Creutzfeldt-Jakob Disease After the Second Dosage of The Novel Pfizer-Biontech Messenger Ribonucleic Acid (Mrna) COVID-19 Vaccination: A Case Report

Uncommon, rapidly progressing, and eventually fatal neurological condition called human prion disease. While most occurrences are sporadic, many can be acquired or inherited. In the current study, we focus on the case of a 63-year-old man who initially had vision problems then exhibits difficulty using his right side for daily activities and who begins to experience frequent falls with gait instability about a week after receiving the second dosage of the novel Pfizer-BioNTech messenger ribonucleic acid (mRNA) COVID-19 vaccination. Confidential findings confirmed the fatal diagnoses of sporadic Creutzfeldt-Jakob disease following a thorough study.

Does Fear of COVID-19 Prolongation Lead to Future Career Anxiety Among Adolescents? The Mediating Role of Depressive Symptoms.

The outbreak of the COVID-19 pandemic has influenced the lives of individuals from all different age groups. In particular, the prolongation of COVID-19 and the emergence of virus variants, such as Omicron, Delta and Alpha, have caused trauma to university students amid expectations that the associated economic, social, and psychological outcomes could affect their future careers. The current study, therefore, examines how the fear of COVID-19 prolongation may affect future career anxiety (FCA) among adolescents and to what extent depressive symptoms may determine such a relationship. Using a cross-sectional design, a survey was conducted to assess depressive symptoms, FCA, and fear of COVID-19 prolongation among 605 university students in Oman, an Arabic-speaking country located in the Gulf region. Using structural equation modeling, the results showed that there were significant standardized direct effects (unmediated) of fear of COVID-19 on depression from COVID-19 and of depression from COVID-19 on FCA. Additionally, depression from COVID-19 mediated the relationship between fear of COVID-19 and FCA. The results are discussed within the Omani context of mental health service accessibility challenges and the dominant culture of perceiving mental health services with social stigma. Supplementary Information: The online version contains supplementary material available at 10.1007/s40653-022-00506-w.

Prevalence of enteric viruses in wastewater in Egypt after the COVID-19 pandemic.

People's hygienic habits greatly affect the spreading rate of enteric viruses. After the COVID-19 pandemic, many people followed announced precautions and improved their hygienic status to protect themselves from SARS-CoV-2 infection. Here, we studied if this indirectly affected the prevalence of enteric viruses in Egypt. A total of 21 samples (one sample per week) were collected from the Zenin wastewater treatment plant (WWTP) through the period between August 2021 and March 2022. Detection of adenovirus, hepatitis A virus (HAV), and rotavirus showed their presence in 66, 14.3, and 9.5% of the collected samples, respectively. Comparing those percentages to previously published data concerned with the detection of the same viruses from the same WWTP or others revealed a remarkable decrease in the prevalence of the three viruses after the COVID-19 pandemic. This allows the conclusion that safety precautions against SARS-CoV-2 lead indirectly to a reduction of adenovirus, HAV, and rotavirus prevalence rates.

Adaptation of AL-TST active learning model in hybrid classroom: Findings from teaching during COVID-19 pandemic in Egypt

Since COVID-19 began, the ways of teaching and learning have changed drastically. Traditional teaching methods are shifted to technology-mediated methodologies such as asynchronous online learning, hybrid learning, blended learning, hy-flex learning, on-demand learning, and competency-based online learning. Due to the COVID-19 pandemic, traditional active learning models, compelling but complex, are assumed not to fit in the hybrid classroom because of the affordance and integration of various distance learning technologies. Hence, in the research, a conceptual active learning model for a hybrid classroom, namely AL-TST (Active Learning-Theory, Strategy, Technology), is used to deliver a STEM course in an Egyptian university. The course was an 8-week-long course designed for 3rd grade (i.e., university junior) students enrolled in the university. At the beginning of the course, the instructor created student-centered lecture contents using the adopted AL-TST model in a hybrid environment. Data (N=76) were collected using the university's learning management system (LMS). Students’ Course Work Grades, Final Exam Grades and Total Exam Grades are analyzed using a one-sample t-test. The analysis indicated no significant result in students’ Course Work Grades, Final Exam Grades and Total Exam Grades. However, the findings indicated some valuable educational insights, such as improving a conceptual active learning model, the perception of course design, and teaching during the crisis.

