Health Science Monitor
Volume:1 Issue: 1, Summer 2022

 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.

The rapid industrial development and agricultural growth and the indiscriminate production of pollutants have faced problems for human societies and the environment. Accumulation and pollution of heavy metals in the environment are the main important problems that as a result of human activities through extraction from ore and processing for various applications has led to the release of these elements in the environment. Heavy metals are non-biodegradable, so they could accumulate in the environment and subsequently contaminate the food chain. Some heavy metals are known as carcinogens, endocrine disruptors and mutagens, and this is a serious threat for human health. Therefore, today, the removal of heavy metal pollutions from the environment has been received special attention by researchers. In the application of physicochemical methods for this purpose, there will be serious limitations such as the need for chemical substances, high cost, the need for specialized equipment and skills, changes in soil characteristics, and disruption of native soil microflora. In contrast, phytoremediation is a better solution to the problem. The use of plants and natural soil microbes to reduce the concentration or toxic effects of pollutants in the environment is called phytoremediation. It is considered as a cost-effective, efficient, new, environmentally friendly and highly adoptable technology. New efficient metal superaccumulator plants are being investigated for applications in phytoremediation and plant extraction. This review article comprehensively discusses the background, concepts, processes and mechanisms in plant remediation of heavy metals.

The Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, caused COVID-19, is a pandemic disease and recently become dangerous to humans in all over the world. There is no efficient medicine for treatment of infected patients, and thousands of people are dying. Real Time-Polymerase Chain Reaction (RT-PCR) is a gold standard assay for COVID-19 diagnosis. Currently, RT-PCR assay is the routine method for detecting COVID-19, and several specific primers for different genes of SARS-CoV-2 are used. However, RT-PCR is not a perfect test, and its reliability depends on the sampling quality and accuracy of the equipment. Mutation occurrence in the virus genome and impurity in extracted RNA samples of the virus might lead to false results. Many efforts are carried out to detect SARS-CoV-2 according to various serological methods such as Enzyme-Linked Immunosorbent Assay (ELISA), colloidal gold-based, and chemiluminescence assays. Finally, the combination of molecular methods like RT-PCR and serological assays such as ELISA can improve the validity and reliability of aforementioned tests. Here, we summarize the results of some molecular and serologic tests for SARS-CoV-2 detection.

Estimates and available statistics indicate that, on average, 20% to 30% of the costs of a hospitalized patient are related to drugs and consumables, and on the other hand, more than 95% of treatment processes depend on drugs and medical supplies. Consumption requirements for the inpatients are divided into two main categories: 1) Consumption requirements are mentioned with the same name and with a specific number and price in the patient's invoice. 2) Hoteling consumption requirements that the number and amount of this consumption requirements are not recorded anywhere in the patient's file, and the costs of this consumption are covered by three percent of the six percent of nursing services.

This descriptive cross-sectional study was performed as a prospective observation in the inpatient emergency department of 'Hazrate Rasoole Akram Hospital in Tehran in the second half of 2018, and 898 patients were monitored.

The study ward had 40 inpatient bed counts, where 898 patients were admitted and hospitalized. The Inpatient Bed Occupancy Rate was over 100%, and the inpatient bed count day was 1706. The frequency distribution of hospitalized patients was 47 different diseases. The cost of their hoteling consumables was 976,776,808 Rials that 186,909,360 Rials of those costs were collected from various insurance deposits and cash receipts, and the rest of the costs, which was equivalent to 81% of the cost of hoteling consumables in December, remained uncollected.

Considering that the cost of Hoteling consumption requirements is provided as 3% of the 6% of nursing services, and on the other hand, the list of Hoteling consumption requirements in different insurances and hospital revenue units is not clear, it is suggested: Firstly, a complete and telling list of hoteling medical consumables should be prepared and communicated, and secondly, according to the existing inflation and the inadequacy of the daily bed tariff and market conditions, the relevant percentage should be adjusted to cover the costs.

 Learning is a complex process in which various factors are involved. The quality of the educational environment is one of the most important factors indicating the quality of education and students' learning. Thus, this study aimed to evaluate the learning environment for students of Behbahan Faculty of Medical sciences.

This descriptive-analytical cross-sectional study was conducted at Behbahan Faculty of Medical Sciences with 115 students. Dundee Ready Education Environment Measure (DREEM) model was used for data collection that obtained 50 questions in five domains, including students' perceptions of learning, teachers' perceptions of teachers, students' academic self-perceptions, and students' perceptions of educational atmosphere and students' social self-perceptions. Data analysis was performed using descriptive statistics (frequency, percentage, mean and standard deviation) and one-way ANOVA.

The mean score of the overall DREEM score was 150.28 +13.03 out of a maximum of 200, showing favorable educational quality (75%). The lowest DREEM score was 64% in students' perceptions of learning, and the highest DREEM score was 79% in both students' perceptions of the professors and students' perceptions of the educational atmosphere. The mean difference for the DREEM subscale scores in different fields of study was statistically significant in all DREEM domains (p <0.05).

Overall, the finding of this study showed that the educational environment of Behbahan Faculty of Medical Sciences was assessed as favorable by the majority of the students. But, considering the difference in results in different fields of study, the strengths and weaknesses of disciplines should be considered to improve the educational environments.

 Aging is a vital phenomenon that happens to everyone. In different cultures, there are diverse beliefs and attitudes towards aging. This study aimed to determine the attitude of Fasa residents towards the elderly.

This descriptive cross-sectional study was conducted on 800 individuals between 13 and 60 years old by cluster sampling. The data collection tool was Kogan's Attitude Survey. Data were analyzed using paired t-test, independent t-test, Pearson correlation coefficient, and ANOVA by spss24 software.

The mean and standard deviation of the age of participants was 41.7 ± 5.17. The score mean of attitude toward the elderly was 117.34 ± 9.75. 51.34% of the individual had a positive attitude, and 49.66% had a negative attitude toward aging. There was a significant relationship between gender and attitude toward the elderly (P = 0.001), positive and negative attitude (P=0.002), and mean attitude scores in terms of marital status in both sexes (P<0.05).

The results showed that the community's view of the elderly has become more negative than in the past. Cultural change and the influence of different cultures on the country have led to a negative attitude towards the elderly. We can make people ready to accept the elderly in the coming years by educating families properly from childhood and at school.

 Improving the health reform plan implementation in terms of quality of health services and fulfilling the goals requires a review of the status quo of midwifery services and identification of their strengths, weaknesses, and challenges, especially in the health sector. The present study was conducted to investigate and comprehensively understand the challenges of midwifery services in the primary health care(PHC) system and provide recommendations for improving midwifery services.

This Qualitative descriptive design study selected 44key informants using purposive sampling and collected their views on midwifery positions and midwifery tasks performed in the PHC system through semi-structured interviews conducted in 2015-16. These informants included 12 midwives, 10 public health experts, 10 faculty members, and 12 managers and officials, who worked in educational and healthcare centers affiliated with Kashan University of Medical Sciences, Kashan, Iran. The transcribed interviews were analyzed in MaxQDA-10 using conventional content analysis.

After analysis, the data of this study were classified into 14 categories and 6 themes. From the participants' point of view, the midwifery services challenges of the health system transformation plan in the field of health were: policymaking and management, rights, referral system, satisfaction, costs, and development and promotion. Most of the extracted codes are related to policymaking and management.

The present findings showed the midwifery sector needs in the Iranian health system for reforms. It is recommended that the role of faculties in training human resources for the health sector be emphasized and new training requirements be integrated into the curriculum by educational departments promptly.

 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.