Internet Addiction and Sleep Disorders among Medical Students.

Background: Considering the increasing use of the Internet in Iranian society, especially among students, and the importance of sleep quality, the present study investigated the relationship between sleep quality and Internet addiction among medical students in Shiraz. Methods: In this descriptive-analytical study, the sample included students of the Shiraz University of Medical Sciences who were selected by a multistage sampling method in 2018. Each faculty was considered to be stratified, and the samples were selected from all strata by simple random sampling. A total of 400 student questionnaires were eligible for analysis. The level of sleep disturbance was measured using the Pittsburgh Sleep Quality Index (PSQI), and Young's Internet Addiction Test (IAT) was used to evaluate Internet addiction. Cronbach's alpha for PSQI and IAT was 0.77 and 0.93, respectively. Results: 109 (%28.9) and 58 (%14.4) of all the people under study were at risk of Internet addiction or poor sleep quality, respectively. The highest percentage of poor sleep quality was in those who were addicted to the Internet (60%), but the lowest percentage was observed in the group without addiction (27%). In addition, there was a significant correlation between Internet addiction and subjective sleep quality (r = 0.191, p = 0.05), sleep latency (r = 0.129, p = 0.01), sleep duration (r = 0.119, p = 0.01), habitual sleep efficiency (r = 0.186, p = 0.05), sleep disturbances (r = 0.169, p = 0.01), use of sleeping medication (r = 0.203, p = 0.05), and daytime dysfunction (r = 0.188, p = 0.05). Conclusion: These findings help national health officials and planners in Iran to design appropriate and effective interventions to improve students' health and prevent Internet addiction.

Awareness, Treatment, and Control of Hypertension among 10663 Adults Based on the Baseline Data of the Kherameh Cohort Study.

Background: A comprehensive strategy to reduce the complications of hypertension (HTN) should include prevention approaches, such as increasing awareness, early diagnosis, and adequate treatment. The present study aimed to assess awareness, treatment, and control of HTN and their related factors in Kherameh, Iran. Methods: This cross-sectional study was performed on 10 663 individuals aged between 40 and 70 years using the Kherameh cohort data. HTN was defined as either systolic/diastolic blood pressure ≥140/90 mmHg or taking medications. Logistic regression was used to investigate the relationship between awareness, treatment, and control of HTN and demographic factors, comorbidities, and a family history of diseases. Results: Out of 10 663 participants, 4719 (44.3%) were men, and the average age of the participants was 51.94±8.27 years. The rates of the prevalence, awareness, treatment, and control of HTN were 27.7% (95% CI, 26.86 to 28.54), 80.3% (95% CI, 79.56 to 81.04), 78% (95% CI, 77.22 to 78.78), and 53.6% (95% CI, 52.66 to 54.54), respectively. Age, gender, body mass index, and cardiovascular disease were associated with all the dependent variables in the regression model. Additionally, occupation, diabetes, chronic diseases, a history of cardiovascular disease in first and second-degree relatives and a history of chronic diseases in second-degree relatives were related to all the dependent variables except for treatment. Conclusion: A high percentage of the patients were aware of their disease, but a smaller proportion were on medication. Consequently, about half the patients had blood pressure below 140/90 mmHg.

Aromatic Fingerprints: VOC Analysis with E-Nose and GC-MS for Rapid Detection of Adulteration in Sesame Oil.

Food quality assurance is an important field that directly affects public health. The organoleptic aroma of food is of crucial significance to evaluate and confirm food quality and origin. The volatile organic compound (VOC) emissions (detectable aroma) from foods are unique and provide a basis to predict and evaluate food quality. Soybean and corn oils were added to sesame oil (to simulate adulteration) at four different mixture percentages (25-100%) and then chemically analyzed using an experimental 9-sensor metal oxide semiconducting (MOS) electronic nose (e-nose) and gas chromatography-mass spectroscopy (GC-MS) for comparisons in detecting unadulterated sesame oil controls. GC-MS analysis revealed eleven major VOC components identified within 82-91% of oil samples. Principle component analysis (PCA) and linear detection analysis (LDA) were employed to visualize different levels of adulteration detected by the e-nose. Artificial neural networks (ANNs) and support vector machines (SVMs) were also used for statistical modeling. The sensitivity and specificity obtained for SVM were 0.987 and 0.977, respectively, while these values for the ANN method were 0.949 and 0.953, respectively. E-nose-based technology is a quick and effective method for the detection of sesame oil adulteration due to its simplicity (ease of application), rapid analysis, and accuracy. GC-MS data provided corroborative chemical evidence to show differences in volatile emissions from virgin and adulterated sesame oil samples and the precise VOCs explaining differences in e-nose signature patterns derived from each sample type.