COVID-19 Semantic Pneumonia Segmentation and Classification Using Artificial Intelligence.

Coronavirus 2019 (COVID-19) has become a pandemic. The seriousness of COVID-19 can be realized from the number of victims worldwide and large number of deaths. This paper presents an efficient deep semantic segmentation network (DeepLabv3Plus). Initially, the dynamic adaptive histogram equalization is utilized to enhance the images. Data augmentation techniques are then used to augment the enhanced images. The second stage builds a custom convolutional neural network model using several pretrained ImageNet models and compares them to repeatedly trim the best-performing models to reduce complexity and improve memory efficiency. Several experiments were done using different techniques and parameters. Furthermore, the proposed model achieved an average accuracy of 99.6% and an area under the curve of 0.996 in the COVID-19 detection. This paper will discuss how to train a customized smart convolutional neural network using various parameters on a set of chest X-rays with an accuracy of 99.6%.

Recent progress of metal organic frameworks-derived composites in adsorptive removal of pharmaceuticals

In the last two years, excessive amounts of antibiotics and other drugs were used to minimize the symptoms of COVID-19 as well as other newly discovered modified viruses. This has daily resulted in generation and release of substantial amounts of these drugs and their metabolites to water systems as hazardous pollutants. Upon entrance of such pharmaceutical residues into water, they incorporate, interfere and uptake by a number of activities. That is why;pharmaceutical residues or pollutants in wastewater must be effectively removed before being evacuated to environments. Water treatment via adsorption technique is highly recommended due to the wide varieties of adsorbents, biosorbents and nanosorbents with their various advantages as cost effective, high surface area, excellent binding ability and superior capacity for all pollutants. Metal-organic frameworks (MOFs) and MOFs-derived composites are representing an important class of adsorbents as porous or spongy materials and thus, recently attracted research interests and industrial applications in different disciplines. The reasons for such high global performance of developed MOFs-derived composites are mainly devoted to their incorporated characteristics as high surface area, tunable micro-porosity, spongy like structures and low density-formed skeleton. Therefore, MOFs-derived composites are regarded as perfect functional porous materials for removal of various pollutants as pharmaceuticals and drugs. For this reason, the present review is devoted to cover the recent applications of modified and functionalized MOFs-derived composites for adsorbing various types of pharmaceuticals from water and wastewater. The future developments of MOFs-derived composites are also pointed out to summarize the major gaps as guidelines for progress of MOFs-derived materials in the coming few decades.

Carbohydrates Metabolic Signatures in Immune Cells: Response to Infection.

Introduction: Metabolic reprogramming in immune cells is diverse and distinctive in terms of complexity and flexibility in response to heterogeneous pathogenic stimuli. We studied the carbohydrate metabolic changes in immune cells in different types of infectious diseases. This could help build reasonable strategies when understanding the diagnostics, prognostics, and biological relevance of immune cells under alternative metabolic burdens. Methods: Search and analysis were conducted on published peer-reviewed papers on immune cell metabolism of a single pathogen infection from the four known types (bacteria, fungi, parasites, and viruses). Out of the 131 selected papers based on the PIC algorithm (pathogen type/immune cell/carbohydrate metabolism), 30 explored immune cell metabolic changes in well-studied bacterial infections, 17 were on fungal infections of known medical importance, and 12 and 57 were on parasitic and viral infections, respectively. Results and Discussion: While carbohydrate metabolism in immune cells is signaled by glycolytic shift during a bacterial or viral infection, it is widely evident that effector surface proteins are expressed on the surface of parasites and fungi to modulate metabolism in these cells. Conclusions: Carbohydrate metabolism in immune cells can be categorized according to the pathogen or the disease type. Accordingly, this classification can be used to adopt new strategies in disease diagnosis and treatment.

Addition of Camellia sinensis extract to water to disinfect respiratory viruses accumulated over different surfaces.

