دکتر مرتضی سدهی

Anxiety is one of the most important symptoms after orthopedic surgery which can have various side effects and negatively affect the process of recovery after surgery. The present study was conducted with the aim of evaluating the effect of Melissa Officinalis tea and Matricaria Chamomilla tea on anxiety of patients after lower limb orthopedic surgery.

This was a randomized controlled trial which was conducted on 96 patients after lower-limb orthopedic surgery in Ayatollah Kashani hospital in Shahrekord, Iran. Patients were selected with convenience sampling method and allocated to either Melissa Officinalis, Chamomile or control group. For Melissa Officinalis group, 1g of Melissa tea, for Chamomile group 1g chamomile tea and for control group black tea was prescribed 3 times a day. Anxiety of participants was evaluated before start of interventions and after each step of interventions using Spielberger’s state anxiety inventory. The mean score of anxiety before interventions and the mean changes of anxiety score after each step of interventions compared between groups. Data were analyzed with descriptive and analytical statistics using SPSS software.

the results of Tukey’s post-hoc test showed that after the second step of interventions till the last step, the mean change of anxiety score in Melissa Officinalis and Chamomile group was significantly greater than in the control group (P<0.05).   

The results of this study show that prescription of Melissa Officinalis and Chamomile tea is effective in reducing anxiety of patients after lower limb orthopedic surgery.

Nonparametric regression can usually be used when the distribution of the dependent variable does not follow the property of normality. In this study, due to the nature of the variables, a bivariate Capula regression model was used to identify the factors affecting the liver enzymes (ALT and AST) and the relationship between these enzymes. This type of regression is suitable when the response variables have a relatively high degree of skewness and interdependence.

In this cross-sectional study, a sample of 2000 participants in the Shahrekord cohort study were randomly selected. To achieve the Capula regression model, the inverse Gaussian margin distribution and the Gumble joint function were selected according to the Akaike criterion. Gamlss, Copula, and Ggrm statistical packages were used in the R software.
(Ethic code: 3316)

According to the findings, some variables were identified as effective factors on the concentration of ALT and AST enzymes through marginal distribution parameters and Capula function. Blood urea, triglyceride, GGT, ALP, and BMI had a nonlinear and significant effect on the mean concentration of the ALT enzyme. The BMI, GGT, ALP, LDL, and HDL (nonlinearly), as well as blood urea (linearly), had a significant effect on the mean concentration of AST enzyme. Finally, the variables of BMI, triglycerides, GGT, and ALP affect the relationship between the concentration levels of the liver enzymes (ALT and AST).

Using this model, in addition to identifying the effective factors, it is possible to distinguish between linear and nonlinear relationships between independent and dependent variables.

The birth of a premature baby can change all the emotions of parents and replace stress. It is necessary to identify the factors that aggravate and relieve maternal stress and adopt strategies to reduce it. The aim of this study was to analyze the effect of supportive care program based on Bandura’s self-efficacy on aggravating and stress relieving factors of mothers of neonates admitted to the neonatal intensive care unit

A quasi-experimental study was performed in 2020 on 90 mothers of premature infants admitted to the neonatal intensive care unit of a Hospital in Shahrekord by convenience sampling method. The experimental group has received an educational intervention based on the theory of self-efficacy in four two-hour sessions with the presentation of an educational booklet. Data were collected using a self-designed questionnaire before and after the intervention in both groups and were analyzed using SPSS software version 24 performing paired t-test, independent t-test, and Chi-square.

The results showed that the mean score of stressors in mothers of preterm infants in the control group decreased from 42.73 ± 6.26 to 42.62 ± 7.09, which was not statistically significant (P= 0.906) while in the experimental group it was decreased from 48.78 ± 4.36 to 25.33 ± 4.84, which was statistically significant (P <0.001). The mean score of soothing agents for mothers of preterm infants increased from 2.98 ± 2.61 to 3.07 ± 2.29 in the control group which was not statistically significant (P = 0.415) and in the experimental group it was increased from 4.51±1.72 to 7.29 ± 1.16, which was statistically significant (P <0.001).

Based on the findings of the study, it is recommended to implement interventions to make parents of premature infants, especially mothers ready to face the baby in the intensive care unit.

Urinary stones are the third most common genital tract disease after urinary tract infections and prostate diseases. Extracorporeal Shock Wave Lithotripsy (ESWL) is the most common treatment for more than half of all kidney stones. Most patients experience pain during the surgery, despite the use of pain killer after ESWL. This study aimed to investigate the effect of prayer on pain in the patients who underwent ESWL.

This clinical trial was performed on 120 patients who were referred to Ayatollah Kashani Hospital, Shahrekord, Iran, for ESWL in 2018. Patients were divided into two experimental and control groups. In addition to the reception of routine care, the test group repeated the prayer “peace be upon Mohammad and his descendants” during ESWL. However, the control group only received routine care. The pain score was determined using McGill Pain Questionnaire and the collected data were analyzed in SPSS software (version 20) through descriptive statistics and t-test.

