rahim roozbahani

There are variables whose influence on the risk of tuberculosis (TB) recurrence change over time. Therefore, this study aimed to assess the time-dependent effects of these variables on the hazard of TB recurrence.

In this historical cohort study, data were collected from 4,564 TB patients who were referred to the TB research center of Dr. Masih Daneshvari Hospital, Tehran, from 2005 to 2015, in order to evaluate factors affecting the hazard of TB recurrence in terms of time dependency or time constancy. Data were analyzed in STATA 14 software using a statistical test based on Schoenfeld residuals, the time-dependent effects method, and the time-varying effects model (considering time function as f (t) = t).

The results showed that only the impact of the variables of drug adverse effects and passive smoker were inconstant over time and had time-dependent effects, and they also influenced the hazard of TB recurrence. Also, the effect of the two mentioned variables on the hazard of TB recurrence displayed a decreasing and increasing trend with time, respectively.

Using the time-varying effects model in the study of the hazard of TB recurrence allows evaluating the time-dependent effects of the studied variables and also can differentiate them from the time-independent variables.

The novel coronavirus disease (COVID-19) was first reported in late November 2019. Identifying the characteristics of this disease can help make decisions and control the pandemic. The present study aims to determine the characteristics and clinical results of patients with COVID-19 hospitalized in Masih Deneshvari Hospital in Tehran, Iran.

In this cross-sectional study, the study population consists of all patients diagnosed with COVID-19 and hospitalized in Masih Deneshvari Hospital in Tehran from February to May 2020. By using a prepared checklist, the demographic and disease-related characteristics of patients were extracted,. The data were then entered into SPSS software and were analyzed using descriptive statistics and chi-square test. P<0.05 was considered statistically significant.

In this study, 700 patients were examined, of whom 451 (64.4%) were male and the rest were female. The most common symptoms were shortness of breath (97.8%) and cough (96.4%). The highest rate of infection was in the age group of 56-65 years. Also, 640 (91.4%) had underlying diseases and 42% were overweight (BMI: 25-29.9 kg/m2). Moreover, 88.2% were recovered and 11.8% were died.

Most of the people infected with COVID-19 admitted to the study hospital were male and middle-aged. Analyzing the relationship between different factors in epidemiological studies can increase the attention of caregivers and decision makers to reduce the spread of the diseases.

Coronavirus disease 2019 (COVID-19) is a newly emerged disease with many unknown facets, so both the treatment and the cause of spreading this disease have remained mysterious so far.
Based on the information of 4372 patients with COVID-19 referring to Dr. Masih Daneshvari Hospital in Tehran, the time-dependent changes in COVID-19 severity were investigated in this study using correlation analysis.
According to the results of this study, on average 154.80 patients were infected with mild to moderate COVID-19, and 39.06 were infected with severe COVID-19. The results of this study also indicated a descending trend in the number of patients with mild to moderate COVID-19 (r=-0.40, P-value=0.004) and an ascending trend in the number of patients with severe COVID-19 (r=0.43, P-value=0.003) overtime on a daily basis so that almost two patients were removed from those with mild to moderate COVID-19 and one was added to the patients with severe COVID-19 on average per day.
Based on the current study results, it is concluded that COVID-19 severity will not be constant over time, and there is a probability of COVID-19 becoming more aggressive. Therefore, by the lack of timely control of the disease over time, we will witness an increased number of patients with severe COVID-19 and an increased number of hospitalizations in the intensive care unit (ICU) ward.

COVID-19 pandemic is a serious concern of the World Health Organization and is considered the most important global health challenge. This study aimed to study the effect of health measures and social restrictions on the COVID-19 epidemic based on the susceptible-infectious-quarantine-recovered (SIQR) mathematical model.

Using the SIQR model, we assessed the effect of health measures and social restrictions on the COVID-19 epidemic by considering different values for the reproductive rate parameter and constant values for the recovery rate and quarantined rate (or disease detection rate).

