Small Molecules of Lactoferrin Modification by Ionic Liquids Against SARS-CoV-2: Molecular Docking Enhanced the Results (preprint)

Ionic liquid MIE-NH2 displays a new role of development of modification of glycoproteins of lactoferrin through a reductive amination mechanism to synthesize versatile pharmaceuticals. This work introduces a new strategy of modification of Lactoferrin by using methylimidazolium N-ethylamine, this ionic liquid MIE-NH2 linked to N-glycans in Lactoferrin derivatives. Using UPLC/ESI-QTOF and MALDI-TOF mass spectrometry to perform and detect the modifying of ionic liquid-linked glycoproteins. Relevantly, modifying the lactoferrin by MIE-H2 as a small molecule of ion liquid lactoferrin (IL-Lf), which could be a potential antiviral drug and it is achieved by inhibiting various targets. The probability of the lactoferrin modified as a small IL-Lf-molecule to inhibit any of these targets was investigated to find out its potency as a SARS-CoV-2 inhibitor. Molecular docking disclosed the activity of modifying glycoproteins - small IL-Lf-molecules containing amino groups and interaction with targeted Mpro, RdRp, TMPRSS2, and PLpro. Clinically, this study shows the a volubility to provide small IL-Lf-molecules as significantly important drugs that target main protease (Mpro), RNA dependent RNA polymerase (RdRp), transmembrane protease serine 2 (TMPRSS2), and Papain-like protease (PLpro).

The Role of Institutionalization Police Support in Emergency Situation: Evidence from Indonesia

Role of Police Supporting Institutions in an Emergency in Indonesia. Regulations related to police duties and the condition of medical personnel are actually at the forefront of emergencies and pandemic disasters, but in Indonesia the police also called the front guard in efforts to prevent the emergency spread of Covid-19. It can be seen if there is gaps in the implementation of police duties during an emergency. This study aims to find out the existence of police as the institution that having mandate to manage and handle emergencies situation such as pandemic of COVID-19. This study used doctrinal legal research as one of the legal research methods. The findings show that management of health protection in Indonesia particularly in pandemic situation had not maximal. As can be seen there are several barriers to Indonesian Police in handling the emergency situations. Firstly, the internal problem in the institution, then it needs a revitalization. Secondly, the lack of adequate funding for the police's performance. Thirdly, as well as the external cause is the lack of publick awareness or the culture of society to be able to cooperate with the police in preventing the spread of COVID-19 in Indonesia. © 2023, Lembaga Contrarius Indonesia. All rights reserved.

Exploring the Role of Technology Application in Tourism Events, Festivals and Fairs in the United Arab Emirates: Strategies in the Post Pandemic Period

This chapter aims to explore the role of technology application in tourism events, festivals, and fairs in the The United Arab Emirates (UAE) during the post-pandemic period of COVID-19. The chapter specifically focuses on various technical Apps based on the latest technology that may affect tourism events, festivals, and fairs. Existing literature lacks the ubiquitous role of technology Apps in sustainable tourism development in collaboration with tourism festivals, events, and fairs. The study identifies how tourists are affected by technology application, revealing in particular an increased tourism development and how tourists are continually enthralled by and attracted to tourism festivals, events, and fairs due to the advancement of the latest technology application in tourism. In this chapter, the perspective of the UAE is brought into the discussion. The chapter reveals that technology application in tourism festivals, events, and fairs can ensure sustainable tourism development in the UAE, especially in the post-pandemic period of COVID-19. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022.

ST-SEGMENT-ELEVATION MYOCARDIAL INFARCTION in COVID-19

Introduction: The Severe Acute Respiratory Syndrome Coronavirus-2 have been associated with cardiovascular adverse events including acute myocardial infarction due to a prothrombotic and hypercoagulable status, and endothelial dysfunction. Case report: We report the case of a 62-year-old women, admitted to the hospital via the emergency room for acute chest pain and dyspnea. A nasopharyngeal swab was positive for COVID19 real-time reverse transcriptase-polymerase chain reaction 11 day ago. On admission, she was hypotensive with systolic blood pressure measering 87 mmHg and tachycardic with 117 beats/min, oxygen saturation (SO2) was 94%. An 18-lead ECG revealed an infero-postero-lateral ST-elevation myocardial infarction with right ventricular involvement and a seconddegree- Mobitz Type 1 atrioventricular block. The coronary angiography from the right femoral artery showed acute thrombotic occlusion of the first diagonal branch with TIMI 0 flow and acute thrombotic occlusion of proximal right coronary artery with TIMI 0 flow. The most likely diagnosis was myocardial infarction secondary to a non-atherosclerotic coronary occlusion. The angioplasy was performed with dilatations with a semi compliant balloon, bailout implant of BMS, manual thrombus aspiration and intracoronary injection of tirofiban in the right coronary artery. The myocardial revascularization was ineffective. The patient developed significant severe hemodynamic instability and cardiac arrest for pulseless electric activity after 24 hours. Conclusion(s): The COVID-19 outbreak implies deep changes in the clinical profile and therapeutic management of STEMI patients who underwent PCI. At present, the natural history of coronary embolism is not well understood;however, the cardiac mortality rate are hight. This suggests these patients require further study to identify the natural history of the condition and to optimize management to improve outcome.

