Choi Yong-Jeon

Aim and objectives: This study aimed to review the state of telemedicine legislations in the United States and South Korea and undertakes a comparative study of the legislations pre- and post-COVID-19 pandemic as groundwork for a more meaningful adoption and execution of telemedicine. Methodology: A comprehensive desk research for regulatory and legal frameworks in telemedicine was performed. The process entailed making an online search for regulations, laws, policy briefs, case studies, green papers, reports, and policy recommendations in the period 1990-2021. Results and Findings: A major concern over telemedicine law in the pre- and post-Covid-19 healthcare system can largely be linked to legislative barriers. There are significant differences in telemedicine legislations between the two countries before and after the COVID-19 pandemic. Like South Korea, the US federal governments also lifted restrictions to telemedicine. However, in the situations between the two countries differed substantially in the pre-COVID-19 healthcare system. South Korea completely banned telemedicine, which barred telemedicine practice across the country. On the other hand, a key legal impediment to telemedicine in the United States is tied to the existence of in-state licensure system and federal reimbursement policies that focus on Medicare. Recommendations: For both South Korea and the United States, there is a need to build regulatory flexibility into telemedicine to cater to demands from a range of cases. Among other elemental factors that can accelerate telemedicine expansion in the US and South Korea would be to develop robust legal standards to mitigate uncertainty and guarantee advantages like data security for using telemedicine in the two countries, and speeding up the processes of telemedicine regulations regardless of geographic delimitations.

Choi Yong-Jeon

Aim and objectives: This study aimed to assess the implications of telemedicine law on health care practice, particularly how it has affected clinicians’ practice during the Covid-19 pandemic. To ensure this, it investigated the costs and benefits of telemedicine by healthcare practitioners in South Korea during the COVID-19 pandemic, and to provide insights as to whether telemedicine continue being regulated in South Korea’s post-pandemic healthcare system. To assess the implications of South Korean telemedicine ban on health care practice, the survey looked at whether telemedicine leads to improved cost-saving and the extent to which telemedicine contributes to hospital avoidance. Method: A quantiative web-based survey was distributed to health practitioners in South Korea’s Daegu Haany University Korean Medicine Hospital. Analysis of data was performed on the basis of parametric statistics and frequency percentages. Findings and Results: The benefits of telemedicine in South Korea during the COVID-19 pandemic can be categorised into three: cost, access, and outcomes. While telemedicine does not seem to have replaced medical examination during Covid-19 pandemic in the context of South Korea’s healthcare system, it helped curtail the spread of the virus. It also avoided the need for patients to visit hospitals. Findings also suggested that telemedicine was useful for caring for patient undergoing palliative treatment or for management of chronic disorders during the pandemic by reducing hospital visitation. Conclusion and recommendations: There is an upward acceptance of remote consultations to improve the ease of use of health care for underserved communities. Healthcare providers suggest a need to lift the legislative restrictions on telemedicine for the country’s post-COVID-19 healthcare systems.

Omar Adil Kamil, Shaymaa W. Al-Shammari

Recently, researchers have shown an increased interest in achieving accurate brain tumor classification using the Internet of Things (IoT). The brain is one of the most complex organs in the human body, with billions of cells. A brain tumor is caused by uncontrolled, abnormal cell growth that disrupts normal brain function and destroys healthy cells. The study aims to achieve a simple application for classifying brain tumors and improve the accuracy of the classification methods. The suggested classification system adopts the idea of optimizing the convolutional neural network (CNN) model using the optimization approach and extracting features from brain MRI images. The accuracy of this proposed method on the test set is 98.57%, and it was proven to be better in terms of accuracy. The second part of the proposed system is the IoT, which makes the system applicable for everyone anywhere everywhere to get accurate classification for brain tumors.

Md. Rahimullah Miah, AAM Shazzadur Rahman, Md. Shahariar Khan, Mohammad Abdul Hannan, Md. Sabbir Hossain, Chowdhury Shadman Shahriar, S. A. M. Imran Hossain, Mohammad Taimur Hossain Talukdar, Alamgir Adil Samdany, Mohammad Shamsul Alam, Mohammad Basir Uddin, Alexander Kiew Sayok, Shahriar Hussain Chowdhury

Corona is a non-communicable sensor disease spreading worldwide through misusing of processed radio frequency. So far higher authorities of health services are facing the undesirable escalating causes of coronavirus towards human beings as a very scientific puzzle comprehensive issue. The study aims to evaluate the maltreating of wireless sensor networks that affect individuals within the body boundary area. Wireless sensor data were collected from individual’s profile, diagnosis and sensor node records at laboratory experiments. The study shows the effect of processed sensor nodes among individual’s body organs to compare with the existing environments. The study illustrates all individuals suffer from sensor disease due to reflecting of wavered sensors at open eyes sights with high speed electromagnetic-radio tracking systems. The overweight and obesity patients are sick from corona disease at less sensor time in a dark environment than that of light conditions. These findings replicate the severe global one health security that the expert provides in active eyes within geographic locations. Systematic healthcare awareness is essential for treatment with medical technological devices but such consciousness is poorly recognized and medication supports are still below par. The study suggests upcoming healthcare paths of a new dynamic alternative approach to promote global public health security concerning Sensor Health Policy and Sustainable Development Goals 2030.