New precautions have become part of our daily life since COVID-19 pandemic such as wearing masks, maintaining distance and disinfecting products bought from markets before using them which is exhausting. We aimed to test the inhibitory effect of Camellia sinensis (black tea) water extracts on respiratory viruses and the inhibition of viruses accumulated over different surface types after being soaked in water supplemented with the extracts. Two water extraction methods (extract A: maceration at 80 °C for 30 min and extract B: boiling for 40 min) were applied; extracts were analyzed by high-performance liquid chromatography to detect polyphenolic compounds. Results showed that 200 µg/ml of extract A and 50 µg/ml of extract B in water caused 100% inhibition of influenza A (enveloped virus) virus after 1.5 h and similar results were obtained for adenovirus (non-enveloped virus) but at the same concentration of extract A and at 100 µg/ml of extract B. Different surfaces (aluminum, glass, plastic or carton, vegetables of smooth (tomato) or rough (lemon) surfaces and green leaves) were inoculated with both viruses for 20 min and then soaked in the water supplemented with 200 µg/ml of extract A or 100 µg/ml of extract B for 1.5 h, and this resulted in complete inhibition of both viruses.

Analysing biosensor clinical pathogen information using mayfly optimized convolute neural network approach

Viral and bacterial infection diseases are the most common things caused by microbes. Infection diseases are serious issues because of the growth of COVID‐19. Because of the current living situation, clinical pathogens are difficult to identify. Therefore, biosensors have been widely utilized to sense the biomolecules relevant to viruses and bacteria. The biosensors observe the nanoparticles from the pathogens and help improve the infection analysis. The sensor information is processed using machine learning techniques because it consists of several learning patterns. However, the existing methods have multi‐objective optimization problems while analysing the changes in the nanoparticles. This work utilizes a mayfly optimized convoluted neural network (MOCNN) to overcome this research issue. The grid uses the fully convolution layer that processes the extracted biosensor features to determine the infections. The network performance is optimized by applying the exploitation and exploration properties of nuptial dance that help to escape from the local optima solutions. The effective utilization of the optimized training patterns improves the convergence speed and convergence rate compared to traditional methods. From the results, MOCNN ensures 98.97% accuracy, 0.388 error rate, and 0.322833 convergence rate on various iterations with different learning rates. [ FROM AUTHOR] Copyright of Expert Systems is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Digital Transformation of Face-To-Face Focus Group Methodology: Engaging a Globally Dispersed Audience to Manage Institutional Change at the World Health Organization.

Focus group discussions (FGDs) are widely used to obtain qualitative data from purposely selected groups of people. This paper describes how the Learning and Capacity Development (LCD) unit of the World Health Organization (WHO) Health Emergencies Programme (WHE) digitalized FGDs to engage with WHO staff from around the world, to listen, share, and collect their feedback in the development of a WHO learning framework. The impact of the COVID-19 pandemic and the introduction of local lockdowns and travel restrictions resulted in the wide use of digital platforms, such as Zoom, for employee communications and collaboration capable of reaching employees wherever they are working. The LCD/WHE team drew upon the experience of WHO colleagues from human resources, country, and regional offices to set up and hold FGDs in 6 languages with participants from all WHO regions. Building on the findings of a 2019 WHO staff survey, which was part of a comprehensive, organization-wide career development initiative, the digitalized FGDs allowed for the exchange of substantive feedback, novel ideas, and alignment, connecting across different geographies, disciplines, and levels of seniority. As a result, FGDs can be successfully conducted online, but it is essential to remove barriers to participation by adopting a multilingual and flexible approach in multinational and international organizations such as the WHO.

Extracorporeal Membrane Oxygenation in Pregnant Women With COVID-19.

Coronavirus disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and lung involvement is common. Patients with COVID-19 may progress to acute respiratory distress syndrome (ARDS) for which they may require mechanical ventilation. When conventional ventilation strategies are unable to achieve the desired oxygenation and gas exchange, extracorporeal membrane oxygenation (ECMO) might be an option in selected patients. The literature on the use of ECMO in peripartum women with COVID-19 is limited. We present a series of ten cases involving pregnant and recently pregnant women who rapidly developed ARDS after the onset of COVID-19 for which they received ECMO. Nine of the 10 patients survived intensive care unit discharge after a gradual recovery of their pulmonary function and weaning from mechanical ventilation and ECMO. In addition, 9 out of the 10 delivered neonates survived neonatal intensive care unit discharge.

Internet addiction and sleep quality among medical students during the COVID-19 pandemic: A multinational cross-sectional survey.

