What’s new in neuromuscular pathology 2022: myopathy updates and gene therapies

This compilation of new changes in the diagnosis and treatment of muscle and nerve disease is extracted from the latest publications from the European Neuromuscular Centre International workshops, FDA.gov and clinicaltrials.gov

Screening the Drug Sensitivity Genes Related to GEM and CDDP in the Lung Cancer Cell-lines

Background and objective Screening of small-cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC) cell lines with gemcitabine hydrochloride (GEM) and cisplatin (CDDP) related to drug sensitivity gene might clarify the action mechanism of anti-cancer drugs and provide a new clue for overcoming drug resistance and the development of new anti-cancer drugs, and also provide theoretical basis for the clinical treatment of individual. Methods The drug sensitivity of CDDP and GEM in 4 SCLC cell lines and 6 NSCLC cell lines was determined using MTT colorimetric assay, while the cDNA macroarray was applied to detect the gene expression state related to drug sensitivity of 10 lung cancer cell line in 1 291, and the correlation between the two was analysized. Results There were 6 genes showing significant positive correlation (r≥0.632, P < 0.05) with GEM sensitivity; 45 genes positively related to CDDP; another 41 genes related to both GEM and CDDP (r≥± 0.4). Lung cancer with GEM and CDDP sensitivity of two types of drugs significantly related genes were Metallothinein (Signal transduction molecules), Cathepsin B (Organization protease B) and TIMP1 (Growth factor); the GEM, CDDP sensitivity associated genes of lung cancer cell lines mainly distributed in Metallothinein, Cathepsin B, growth factor TIMP1 categories. Conclusion There existed drug-related sensitive genes of GEM, CDDP in SCLC and NSCLC cell lines; of these genes, Metallothinein, Cathepsin B and TIMP1 genes presented a significant positive correlation with GEM drug sensitivity, a significant negative correlation with CDDP drug sensitivity

Curriculum Design of Artificial Intelligence and Sustainability in Secondary School

Artificial Intelligence is revolutionizing numerous sectors with its transformative power, while at the same time, there is an increasing sense of urgency to address sustainability challenges. Despite the significance of both areas, secondary school curriculums still lack comprehensive integration of AI and sustainability education. This paper presents a curriculum designed to bridge this gap. The curriculum integrates progressive objectives, computational thinking competencies and system thinking components across five modules—awareness, knowledge, interaction, empowerment and ethics—to cater to varying learner levels. System thinking components help students understand sustainability in a holistic manner. Computational thinking competencies aim to cultivate computational thinkers to guide the design of curriculum activities

Isolation and Characterization of 89K Pathogenicity Island-Positive ST-7 Strains of Streptococcus suis Serotype 2 from Healthy Pigs, Northeast China

Streptococcus suis is a swine pathogen which can also cause severe infection, such as meningitis, and streptococcal-like toxic shock syndrome (STSS), in humans. In China, most of the S. suis infections in humans were reported in the southern areas with warm and humid climates, but little attention had been paid to the northern areas. Data presented here showed that the virulent serotypes 1, 2, 7, and 9 of S. suis could be steadily isolated from the healthy pigs in the pig farms in all the three provinces of Northeast China. Notably, a majority of the serotype 2 isolates belonged to the 89K pathogenicity island-positive ST-7 clone that had historically caused the human STSS outbreaks in the Sichuan and Jiangsu provinces of China, although the human STSS case caused by S. suis had never been reported in northern areas of China. Data presented here indicated that the survey of S. suis should be expanded to or reinforced in the northern areas of China

How to Optimize the Supply and Allocation of Medical Emergency Resources During Public Health Emergencies

The solutions to the supply and allocation of medical emergency resources during public health emergencies greatly affect the efficiency of epidemic prevention and control. Currently, the main problem in computational epidemiology is how the allocation scheme should be adjusted in accordance with epidemic trends to satisfy the needs of population coverage, epidemic propagation prevention, and the social allocation balance. More specifically, the metropolitan demand for medical emergency resources varies depending on different local epidemic situations. It is therefore difficult to satisfy all objectives at the same time in real applications. In this paper, a data-driven multi-objective optimization method, called as GA-PSO, is proposed to address such problem. It adopts the one-way crossover and mutation operations to modify the particle updating framework in order to escape the local optimum. Taking the megacity Shenzhen in China as an example, experiments show that GA-PSO effectively balances different objectives and generates a feasible allocation strategy. Such a strategy does not only support the decision-making process of the Shenzhen center in terms of disease control and prevention, but it also enables us to control the potential propagation of COVID-19 and other epidemics. © Copyright © 2020 Wang, Deng, Yuan, Zhang, Zhang, Cai, Gao and Kurths