The present study aimed to systematically search in app stores and intended to carry out content analysis of free Persian mobile health apps in the management of COVID-19 and, ultimately determine the relationship between the popularity and quality of these apps. According to a researcher-made checklist including five axes of ease of use, privacy, data sharing, education, and monitoring, four app markets such as Myket, Bazzar, Google Play and App Store were searched from May 2021 up to now. The findings showed that all selected apps performed well in terms of ease of use and privacy but they needed to be improved in terms of education, monitoring, and data sharing. Also, there was no significant relationship between the popularity and quality of these apps. Owing to the high penetration rate of smartphones in Iran and the low popularity of COVID-19 apps, government, developers, and investors are required to improve the quality of apps and their marketing.

Background: The rapid coronavirus disease 2019 (COVID-19) outbreak has overwhelmed many healthcare systems worldwide and put them at the edge of collapsing. As the capacity of intensive care units (ICUs) is limited, deciding on the proper allocation of required resources is crucial. Objectives: This study aimed to create a machine learning (ML)-based predictive model of ICU admission among COVID-19 in-hospital patients at the initial presentation. Methods: This retrospective study was conducted on 1225 laboratory-confirmed COVID-19 hospitalized patients during January 9, 2020 - January 20, 2021. The top clinical parameters contributing to COVID-19 ICU admission were identified based on a correlation coefficient at P-value < 0.05. Next, the predictive models were constructed using five ML algorithms. Finally, to evaluate the performances of models, the metrics derived from the confusion matrix, classification error, and receiver operating characteristic were calculated. Results: Following feature selection, a total of 11 parameters were selected as the top predictors to build the prediction models. The results showed that the best performance belonged to the random forest (RF) algorithm with the mean accuracy of 99.5%, mean specificity of 99.7%, mean sensitivity of 99.4%, Kappa metric of 95.7%, and root mean squared error of 0.015. Conclusions: The ML algorithms, particularly RF, enable a reasonable level of accuracy and certainty in predicting disease progression and ICU admission for COVID-19 patients. The proposed models have the potential to inform frontline clinicians and health authorities with quantitative tools to assess illness severity and optimize resource allocation under time-sensitive and resource-constrained situations.

Background: Early screening and diagnosis of breast cancer (BC) is critical for improving the quality of care and reducing the mortality rate. Objectives: This study aimed to construct and compare the performance of several machine learning (ML) algorithms in predicting BC. Methods: This descriptive and applied study included 1,052 samples (442 BC and 710 non-BC) with 30 features related to positive and negative BC diagnoses. The data mining (DM) process was implemented using the selected algorithm, including J-48 and random forest (RF) decision tree (DT), multilayer perceptron (MLP), Naïve Bayes (NB), Adaboost (AB), and logistics regression (LR) classifier. Then, we obtained the best algorithm by comparing their performances using the confusion matrix and area under the receiver operator characteristics (ROC) curve (AUC). Finally, we adopted the best model for BC prognosis. Results: The results of evaluating various DM algorithms revealed that the J-48 DT algorithm had the best performance (AUC = 0.922), followed by the AB, MLP, LR, and RF algorithms (AUC: 0.899, 0819, 0.716, and 0.703, respectively). Also, the NB algorithm achieved the lowest performance in this regard (AUC = 0.669). Conclusions: The ML presents a reasonable level of accuracy for an early diagnosis and screening of breast malignancies. Also, the empirical results showed that the J-48 DT algorithm yielded higher performance than other classifiers.

The impact of socioeconomic status (SES) on children is among the most debated issues in human rights. By reviewing the literature, this study aims to identify socioeconomic mechanisms affecting children’s health. The child’s economic operations are influenced by adults. According to several studies, children from middle- and high-SES families, unlike low-SES children, have precise and logical policies, because their parents provide logical explanations in response to their children, and consequently, their children have more cultural capital. This is the family that gives the child sociolinguistic competences. This review study showed that growth rate, nutritional quality, mental health, academic performance, intelligence quotient, mortality rate, and accidents were associated with the economic status of parents, especially mothers. Therefore, it is necessary to implement training programs on proper nutrition, accident prevention, dental health, and psychological interventions for families with low SES.

Vasodilators are drugs that induce or start the widening of blood vessels and are commonly applied to treat disorders with irregularly high blood pressure, including hypertension, congestive heart failure, and angina. The present study aims to systematically review the studies on the vasodilation effects of medicinal herbs. The study was done according to the 06- Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines and registered in the CAMARADES-NC3Rs Preclinical Systematic Review and Meta-Analysis Facility (SyRF) database. Various English databases, such as Scopus, PubMed, Web of Science, EMBASE, and Google Scholar, were used to find publications about the vasodilation effects of medicinal herbs with no date limitation. The searched terms and keywords words were: "medicinal herbs", "medicinal plants", "vasodilator", "vasorelaxant", "hypertension", "high blood pressure", "vasodilation", "extract", "essential oil". Out of 1820 papers (up to 2020), 31 papers met the inclusion criteria and were reviewed. The most important medicinal plants with vasodilation/vasorelaxant activity belonged to the family Asteraceae (19.4%) followed by Zingiberaceae (9.7%). Aerial parts (30.5%), leaves (30.5%), followed by roots (11.1%) were the most common parts used in the studies. The findings showed that ethanolic extract (33.3%), followed by aqueous extract (22.2%) and methanolic extract (19.4%) was the frequency used extraction methods, whereas the essential oil (13.9%) and hydroalcoholic extract (8.3%) were the second most used herbal remedies. The results of the current review study revealed that the plant vasodilatory agents were might be used as an alternative and complementary source to treat hypertension as they had lower important toxicity. Nevertheless, more investigations, particularly clinical trials, are needed to clear this suggestion.