Iranian medical students' E-learning continuance intention after end of COVID-19 pandemic.

BACKGROUND: Coronavirus disease (COVID-19) pandemic caused the closure of many face-to-face classes in Iran's universities of medical sciences, so e-learning was adopted as the alternative method. This study aims to examine the medical students' perspectives on e-learning continuance intention. MATERIALS AND METHODS: In this quantitative study, the population included 1,453 students and the statistical sample size was determined to be 305 students using the Cochran formula. The participants were selected using stratified sampling method based on the field of study and the data were collected by e-learning evaluation questionnaire. The data were analyzed using SPSS 26.0 in addition to descriptive statistics. RESULTS: The results showed the mean perceived autonomy, perceived competence, and communication in e-learning, intrinsic motivation, information quality, e-learning applicability and students' satisfaction with e-learning courses were 2.61, 2.81, 2.91, 3.03, 2.98, 2.92, and 3.31, respectively. There was no significant correlation between the competence and applicability, user satisfaction and e-learning continuance intention. Moreover, e-learning continuance intention had the highest correlation with students' satisfaction (0.787) and information applicability (0.784), respectively. CONCLUSION: Medical students had a tendency to continue e-learning, even after controlling the COVID-19 outbreak, and health education policymakers can be of use in this opportunity to developing educational services.

Designing a comprehensive minimum dataset for patients with COVID-19 in Iranian hospital information systems.

BACKGROUND: The Minimum Data Set (MDS) plays a vital role in data exchange, collection and quality improvement. In the context of the COVID-19 pandemic, there is a need for a tailored MDS that aligns with the specific information needs of the Iranian community and integrates seamlessly into the country's Hospital Information Systems (HIS). OBJECTIVE: The study aimed to develop a comprehensive MDS for COVID-19 patients in Iran, with objectives to identify essential data elements and integrate the MDS into HIS, enhancing data exchange and supporting decision-making. METHOD: This study employed a comparative-descriptive approach to design COVID-19 patient data elements based on World Health Organisation and Centers for Disease Control and Prevention guidelines. The Delphi technique involved 35 experts in two rounds for checklist refinement. The finalised MDS consisted of 9 main terms and 80 sub-terms, analysed using descriptive statistics and IBM SPSS software. RESULTS: Of 35 experts involved with the study, 69% were male and 31% female, and Health Information Management experts were the majority (34%). The refined MDS for COVID-19 in Iran comprises 50 data elements, while 30 elements were excluded. The MDS includes 8 main terms and 80 sub-terms, with unanimous approval for identity, underlying disease, and treatment sections. CONCLUSION: The customised MDS for COVID-19 patients in Iran addresses data collection challenges and supports effective disease prevention and management. By providing comprehensive and reliable information, the MDS enhances healthcare quality, facilitates timely access to medical records, and fosters integrated health services.

Diagnosis of COVID-19 Disease in Chest CT-Scan Images Based on Combination of Low-Level Texture Analysis and MobileNetV2 Features.

Since two years ago, the COVID-19 virus has spread strongly in the world and has killed more than 6 million people directly and has affected the lives of more than 500 million people. Early diagnosis of the virus can help to break the chain of transmission and reduce the death rate. In most cases, the virus spreads in the infected person's chest. Therefore, the analysis of a chest CT scan is one of the most efficient methods for diagnosing a patient. Until now, various methods have been presented to diagnose COVID-19 disease in chest CT-scan images. Most recent studies have proposed deep learning-based methods. But handcrafted features provide acceptable results in some studies too. In this paper, an innovative approach is proposed based on the combination of low-level and deep features. First of all, local neighborhood difference patterns are performed to extract handcrafted texture features. Next, deep features are extracted using MobileNetV2. Finally, a two-level decision-making algorithm is performed to improve the detection rate especially when the proposed decisions based on the two different feature set are not the same. The proposed approach is evaluated on a collected dataset of chest CT scan images from June 1, 2021, to December 20, 2021, of 238 cases in two groups of patient and healthy in different COVID-19 variants. The results show that the combination of texture and deep features can provide better performance than using each feature set separately. Results demonstrate that the proposed approach provides higher accuracy in comparison with some state-of-the-art methods in this scope.

Performance evaluation of selected machine learning algorithms for COVID-19 prediction using routine clinical data: With versus Without CT scan features.

