hamidreza ghaffari

Diabetic nephropathy and peripheral neuropathy complications are attributed to the elevated concentration of the pro-inflammatory cytokine TNF-α. This cytokine, recognized as a crucial biomarker, plays a pivotal role in investigating the pathogenesis of diabetes due to its level fluctuations in glomerular, tubular damage, and fibrosis. This study explores the dose-dependent effect of magnesium sulfate on the serum concentration of TNF-α in an animal model induced with paclitaxel.

Twenty-four male Wistar rats with a mean weight of 200±40 g were randomly divided into four groups: three experimental groups (E1, E2, and E3) and a control group, each comprising six mice. To induce neuropathy, paclitaxel (10 mg/kg) was intraperitoneally administered every other day. To prevent neuropathy occurrence, magnesium sulfate (doses of 75, 150, and 300 mg/kg) was intraperitoneally prescribed daily. Sampling was conducted after 10 days, and serums were separated to measure TNF-α concentration using the ELISA method.

The mean±SD serum level of TNF-α in the E1 group (2.31±0.26 pg/ml) significantly increased compared to the control group (1.46±0.21) (P<0.05). In the E3 group, the mean±SD serum level of TNF-α (4.54±1.32) also significantly increased compared to the control group. However, there was an insignificant increase in TNF-α serum level in the E2 group (3.34±1.08) compared to the control group (P>0.05).

This study, confirming the rise in TNF-α serum level induced by paclitaxel, demonstrates that the dose-dependent administration of magnesium sulfate (300 and 150 mg/kg) reduces the potential incidence of nephropathy and peripheral neuropathy by lowering TNF-α serum levels

Meta-heuristic algorithms are problem solving methods that are modeled on the events in nature or the behavior of living beings so that optimal solutions can be extracted. Collective intelligence algorithms [1] are a kind of meta-heuristic algorithms that are modeled on the behavior of living beings that live in a group and social life, such as hunting behavior, hyena optimization algorithm, whale optimization algorithm, etc. Is. Meta-heuristic algorithms can be divided into different categories based on the method of problem solving, one of which is shown in the research [2] in 2020 according to the diagram in Figure (1) and can be seen. Meta-heuristic algorithms are divided into 4 different groups and categories based on their performance:Figure 1: Classification of meta-heuristic algorithms into different categories [2]Meta-heuristic algorithms are used in various fields, one of which is the optimization of machine learning and deep learning parameters. One of the applications of machine learning and deep learning is in weather forecasting. In this article, to improve the accuracy of the LSTM network, the optimization of important features using the learning method incrows has been used.2. LSTM learning network In the short term, long memory neural networks are actually a type of recurrent neural networks [3].In the LSTM network, with the help of the sigmoid function that is applied element by element, the input, forgetting and output gate layers produce vectors whose all dimensions are between zero and one or close to both. The general structure of LSTM deep learning neural network is as shown in Figure (2):Figure 2. The structure of long memory networks, in the short term 3. proposed model Figure (3). the framework of the proposed method is shown. The evaluation or minimization function of the following two factors shows how well a feature vector has competence:• Average prediction error with neural networ• Number of features selected The calculation of error index E is as follows:the population of crows is stored in a matrix:Each crow needs to remember the most optimal position:In the crow learning algorithm, there are two phases of horizontal and vertical learning. Vertical learning from parents is horizontal learning from brothers and sisters. It is used to select a sister or vector randomly. 6 k= 3+[rand×(i-3)] & i≥3 In the crow's learning algorithm, the probability of receiving a reward for crows is equal to Rpprob. lf is the value of the learning factor in crows. Amount of reward for crows: Reinforcement of learning for parents is used in the Crow algorithm as follows. It is used to search for food with the stealing mechanism as follows: 4-Implementation and analysis 4-1-Implementation parameters Table (1): Implementation parameters of the proposed method Figure 4: LSTM implementation parameters in the proposed method 4-2-Evaluation indices One of the important indicators for predicting weather conditions is the mean squared error MSE index, and to evaluate the proposed method, you can use the classification and prediction indicators of accuracy, recall and accuracy: 4-3- Analysis of the proposed

In the diagram of figure (5), the prediction error in the feature selection phase in combination with the neural network is shown, and in figure (6), the output of LSTM deep learning in weather forecasting is depicted. Figure 5: Reduction of prediction error in feature selection phase with 10 iterations Figure 6: Reducing the prediction error in the classification phase with LSTM Table 2: Average prediction indices of the proposed method Figure 7: Comparison of the MSE error of the proposed method with predictionmethods Figure 8: Comparison of the accuracy of the proposed method with prediction methods Figure 9: Comparison of recall of the proposed method with prediction methods Figure 10: Comparing the precision of the proposed method with prediction methods Figure 11: Comparison of the accuracy index of the proposed method in weather forecasting 5.

