Background Yoga can reduce the risk of preterm delivery, cesarean section (CS), and fetal death. The aim of the present study was to investigate the effects of Yoga on pregnancy, delivery, and neonatal outcomes. Methods This was a clinical trial study and using the random sampling without replacement 70 pregnant women entered Hatha Yoga and control groups according to the color of the ball they took from a bag containing two balls (blue or red). The data collection tool was a questionnaire pregnancy, delivery, and neonatal outcomes. The intervention in this study included pregnancy Hatha Yoga exercises that first session of pregnancy Yoga started from the 26th week and samples attended the last session in the 37th week. They exercised Yoga twice a week (each session lasting 75 min) in a Yoga specialized sports club. The control group received the routine prenatal care that all pregnant women receive. Results The results showed that yoga reduced the induction of labor, the episiotomy rupture, duration of labor, also had a significant effect on normal birth weight and delivery at the appropriate gestational age. There were significant differences between the first and second Apgar scores of the infants. Conclusion The results of the present study showed that Yoga can improve the outcomes of pregnancy and childbirth. They can be used as part of the care protocol along with childbirth preparation classes to reduce the complications of pregnancy and childbirth. Trial registration IRCT20180623040197N2 (2019-02-11).

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Occurring early PE is predicted in majority of traumatic patients requiring ICU admission especially in older ones, patients with long bone fractures and those with more severe injury.

Background Centers for Disease Control and Prevention data showed that about 40% of coronavirus disease 2019 (COVID-19) patients had been suffering from at least one underlying medical condition were hospitalized; in which nearly 33% of them needed to be admitted to the intensive care unit (ICU) to receive specialized medical services. Our study aimed to find a proper machine learning algorithm that can predict confirmed COVID-19 hospital admissions with high accuracy. Methods We obtained data on daily COVID-19 cases in regular medical inpatient units, emergency department, and ICU in the time window between 21 July 2020 and 21 November 2021. Data for the first 183 days (training data set) were used for long short-term memory (LSTM) network, adaptive neuro-fuzzy inference system (ANFIS), support vector regression (SVR) and decision tree model training, whilst the remaining data for the last 60 days (test data set) were used for model validation. To predict the number of ICU and non-ICU patients, we used these models. Finally, a user-friendly graphical user interface unit was designed to load any time series data (here the trend of population of COVID-19 patients) and train LSTM, ANFIS, SVR or tree models for the prediction of COVID-19 cases for one week ahead. Results All models predicted the dynamics of COVID-19 cases in ICU and non- wards. The values of root-mean-square error and R2 as model assessment metrics showed that ANFIS model had better predictive power among all models. Conclusion Artificial intelligence-based forecasting models such as ANFIS system or deep learning approach based on LSTM or regression models including SVR or tree regression play a key role in forecasting the required number of beds or other types of medical facilities during the coronavirus pandemic. Thus, the designed graphical user interface of the present study can be used for optimum management of resources by health care systems amid COVID-19 pandemic.

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