The scores of pain variations in the prayer (intervention) and control groups were estimated at -11.10±10.13 and -1.85±8.67, respectively. Based on the results obtained from the Tukey test, there was a significant difference between the two groups in terms of pain variations (P=0.001). There was a significant difference between the mean drug use in the control and experimental groups (P=0.01).

This study showed that prayer therapy reduced the pain of the patients who underwent ESWL.

ICU is one of the vital departments in the hospital to reduce mortality and morbidity. The department where nurses face complex situations that require decision-making skills. In fact, problem solving is an important and complex issue clinical practice, and decision-making is an important indicator. The purpose of the study was to investigate the effect of SBAR-based patient safety education program on nurseschr('39') clinical decision making in intensive care unit nurses.

The quasi-experimental study was performed on 60 nurses working in intensive care units of Ayatollah Kashani and Hajar hospitals in Shahrekord, Iran in 1398. The subjects were randomly assigned to two experimental and control groups (30 people each). For the experimental group, five SBAR-based training program was held (Situation, Background, Assessment, and Recommendation). Data were collected using Lori et al. (2001) clinical decision making questionnaire before and one month after the intervention respectively. The collected data were analyzed using analysis of covariance in SPSS software (version 22).

According to the findings, the clinical decision-making score of the test group nurses before the intervention was 69.1± 5.44 and after the intervention was 80.8 ± 7.22 (P<0.001). In the control group, it was 70.6 ±6.94 before intervention and 71.1 ± 6.72 after intervention (P = 0.214). The rate of changes in the experimental group was 11.7 ± 3.79 and in the control, group was 0.53 ± 2.3. The results of the study showed that the implementation for the patient safety-training program based on SBAR technique had a significant effect on the mean score of clinical decision making of nurses.

The program training based on SBAR technique is effective on nurses chr('39')clinical decision-making. Therefore, it is suggested that HEC and planners benefit from this technique.

Nowadays the use of mobile health technology to improve health is a great opportunity for developing countries, to have a more efficient health system by telemedicine education. So the purpose of this study was to investigate the effect of mobile-based self-care application on the balance of people with multiple sclerosis.

The present study is a clinical trial study performed in Chaharmahal and Bakhtiari province in 2019. The study units were selected by convenience sampling and were divided into two groups of experimental and control. At first, balance level of the patients was measured by Tinetti (Tinetti Performance Oriented Mobility Assessment) questionnaire. Then, for the experimental group, the self-care application was installed on their cell phone and for two months was used by patients. The control group received no intervention during this period. The application usage rate was measured by self-report checklists. After two months balance level was again measured in both groups. Data were analyzed by using SPSS-21 software and chi-square and independent and paired t-test.

Participants in two groups had no significant difference in terms of demographic characteristics and underlying variables including: Age (P = 0.736), age groups (P = 0.955), gender (P = 0.722), education (P = 0.880), marital status (P = 0.358), and occupation (P = 0.172). Before intervention, the mean of balance score was 10.61 ± 3.78 in the control group and 10.55 ± 2.96 in the experimental group, that was not statistically significant (P = 0.945). It was while, two months after intervention the mean balance score in the control group was 10.52 ± 3.63 and in the experimental group was 12.00 ± 2.47 and the difference was statistically significant (P = 0.049).

The results show that implementation mobile-based self-care applications is effective in improving the balance of the patients with multiple sclerosis. Therefore, mobile applications of this supportive method could be useful to manage their disease and to obtain self-care skills.

Discriminant analysis and classification is one of the most practical statistical parts in various scientific scopes. Classical statistics methods have such prior hypothesizes that they should be considered; otherwise using these methods can cause some noticeable errors. For this reason, researchers tend to choose the methods with less limitation, such as nervation, so they dont need to a specific kind of prior hypothesis. For conducting this research, is used logistic regression method, quadratic discriminant and artificial neural network method for comparison. In this study the participants are 1000 case-control data, who suffered from Myocardial Infarction. Neural network analysis was done with error least squares criterion, and is used Error Back-Propagation algoritm. Then is compared the function of three different BFGS Conjugate, gradient and Gradient descent algorithm of neural network for diagnosis of the stated illness. For analyzing data, is used SPSS, STATISTICA and R-SAS softwares. Based on quadratic discriminant method, prediction error percent, prediction correct percent, sensitivity, specificity and area under the roc curve are 10.15, 89.85, 0.8888, 0.9083 and 0.922, based on logistic regression method these measurements are 10.88, 89.12, 0.8743, 0.9110 and 0.941. And for artificial neural network method these measurements are 3.97, 96.03, 0.9561, 0.9644 and 0.966. There was a meaningful difference between are a under the Rroc curve for these three methods. Also, between three different algorithms, nervation algorithm BFGS has had the best function. The present findings show that the artificial nervation is more accurate for diagnosising Myocardial Infarction illness compared with logistic regression and quadratic discriminant methods.