LSTM network is a deep learning method that can be used to predict weather conditions. In the proposed method to increase the prediction accuracy of LSTM neural network, intelligent feature selection is used using a combination of crow learning algorithm and crow search. Experiments showed that the proposed method has an accuracy of 96.92%, a sensitivity of 95.82%, and an accuracy of 96.34%, and it is more accurate for predicting weather conditions than multilayer neural network, recurrent neural network, and LSTM method.

A gastric ulcer is a mucosal lesion of the stomach that may occur after taking nonsteroidal anti-inflammatory drugs such as aspirin, especially in cardiovascular diseases. The aim of this study was to evaluate the cytoprotective effects of the aqueous extract of oak (Jaft) against the mucosal damage effects of aspirin.

Seventy-two female Wistar rats weighing 200‒240 g were prepared and divided into 6 groups. Groups I, II, and III received the Jaft extract at doses of 250 mg/kg/d, 500 mg/kg/d, and 750 mg/kg/d, respectively, and Group IV received omeprazole at a dose of 200 mg/kg/d. Groups V (patient’s control) and VI (healthy control) both received saline (0.9%) at 1 mL/kg/d . Mice were given aspirin (200 mg/kg/d) for inducing the gastric ulcer. After 14 days, they were anesthetized with ether, their stomachs were removed, and the blocks of tissue were prepared. The tissues were stained using the hematoxylin-eosin (H&E) dye and analyzed by the Olympus light microscope and OLYSIA software. Finally, IBM SPSS 26.0 software was used for statistical analysis of the data.

The means ± standard deviation (SD) of the mucosal thickness in groups taking the Jaft extracts (250, 500, and 700 mg) decreased compared to the aspirin group (P < 0.05). In addition, the mean ± SD of the mucosal gland thickness and mucosal folds in group V (with aspirin) decreased compared to the 250, 500, and 700 extract groups (P > 0.05). The epithelial cell destruction, edema, venous congestion, and destruction of the capillaries of the mucosal and sub-mucosal areas in the extract groups decreased compared to the V group. Likewise, the number of mucosal cells in the gastric gland and the size of gastric parietal cells in the extract groups decreased compared to the V group.

This study generally elucidated the cytoprotective effect of the Jaft extract in the mouse model of gastric ulcer.

One of the most important and practical issues in today's world is weather forecasting. Forecasting the weather condition reduces the losses and damages of critical weather conditions. Weather forecasting can be effective in applications such as agriculture and air transportation. One of the methods that can be used to learn and predict the weather is deep learning networks, including convolutional neural networks. One of the important challenges of the convolutional neural network is that it performs feature selection unintelligent by using a number of convolution operations. In this article, a collective intelligence method is presented to improve the accuracy of forecasting weather conditions by convolutional neural network. The evaluations using the data sets related to weather conditions show that the proposed method, which has an accuracy and sensitivity of 96.32% and 96.14%, respectively, compared to the CNN deep learning network, has been able to achieve prediction accuracy of about increase by 8.35%.

Gamma rays are widely used in medicine despite their harmful effects on health. Our study evaluated the protective effects of melatonin on kidney, heart, and liver tissues.

Seventy-two adult male Wistar rats were categorized into nine groups. Groups 2 and 3 only received whole-body γ-ray irradiation (WBI) (2.0 Gy), no melatonin,  interval time (IT) 8hr and 24hr. Groups 4 and 5 received WBI (8.0 Gy, no melatonin, 8hr and 24hr). Groups 6 and 7 received melatonin at 60 minutes (min) before WBI(2.0 Gy). Groups 8 and 9 received melatonin before WBI(8.0 Gy). All the rats were sacrificed 8 or 24 h after the experiments for laboratory and histopathological analysis. Serum levels of Blood Urea Nitrogen(BUN), Creatinine (Cr), Lactate dehydrogenase (LDH), Potassium (K), Gamma-glutamyl Transferase (GGT), Serum Glutamic-Oxaloacetic Transaminase(SGOT), Serum Glutamic-Pyruvic Transaminase(SGPT), Alkaline Phosphatase(ALP), c-reactive protein(CRP), Troponine(TPO)  and histological features of liver, heart and kidney tissues were assessed. Statistical analysis was performed by One-Way ANOVA.