Environmental Engineering Applications of Electronic Nose Systems Based on MOX Gas Sensors.

Nowadays, the electronic nose (e-nose) has gained a huge amount of attention due to its ability to detect and differentiate mixtures of various gases and odors using a limited number of sensors. Its applications in the environmental fields include analysis of the parameters for environmental control, process control, and confirming the efficiency of the odor-control systems. The e-nose has been developed by mimicking the olfactory system of mammals. This paper investigates e-noses and their sensors for the detection of environmental contaminants. Among different types of gas chemical sensors, metal oxide semiconductor sensors (MOXs) can be used for the detection of volatile compounds in air at ppm and sub-ppm levels. In this regard, the advantages and disadvantages of MOX sensors and the solutions to solve the problems arising upon these sensors' applications are addressed, and the research works in the field of environmental contamination monitoring are overviewed. These studies have revealed the suitability of e-noses for most of the reported applications, especially when the tools were specifically developed for that application, e.g., in the facilities of water and wastewater management systems. As a general rule, the literature review discusses the aspects related to various applications as well as the development of effective solutions. However, the main limitation in the expansion of the use of e-noses as an environmental monitoring tool is their complexity and lack of specific standards, which can be corrected through appropriate data processing methods applications.

Determining the shelf life and quality changes of potatoes (Solanum tuberosum) during storage using electronic nose and machine learning.

The activities of alpha-amylase, beta-amylase, sucrose synthase, and invertase enzymes are under the influence of storage conditions and can affect the structure of starch, as well as the sugar content of potatoes, hence altering their quality. Storage in a warehouse is one of the most common and effective methods of storage to maintain the quality of potatoes after their harvest, while preserving their freshness and sweetness. Smart monitoring and evaluation of the quality of potatoes during the storage period could be an effective approach to improve their freshness. This study is aimed at assessing the changes in the potato quality by an electronic nose (e-nose) in terms of the sugar and carbohydrate contents. Three potato cultivars (Agria, Santé, and Sprite) were analyzed and their quality variations were separately assessed. Quality parameters (i.e. sugar and carbohydrate contents) were evaluated in six 15-day periods. The e-nose data were analyzed by means of chemometric methods, including principal component analysis (PCA), linear data analysis (LDA), support vector machine (SVM), and artificial neural network (ANN). Quadratic discriminant analysis (QDA) and multivariate discrimination analysis (MDA) offer the highest accuracy and sensitivity in the classification of data. The accuracy of all methods was higher than 90%. These results could be applied to present a new approach for the assessment of the quality of stored potatoes.

A population-based study of obesity and its complications in southern Islamic Republic of Iran.

Background: General and central obesity are important risk factors for chronic diseases and health-related outcomes. Aims: We determined the prevalence of obesity and related complications among individuals aged 40-70 years in Kherameh, southern Islamic Republic of Iran. Methods: This cross-sectional study included 10 663 people aged 40-70 years who participated in the first phase of the Kherameh cohort study. Data were collected on demographic characteristics, history of chronic diseases, family history of diseases, and various clinical measures. We used multiple logistic regression analysis to establish the relationships between general and central obesity, and related complications. Results: Of the 10 663 participants, 17.9% had general obesity and 73.5% had central obesity. In people with general obesity, the odds of having the non-alcoholic fatty liver disease and cardiovascular disease were 3.10 times and 1.27 times higher than in individuals with normal weight, respectively. People with central obesity had higher odds of having other components of metabolic syndrome such as hypertension (OR: 2.87; 95% CI: 2.53-3.26), high triglyceride levels (OR: 1.71; 95% CI: 1.54-1.89), and low high-density lipoprotein cholesterol levels (OR: 1.53; 95% CI: 1.37-1.71) than those without central obesity. Conclusions: The study showed a high prevalence of general and central obesity and health-related effects, and its association with several comorbidities. Given the level of obesity-related complications found, primary and secondary prevention interventions are needed. The results may help health policymakers establish effective interventions to control obesity and related complications.