Infant's growth health is one of the important elements effectively on social health. Accordingly, recognizing effective factors on infant's physical growth as well as identifying essential procedures to preserve and improve their health matters is very important. The aim of this study was to determine influential factors on growth parameters including height, weight and head circumference of infants using quantile regression analysis. This cohort analytic descriptive study was conducted in Islamic research committee of Shahrekord University of Medical Sciences from Ramadan 2006 to 2009. 92 healthy infants including entrance criteria were studied. The impacts of effective factors on growth parameters like height, weight and head circumference in various quantiles were compared using quantile regression model. Growth parameters have a direct relationship with the birth weight of infants. The direct relationship of mother's age was pretty apparent up to 6th month. Birth rank had a reverse effect on growth parameters. Iron consumption at the 20 and 40 quantiles of height and feeding of mother’s milk at the second deciles of head circumference was significant (P<0.05). Due to fit of various regression lines, obtained results indicated that quantile regression is more comprehensive than a linear regression model. Effectiveness of variables of father's education, birth rank, mother's Body Mass Index, the number of family members, iron intake, vitamin and feeding mother milk was not observed by linear regression. Meanwhile, a greater attention is needed to girl's growth especially during the first 6 month after birth.

Modeling is one of the most important ways for explanation of relationship between dependent and independent response. Since data, related to number of blood donations are discrete, to explain them it is better to use discrete variable distribution like Poison or Negative binomial. This research tries to analyze numerical methods by using neural network approach and compare it by classic statistical methods to choose better way to predict the number of blood donations. In this study, data were collected from blood donors at the blood center of the Sharekord and then four methods were compared by neural network approach. These methods are: Poisson regression model and its zero inflated, Negative binomial models and its zero inflated.To learn neural network approach, (BFGS) Broyden–Fletcher–Goldfarb–Shanno algorithm was used. To choose the best model, mean-square error (MSE) was used. The best network structure in teaching data was chosen and neural network approach resolution was compared by them, to choose the best approach for prediction the number of blood donations. The MSE for Poisson regression model, Poisson regression with zero inflated, negative binomial and negative binomial with zero inflated are respectively 2.71, 1.54, 0.94 and 1.01. For neural network approach 14:17:1 with activation function of hyperbolic tangent in hidden layer and output layer 0.056 is achieved. The results showed that, according to amount of MSE, neural network approach is the best method with highest accuracy to predict the number of blood donations rather than other methods examined in this article.

In analyzing survival data using conventional methods of classical statistics requires some basic assumptions for data. Artificial neural networks as a modern modeling method can be used in situations where classic models have restricted application because their assumptions are not met. This study is compared survival of patients with breast cancer using artificial neural network and Cox regression models. This historical cohort study, include data from 161 patients with breast cancer in Ardabil province in the years 2002-2007 were diagnosed as having cancer. 68.9% of data dividing as training data set and 31.1% of data dividing as validation data set. Artificial neural networks and Cox regression models are fitted to data. Predictive accuracy and area under ROC used to compare models. Between neural network models, models with SCG, OSS and LM learning algorithms with predictive accuracy of 94, 90 and 78 percent for validation data, had the highest efficiency respectively. Areas under ROC for these models are 0.991, 0.972 and 0.837 respectively and 0.869 for Cox regression model. This study shaw that if suitable architecture and algorithms are selected for artificial neural network model, this model will be more efficient than the Cox regression model to predict the survival situation of patients with breast cancer.

In modeling process, correlation between covariates causes multicolinearity that may reduce efficiency of the model. This study was aimed to use principal component analysis to eliminate the effect of multicolinearity in logistic regression and neural network models, and to determine its effect on the accuracy of predicting metabolic syndrome in a sample of individuals participating in the Tehran Lipid and Glucose Study. A total of 347 participants from the Cohort section of the Tehran Lipid and Glucose Study (TLGS) were evaluated. The subjects were free of metabolic syndrome, according to the ATPIII criteria, at the beginning. Logistic regression, logistic regression with principal components, neural network and neural network with principal components models were fitted to the data. The ability of the models in predicting metabolic syndrome was compared using ROC analysis and kappa statistics. The area under receiver operating characteristic (ROC) curve for logistic regression, logistic regression with principal components, neural network and neural network with principal component were estimated as 0.749, 0.790, 0.890 and 0.927 respectively. Sensitivity of the models was calculated as 0.483, 0.435, 0.836 and 0.919 and their specificity as 0.857, 0.919, 0.892 and 0.964 respectively. The kappa statistic for these models was 0.322, 0.386, 0.712 and 0.886 respectively. the study shows that the prediction accuracy of models based on principal components is better than that of models based on primary covariates, so in the presence of multicolinearity, models based on principal components are efficient for predicting metabolic syndrome.