BACKGROUND: The emergence of the COVID-19 pandemic has affected the lives of many people, including medical students. The present study explored internet addiction and changes in sleep patterns among medical students during the pandemic and assessed the relationship between them. METHODS: A cross-sectional study was carried out in seven countries, the Dominican Republic, Egypt, Guyana, India, Mexico, Pakistan, and Sudan, using a convenience sampling technique, an online survey comprising demographic details, information regarding COVID-19, the Pittsburgh Sleep Quality Index (PSQI), and the Internet Addiction Test (IAT). RESULTS: In total, 2749 participants completed the questionnaire. Of the total, 67.6% scored above 30 in the IAT, suggesting the presence of an Internet addiction, and 73.5% scored equal and above 5 in the PSQI, suggesting poor sleep quality. Internet addiction was found to be significant predictors of poor sleep quality, causing 13.2% of the variance in poor sleep quality. Participants who reported COVID-19 related symptoms had disturbed sleep and higher internet addiction levels when compared with those who did not. Participants who reported a diagnosis of COVID-19 reported poor sleep quality. Those living with a COVID-19 diagnosed patient reported higher internet addiction and worse sleep quality compared with those who did not have any COVID-19 patients in their surroundings. CONCLUSION: The results of this study suggest that internet addiction and poor sleep quality are two issues that require addressing amongst medical students. Medical training institutions should do their best to minimize their negative impact, particularly during the current COVID-19 pandemic.

Cross-cultural prevalence of sleep quality and psychological distress in healthcare workers during COVID-19 pandemic.

BACKGROUND: Poor quality sleep and emotional disturbances are expected in times of crisis. COVID-19 has severely impacted healthcare worldwide and with that comes the concern about its effects on healthcare workers. The purpose of the present study was to assess sleep quality and psychological distress in healthcare workers during the COVID-19 pandemic. METHODS: The present work is a multi-centric cross-sectional study targeting healthcare workers from India, Pakistan, and Nepal. It used an online version of the Pittsburg Sleep Quality Index and the General Health Questionnaire, and data were analyzed using SPSS V.24. RESULTS: A total of 1790 participants completed the questionnaire. Of the 1790 participants, 57% reported poor sleep quality, and 10% reported a high level of psychological distress. A cross-cultural comparison found some differences between the different groups of participants. The details of the differences were further explored in the article. CONCLUSION: The present study highlights that a significant proportion of healthcare workers are affected by poor sleep quality and psychological distress during the COVID-19 pandemic. It also emphasizes the imperative to provide them with psychosocial support to avoid potential short- and long-term psychological consequences of these troubling times.

Developing the first-ever global learning strategy to frame the future of learning for achieving public health goals.

Health is at the forefront of everyone's mind. Every country on the planet is currently experiencing the COVID19 pandemic, which is not just causing death and disease, but damaging societies and economies on a significant scale across the world.1 The current pandemic has brought into sharp focus the socio-economic factors and inequities in how people experience health and ill health and shone a spotlight on the need for lifelong learning so that health professionals, volunteers, and the public alike can adapt and respond to health threats. But even before the pandemic, the world was changing profoundly in the past few decades, and these changes impacted people's health. Unfortunately, the role of education and learning in the health domain has not kept pace with these changes. Many factors converge to create a major evolution, and some say, revolution, in how education, training and learning for health can and should be leveraged to protect and promote people's health. This paper summarizes some of the critical ideas of an 18-month process to create the first-ever global learning strategy for health by the World Health Organization.

COVID-19 outbreak in Mauritania: epidemiology and health system response.

