Gene expression changes reveal the impact of the space environment on the skin of International Space Station astronauts.

BACKGROUND: The various types of ionizing radiation and altered gravity in the space environment present a risk to humans during space missions. Changes in the space environment lead to skin diseases, affecting the status of the aviators to fly. Therefore, it is important to explore the molecular-level changes in the skin during space missions. OBJECTIVES: Bioinformatics analysis of gene arrays from hair follicle tissue of 10 astronauts was performed to explore changes in gene expression before, during and after space missions. METHODS: First, STEM (Short Time-series Expression Miner) software was used to identify the expression patterns of hair follicle genes of astronauts pre-, in- and postflight. Gene Ontology Enrichment Analysis was then performed to explore the gene functions within the module. Protein-protein interaction network analysis was performed on skin-related genes. The transcriptional regulatory network within the module was constructed using the TRRUST database. The circadian rhythm-related genes within the module were screened using the MSigDB (Molecular Signatures Database). RESULTS: Based on differential expression analysis between the two groups, there were 327 differentially expressed genes after the astronauts entered space compared with preflight, and only 54 differentially expressed genes after returning to Earth. This outcome suggests that the expression of most genes can be recovered on return to the ground, but there are a small number of genes whose expression cannot be recovered in a short period of time. Based on time series analysis, 311 genes showed increased expression on entry into space and decreased expression on return to Earth. The genes of this expression pattern were associated with skin development, keratinocyte differentiation and cornification. Ten hub genes were identified as skin-related genes within the module, as well as nine transcription factors and three circadian genes. One hundred and seventy-nine genes decreased in expression after entry into space and increased on return to Earth. By reviewing the literature, we found that four of the genes, CSCD2, HP, CXCR1 and SSTR4, are associated with skin diseases. CONCLUSIONS: Through bioinformatics analysis, we found that the space environment affects skin keratinocyte differentiation, leading to skin barrier damage and inflammatory responses, and that this effect was decreased after return to Earth.

Influences of Daily Life Habits on Risk Factors of Stroke Based on Decision Tree and Correlation Matrix.

PURPOSE: To explore the influences of smoking, alcohol consumption, drinking tea, diet, sleep, and exercise on the risk of stroke and relationships among the factors, present corresponding knowledge-based rules, and provide a scientific basis for assessment and intervention of risk factors of stroke. METHODS: The decision tree C4.5 algorithm was optimized and utilized to establish a model for stroke risk assessment; then, the main risk factors of stroke (including hypertension, dyslipidemia, diabetes, atrial fibrillation, body mass index (BMI), history of stroke, family history of stroke, and transient ischemic attack (TIA)) and daily habits (e.g., smoking, alcohol consumption, drinking tea, diet, sleep, and exercise) were analyzed; corresponding knowledge-based rules were finally presented. Establish a correlation matrix of stroke risk factors and analyze the relationship between stroke risk factors. RESULTS: The accuracy of the established model for stroke risk assessment was 87.53%, and the kappa coefficient was 0.8344, which was superior to that of the random forest and Logistic algorithm. Additionally, 37 knowledge-based rules that can be used for prevention of risk factors of stroke were derived and verified. According to in-depth analysis of risk factors of stroke, the values of smoking, exercise, sleep, drinking tea, alcohol consumption, and diet were 6.00, 7.00, 8.67, 9.33, 10.00, 10.60, and 10.75, respectively, indicating that their influence on risk factors of stroke was reduced in turn; on the one hand, smoking and exercise were strongly associated with other risk factors of stroke; on the other hand, sleep, drinking tea, alcohol consumption, and diet were not firmly associated with other risk factors of stroke, and they were relatively tightly associated with smoking and exercise. CONCLUSIONS: Establishment of a model for stroke risk assessment, analysis of factors influencing risk factors of stroke, analysis of relationships among those factors, and derivation of knowledge-based rules are helpful for prevention and treatment of stroke.

Analysis of behavioral disability and construction of a disability scale for the elderly in Shanghai.

BACKGROUND: With the gradual aging of China's population development, the number of disabled elderly has increased significantly. OBJECTIVE: In order to better solve the problem of life care for these elderly people, it is necessary to conduct in-depth and detailed research on the specific conditions of disabled elderly people, in order to differentiate different conditions for care and set appropriate insurance provisions. METHODS: Based on the detailed analysis of the basic behavioral ability of the elderly, and referring to the International Disability standards, this paper refines the three basic living ability indicators of physiological behavior, cognitive behavior and interpersonal behavior, and integrates the cultural elements of assimilation, continuity, fusion and cohesion with Chinese characteristics. A more systematic and perfect five-level disability scale which conforms to the national conditions of China is designed. RESULTS: The disability of the elderly in Shanghai was investigated with the newly constructed scale, and a detailed analysis and five-level division were made. CONCLUSION: Experiments show that the results of this study can more effectively establish the disability level of the elderly in China.

Assessment of the risk factors in the daily life of stroke patients based on an optimized decision tree.

BACKGROUND: Stroke is a leading cause of mortality and disability, which can be affected by people's daily living habits. OBJECTIVE: To investigate the effects of main daily living habits (smoking, drinking, diet, vegetable and fruits consumption, and exercise) on stroke risk in patients and provide the scientific basis for the assessment of the risk factors, a novel risk analysis model of the stroke is proposed. METHODS: A data mining method using decision trees which adopted the optimized C4.5 algorithm is presented. It is able to deal with the unbalanced data problem of the classification. Meanwhile, the proposed method has been verified on a clinical dataset of 23,682 patients with 21 risk factors. RESULTS: The overall accuracy and kappa coefficient for stroke risk classification has reached 84.88% and 0.7763, respectively. Through the generated knowledge rules, it demonstrates that the behavioral habits in daily life have an indirect effect on the risk of stroke. While, it has an obvious effect on stroke when hypertension, diabetes mellitus, hypercholesterolemia, and BMI risk factors exist. In addition, it was observed that the aforementioned five daily living habits have a decreased impact on the stroke. CONCLUSIONS: It is anticipated that the proposed system could help in reducing the risk, mortality, and disability of stroke, and provide clinical decision support for the treatment of stroke.