Improved Barnacle Mating Optimizer-Based Least Square Support Vector Machine to Predict COVID-19 Confirmed Cases with Total Vaccination

Every country must have an accurate and efficient forecasting model to avoid and manage the epidemic. This paper suggests an upgrade to one of the evolutionary algorithms inspired by nature, the Barnacle Mating Optimizer (BMO). First, the exploration phase of the original BMO is enhanced by enforcing and replacing the sperm cast equation through Levy flight. Then, the Least Square Support Vector Machine (LSSVM) is partnered with the improved BMO (IBMO). This hybrid approach, IBMO-LSSVM, has been deployed effectively for time-series forecasting to enhance the RBF kernel-based LSSVM model since vaccination started against COVID-19 in Malaysia. In comparison to other well-known algorithms, our outcomes are superior. In addition, the IBMO is assessed on 19 conventional benchmarks and the IEEE Congress of Evolutionary Computation Benchmark Test Functions (CECC06, 2019 Competition). In most cases, IBMO outputs are better than comparison algorithms. However, in other circumstances, the outcomes are comparable.

Hybrid deep neural network for automatic detection of COVID-19 using chest x-ray images

The 2019 coronavirus (COVID-19), started in China, spreads rapidly around the entire world. In automated medical imagery diagnostic technique, due to presence of noise in medical images and use of single pre-trained model on low quality images, the existing deep network models cannot provide the optimal results with better accuracy. Hence, hybrid deep learning model of Xception model & Resnet50V2 model is proposed in this paper. This study suggests classifying X-ray images into three categories namely, normal, bacterial/viral infections and Covid positive. It utilizes CLAHE & BM3D techniques to improve visual clarity and reduce noise. Transfer learning method with variety of pre-trained models such as ResNet-50, Inception V3, VGG-16, VGG-19, ResNet50V2, and Xception are used for better feature extraction and Chest X-ray image classification. The overfitting issue were resolved using K-fold cross validation. The proposed hybrid deep learning model results high accuracy of 97.8% which is better than existing techniques.

The Impact of Covid-19 Vaccines on Sperm Quality

Objective: To determine the impact of Covid-19 vaccines on sperm quality. Study Design: Case control study Place and Duration of Study: Department of Diabetes & Endocrinology, Chandka Medical College Hospital Larkana from 1st July 2022 to 31st December 2022. Methodology: Patients were enrolled as 50 those who had PCR confirmed Covid 19 history and 50 those who never got Covid-19. On this basis those cases who had a Covid-19 history were placed in group A while those who did not had Covid-19 history were placed in Group B. Patients clinical history including anamnesis, marital status, cryptorchidism, operative varicocele, or any chronic ailment were documented. A counting chamber was used for sperm count in a 100 square area. Spermatozoa was measured as either rapid-progressively motile (Type a), or as slow-progressively-motile (Type b), or as situ motile (Type c), and finally as immobile (Type d). The total semen sperm count was gained by multiplication of concentration of sperm with its volume. Result(s): Volume and concentration was significantly different in both study groups. Difference in tail anomaly was also observed. In group A, it was 29.20 +/- 10.26 while 27.59 +/- 12.31 was the value of group B. Almost equal number of participants were married. Azoospermia was only found among Covid patients. Conclusion(s): Azoospermia was only found in Covid patients and no such results were obtained from Covid negative patients.Copyright © 2023 Lahore Medical And Dental College. All rights reserved.

Automated COVID-19 diagnosis and classification using convolutional neural network with fusion based feature extraction model.