The results indicated that with increasing the level of social restrictions and health measures equivalent to 20, 40, 60, and 80%, the reproductive rate of the COVID-19 reduced from 2.5 to 2, 1.5, 1, and 0.5, respectively. Also, with increasing the levels of social restrictions and health measures, a smaller percentage of people in the community became infected. Considering the level of social restrictions equal to 20, 40, 60, and 80% during the COVID-19 epidemic, about 60, 50, 35, and 10% of the individuals were infected with COVID-19, respectively.

The study of the impact of health measures and social restrictions on the COVID-19 epidemic will provide appropriate information on how the disease spreads and also help researchers select the proper level of these measures and restrictions to prevent further spread of COVID-19 disease.

Diagnosis and treatment of patients with multidrug-resistant tuberculosis (MDR-TB) are very important. Hence, it is necessary to predict and diagnose these patients based on individual, demographic and clinical characteristics before starting treatment. This study aimed to predict MDR-TB in TB patients using the perceptron artificial neural networks (ANNs) model.

This retrospective cohort study was conducted on 1,050 TB patients who have been treated in Masih Daneshvari Hospital, Tehran, Iran from 2005 to 2015. Data on personal and demographic information, as well as medical data such as drug therapy, final outcome of treatment, and the diagnosis of MDR-TB, were collected from the patients' medical records.

The results of this study indicated that the predictive power of MDR-TB for both training and testing groups was 85% and 80%, respectively. Also, the variables of marital status, education, drug use, being imprisoned, extrapulmonary TB, history of comorbidities, AIDS, patients' age, and family size were identified as very effective factors. However, variables of residence, smoking history, contact with a TB person, pulmonary TB, drug side effects, nationality, and diabetes were found as effective factors in predicting the development of MDR-TB.

Application of the perceptron ANNs model in the study of MDR-TB is able to create new horizons in the diagnosis of these patients due to high predictive accuracy.

Nowadays, with the coronavirus disease-2019 (COVID-19) pandemic, millions of people have been infected with the coronavirus, and most countries in the world have been unable to treat and control this condition. The aim of this study was to estimate and predict the COVID-19 prevalence rate based on multilayer perceptron artificial neural network (MLP-ANN) model.

In this cross-sectional study, based on the information of 4,372 patients with COVID-19 referred to Dr. Masih Daneshvari Hospital in Tehran, the prevalence rate of this disease was estimated. In addition, considering the role of the health measures and social restrictions, the trend of this index based on the MLP-ANN model was predicted.

According to the results of this study, the prevalence of COVID-19 increased by an average of 7.05 per thousand people daily during the 48 days from the onset of the epidemic, and it reached about 341.96 per thousand people. Based on the MLP-ANN model with a lack of attention to the health measures by individuals in the community and failure to reduce social restrictions by the government, the COVID-19 prevalence increased by an average of 1.03 per thousand people per day. While in the case of attention to the health measures by the people and continued social restrictions by the state, the prevalence of this disease decreased by an average of 2.13 per thousand people, daily.

The study on the prevalence of COVID-19 disease and prediction of the trend of this index provides researchers with useful information about the role of the health measures and social restrictions in controlling this disease.

Today, due to progressing technology and improving the standard of living of humans, the study of diseases has become more complex. This complexity has led to using new methods, such as the model of artificial neural networks (ANNs), to study many chronic diseases, especially tuberculosis (TB). The present study aimed to investigate the mechanism of disease relapse events by applying a multilayer perceptron artificial neural network (MLP-ANN) model among TB patients.

This retrospective cohort study examined information of 4,564 TB patients treated in Masih Daneshvari Hospital, Tehran, Iran, from 2005 to 2015. TB disease relapse was considered as a study event, and the relapse mechanism was investigated using an MLP-ANN model consisting of three layers.

