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1.
Asian J Androl ; 25(2): 240-244, 2023.
Article in English | MEDLINE | ID: mdl-36348579

ABSTRACT

The effects of the coronavirus disease 2019 (COVID-19) pandemic on male fertility have received considerable attention because human testes contain high levels of angiotensin-converting enzyme-2 receptors, through which severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) can enter. Early studies showed decreases in semen quality during and after recovery from COVID-19. However, no semen quality studies have examined the effects of widespread subclinical and mild disease, as well as changes in lifestyle, psychosocial behavior, intake of dietary supplements, and stress. This cross-sectional study compared semen quality parameters in male partners of infertile couples between men who underwent semen analysis before the COVID-19 pandemic (prepandemic group) and men who underwent semen analysis during the pandemic period (pandemic group); the analysis sought to clarify the overall effects of the pandemic. No participants in the pandemic group had experienced clinically overt disease. Among the 239 participants, mean body weight (P = 0.001), mean body mass index (P < 0.001), median sperm concentration (P = 0.014), total sperm count (P = 0.006), and total percentages of motile (P = 0.013) and abnormal cells (P < 0.001) were significantly greater in the pandemic group (n = 137) than those in the prepandemic group (n = 102). Among abnormal cells, the percentages of cells with excess residual cytoplasm (P < 0.001), head defects (P < 0.001), and tail defects (P = 0.015) were significantly greater in the pandemic group than those in the prepandemic group. With the exception of morphology, the overall semenogram results were better in the pandemic group than those in the prepandemic group.


Subject(s)
COVID-19 , Infertility, Male , Humans , Male , Pandemics , Cross-Sectional Studies , Testis , SARS-CoV-2 , Semen , Semen Analysis , Sperm Count
2.
Asian Pac J Cancer Prev ; 19(9): 2631-2635, 2018 Sep 26.
Article in English | MEDLINE | ID: mdl-30256071

ABSTRACT

Objective: This method is to reduce the risk of imprecise diagnosis associated with poor-quality CT images, this paper presents a new technique designed to enhance the quality of medical CT images. The main objective is to improve the appearance of CT images in order to obtain better visual interpretation and analysis, which is expected to ease the diagnosis process. The proposed technique involves applying a deblocking filter is to enhance the visual nature of a picture by diminishing the blocking artifacts. The appearance of a picture isn't clear while an antique happens. The proposed deblocking filter calculation gives a strategy to expel the ancient rarities by smoothing the sharp edges of a picture. Methods: With a specific goal to lessen the quantity of information access, multifaceted nature and consequently to upgrade the proficiency, a six-staged pipelined structure for picture pixels are proposed. Besides, to enhance the subjective and target nature of a picture the deblocking filter performs identification of the antique at the coded square limits and weakens them by applying a chose filter. Result: The proposed algorithm is implemented in HDL using Xilinx FPGA. The input image is converted into decimal pixel values using Matlab and this value is used as the input in HDL. The proposed algorithm is compared with other blocking algorithms. Conclusion: To design an effective deblocking filter with low cost, low complexity and high intensity, pipeline based systems are used. In addition to that the number of memory accesses and timing efficiency also be reduced using this method. The deblocking filtering operations can also easily perform in parallel on multiple processors by using six-stage of pipelined, two-line deblocking filter. The parameter mean, variance, standard deviation, resolution, contrast and PSNR values are compared with the previous method. Hence it shows the implementation of deblocking filter using pipelining is more efficient than others.


Subject(s)
Algorithms , Image Enhancement/methods , Image Processing, Computer-Assisted/methods , Neoplasms/diagnostic imaging , Neoplasms/diagnosis , Tomography, X-Ray Computed/methods , Artifacts , Humans
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