COVID-19 was first identified in December 2019 at Wuhan, China. At present, the outbreak of COVID-19 pandemic has resulted in severe consequences on both economic and social infrastructures of the developed and developing countries. Several studies have been conducted and ongoing still to design efficient models for diagnosis and treatment of COVID-19 patients. The traditional diagnostic models that use reverse transcription-polymerase chain reaction (rt-qPCR) is a costly and time-consuming process. So, automated COVID-19 diagnosis using Deep Learning (DL) models becomes essential. The primary intention of this study is to design an effective model for diagnosis and classification of COVID-19. This research work introduces an automated COVID-19 diagnosis process using Convolutional Neural Network (CNN) with a fusion-based feature extraction model, called FM-CNN. FM-CNN model has three major phases namely, pre-processing, feature extraction, and classification. Initially, Wiener Filtering (WF)-based preprocessing is employed to discard the noise that exists in input chest X-Ray (CXR) images. Then, the pre-processed images undergo fusion-based feature extraction model which is a combination of Gray Level Co-occurrence Matrix (GLCM), Gray Level Run Length Matrix (GLRM), and Local Binary Patterns (LBP). In order to determine the optimal subset of features, Particle Swarm Optimization (PSO) algorithm is employed. At last, CNN is deployed as a classifier to identify the existence of binary and multiple classes of CXR images. In order to validate the proficiency of the proposed FM-CNN model in terms of its diagnostic performance, extension experimentation was carried out upon CXR dataset. As per the results attained from simulation, FM-CNN model classified multiple classes with the maximum sensitivity of 97.22%, specificity of 98.29%, accuracy of 98.06%, and F-measure of 97.93%.

Clinical Profile and Outcomes of Epidemic Dropsy Patients Attending a Tertiary Care Centre in Assam, India.

Background The clinical condition of epidemic dropsy is caused by the consumption of edible oils contaminated with Argemone mexicana oil. Two of the most toxic alkaloids found in argemone oil are sanguinarine and dehydrosanguinarine, which cause capillary dilation, proliferation, and increased permeability. Extreme cardiac decompensation leading to congestive heart failure and glaucoma resulting in blindness are the most serious consequences of epidemic dropsy.  Materials and methods All patients attending the medicine department of Tezpur Medical College and Hospital with clinical features of epidemic dropsy were included in the study after obtaining informed consent. All patients, after a complete history, underwent a thorough clinical examination, and findings were recorded using a pre-formed proforma. Along with routine blood examination, patients were also evaluated with echocardiography, ECG, and chest X-ray. Cooking oil samples obtained from patients were investigated for the presence of sanguinarine in a standardized laboratory with the help of the district authority. The statistical analysis was done using MS Excel 2017. Results Out of 38 patients, 36 were male (94.7%), and only two were female (5.2%). Male to female ratio was 18:1. This difference in sex ratio may be due to the fact that only severely ill patients attended our tertiary care hospital. In contrast, moderate and mildly ill patients were treated in local hospitals. The mean age of patients was 28.1 years, and the mean length of hospital stay was eight days. Bilateral pitting type of ankle edema was the most common clinical manifestation, and all 38 patients (100%) exhibited edema. A total of 76% of patients had dermatological manifestations. Sixty-two percent of patients had gastrointestinal manifestations. In cardiovascular manifestation, persistent tachycardia was seen in 52% of patients, pansystolic murmur was best heard in the apical area in 42% of patients, and 21 percent had evidence of a raised jugular venous pressure (JVP). Five percent of patients had pleural effusion. Sixteen percent of patients had ophthalmological manifestations. Eight patients (21%) required ICU care. The in-hospital fatality rate was 10.53% (n=4). Of the expired patients, 100% were male. The most common cause of death was cardiogenic shock (75%), followed by septic shock (25%). Conclusion From our study, it was found that most of the patients were male, with an age group of 25-45 years. The most common clinical manifestation was dependent edema, along with signs of heart failure. Other common manifestations were dermatological and gastrointestinal. The severity and outcome were directly related to the delay in seeking medical consultation and diagnosis.

Chest CT utilization in COVID-19: a dosimetric and diagnostic-quality study.

The purpose of this study is to look at the variations in chest computed tomography (CT) use, radiation dose and image quality in the 2019 novel coronavirus (COVID-19) pneumonia patients in Saudi Arabia. This is a retrospective study of 402 patients with COVID-19, who were treated between February and October 2021. Radiation dose was estimated using metrics of volume CT dose index (CTDIvol) and size-specific dose estimate (SSDE). The imaging performance of the CT scanners was evaluated by measuring different parameters, such as resolution and CT number uniformity, with an ACR-CT accreditation phantom. Expert radiologists assessed the diagnostic quality and occurrence of artefacts. For all of the image quality parameters tested, the majority of the scanner sites (80%) were found to be within the suggested acceptance limits. Ground-glass opacities were the most common finding in our patient sample (54%). On chest CT exams with typical appearance of COVID-19 pneumonia, the most respiratory motion artefacts (56.3%) were present, followed by those with indeterminate appearance (32.2%). There were significant differences in CT utilization, CTDIvol and SSDE across the collaborated sites. The use of CT scans and radiation doses varied in the COVID-19 patients, highlighting the optimizations of CT protocols at participating sites.