Qusay H. Tawfeeq, Ahmed H. Y. Al-Noori, Amjed N. Jabir

The most essential trait of any door locker security system is to check the identity of person who come in through that door. However, instead of surveillance, devices that using passwords or pin code, the unique features in people faces image and their voices signal can be considered as biometric trait to verify them. These characteristics that cannot be edited, copied or stolen easily. The level of security can be improved efficiently by using the double biometrics including face recognition and speaker recognition techniques simultaneously, to achieve high accuracy. This system is developed to deny theft in highly secure areas such as home, bank and other places for both stranger detection and for door locker security. This system is tested with the Windows operatingsystem environment and then adapted to the Unixoperating system when running on the Raspberry Pi 3 platform using python 3.7. Raspberry Pi electronic board is a single-board computers operated on Battery power supply, wireless internet connectivity by using USB modem, it connects tocamera, microphone, LED and a12 voltdoor locker. When the person stands front the door, the camera and microphone of raspberry pi, are used toverify the person face image and voice signal to determine whether this person is verified authentic or just imposter. Since the person is verified the system will unlock the door. Otherwise, it will send an alarm and takea photo capture of that imposter personand then send it to the authorized person using Gmail and SMSmessages.

Mohamed L. Salem, Ahmed M. Azlohairy, Ismail Atia, Heba Wassfy, Abdel-Aziz A. Zidan, Gábor Gyulai

Cell differentiation processes of stem cells have unique pathways controlled by certain genes including EYA1 (Eyes Absent), SIX1 (Sine oculis homeobox), and SOX9 (Sry-related high mobility group BOX). It was reported that these genes act as transcriptional factors (TFs) for the repair and regeneration of the progenitor cells as well as of damaged tissues, however the function of these genes are not fully characterized and understood. Different bioinformatics tools were used in this study to analyze four transcriptional factors (TFs of EYA1, SIX1 and SOX9) in human and compare them with their orthologs in eleven different organisms by tools to provide more knowledge about their functions in stem cells. The present investigations showed that SOX9 was neither present in Ceanorhabditis elegans (nematode; roundworm) nor in Cavia porcellus (domestic guinea pig). We applied multiple sequence alignments (MSA) for selection isoforms of SOX9 protein showed conserved domains among different species. EYA1 protein showed wide range of alternative isoforms in Homo sapiens. High levels of SIX1 were detected in all human body tissues. Gene expressions were investigated to determine the expressions of genes in body tissues. The interaction between TFs proteins was confirmed by gene interaction network (GIN) which showed close relations between EYA1 and SIX1. The results of this study shed a light on the differential gene expression level of studied genes in different tissues damaged by cancer and their different repair mechanisms. The presence or absence of these TF genes may be an indicator of the developmental differentiation between invertebrates and vertebrates.

Nidal Essa, Abdelrahman H. Abdelmoneiom, Razan M. Badawi, Afnan Mohamed Mohamed Khair, Rania Anwer, Amel Nasir Eltayeb Ali, Tebyan Ameer Abdelhameed Abbas, Mohamed A. Hassan

Luteinizing hormone beta subunit (LH-B) (protein ID P01229) is gonadotropin hormone secreted from the anterior pituitary belongs to the glycoprotein family, mapped on chr19p13.3 and consists of three exons, three transcript variants with two phenotypes encoded for one protein known as Lutropin subunit beta (P01229) It has a central role in promoting spermatogenesis and ovulation by stimulating the testes and ovaries for steroidogenesis. PCOS is a common endocrinopathy affecting of women within the reproductive age, abnormal ovulation associated with a high level of LH as a result of gene polymorphisms lead to infertility problems. In this study, we used various computational approaches to identify nsSNPs which probably be deleterious to the structure and/or function of LH-B protein that might be associated with polycystic ovary syndrome. The data on human LH-B gene was retrieved from dbSNP/NCBI. Eleven different bioinformatics prediction algorithms; SIFT, Polyphen, PROVEAN, SNAP2, Pmut, PhD-SNP, I-Mutant and Project Hope were used to analyze the effect of nsSNPs on functions and structure of the LH-B protein, and RaptorX for protein modeling and Chimera for visualization of the model, in addition, we used PolymiRTS to detect SNPs on miRNA binding sites. After retrieval of SNPs from the NCBI database, 140SNPs were classified as missense SNPs. From functional analysis software, 39 SNP were predicted to be deleterious then they analyzed by disease-related software 13 SNPs, when checked for protein stability, 12 of them decreased protein stability and one SNP increased its stability. Hence, application of these 13 novel mutations through genetic studies will contribute towards our understanding of the pathophysiology of PCOS.