INTRODUCTION: In Africa, the first case of COVID-19 was reported in February 2020. Mauritania's first case was confirmed in March 2020. METHODOLOGY: We provide an update of the COVID-19 epidemic in Mauritania as of December 2020, and describe the country's Health System Response. RESULTS: In total, 133,749 diagnostic tests were performed, 14,364 (10.7%) were positive (309 cases/100,000 inhabitants). Case fatality rate was 2.4%. The 20-39 year-olds (41%) and males (59.1%) were most commonly affected. Comorbidities among fatal cases included cardiovascular diseases (44.8%) and diabetes (37.1%). Clinical symptoms included fever (57%), cough (52%), running nose (47%) and headache (26%). After the first case, prevention measures were progressively tightened, and quarantine implemented for all suspected cases. Schools and universities were closed, and flights to Mauritania suspended. Restaurants and cafeterias were closed, and night curfews installed. Friday prayers were suspended nationwide, and movements between regions restricted. These measures helped to contain the spread of SARS-CoV-2 during the first pandemic wave, which peaked in June 2020 with low rates. However, the number of daily cases reached high levels in December 2020, during the second wave (40.1% of all cases and 48.9% of deaths). During the first wave, there were 38 ICU beds nationwide, but the ICU's capacity increased in short time. CONCLUSIONS: Mauritania has passed through the first pandemic wave with relatively low case fatality rates, currently being at the end of the second wave. As the country's health system is very vulnerable, there is a need for strict public health measures during epidemics.

The Effects of COVID-19 Spread on Oil Markets: The Case of the Top 10 Producing Countries (preprint)

This paper attempts to investigate the effects of Coronavirus spread on oil companies. Coronavirus spread has been measured by cumulative cases, new cases, cumulative deaths and new deaths, on daily basis from March 15, 2020, to August 31, 2020. This has been applied to the top 10 oil companies in the world in terms of production. Using panel data analysis according ARDL technique, the study has investigated the short-run relationship between Coronavirus spread and abnormal return of oil companies. Besides, it has tested the long-run relationship and causality. Results support the short-run negative response of oil market to the spread of Coronavirus measured by cumulative and new cases and deaths, during the whole period. During March and April, oil markets seems to be sensitive to cumulative cases and deaths, while during May, it tends to be sensitive to new cases. Results do not support any negative effects during June, July and August.

Epidemiological and clinical presentations of hospitalized COVID-19 patients in Libya: An initial report from Africa.

BACKGROUND: The first case of the novel coronavirus disease 2019 (COVID-19) in Libya was diagnosed in March 2020. We aimed to determine the epidemiological, clinical, and laboratory characteristics of COVID-19 in Libya. METHOD: In this retrospective descriptive study, we analyzed the demographics, initial clinical presentation, history, comorbidities, laboratory findings, complications, and outcomes of hospitalized patients with COVID-19 at several centers in the Western part of Libya between March 24, 2020, and December 3, 2020. RESULTS: The study included 811 (67.2%) men and 396 (32.8%) women. The median (interquartile range [IQR]) age was 56 (40-64). A total of 173 (14.3%) patients developed respiratory distress syndrome, while 70 (5.8%) developed circulatory shock and hypotension; 190 (15.7%) were admitted to the intensive care unit. Acute cardiac injury occurred in 27 (2.2%) patients, and 45 (3.7%) developed arrhythmia. Acute kidney injury occurred in 44 (3.6%) patients. Of the patients admitted during the study period, 149 (12.3%) died. The predominant comorbidities ordered in a descending manner were as follows; diabetes mellitus, presented 490 (40.6%), hypertension in 414 (34.3%), chronic kidney disease in 114 (9.4%), and lung diseases in 103 (8.5%). The total white blood cell, neutrophil; monocyte; D-dimer; creatinine kinase; creatine kinase-MB; creatinine; total bilirubin; alanine and aspartate aminotransferase; and hypersensitive troponin were increased among non-survivors, whereas lymphocyte and platelet counts were decreased among non-survivors. CONCLUSION: This is the first report of the clinical presentations and laboratory findings in patients hospitalized with COVID-19 in Libya. Libyan authorities must implement several restrictions to control the pandemic. However, incoming international travelers pose a challenge to the local authorities, especially with the recent discovery of new variants.

The Effects of COVID-19 Spread on Oil Markets: The Case of the Top 10 Producing Countries (preprint)

This paper attempts to investigate the effects of Coronavirus spread on oil companies. Coronavirus spread has been measured by cumulative cases, new cases, cumulative deaths and new deaths, on daily basis from March 15, 2020, to August 31, 2020. This has been applied to the top 10 oil companies in the world in terms of production. Using panel data analysis according ARDL technique, the study has investigated the short-run relationship between Coronavirus spread and abnormal return of oil companies. Besides, it has tested the long-run relationship and causality. Results support the short-run negative response of oil market to the spread of Coronavirus measured by cumulative and new cases and deaths, during the whole period. During March and April, oil markets seems to be sensitive to cumulative cases and deaths, while during May, it tends to be sensitive to new cases. Results do not support any negative effects during June, July and August.

