal functionals-a functional that captures fluctuations in electronic density and a functional that incorporates gradient information. Since the analytical functional forms of the kernels present in these nonlocal terms are not known from theory, we propose a basis function expansion to model these seemingly difficult nonlocal quantities. This allows us to easily reconstruct kernels for any system using only a few structures. The proposed method is able to learn kinetic energy densities and total kinetic energies of molecular and periodic systems, such as H2, LiH, LiF, and a one-dimensional chain of eight hydrogens using data from Kohn-Sham density functional theory calculations for only a few structures.

Synergetic Effect of EP1 Receptor Antagonist and (-)-Epigallocatechin-3-gallate in Hepatocellular Carcinoma.

Epigallocatechin-3-gallate (EGCG), the principal catechin of green tea, modulates different molecular mechanisms underlying hepatocellular carcinoma (HCC). Accumulating studies showed that the activation of prostaglandin (PG) receptor EP1 promotes cell migration and invasion in different cancers, which could be inverted by blocking the EP1 receptor. This study investigated the synergetic effects of EP1-selective antagonist ONO-8711 and EGCG treatment on HCC to better understand the potential strategy to treat HCC. We found that EGCG significantly inhibited PGE2 and EP1-selective agonist induced migration of HCC cells and increased the ratio of Bax/Bcl-2 even in the presence of ONO-DI-004 or PGE2. ONO-8711 significantly inhibited PGE2-induced HCC proliferation while increased the inhibitory effect of EGCG on HCC cell viability and migration ability compared with EGCG alone. These findings suggest that a combination of ONO-8711 and EGCG is a potential treatment for HCC therapy.