Noel Dougba Dago, Inza Jesus Fofana, Nafan Diarrassouba, Mohamed Lamine Barro, Jean-Luc Aboya Moroh, Olefongo Dagnogo, Loukou N’Goran Etienne, Martial Didier Saraka Yao, Souleymane Silué, Giovanni Malerba

Data harvesting, data pre-treatment and as well data statistical analysis and interpretation are strongly correlated steps in biological and as well agronomical experimental survey. In view to make straightforward the integration of these procedures, rigorous experimental and statistical schemes are required, playing attention to process data typologies. Numerous researchers continue to generate and analyse quantitative and qualitative phenotypical data in their agronomical experimentations. Considering the impressive heterogeneity and as well size of that data, we proposed here a semi-automate analysis procedure based on a computational statistical approach in R programming environment, with the purpose to provide a simple (programmer skills are not requested to users) and efficient (few minute are needed to get output files and/or figures) and as well flexible (authors can add own script and/or bypassed some functions) tool pointing to make straightforward heterogenic metric data interactions in biostatistics survey. The pipeline starts by loading a row data matrix followed by data standardization procedure (if any). Next, data were processed for a multivariate descriptive and as well analytical statistical analysis, comprising data quality control by providing correlation matrix heat-map and as well as p-value clustering analysis graphics and data normality assessment by Shapiro-Wilk normality test. Then, data were handled by principal component analysis (PCA) including PCA n factor survey in discriminating needed factors component explaining data variability. Finally data were submitted to linear and/or multiple linear regression (MLR) survey with the purpose to link mathematically managed data variables. The pipeline exhibits a high performance in term of time saving by processing high amount and heterogenic quantitative data, allowing and/or providing a complete descriptive and analytical statistical framework. In conclusion, we provided a quick and useful semi-automatic computational bio-statistical pipeline in a simple programming language, exempting the researchers to have skills in advanced programming and statistical technics, although it is not exhaustive in terms of features.

Nidal Essa, Enas A. Osman, Hadeel M. Yousif, Kutuf A. Albushra, Amel Nasir Eltayeb Ali, Tebyan Ameer Abdelhameed Abbas, Mohamed A. Hassan

Background: Follicular stimulating hormone beta subunit (FSH-B) as gonadotropin hormone secreted from the anterior pituitary belongs to glycoprotein family located on 11p14.1 and consists of three exons. It is responsible for follicular growth and ovarian steroidogenesis in females and spermatogenesis in males. PCOS is a common endocrinopathy affecting 4-20% of women within the reproductive age the pathophysiological process is not fully understood and lowering of serum FSH level due to gene polymorphism lead to abnormal folliculogenesis and irregular menstrual cycle.In this study, we used various computational approaches to identify nsSNPs which probably be deleterious to the structure and/or function of FSH-B protein that might be associated with polycystic ovary syndrome. Methods: The data on human FSH-B gene was retrieved from dbSNP/NCBI. Eleven different bioinformatics prediction algorithms; SIFT, Polyphen, PROVEAN, SNAP2, Pmut, PhD-SNP, I-Mutant and Project Hope were used to analyze the effect of nsSNPs on functions and structure of the FSH-B protein, and RaptorX for protein modeling and Chimera for visualization of the model, in addition, we used PolymiRTS to detect SNPs on miRNA binding sites. Results: After retrieval of SNPs from the NCBI database, 164SNPs were classified as missense SNPs. From functional analysis softwares, 84 SNP were predicted to be deleterious then they analyzed by disease related softwares 13 SNPs, when checked for protein stability, 12 of them decreased protein stability and one SNP increased its stability. Conclusion: Consideration should be taken to these 13 novel mutations when we are carrying out genetic studies through human samples. Keywords PCOS, In Silico analysis, FSH-B, Folliculogenesis, Computational analysis

Salah H. Abid, Jinan H. Farhood

Many studies discussed different numerical representations of DNA sequences, while far fewer studies deal with image analysis for aspects related with DNA. In this paper, we proposed new algorithm for image similarity to compare among variance covariance matrix eigenvalues images of Fast Fourier Transform (FFT) for numerical values representation of DNA sequences of five organisms, Human, E. coli, Rat, Wheat and Grasshopper. This algorithm is based on randomized block design model. It should be noted that it is the first time that the variance covariance matrix eigenvalues of Fast Fourier Transform (FFT) for numerical values representation of DNA sequences, is used in an analysis like this and related analyzes.