BACKGROUND: Given coronavirus disease (COVID-19's) unknown nature, diagnosis, and treatment is very complex up to the present time. Thus, it is essential to have a framework for an early prediction of the disease. In this regard, machines learning (ML) could be crucial to extract concealed patterns from mining of huge raw datasets then it establishes high-quality predictive models. At this juncture, we aimed to apply different ML techniques to develop clinical predictive models and select the best performance of them. MATERIALS AND METHODS: The dataset of Ayatollah Talleghani hospital, COVID-19 focal center affiliated to Abadan University of Medical Sciences have been taken into consideration. The dataset used in this study consists of 501 case records with two classes (COVID-19 and non COVID-19) and 32 columns for the diagnostic features. ML algorithms such as Naïve Bayesian, Bayesian Net, random forest (RF), multilayer perceptron, K-star, C4.5, and support vector machine were developed. Then, the recital of selected ML models was assessed by the comparison of some performance indices such as accuracy, sensitivity, specificity, precision, F-score, and receiver operating characteristic (ROC). RESULTS: The experimental results indicate that RF algorithm with the accuracy of 92.42%, specificity of 75.70%, precision of 92.30%, sensitivity of 92.40%, F-measure of 92.00%, and ROC of 97.15% has the best capability for COVID-19 diagnosis and screening. CONCLUSION: The empirical results reveal that RF model yielded higher performance as compared to other six classification models. It is promising to the implementation of RF model in the health-care settings to increase the accuracy and speed of disease diagnosis for primary prevention, screening, surveillance, and early treatment.

Efficacy of naproxen in the management of patients hospitalized with COVID-19 infection: A randomized, double-blind, placebo-controlled, clinical trial.

BACKGROUND AND AIMS: The current study was done to examine the efficacy of naproxen in the management of patients with COVID-19 infection. METHODS: This randomized, double-blind, placebo-controlled, clinical trial was done on hospitalized adult patients with confirmed COVID-19 infection. Patients were randomly assigned to receive either naproxen (two capsules per day each containing 500 mg naproxen sodium) or placebo (containing starch) for five days along with the routine treatment that was nationally recommended for COVID-19 infection. Clinical symptoms of COVID-19 infection, the time to clinical improvement, blood pressure, laboratory parameters, and death due to COVID-19 infection were considered as the outcome variables in the present study. RESULTS: Treatment with naproxen improved cough and shortness of breath in COVID-19 patients; such that, compared with placebo, naproxen intake was associated with 2.90 (95% CI: 1.10-7.66) and 2.82 (95% CI: 1.05-7.55) times more improvement in cough and shortness of breath, respectively. In addition, naproxen administration resulted in a significant increase in mean corpuscular volume (MCV) and had a preventive effect on the reduction of systolic blood pressure in COVID-19 patients. CONCLUSION: Treatment with naproxen can improve cough and shortness of breath in COVID-19-infected patients. Further studies are required to confirm our findings.

The impact of sofosbuvir/daclatasvir or ribavirin in patients with severe COVID-19.

OBJECTIVES: Sofosbuvir and daclatasvir are direct-acting antivirals highly effective against hepatitis C virus. There is some in silico and in vitro evidence that suggests these agents may also be effective against SARS-CoV-2. This trial evaluated the effectiveness of sofosbuvir in combination with daclatasvir in treating patients with COVID-19. METHODS: Patients with a positive nasopharyngeal swab for SARS-CoV-2 on RT-PCR or bilateral multi-lobar ground-glass opacity on their chest CT and signs of severe COVID-19 were included. Subjects were divided into two arms with one arm receiving ribavirin and the other receiving sofosbuvir/daclatasvir. All participants also received the recommended national standard treatment which, at that time, was lopinavir/ritonavir and single-dose hydroxychloroquine. The primary endpoint was time from starting the medication until discharge from hospital with secondary endpoints of duration of ICU stay and mortality. RESULTS: Sixty-two subjects met the inclusion criteria, with 35 enrolled in the sofosbuvir/daclatasvir arm and 27 in the ribavirin arm. The median duration of stay was 5 days for the sofosbuvir/daclatasvir group and 9 days for the ribavirin group. The mortality in the sofosbuvir/daclatasvir group was 2/35 (6%) and 9/27 (33%) for the ribavirin group. The relative risk of death for patients treated with sofosbuvir/daclatasvir was 0.17 (95% CI 0.04-0.73, P = 0.02) and the number needed to treat for benefit was 3.6 (95% CI 2.1-12.1, P < 0.01). CONCLUSIONS: Given these encouraging initial results, and the current lack of treatments proven to decrease mortality in COVID-19, further investigation in larger-scale trials seems warranted.