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Objective: The aim of this work was to know the prevalence of Chlamydophila pneumoniae and Mycoplasma pneumoniae in coronavirus disease 2019 (COVID-19) patients in Jordan. Also, to assess a TaqMan real-time polymerase chain reaction (PCR) assay in detecting these two bacteria. Methods: This is a retrospective study performed over the last five months of the 2021. All nasopharyngeal specimens from COVID-19 patients were tested for C. pneumonia , and M. pneumoniae. The C. pneumoniae Pst-1 gene and M. pneumoniae P1 cytadhesin protein gene were the targets. Results: In this study, 14 out of 175 individuals with confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection (8.0%) were co-infected with C. pneumoniae or M. pneumoniae. Co-infection with SARS-CoV-2 and C. pneumoniae was reported in 5 (2.9%) patients, while 9 (5.1%) patients had M. pneumoniae and SARS-CoV-2 co-infection. The mean (+/- std) of the correlation coefficient of the calibration curve for real-time PCR analysis was -0.993 (+/- 0.001) for C. pneumoniae and -0.994 (+/- 0.003) for M. pneumoniae. The mean amplification efficiencies of C. pneumoniae and M. Pneumoniae were 187.62% and 136.86%, respectively. Conclusion: In this first study based in Jordan, patients infected with COVID-19 have a low rate of atypical bacterial co-infection. However, clinicians should suspect co-infections with both common and uncommon bacteria in COVID-19 patients. Large prospective investigations are needed to give additional insight on the true prevalence of these co-infections and their impact on the clinical course of COVID-19 patients.
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Objective: Patients diagnosed with chronic disease may experience psychological symptoms including depression, anxiety, insomnia, and fatigue, all of which may adversely affect their quality of life (QoL). The main objective of this study is to identify the level of QoL, to know the prevalence of these symptoms among chronic disease patients in Oman during the third wave of coronavirus disease 2019 (COVID-19) pandemic, and to explore the contributing factors. Methods: A cross-sectional and descriptive correlational design was used. Convenience sampling was used to recruit participants. Data were collected using the Functional Assessment of Chronic Therapy (FACT)-General, Hospital Anxiety and Depression Scale, the Insomnia Severity Index, and the FACT-Fatigue subscale via Qualtrics ® software. Linear regression analyses were used to explore factors that were associated with QoL. Results: Of 990 patients with chronic disease who participated, the mean total QoL score was 67.7 (standard deviation = 16.1). Participants aged above 51, those with a basic education, those with heart disease, or those with more than one comorbidity had a significantly lower QoL. Linear regression revealed that the main factors associated with lower QoL included heart disease (β = 0.05, P = 0.02), diabetes (β = 0.12, P < 0.01), having taken one dose of COVID-19 vaccine (β = 0.05, P = 0.04), anxiety (β = -0.24, P < 0.01), depression (β = -0.31, P < 0.01), insomnia (β = -0.12, P < 0.01), and fatigue (β = 0.27, P < 0.01). Conclusions: The COVID-19 pandemic has significantly reduced the individuals' level of QoL and affected the mental health of patients diagnosed with chronic diseases. Appropriate strategies to monitor psychological problems and interventions to prevent and reduce these among such patients are needed. © Medknow. All rights reserved.
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Lack of access to the patient medical record (90.6%) was the major barrier for the integration of pharmaceutical care into practice. The majority of participants (93.0%) encouraged creating a website that provides pharmaceutical care. Furthermore, 45.1% would pay for such a service if present. Moreover, the majority (89.8%) agreed that creating a comprehensive database for patients' data will help in decreasing medical errors. Among the four aspects of pharmaceutical care (technical, psychosocial, communication and administrative) that were assessed for students and pharmacist's, general weakness in all aspects was noticed. This study highlights that absence of proper documentation of patient medical information raises the risk of medical problems and is considered the most documented barrier for the integration of pharmaceutical care. This emphasizes the future role of telemedicine and the availability of a specialized website and database repository that stores patient's information to ensure the continuity of care even during pandemics.
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In this work, the optimization-based method is implemented to investigate the effectiveness of lockdown strategies undertaken to contain the COVID-19 during the first two waves in Malaysia. The well-known Susceptible-Infected-Removed (SIR) epidemiological model was fitted to the actual data of infected cases from the official press to closely reflect the observed COVID-19 outbreak in Malaysia. The Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) were implemented to determine the daily transmission rate beta(t) that fits the SIR model to the actual data. The best fitness value of PSO is mostly stable at approximately 37.5 with the best value of 37.41 at a population size of 1000, whilst the best value for GA slowly decreased to the best value of 47.45 at a population size of 1000. In addition, PSO requires a lower number of iterations to reach the optimum fitness value for the same population size as compared to GA, while GA is too far to reach the convergence. As the removal rate (gamma) is a constant value fixed at 0.1, the optimized beta(t) values indicate a high basic reproduction number (average R0 = 1.23) obtained before the Movement Control Order (MCO), followed by a considerable decrease to an average R0 value of 1.23 during the MCO. During the Conditional MCO and Recovery MCO, the basic reproduction number was slightly decreased to an average R0 value less than 1. This is an indication of the success of the government to contain the pandemic during the first two waves as the R0 has been kept below than 1.