Based on an MLP-ANN model comprising three layers, the power to accurately predict disease relapse in TB patients was 96%. Also, variables of family size, adverse effects of exposure to cigarette smoke, patient age, and education as very effective factors, and marital status, history of drug use, imprisonment, pulmonary TB, diabetes, and AIDS as effective factors were identified in predicting the mechanism of TB disease relapse.

Using an ANN model in the study of TB relapse due to its flexibility and high predictive accuracy can clarify any ambiguous aspects of this disease.

COVID 19 pandemic and the confusion of the world at the beginning of the epidemic affected many aspects of the life and health care. In this regard, organdonation as a vital approach for life saving in patients on the waiting list was influenced too. This essential treatment requires the provision of vital organs from the brain death cases, which is a sensitive, accurate and lengthy process. This process begins with the identification of Glasgow Coma Scale (GCS) cases less than 5 and is followed by organ harvesting and assignment to waiting list patients.Organ donation and factors related to its process have been fluctuated during three specific time periods, including the first and second year of the epidemic with the year before the epidemic. This decrease in the number of donations has been felt worldwide and it has been reported that this number has decreased significantly even in the amount of blood donation.Numerous barriers to the treatment system during the epidemic, limitations of surgeries except in emergencies, asymptomatic patients, and many unknown aspects of the disease have shown that the policies and approaches of procurement centers need to be changed to continue efforts in this situation. New protocols (according to the needs of these days) should be developed and implemented according to the conditions ahead.

The success of treatment strategies to control the disease relapse requires determining factors affecting the incident short-time and long-time of disease relapse. Therefore, this study was aimed to identify the factors affecting of short-and long-time of occurrence of disease relapse in patients with tuberculosis (TB) using a parametric mixture cure model.

In this historical cohort study; the data was collected from 4564 patients with TB who referred to the Tuberculosis and Lung Diseases Research Center of Dr. Masih Daneshvari Hospital from 2005 to 2015. In order to evaluate the factors affecting of short-and long-time of occurrence of disease relapse, a parametric mixture cure model was used.

In this study, the estimation of the annual incidence of TB relapse showed that the probability of recurrence in the first year is 1% and in the third and tenth years after treatment is 3% and 5%, respectively. In addition, the results of this study showed that the variables of residence, exposure to cigarette smoke, adverse effects of drug use, incarceration, and pulmonary and extra- pulmonary tuberculosis were the factors affecting the short-time recurrence of TB. The variables of drug use, pulmonary and extra- pulmonary tuberculosis, and also incarceration affected the long-term recurrence of this disease.

Cure models by separating factors affecting the short-time occurrence from the long-time occurrence of disease relapse can provide more accurate information to researchers to control and reduce TB relapse.

 The development of treatment methods and increasing the survival of patients with tuberculosis (TB) has led to the complication of relationships between independent and dependent variables associated with this disease. Therefore, it is important to use new methods to model the TB process that can accurately estimate the current situation. This study aimed to model the survival of patients with tuberculosis based on the model of perceptron artificial multilayer neural network (MLP-ANN).

In this retrospective cohort study, the data was collected from 2366 TB patients who were treated in Dr. Masih Daneshvari Hospital in Tehran from 2005 to 2015. To model the predictive power of survival in TB patients, an MLP-ANN model consisting of three layers was applied.

The results of this study showed that based on the MLP-ANN model, the correct predictive power of survival in TB patients is 88.4%. In this study, the variables of patients' age and family size as very effective variables also variables of patients’ gender, marital status, education, adverse drug effects, exposure to cigarette smoke, imprisonment, pulmonary tuberculosis, and AIDS as effective variables in predicting the survival of patients were diagnosed.

In the model of artificial neural networks, no restrictions are considered for the data structure and the type of relationship between variables. Therefore, these models with their flexibility and high accuracy can be one of the best methods for modeling health data.

 The spread of the COVID-19 virus is currently considered the most important global health challenge. Therefore, it is very important to study and simulate the patterns of spread of this disease based on mathematical models. This study aimed to simulate the COVID-19 epidemic based on the SIR model, in Iran.