Trend Analysis of Deaths With Unintentional Poisoning and Years of Life Lost in the South of Iran: 2004-2019.

BACKGROUND: This study was conducted to determine the mortality rate and the years of life lost (YLL) due to unintentional poisoning in Fars province in the south of Iran. Study Design: A cross-sectional study. METHODS: In this study, data from all of the deaths due to unintentional poisoning in the south of Iran between 2004 and 2019 was extracted from the population-based Electronic Death Registry System (EDRS). The Joinpoint Regression method was used to examine the trend of the crude mortality rate, the age-standardized mortality rate (ASMR), and the YLL rate. RESULTS: During the 16-year study period (2004-2019), 1466 deaths due to poisoning occurred in Fars province. Of this number, 75.2% (1103 cases) were in men, and 37.5% (550 cases) were in the age group of 15-29 years. The total YLL due to poisoning during the 16-year study period were 25149 and 8392 in men and women, respectively. According to the joinpoint regression analysis, the 16-year trend of YLL rate due to premature mortality was stable. Moreover, the annual percent change (APC) was -0.7% (95% CI: -4.0 to 2.7, P=0.677) for males and - 0.3% (95% CI: -3.8 to 3.3, P=0.862) for females. CONCLUSION: The trend of crude mortality rate, ASMR and YLL due to unintentional poisonings was stable. Considering the high rate of mortality and YLL due to unintentional poisoning in the age group of 15-29 years, it is essential to take necessary actions in this age group.

How to Identify Roast Defects in Coffee Beans Based on the Volatile Compound Profile.

The aim of this study was to detect and identify the volatile compounds in coffee that was obtained in defect roast processes versus standard roasting and to determine the type and strength of the correlations between the roast defects and the volatile compound profile in roasted coffee beans. In order to achieve this goal, the process of coffee bean roasting was set to produce an underdeveloped coffee defect, an overdeveloped coffee defect, and defectless coffee. The "Typica" variety of Arabica coffee beans was used in this study. The study material originated from a plantation that is located at an altitude of 1400-2000 m a.s.l. in Huehuetenango Department, Guatemala. The analyses were carried out with the use of gas chromatography/mass spectrometry (GC-MS) and an electronic nose. This study revealed a correlation between the identified groups of volatile compounds and the following coffee roasting parameters: the time to the first crack, the drying time, and the mean temperatures of the coffee beans and the heating air. The electronic nose helped to identify the roast defects.

Investigating Changes in pH and Soluble Solids Content of Potato during the Storage by Electronic Nose and Vis/NIR Spectroscopy.

Potato is an important agricultural product, ranked as the fourth most common product in the human diet. Potato can be consumed in various forms. As customers expect safe and high-quality products, precise and rapid determination of the quality and composition of potatoes is of crucial significance. The quality of potatoes may alter during the storage period due to various phenomena. Soluble solids content (SSC) and pH are among the quality parameters experiencing alteration during the storage process. This study is thus aimed to assess the variations in SSC and pH during the storage of potatoes using an electronic nose and Vis/NIR spectroscopic techniques with the help of prediction models including partial least squares (PLS), multiple linear regression (MLR), principal component regression (PCR), support vector regression (SVR) and an artificial neural network (ANN). The variations in the SSC and pH are ascending and significant. The results also indicate that the SVR model in the electronic nose has the highest prediction accuracy for the SSC and pH (81, and 92%, respectively). The artificial neural network also managed to predict the SSC and pH at accuracies of 83 and 94%, respectively. SVR method shows the lowest accuracy in Vis/NIR spectroscopy while the PLS model exhibits the best performance in the prediction of the SSC and pH with respective precision of 89 and 93% through the median filter method. The accuracy of the ANN was 85 and 90% in the prediction of the SSC and pH, respectively.

Physical activity behavior during Covid 19 pandemic among Iranian dwellers in Southern Iran based on planned behavior theory: a SEM analysis.