Determinants of Herding Behavior in The Time Of COVID-19: The Case of Egyptian Stock Market Sectors (preprint)

This paper attempts to research in two parts, the first part aims to study the Herding Behavior in the sectors of the Egyptian Stock Exchange, when the second part aims to study the factors that affect the Herding Behavior according to the identification of those factors. Factors based on the presentation of previous literature related to herd behavior, and these factors are represented in the exchange rate, Stock trading volumes as an indicator of Liquidity, stock market returns, and indicators of the spread of the Corona virus represented in the number of cumulative cases and deaths according to the population in Egypt. During the period from 1/3/2020 to 31/7/2020. Sectors are five sectors of 76 companies, that have dispersion decrease (CSSD), and the sectors are (Basic Resources, Banks, Travel & Leisure, Health Care & Pharmaceuticals, Food, Beverages and Tobacco). The results of the multiple regression models for the sectors in which herd behavior appeared were as follows: - Basic resources determination coefficient (R2) (52.24%), and the variables determining herd behavior in the sector are variables (Corona virus Cumulative Cases, Cumulative Corona virus deaths).- Banks determination coefficient (R2) (66.85%), and the variables determining herd behavior in the sector are variables (Stock Market Return, Sector Trading Volumes, Corona virus Cumulative Cases, Cumulative Corona virus deaths).- Travel & Leisure determination coefficient (R2) (49.04%), and the variables determining herd behavior in the sector are variables (Stock Market Return, Exchange Rate, Sector Trading Volumes, Corona virus Cumulative Cases, Cumulative Corona virus deaths).- Health & Care Pharmaceuticals determination coefficient (R2) (41.84%), and the variables determining herd behavior in the sector are variables (Exchange Rate, Corona virus Cumulative Cases, Cumulative Corona virus deaths).- Food, Beverages and Tobacco determination coefficient (R2) (58.87%), and the variables determining herd behavior in the sector are variables (Stock Market Return, Sector Trading Volumes, Corona virus Cumulative Cases, Cumulative Corona virus deaths).

Investment Opportunities in The Time Of (COVID-19) Spread: The Case of Cryptocurrencies and Metals Markets (preprint)

The global financial and economic crises remain a controversial topic among the categories of investors, there are those who can see and seize investment opportunities, and there are some individuals who are proficient in investing in difficult economic conditions and can create opportunities even in the most difficult crises. With the emergence of the corona virus in China, it quickly became a global pandemic and caused tremendous damage to many global financial and economic markets.This paper aims to shed light on investment opportunities in the global markets in light of the spread of the corona virus around the world, by studying the effect of the corona virus on the returns of the cryptocurrency currency and global metals markets traded in the US dollar, where the research period was determined based on the spread of the virus at a level The world, from the date of 25/3/2020 to 25/6/2020, and the best cryptocurrency currencies were chosen in terms of market value and trading during the research period and were Bitcoin, Ethereum and Tether, and the best metals in terms of popularity and trading were gold, silver and copper. Corona virus was measured by indicators of virus spread, which are the number of daily cases, cumulative cases and the number of daily deaths and cumulative deaths, at the level of 213 countries around the world, and the dependent variable represented in the cryptocurrency and metal markets was measured by the daily returns of the investment opportunities that were chosen in each market.The research results showed that the cryptocurrency currency markets are affected by the spread of the corona virus and the independent variable was the most influential (Total Deaths) variable on all investment opportunities in the cryptocurrency market. The Total Deaths variable had more influence on the Gold market, and Total Cases variable had more influence on both Silver and Copper in the metals market.The results also showed that there were no statistically significant differences between the average return on investment for the cryptocurrency currency markets and the metal markets, where the significance of the test reached (0.889), which is greater than the level of significance of 5%, due to the convergence of the average levels of the markets during the period of coronary virus spread throughout the world.The best investment opportunities according to the return on investment index during the research period were, for cryptocurrency currency markets, the return on investment on Ethereum was (72.02%), then Bitcoin (38.98%), then Tether (0.23%), and in relation to the metal markets, the return was on the investment for Silver was (42.16%), then Copper (20.75%), then Gold (8.54%).