In this study, the COVID-19 epidemic was simulated based on the susceptible-infected-recovered (SIR) mathematical model. According to the parameters of this model, different scenarios for this disease were examined. Finally, the simulation of the COVID-19 epidemic based on the values of these parameters was presented for Iran.

According to the results of this study, with increasing the level of social restrictions and health measures, the reproductive rate of COVID-19 decreased, and also with access to effective medicines and vaccines, the recovery rate of this disease increased, and fewer people became infected. Moreover, results showed that with the continuation of social restrictions and attention to health issues by the people in Iran, the peak of COVID-19 is seen within 50 days from the beginning of the epidemic also about 5% of the population is affected by this disease. The end of the initial wave of the disease was predicted at least 100 days after the onset of the epidemic.

A simulation study to evaluate the prevalence of COVID-19 will provide comprehensive and complete information about the role of health care measures and social restrictions to prevent the spread of this disease to health researchers.

Continuing medical education (CME) is an integral part of nursing professionalization, which can be effective in the development of nursing behavior. E-learning can play an important role in CME programs. This study aimed to evaluate the effectiveness of an E-learning program in increasing the nurses’ knowledge and behavior regarding care for venous thromboembolism (VTE) patients.

One-hundred nurses were selected via convenience sampling method and divided into E-learning and traditional education groups. All nurses had access to the Internet, as well as adequate Internet literacy. Each nurse in the E-learning group received three didactic files during the intervention (four weeks), which focused on the VTE risk assessment, methods of prophylaxis, prophylaxis guidelines, diagnosis, pharmacological and non-pharmacological treatments, and patient education. On the other hand, nurses in the traditional education group were taught traditionally by an expert lecturer, who used audiovisual materials for teaching. A pretest-posttest analysis and a checklist were used to evaluate the impact of interventions in the groups. Chi-square and Mann-Whitney tests were also used to analyze the data.

The comparison of knowledge level between the groups showed that E-learning is not superior to traditional learning methods. The mean changes in the nurses’ behavior was 3.16±1.49 in the E-learning group and 2.77±1.26 in the conventional education group. Statistical analysis showed a significant relationship between the score changes and E-learning.

Integration of E-learning in CME programs, besides attendance of traditional courses, can be an effective learning method. We suggest that future studies compare the effects of these methods.

Tobacco smoke is toxic for cells and could be a damaging factor to skin. The purpose of this study was to compare the biophysical properties of skin in smokers and non-smokers.

The study population consisted of 28 current smokers and 24 non-smokers. The hydration of the stratum corneum, trans epidermal water loss, pH, erythema, melanin content, sebum, friction and elasticity parameters (R0, R2, R5) of skin, epidermis and dermis thickness and echodensity were measured on middle forehead, right cheek and right inner arm of participants. Also volume, surface area and depth of right nasolabial folds were measured. The mean of these values in smokers were compared with nonsmokers by independent sample T- test.

Gross elasticity was significantly lower in smokers on forehead (p= 0.048). Thickness of epidermis was higher in smokers in all measured sites but the differences were not statistically significant. Thickness of dermis was higher in smokers in all measured sites too, but only the difference on cheek was statistically significant (p= 0.009). Density of epidermis was lower in smokers in all measured sites, but only the difference on forehead was statistically significant (p= 0.019). Density of dermis was lower in smokers in all measured sites, but only the difference on arm was statistically significant (p= 0.028). Volume and area of nasolabial folds were higher in smokers, but only the difference of area was statistically significant (p = 0.031).

Tobacco smoking could affect the biophysical parameters of skin, especially thickness and density of dermis and epidermis and nasolabial folds.