BACKGROUND: The COVID-19 pandemic restrictions curtailed physical activity. The current study applied an integrated Theory of Planned Behavior to identify the determinants of physical activity behavior and the processes involved in the COVID-19 pandemic. METHODS: A cross-sectional study was conducted in Shiraz city, Southern Iran, among 2500 people who met the inclusion criteria were included in the study. Data were collected using the demographic information questions and questionnaire based on the Theory of Planned Behavior (TPB) constructs. The Questionnaire via WhatsApp, emails, and SMS was shared. Data analysis was performed using SPSS26 and Amos version 24. Mean and standard deviation was used to describe the data. Also, one-way ANOVA and structural equation analysis were used to analyze the data. The significance level in all the tests was considered to be 0.05. RESULTS: One thousand one hundred sixty-nine samples (46.8%) said they had been exercising less than 3 days a week, and 47.6% of them did not have any exercise or physical activities (n = 1191). The mean score of attitudes, SN, PBC, and intention were 9.38 ± 2.07, 9.27 ± 2.03, 9.32 ± 2.05, and 12.29 ± 2.35, respectively. The effect size values demonstrate the independent variables' high coefficient of influence on explaining the theoretical model. According to the results, the factors play an important role in samples' intention (η2 ≥ 0.2, p ≤ 0.05). The effect size of intention on doing physical activities and exercise during the SARS-CoV-2 pandemic is Eta square = 0.777, which means the measure was high. The obtained model was good based on the main goodness of fit indices (Chi2 = 108.6, df = 25, n = 2500, Chi2/df = 4.344, RMSEA = 0.036, AGFI = 0.92, CFI = 0.95, GFI = 0.90, Fornell-Larcker criterion = 0.87, HTMT = 0.89). CONCLUSION: The TPB provides a useful framework to explore psychosocial determinants of physical activity behavior during the pandemic and identify key strategies for program planning aimed at improving exercise among people who were already influenced by quarantine and lockdown restrictions.

Application of MOS Gas Sensors Coupled with Chemometrics Methods to Predict the Amount of Sugar and Carbohydrates in Potatoes.

Five potato varieties were studied using an electronic nose with nine MOS sensors. Parameters measured included carbohydrate content, sugar level, and the toughness of the potatoes. Routine tests were carried out while the signals for each potato were measured, simultaneously, using an electronic nose. The signals obtained indicated the concentration of various chemical components. In addition to support vector machines (SVMs that were used for the classification of the samples, chemometric methods, such as the partial least squares regression (PLSR) method, the principal component regression (PCR) method, and the multiple linear regression (MLR) method, were used to create separate regression models for sugar and carbohydrates. The predictive power of the regression models was characterized by a coefficient of determination (R2), a root-mean-square error of prediction (RMSEP), and offsets. PLSR was able to accurately model the relationship between the smells of different types of potatoes, sugar, and carbohydrates. The highest and lowest accuracy of models for predicting sugar and carbohydrates was related to Marfona potatoes and Sprite cultivar potatoes. In general, in all cultivars, the accuracy in predicting the amount of carbohydrates was somewhat better than the accuracy in predicting the amount of sugar. Moreover, the linear function had 100% accuracy for training and validation in the C-SVM method for classification of five potato groups. The electronic nose could be used as a fast and non-destructive method for detecting different potato varieties. Researchers in the food industry will find this method extremely useful in selecting the desired product and samples.

Knowledge and Technology Used in Capacitive Deionization of Water.

The demand for water and energy in today's developing world is enormous and has become the key to the progress of societies. Many methods have been developed to desalinate water, but energy and environmental constraints have slowed or stopped the growth of many. Capacitive Deionization (CDI) is a very new method that uses porous carbon electrodes with significant potential for low energy desalination. This process is known as deionization by applying a very low voltage of 1.2 volts and removing charged ions and molecules. Using capacitive principles in this method, the absorption phenomenon is facilitated, which is known as capacitive deionization. In the capacitive deionization method, unlike other methods in which water is separated from salt, in this technology, salt, which is a smaller part of this compound, is separated from water and salt solution, which in turn causes less energy consumption. With the advancement of science and the introduction of new porous materials, the use of this method of deionization has increased greatly. Due to the limitations of other methods of desalination, this method has been very popular among researchers and the water desalination industry and needs more scientific research to become more commercial.