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The pandemic of COVID 19 is transformed the people life style over the world. The teaching and learning filed is one of the most sectors were influenced by the procedures, new situations and social distancing through this pandemic. The self-learning via websites and internet used in various methods to teach the people the languages and skills. As well it used to learn the Arabic language which is one of the most widely used languages in the world. It is expected to play a critical role in educational operations and assignments. The aim of this study is to asset people to find and assess the websites for Arabic language learning based on multi criteria to support the learners. As well as to become a reference for teachers and learners to select a good quality website for non-Arabic speakers. These criteria are to evaluate Arabic learning websites to be guidelines based on functionality, usability, and learning content to serve the language skills writing, reading, listening and speaking based on correct grammars. This study focuses on Arabic language learning websites to classify them based on quantitative and qualitative methods to distinguish among them. However, this study will not assess the computer systems for learning which is out of websites scope. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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Introduction: Telemedicine is the delivery of health care services to patients distantly. During the Coronavirus Disease 2019 (COVID-19) pandemic, telemedicine has become an essential implement in delivering healthcare services worldwide. Accordingly, in March 2020, the Primary Health Care Centers (PHCCs) in Qatar has started telephone consultation follow-up appointments in Family Medicine (FM) clinics instead of conventional consultation. Given the limited data about telephone consultations in Qatar, our aim of this study is to investigate the possible impact of telemedicine on chronic disease patients' follow-up compliance. Methods: This study compares the compliance of adult patients with chronic diseases following-up within FM clinics in Qatar's PHCC through telephone consultations with a minimum of three telephone consultations ordered between April to November 2020, in comparison to the compliance of the same group of patients to their prior face-to-face follow-up consultations in FM clinics with a minimum of three face-to-face ordered follow up appointments between April to November 2019. A cross-sectional study will be carried out to investigate the effect of telephone consultation in PHCC on patients' compliance with reference to conventional face-to-face consultation. Patients' data will be received from Health Information Management in twenty-seven PHCCs in Qatar. Conclusion: Due to the limited studies on the effectiveness of telemedicine on patient compliance in FM follow-ups within Qatar's PHCC, comparing patients' follow-up compliance with telephone consultations to their prior face-to-face consultations would be helpful in assessing patients' quality of care delivering within FM clinics. With telecommunication being easily accessible and time-efficient, it is believed, when used correctly, it might improve compliance and adherence to the management prescribed by the physician and follow-up appointments in Qatar's PHCC. In addition, this study will help in providing recommendations that could guide the organization on forming policies to be applied in PHCCs after the resolution of the COVID-19 pandemic.
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The coronavirus pandemic is one of the most fast-spreading diseases in the history, and the transmission of this virus has crossed rapidly over the whole world. In this study, we intend to detect the effect of temperature, precipitation, and wind speed on the Coronavirus infected cases throughout climate seasons for the whole year of epidemic starting from February 20, 2020 to February 19, 2021 with considering data patterns of each season separately; winter, spring, summer, autumn, in Mediterranean European regions, whereas those are located at the similar temperature zone in southern Europe. We apply the panel data approach by considering the developed robust estimation of clustered standard error which leads to achieving high forecasting accuracy. The main finding supports that temperature and wind speed have significant influence in reducing the Coronavirus cases at the beginning of this epidemic particularly in the first-winter, spring, and early summer, but they have very weak effects in the autumn and second-winter. Therefore, it is important to take into account the changes throughout seasons, and to consider other indirect factors which influence the virus transmission. This finding could lead to significant contributions to policymakers in European Union and European Commission Environment to limit the Coronavirus transmissions. As the Mediterranean region becomes more crowded for tourism purposes particularly in the summer season.
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COVID-19 is a coronavirus-caused viral disease that had spread worldwide. Within over six months after its spread in China at the end of 2019, it infected over 10 million persons worldwide and more than 519,000 had perished. Drones are important in decreasing the range of COVID-19 disease outbreaks in most general applications, and especially in medical applications. This paper presents a new application of an autonomous Drone in fast detecting medical face masks by using Deep Learning to classify people based on their mask-wearing with high accuracy by using a classifier implemented based on MobileNetV2 architecture. The training carried out on an artificially created using Tensorflow, Opencv, and Keras. The autonomous Drone controlled by a smart mobile app with help of IoT technology such as the TeamViewer app, which controls the mobile, and the Qground control app to control the Drone through the MAV-link protocol. The objective of this paper is to use intelligent technology to decrease the spread of coronavirus to protecting people. © 2021 IEEE.