Obstructive sleep apnea syndrome (OSAS) is a common disorder in which instability of the upper airways leads to a reduction or cessation of airflow during sleep. Sleep disorders such as OSAS increase the risk of occupational accidents and impaired work performance. Sleep deprivation during shift increases the risk of occupational accidents among health care employees. The purpose of this study was to determine the association between occupational injuries in hospital staff and the risk of sleep apnea. This cross-sectional study was conducted on hospital staff of Masih Daneshvari Hospital in 2012. In this study, the hospital staff’s (715) response to the Berlin questionnaire plus additional information including a history of an occupational accident, night shifts, less than four hours of night sleep, history of smoking, chronic disease and quality of sleep were assessed. Information obtained was analyzed using SPSS 15. In general, 27.6% reported a history of occupational accidents. The incidence of occupational accidents in the high-risk group for sleep apnea was significantly higher than the low-risk group (OR=2.736, CI=1.522-4.917, P=0.001). The results of logistic regression analysis also showed a statistically significant association between occupational accidents and risk of sleep apnea (OR = 2.247, CI = 1.194-4.231, P= 0.012). This study showed that the incidence of occupational accidents in the hospital employees is strongly related to the probability of OSA. Therefore, special attention should be directed to respiratory sleep disorders in order to reduce occupational injuries at hospitals.

The importance of organ donation is increasing world wild. The knowledge and attitude of the medical staffs, including nurses, and their positive motivation in this regard are considered as the advantage to achieve a better transplantation results. This study aimed to investigate nurses’ attitude, awareness and performance about organ transportation in Dr. Masih Daneshvari Hospital. The present study evaluated the opinions of 66 randomly selected, educated nurses who had at least one year experience in different wards of the hospital. Using a Farsi questionnaire, nurses’ attitudes, knowledge and practice data on organ transplantation, as well as their demographic information were surveyed. The validity and reliability of the questionnaire were determined by means of content validity and test-retest (r=0.81) methods, respectively. Using SPSS 16, data analysis was done, hence, descriptive (i.e. absolute and relative frequency tables, mean and standard deviation) and analytical (e.g. Chi2, t-test) statistical results were obtained. The range of age of the majority of participants was 20-40 years old (74.2% woman and 25.8% man). The results showed a significant inverse relationship between the age and the psycho-emotional status of the nurses, regarding the care of brain death patients. The participants’ point of view on the organ transplantation was 12.3% opposite, 17.6% surely agree, 54.7% agree only when there is condition improvement, and 15.4% indifferent.The results also showed that a high percentage of nurses have lack of enough knowledge on organ transplantation. As the educational course and subsequent awareness can be highly effective on their attitude, therefore, the increase of awareness, together with condition improvement, could be important to enhance the care of transplant patients.

Asthma is the most common respiratory disease with an increasing prevalence. On the other hand, obesity is also a challenging disease compromising health in human communities. This study sought to assess the correlation of asthma and body mass index (BMI) in occupational setting. This study was conducted in a cable manufacturing company in 2012. A total of 551 workers from the production (exposed group) and non-production (unexposed group) units were studied. A questionnaire specifically designed for this purpose was filled out for study subjects and then all workers with respiratory symptoms suggestive of asthma thoroughly examined by a physician and medical history was taken from them. Complementary diagnostic tests were also carried out. A total of 11.6% of our understudy subjects had asthma. The prevalence of asthma in exposed subjects with BMI ³ 25 kg/m2 was found to be significantly higher than in exposed workers with BMI< 25 kg/m2 (p< 0.01). However, no significant differences existed in prevalence of asthma between the two subgroups of BMI ³ 25 kg/m2 and BMI< 25 kg/m2in the unexposed group (p>0.05). After adjusting for confounding factors significant associations were observed between BMI and asthma at cut points of 30 kg/m2 and 25 kg/m2 (OR: 8.53 and 2.41, respectively). Our study results showed that prevalence of asthma might be higher in workers with higher BMI who are exposed to occupational asthmogens. This finding highlights the necessity of offering weight loss recommendations in periodic examinations to workers with exposure to occupational asthmogens.