Retraction Note: Hybrid intelligent model for classifying chest X-ray images of COVID-19 patients using genetic algorithm and neutrosophic logic.

[This retracts the article DOI: 10.1007/s00500-021-06103-7.].

How helpful were molecular dynamics simulations in shaping our understanding of SARS-CoV-2 spike protein dynamics?

The SARS-CoV-2 spike protein (S) represents an important viral component that is required for successful viral infection in humans owing to its essential role in recognition of and entry to host cells. The spike is also an appealing target for drug designers who develop vaccines and antivirals. This article is important as it summarizes how molecular simulations successfully shaped our understanding of spike conformational behavior and its role in viral infection. MD simulations found that the higher affinity of SARS-CoV-2-S to ACE2 is linked to its unique residues that add extra electrostatic and van der Waal interactions in comparison to the SARS-CoV S. This illustrates the spread potential of the pandemic SARS-CoV-2 relative to the epidemic SARS-CoV. Different mutations at the S-ACE2 interface, which is believed to increase the transmission of the new variants, affected the behavior and binding interactions in different simulations. The contributions of glycans to the opening of S were revealed via simulations. The immune evasion of S was linked to the spatial distribution of glycans. This help the virus to escape the immune system recognition. This article is important as it summarizes how molecular simulations successfully shaped our understanding of spike conformational behavior and its role in viral infection. This will pave the way to us preparing for the next pandemic as the computational tools are tailored to help fight new challenges.

Multiscale aware classification of COVID-19 from Chest X-Ray using a spatially weighted atrous spatial pyramid pooling CNN (preprint)

COVID-19 is a severe respiratory tract infections which can range from mild to lethal. COVID-19 caused by SARS-CoV-2 can readily spread through direct or indirect contact with an infected person. This high spread rate pressure on the health care systems and requires non time-consuming methods for diagnosing. Convolutional Neural Networks (CNN) show a great success for various computer vision tasks. However, CNN like many computer vision models is a scale-variant model and requires expensive computation. In this paper, a novel micro architecture is proposed for multiscale feature extraction and classification. Proposed CNN learns multiscale features using a pyramid of shared convolution kernels with different dilation, atrous, rates. Proposed CNN is an attention based mechanism that is used to guide and select correct scale for each input. Proposed CNN is an end-to-end trainable Network. It achieved a 0.9929 for F1-score tested on QaTa-Cov19 benchmark dataset with a total of 5,040,571 trainable parameters.

A Hybrid Method for Analyzing the Situation Based on Cumulative Fully Vaccinated and Confirmed Cases of Covid-19 in Malaysia

SARS-CoV-2 is an infection that affects several organs and has a wide range of symptoms in addition to producing severe acute respiratory syndrome. Millions of individuals were infected when it first started because of how quickly it travelled from its starting location to nearby countries. Anticipating positive Covid-19 incidences is required in order to better understand future risk and take the proper preventative and precautionary measures. As a result, it is critical to create mathematical models that are durable and have as few prediction errors as possible. This study suggests a unique hybrid strategy for examining the status of Covid-19 confirmed patients in conjunction with complete vaccination. First, the selective opposition technique is initially included into the Grey Wolf Optimizer (GWO) in this study to improve the exploration and exploitation capacity for the given challenge. Second, to execute the prediction task with the optimized hyper-parameter values, the Least Squares Support Vector Machines (LSSVM) method is integrated with Selective Opposition based GWO as an objective function. The data source includes daily occurrences of confirmed cases in Malaysia from February 24, 2021 to July 27, 2022. Based on the experimental results, this paper shows that SOGWO-LSSVM outperforms a few other hybrid techniques with ideally adjusted parameters. © 2022 IEEE.

Technology Application in the Tourism and Hospitality Industry of the Middle East Asia

The use of technology has arguably benefited the tourism and hospitality industry of the Middle East. Tourists, on the other side, are also privileged for having easier access to scheduling their trips and finding all of the details they need to schedule the perfect trip with the ubiquitous help from the internet. They can also instantly find the necessary information about any chosen destination by browsing the internet. Theoretically, general technology-enhanced tourism and hospitality are relatively well investigated by researchers, meaning that;investigating the effects of technology-based tourism in the Middle East in the challenging pandemic time can be useful. Thus, this chapter is focused on discussing the advancements of the technology-based tourism and hospitality industry in the Middle East, highlighting the COVID-19 and the post-COVID-19 pandemic period. Current scholarly literature on technology-based tourism in the Middle East is brought into the discussion to generate insightful findings for the tourism policy-makers and relevant stakeholders in the Middle East. Results outline the opportunities and challenges of technology-based tourism in the Middle East with theoretical analysis. Although the chapter has limited discussion on a few Middle Eastern countries, it discovers valuable comprehension for the travelers and tourism policy-makers. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022.