Our data did not indicate significant differences in BUN, Cr, K, and CRP between groups with or without melatonin treatment (P>0.05); but differences were significant for LDH, GGT, SGOT, SGPT, ALP, and TPO (P<0.05). The results showed that radiation-induced histopathological effects on the liver, heart, and kidneys were mitigated in the groups six to nine.

The existence of significant differences in serum levels of LDH, GGT, SGOT, SGPT, ALP, and TPO in groups(6 to 9) and amelioration of the histopathological effects of irradiation on the liver, heart, and kidneys in the groups six to nine showed that the melatonin(100 mg/kg)  is able to protect the body in gamma-radiation(2.0 Gy and 8.0 Gy).

Biological aerosol particles smaller than 10 microns in diameter are among the health concerns in hospitals since they remain in the air for a long time and are infectious and easily transported. We aimed to investigate the concentration of Escherichia coli and Staphylococcus aureus bioaerosols and evaluate their risk in the indoor environment of different wards of Khatam al-Anbia hospital, Jask, Iran, in 2020.

This descriptive-analytical study was performed by collecting and analyzing 50 samples from seven different hospital wards. The active sampling of bioaerosols was performed according to the standard method of the National Organization for Occupational Health and Safety and by a pump with a flow rate of 28.3 L/min for 10 minutes. Blood agar and eosin methylene blue were used to detect bacteria. Then, the Monte Carlo simulation technique was used to assess the microbial risk.

The concentration of S. aureus in different wards of the hospital was 4.81 to 18.11 CFU/m3 . The lowest and highest concentrations of S. aureus were in the operating room and general emergency wards, respectively, while the highest and lowest concentrations of E. coli were in the inpatient wards (0 CFU/m3 ) and infectious emergency ward (21.22 CFU/m3 ), respectively. The highest and lowest daily risk of S. aureus was observed in the neonatal and general emergency wards (8.03×10-4 and 3.02×10-4), respectively. Moreover, the lowest and highest daily risk of E. coli was found in the neonatal and male inpatient wards (zero and 7.21×10-3), respectively.

In some hospital wards, the concentration and infection risk of E. coli and S. aureus were found to be higher than the acceptable value. Since high concentrations of airborne bacteria can play an important role in producing nosocomial infections in patients and staff, it is necessary for hospital officials to take corrective measures in equipment control, use proper ventilation systems in the wards, and closely monitor the disinfection process.

Diabetics usually suffer from chronic complications, including microvascular and macrovascular disorders. The capillaries supplying the sexual organs affect normal sexual functions. The erectile process has been shown to be impaired in diabetics. Vascular damage in diabetics may be associated with decreased testosterone production, sperm count, testicular weight, and seminiferous tubule diameter. In this study, we investigated the effects of Aloe vera gel extract (from Sistan and Baluchistan, Iran) on the male genital system in streptozocininduced diabetic rats.

A total of 24 male Wistar rats were included in the study. Diabetes was induced in 12 rats by intraperitoneal injection of a single dose of streptozotocin (STZ). The rats were divided into four groups. The first and third groups received saline (NaCl) by gavage daily. The second and fourth groups received Aloe vera gel extract daily by gavage. Treatments were continued for 30 days. At the end of the treatment period, blood samples were taken and serum glucose and testosterone levels were measured by photometric methods. Histological examinations were performed on the prostate and testes.

Mean weight index, serum levels of glucose and testosterone, mean sperm count, germinal epithelium (GE) thickness, and seminiferous tubule diameter improved significantly in the diabetes + Aloe vera group.

We concluded that consumption of Aloe vera gel extract improved sexual complications in diabetic rats.

Acute respiratory infections are caused by a wide range of viruses. Respiratory spread is the main process of disease transmission. We evaluated neutrophil-to-lymphocyte ratio(NLR), platelet-to-lymphocyte ratio (PLR) and monocyte-lymphocyte ratio (MLR) indices.

This descriptive-analytical study was conducted on patients-files between September 2019 and March 2021. 102 patients were evaluated. 26 men and 30 women were included in the acute coronary group, 16 men and 30 women were included in the non-acute coronary group.

