Perinatal aspects of sars-cov-2 infection during pregnancy: A potential cause for concern

Ever since the beginning of COVID-19 pandemic, uncertainty regarding clinical presentation and differences among various subpopulations exist. With more than 209,870,000 confirmed cases and more than 4,400,000 deaths worldwide, we are facing the new era of health crisis which will undoubtedly impair global health, economic and social circumstances. In the past year, numerous genetic mutations which code SARS-CoV-2 proteins led to the occurrence of new viral strains, with higher transmission rates. Apart from the implementation of vaccination, the effect of SARS-CoV-2 on pregnancy outcome and maternal fetal transmission remains an important concern. Although neonates diagnosed with COVID-19 were mostly asymptomatic or presented with mild disease, the effect on early pregnancy is yet to be evident. While positive finding of SARS-CoV-2 RNA in some samples such as amniotic fluid, placental tissue, cord blood and breast milk exists, additional research should confirm its association with transplacental transmission.Copyright © 2022, Dr. Mladen Stojanovic University Hospital. All rights reserved.

Brownian Motion Fractal Nature Frontiers Within the Matter

One of the main motivations for our research was to find a connection between the Brownian motion of microorganisms within fractal nature, with the idea of developing an appropriate procedure and method to control the microorganism's motion direction and predict the position of the microorganism in time. In this paper, we have followed the results of the very rear microorganism's motion sub-microstructures in the experimental microstructure analysisFractals already observed and published. All of these data have been good basis to describe the motion trajectory by time interval method and fractals. We successfully defined the diagrams in two and three-dimensions and we were able to establish the control of Brownian chaotic motion as a bridge between chaotic disorders to control disorder. This significant study opens a new possibility for future investigation and the new potential of total control of the microorganism motion. These perspectives and findings provide significant data for getting more information from these bio systems. They can also be applied, based on self-similarities and biomimetics, to particle physical systemMatterFractalss and matter, generally. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Fuzzy enhancement and deep hash layer based neural network to detect Covid-19

This paper proposes a deep learning framework for Covid-19 detection by using chest X-ray images. The proposed method first enhances the image by using fuzzy logic which improvises the pixel intensity and suppresses background noise. This improvement enhances the X-ray image quality which is generally not performed in conventional methods. The pre-processing image enhancement is achieved by modeling the fuzzy membership function in terms of intensity and noise threshold. After this enhancement we use a block based method which divides the image into smooth and detailed regions which forms a feature set for feature extraction. After feature extraction we insert a hashing layer after fully connected layer in the neural network. This hash layer is advantageous in terms of improving the overall accuracy by computing the feature distances effectively. We have used a regularization parameter which minimizes the feature distance between similar samples and maximizes the feature distance between dissimilar samples. Finally, classification is done for detection of Covid-19 infection. The simulation results present a comparison of proposed model with existing methods in terms of some well-known performance indices. Various performance metrics have been analysed such as Overall Accuracy, F-measure, specificity, sensitivity and kappa statistics with values 93.53%, 93.23%, 92.74%, 92.02% and 88.70% respectively for 20:80 training to testing sample ratios;93.84%, 93.53%, 93.04%, 92.33%, and 91.01% respectively for 50:50 training to testing sample ratios;95.68%, 95.37%, 94.87%, 94.14%, and 90.74% respectively for 80:20 training to testing sample ratios have been obtained using proposed method and it is observed that the results using proposed method are promising as compared to the conventional methods.

Brownian fractal nature coronavirus motion

The goal of our research is to establish the direction of coronavirus chaotic motion to control corona dynamic by fractal nature analysis. These microorganisms attaching the different cells and organs in the human body getting very dangerous because we don't have corona antivirus prevention and protection but also the unpredictable these viruses motion directions what resulting in very important distractions. Our idea is to develop the method and procedure to control the virus motion direction with the intention to prognose on which cells and organs could attach. We combined very rear coronavirus motion sub-microstructures images from worldwide experimental microstructure analysis. The problem of the recording this motion is from one point of view magnification, but the other side in resolution, because the virus size is minimum 10 times less than bacterizes. But all these images have been good data to resolve by time interval method and fractals, the points on the motion trajectory. We successfully defined the diagrams on the way to establish control over Brownian chaotic motion as a bridge between chaotic disorder to control disorder. This opens a very new perspective to future research to get complete control of coronavirus cases. © 2021 World Scientific Publishing Company.