Impact of Coffee Bean Roasting on the Content of Pyridines Determined by Analysis of Volatile Organic Compounds.

The aim of the study was to analyze the process of roasting coffee beans in a convection-conduction roaster (CC) without a heat exchanger and a convection-conduction-radiation roaster (CCR) with a heat exchanger for determination of the aroma profile. The aroma profile was analyzed using the SPME/GC-MS technique, and an Agrinose electronic nose was used to determine the aroma profile intensity. Arabica coffee beans from five regions of the world, namely, Peru, Costa Rica, Ethiopia, Guatemala, and Brazil, were the research material. The chemometric analyses revealed the dominance of azines, alcohols, aldehydes, hydrazides, and acids in the coffee aroma profile. Their share distinguished the aroma profiles depending on the country of origin of the coffee beans. The high content of pyridine from the azine group was characteristic for the coffee roasting process in the convection-conduction roaster without a heat exchanger, which was shown by the PCA analysis. The increased content of pyridine resulted from the appearance of coal tar, especially in the CC roaster. Pyridine has an unpleasant and bitter plant-like odor, and its excess is detrimental to the human organism. The dominant and elevated content of pyridine is a defect of the coffee roasting process in the CC roaster compared to the process carried out in the CCR machine. The results obtained with the Agrinose showed that the CC roasting method had a significant effect on the sensor responses. The effect of coal tar on the coffee beans resulted in an undesirable aroma profile characterized by increased amounts of aromatic volatile compounds and higher responses of Agrinose sensors.

A Machine Learning Method for Classification and Identification of Potato Cultivars Based on the Reaction of MOS Type Sensor-Array.

In response to one of the most important challenges of the century, i.e., the estimation of the food demands of a growing population, advanced technologies have been employed in agriculture. The potato has the main contribution to people's diet worldwide. Therefore, its different aspects are worth studying. The large number of potato varieties, lack of awareness about its new cultivars among farmers to cultivate, time-consuming and inaccurate process of identifying different potato cultivars, and the significance of identifying potato cultivars and other agricultural products (in every food industry process) all necessitate new, fast, and accurate methods. The aim of this study was to use an electronic nose, along with chemometrics methods, including PCA, LDA, and ANN as fast, inexpensive, and non-destructive methods for detecting different potato cultivars. In the present study, nine sensors with the best response to VOCs were adopted. VOCs sensors were used at various VOCs concentrations (1 to 10,000 ppm) to detect different gases. The results showed that a PCA with two main components, PC1 and PC2, described 92% of the total samples' dataset variance. In addition, the accuracy of the LDA and ANN methods were 100 and 96%, respectively.

Chrysin reduced acrylamide-induced neurotoxicity in both in vitro and in vivo assessments.

BACKGROUND: Acrylamide (ACR) is a well-known industrial toxic chemical that produces neurotoxicity, which is characterized by progressive central and peripheral neuronal degeneration. Chrysin is a natural, biologically active flavonoid compound, which is commonly found in many plants. The antioxidant and neuroprotective properties of chrysin have been demonstrated. METHODS: In this study, the possible effect of chrysin on ACR-induced toxicity was evaluated in both in vitro and in vivo experiments. PC12 cells were used as a suitable in vitro model. Cells were exposed to chrysin (0.5-5 µM) for 12 and 24 h, and then ACR in IC50 concentration was added to the cells. Finally, cell viability was determined using (4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium assay. For in vivo assay, Wistar rats were treated with ACR (50 mg/kg i.p. for 11 days) alone or in combination with chrysin (12.5, 25, and 50 mg/kg). At the end of treatment, behavioral index was evaluated. RESULTS: ACR decreased cell viability and pre-treatment with chrysin (0.5-5 µM) significantly decreased ACR-induced cytotoxicity in the time- and dose-dependent manner. In Wistar rats, exposure to ACR significantly induced severe gait abnormalities, but treatment with chrysin (50 mg/kg) reduced ACR-induced neurotoxicity in animals. CONCLUSION: In the current study, chrysin exhibited neuroprotective effect on PC12 cells as an in vitro model and also on Wistar rats.