The mean age of women (53.0 ± 1.9) was higher than that of men (50.2 ± 2.02) in the acute coronary group. The mean NLR index in coronary men (65 ± 1.47) was higher than non-acute coronary men (54 ± 1.76). It was also higher in coronary women (54 ± 2.78 ± 10) than non-acute coronary women (24 ± 1.97) (p value > 0.05). PLR index of coronary men (85 ± 43.15) was lower than the non-acute men (48.3 ± 48.15). But this index was lower in coronary women (36.3 ± 5.22) than the non-acute women (2.4 ± 2.25) (p value > 0.05). The mean of MLR index in coronary men (0.16 ± 0.025) was less than the coronary women (0.03 ± 0.26) (p value > 0.05). The mean MLR index in non-acute men (0.04 ± 0.22) was lower than the non-acute women (0.02 ± 0.23) (p value > 0.05). The mean MLR in the coronary men (0.025 ± 0.16) was lower than non-acute men (0.04 ± 0.22). This index was higher in coronary women (0.03 ± 0.26) than the non-acut women (0.02 ± 0.23) (p value > 0.05).

The changes in NLR, PLR and MLR indices were in accordance with global studies but were not significant. It may be due to immune responses of patients and laboratory errors (sampling and performing techniques)

Historical documents are always of interest to historians and linguists. Important documents are usually digitized by segmentation and identification methods. Digitization of documents is very important for research on these documents and their protection. This article proposes a general classification and recognition framework for the images of Persian historical documents. First, pre-processing is done on documents by removing noises, removing skew, removing stamps, etc., and the document image becomes a two-level image. In the second step, a method of dividing the text of the document into lines is proposed. In the third stage, a method of dividing lines into sub-words of Persian script is presented and the sub-words of these documents are extracted, then deep networks are used to train frequent sub-words and recognize them, and the results are reported based on different criteria.

Today, one of the widely used fields in artificial intelligence is text mining methods, which due to the expansion of virtual space and the increase in the use of media and social messengers, and on the other hand, the ability of these methods to extract the desired information from a very large volume of Unstructured text files have a special place. for example, one of its applications can be mentioned in spam detection. Nowadays, the presence of spam content in social media is increasing drastically, and therefore spam detection has become critical. Users receive many text messages through social networks. These messages contain malicious links, programs, etc., and it is necessary to identify and control spam texts and emails to improve social media security. There are various techniques for this, among which neural networks have shown more effective results. In this article, an approach based on deep learning using an LSTM neural network and Glove word embedding method is introduced to display text word vectors to detect spam emails. The results of the proposed model have been evaluated using accuracy criteria. This model has shown successful and acceptable performance by achieving 98.39% and 99.49% accuracy on two different data sets.

Adequate amniotic fluid volume is one of the most important aspects of fetal health and Oligohydramnios refers to amniotic fluid volume that is less than expected for gestational age. The aim of this study was to compare the trend of amniotic fluid changes in the two groups of oral and injectable before and after fluid therapy.

In the present study, pregnant women with oligohydramnios in the third trimester referring to Amir Al-Momenin Hospital (Zabol, 2019) were randomly divided into two groups of case. The first group received oral water and the second group was treated with serum therapy. Eventually, all patients underwent ultrasonography and their AFI was recalculated and compared. Data was analyzed using SPSS 20 statistical software.

The results of the study showed that there was no statistically significant difference between the amniotic fluid index before fluid therapy in the oral and injectable (serum therapy) groups (p = 0.426). After fluid therapy, the amniotic fluid index in the oral group was significantly higher than the injected group (p <0.001).Although in both oral and injectable groups, the amniotic fluid index increased significantly after treatment (p <0.001), the increase in the oral group was significantly higher than the injectable group.

The results of this study showed that oral and intravenous fluid therapy improves the amniotic fluid index more than injectable fluid therapy.

Cleft Lip and Palate (CL±CP) are congenital malformations that occur in two forms CL±CP and CL only. The aim of this study was to investigate the prevalence of CL±CP and associated risk factors in a group of newborns in Zabol in 2020.

In the present descriptive-analytical study, we examined all newborns born at Amir Al-Momenin (AS) Hospital in Zabol between April 2018 and the end of March 2019. Eighty-eight births in which this complication occurred were studied.

