ABSTRACT
Objective@#To explore the association between urinary metals and lung function among college students, and to provide a theoretical basis for related research on metal exposure and lung function injury.@*Methods@#A total of 45 healthy college students were recruited from North China University of Science and Technology in Caofeidian between 2017-2018. During the four seasons, information was obtained from questionnaires and physical examinations, lung function parameters were assessed, including FVC, FEV1, PEF, FEV1/FVC and FEF 25-75 , and morning urine samples were collected simultaneously. The urinary levels of 15 metals were measured by inductively coupled plasma mass spectrometry (ICP/MS); a Kruskal Wallis H test was used to compare differences in urinary metals during the four seasons; and a mixed effect model was used to assess correlations between urinary metals and lung function.@*Results@#There were significant differences in the levels of urinary chromium, iron, nickel, copper, zinc, arsenic, selenium, selenium, molybdenum, cadmium, antimony and lead from 15 metals over the four seasons ( H =9.79- 20.61 , P <0.05). The differences observed in five lung function parameters over the four seasons were statistically significant ( F =61.72, 45.30, 47.61, 25.47, 35.13, P <0.05). The linear mixed effect model analysis showed that urinary concentrations of vanadium, manganese, iron, cobalt, nickel and antimony were negatively correlated with FEV1( B =0.202, 0.192, 0.181, 0.154, 0.131 , 0.283); urinary concentrations of aluminum, vanadium, manganese, iron, cobalt, nickel, zinc, cadmium, and antimony were negatively correlated with FVC ( B =0.252, 0.290, 0.292, 0.271, 0.201, 0.180, 0.171, 0.163, 0.381); urinary concentrations of manganese and antimony were negatively correlated with PEF ( B =0.291, 0.354)( P <0.05).@*Conclusion@#The increase of multiple metal concentrations among college students was related to lung function decline, the long term metal exposure might lead to lung function damage. So environmental metal pollution should be controlled.
ABSTRACT
BACKGROUND: Super-resolution reconstruction has been extensively studied and used in many fields,such as medical diagnostics,military surveillance,frame freeze in video,and remote sensing.OBJECTIVE: In order to obtain high-resolution magnetic resonance images,gradient magnetic field is required and the signal-to-noise will be reduced due to the decrease in voxel size with traditional scan.The present study used a self-adaptive regularized super-resolution reconstruction algorithm to acquire high-resolution magnetic resonance images from four half-pixel-shifted low resolution images.METHODS: The least squares algorithm was used as a cost function.The dedvative of the cost function was calculated to obtain an iterative formula of super-resolution reconstruction.In the process of iterative process,the parameter and step size of image resolution were regularized.RESULTS AND CONCLUSION: The new regularization parameter makes cost function of the new algorithm convex within the definition region.The piori information is involved in the regularization parameter that can improve the high-frequency components of the restored image.As shown from the results obtained in the phantom imaging,the proposed super-resolution technique can improve the resolution of magnetic resonance image.