KNOWLEDGE OF DOCTORS TOWARD THE STANDARD PRECAUTIONS AND PREVENTIVE MEASURES AGAINST CORONAVIRUS DISEASE (COVID-19), KHARTOUM, SUDAN 2020

Intro Coronavirus disease 2019 (COVID-19) pandemic caused large numbers of morbidities and fatalities. Health care workers have greater risk of contracting the disease. This risk is even higher with sub-optimal adherence to prevention and control measures. This study assessed the knowledge of doctors in Khartoum state toward the standard precautions and protective methods against COVID-19. Methods A cross-sectional web-based study was conducted in 2020. The questionnaire was developed and the results were evaluated based on the Sudan Federal Ministry of Health and WHO guidelines on standard precautions needed against COVID-19 particularly use of personal protective equipment (PPE) and hand hygiene (HH) in hospital settings. Findings 165 doctors working in Khartoum State hospitals were included with a mean age of 27.5 +/- 4.1 years. Fifth of them dealt with COVID-19 patients, while 69.7% of them worked in an area where there is COVID-19 patients. The study revealed that most of the participants demonstrated an average level of knowledge 72% toward the needed PPE in all the situations. The weakest area was the knowledge regarding the equipment needed during the physical examination of patients with respiratory symptoms. Additionally, the vast majority had a poor level of knowledge regarding the right method of using PPE 81.8%. 82.9% of the participants showed poor knowledge toward HH practices. The study revealed that Those who had dealt with COVID-19 patients before, and those who received any form of training also had higher levels of knowledge p- value 0.017 and p-value 0.001, respectively. Conclusion Overall, the participants had a poor level of knowledge toward the standard precautions. The level of knowledge significantly rises with experience and training.

Application of Artificial Intelligence to Predict CO2 Emissions: Critical Step towards Sustainable Environment

Prediction of carbon dioxide (CO2) emissions is a critical step towards a sustainable environment. In any country, increasing the amount of CO2 emissions is an indicator of the increase in environmental pollution. In this regard, the current study applied three powerful and effective artificial intelligence tools, namely, a feed-forward neural network (FFNN), an adaptive network-based fuzzy inference system (ANFIS) and long short-term memory (LSTM), to forecast the yearly amount of CO2 emissions in Saudi Arabia up to the year 2030. The data were collected from the "Our World in Data” website, which offers the measurements of the CO2 emissions from the years 1936 to 2020 for every country on the globe. However, this study is only concerned with the data related to Saudi Arabia. Due to some missing data, this study considered only the measurements in the years from 1954 to 2020. The 67 data samples were divided into 2 subsets for training and testing with the optimal ratio of 70:30, respectively. The effect of different input combinations on prediction accuracy was also studied. The inputs were combined to form six different groups to predict the next value of the CO2 emissions from the past values. The group of inputs that contained the past value in addition to the year as a temporal index was found to be the best one. For all the models, the performance accuracies were assessed using the root mean squared errors (RMSEs) and the coefficient of determination (R2). Every model was trained until the smallest RMSE of the testing data was reached throughout the entire training run. For the FFNN, ANFIS and LSTM, the averages of the RMSEs were 19.78, 20.89505 and 15.42295, respectively, while the averages of the R2 were found to be 0.990985, 0.98875 and 0.9945, respectively. Every model was applied individually to forecast the next value of the CO2 emission. To benefit from the powers of the three artificial intelligence (AI) tools, the final forecasted value was considered the average (ensemble) value of the three models' outputs. To assess the forecasting accuracy, the ensemble was validated with a new measurement for the year 2021, and the calculated percentage error was found to be 6.8675% with an accuracy of 93.1325%, which implies that the model is highly accurate. Moreover, the resulting forecasting curve of the ensembled models showed that the rate of CO2 emissions in Saudi Arabia is expected to decrease from 9.4976 million tonnes per year based on the period 1954–2020 to 6.1707 million tonnes per year in the period 2020–2030. Therefore, the finding of this work could possibly help the policymakers in Saudi Arabia to take the correct and wise decisions regarding this issue not only for the near future but also for the far future.