The prevalence of CL±CP in the total study population was 0.22% or 2.22 newborns per 1000 live births. In the present study, 35.2% had left unilateral CL±CP (n = 31), 31.8% had CL (n = 28), 20.5% had bilateral CL±CP (n = 18), 10.2% (n = 9) had CL±CP unilateral at the right side, CL 1.1% (n = 1) and 1.1% had medial cleft. The most common disorders in boys were CL, CL and unilateral left lip. In girls, CL± unilateral CP were the most common complications. Babies with left unilateral cleft lip and palate had the highest rate of anomalies.

The present study showed that the prevalence of CL±CP in newborns in Sistan region is higher than national level. The prevalence of unilateral CL±CP on the left side and cleft palate is one of the most common types of CL±CP. This anomaly occurred more frequently in males.

The aim of the present study was to investigate the effect of exposure to hookah smoke on the respiratory capacity of employees working in hookah cafes in Bandar Abbas.

A total of 75 employees of hookah cafes and 64 people in the control group were the target population. Participants without a history of smoking, diabetes, and hypertension were included in the study. First, the height and weight of subjects were measured and then a respiratory test was performed by an occupational medicine specialist. At the same time, a checklist was completed, which contained demographic characteristics, history of working in hookah cafes, pulmonary diseases, hypertension, smoking, exercising, and a second job.

The mean age of the case and control groups was found to be 31.41 and 30.73 years, respectively. The mean values of the indices in the case and the control groups were as follows: forced expiratory volume (FEV1): 84.4% and 89.9%, forced vital capacity (FVC): 91.5% and 91.1%, forced expiratory flow 25-75 (FEF25-75): 78.7% and 75.9%, and peak expiratory flow (PEF): 87.2% and 95.2%, respectively. A significant relationship was found between exposure to hookah smoke and the lung capacity of employees working in hookah cafes (P <0.001).

Based on the findings of the study and in order to reduce passive exposure to hookah smoke and its negative consequences, officials should review and apply strict rules on hookah use and monitor and control the air quality inside hookah cafes.

Tobacco consumption is one of the major public health concerns worldwide which can have adverse effects on human health. An awareness of the status of tobacco consumption and identification of its affecting factors constitute the first step in tobacco control programs. The aim of this study was to investigate the prevalence of cigarette and waterpipe consumption among residents in Bandar Abbas in Hormozgan province.

In general, 853 residents of Bandar Abbas were chosen through the cluster sampling method and by referring to their houses and interviewing them. Data were analyzed by the chi-square test, along with the odds ratio (OR) with a confidence interval (CI) of 95%.

The prevalence of cigarette and waterpipe consumption in men and women was 14.8% and 0.9%, as well as 5.6% and 5.4%, respectively. Male gender (OR=18.26; 95% CI: 6.58-50.65), marital status of those married once (OR=2.91; 95% CI: 1.24-6.85), academic education (OR=0.48; 95% CI: 0.25-0.93), father’s diploma and above (OR=0.35; 95% CI: 0.15 – 0.83), mother’s diploma and above (OR=0.36; 95% CI: 0.13-0.99) were associated with cigarette smoking. The other related factors were employee (OR=1.99; 95% CI: 1.20-3.30), self-employed (OR=3.13; 95% CI: 1.87- 5.24), and age (OR=12.57; 95% CI: 2.86-55.24 for age ≥ 50 years). No significant relationship was found between demographic factors and tobacco smoking.

Cigarette and waterpipe smoking is a multidimensional health problem and has a close relationship with different demographic and social factors. Planning for enhancing health and mitigating the damages resulting from tobacco consumption is essential for promoting healthcare.

Nanotechnology is a field of applied knowledge. The use of nanotechnology and silver nanoparticles has been studied in recent years to control bacterial diseases. This study aimed to investigate the antibacterial effect of ceramic filter pads containing silver nanoparticles in the control of E. coli.

The present work was an experimental study. Silver nanoparticles were synthesized by dissolving silver nitrate (AgNO3) in ethanol and reducing silver in an alcoholic medium. Characteristics of filters were evaluated using BET, SEM, XRF, and XRD tests. Ethical considerations were observed in all stages of the study.

The results showed that the removal efficiency of E. coliwas 36.2-66.7% by conventional filters and reached up 99-100% by ceramic filters coated with silver nanoparticles

The results of this study showed that ceramic filter media containing silver nanoparticles have considerable antimicrobial effects. By generalizing the results of this study, these ceramicfilter media can be used in the treatment and filtration of unsafe water and thus control of pathogens in drinking water.

Objective:

 Infertility is affected by various factors. The hookah use can aggravate this problem. This study aims to determine the effect of hookah use on male infertility.

Methods :

This is a cross-sectional study on 40 men with hookah use and 40 non-users who referred to Imam Reza Hospital in Bandar Abbas, Iran in 2019. Data were collected using a researcher-made questionnaire, and semen parameters were analyzed. Independent t-test, Mann-Whitney U test, and Chi-square test were used to assess the relationship of hookah use with infertility-related parameters in abnormal and normal sperms of the two groups.

Results :

There was no significant difference between the two groups in terms of demographic parameters; hookah use was more in young and employed men with academic education. Means of sperm count,  motile sperm, sperms with progressive sperm motility, inactive sperms with progressive motility, and normal-shape sperms in the hookah users were 22±12, 35±10, 6±3, 21±6, 34±8 %, respectively, which were significantly lower than in non-users.

Conclusion:

 Given the adverse effects of hookah use on male fertility, health managers should increase the awareness of people about its effects.

Waterpipe use has grown significantly in recent years. Hookah use has been shown to be associated with various human health problems such as cardiovascular disease, respiratory disease, and cancer. The aim of this review study was to investigate the pollutants in the indoor air of waterpipe cafes. An automated literature search was conducted to identify any relevant studies published up to February 2022. For this search, international digital databases (PubMed, ScienceDirect, and Scopus) were chosen. Multiple keywords were used in the systematic literature review, including "hookah" OR "waterpipe" OR "hubble-bubble" OR "narghile" OR "shisha" AND "café" AND "indoor air." After removing duplicate papers and analyzing titles, abstracts, and full texts, 25 studies remained for analysis and interpretation. Particulate matter, polycyclic aromatic hydrocarbons (PAH), BTEX compounds, carbon monoxide (CO), nicotine, aldehydes, and bioaerosols were found to be the most prevalent and challenging contaminants in the indoor air of waterpipe cafes. Because of the growing popularity of waterpipe use in cafes, particularly among young people and teenagers, and the high concentration of pollutants in this area, as well as the long-term presence of individuals in these areas, it is necessary for officials to adopt restrictive laws in these places.

The aim of this study was to investigate the concentration of potentially toxic elements (arsenic, lead, copper, cadmium and mercury) in black tea imported to southern Iran and to assess the risk of carcinogenic and non-carcinogenic exposure to consumers.

For this purpose, 94 samples of black tea from 15 brands imported from India in 2021 were selected. Heavy metals concentrations were measured using an atomic absorption spectrometer (GBC model SAVANTAA). After determining the concentration of heavy metals in black tea samples, health risk assessment was determined using Montocarlo simulation technique.

According to the results, the average concentrations of heavy metals namely arsenic, lead, copper, cadmium and mercury were 0.03±0.02, 0.02±0.16, 15.67±7.69, 0.02±0.01 and 0.006±0.005 mg/kg, respectively. The hazard quotient (HQ) of the heavy metals arsenic, lead, copper, cadmium and mercury were 1.07×10-2, 6.37×10-3, 3.45×10-5, 2.05×10-2 and 7.19×10-4, respectively.

Therefore, according to the findings, it can be concluded that the concentrations of potentially toxic elements (arsenic, lead, copper, mercury and cadmium) in black tea were consistent with the standard level of Iran and World Health Organization. Additionally, the average carcinogenic risk index for arsenic metal was 4.49×10-6, which is much lower than the acceptable level of carcinogenic risk (10-6). Therefore, the concentrations of five potentially toxic elements in the studied black tea did not show any significant risk for consumers.

Objective :

Among the pollutants in water sources, heavy metals are of great concern due to their non-biodegradability characteristics, their ability to accumulate in the food chain, and their adverse and chronic effects on human health. Therefore, this study aims to assess the health risk of exposure to heavy metals through drinking water in Bandar Abbas, Iran.

Methods:

 This descriptive-analytical study was performed on drinking water samples in Bandar Abbas, Iran in 2020. Forty-two samples were taken from the drinking water distribution networks, the point-of-use water treatment devices, and municipal water treatment centers. Heavy metal concentrations in the samples were measured using the inductively coupled plasma mass spectrometry method. The carcinogenic risk of heavy metals was assessed according to probabilistic risk assessment method and Monte Carlo simulation technique.

Results :

The mean concentrations of arsenic, nickel, lead, and cadmium in drinking water were 1.25, 0.0713, 1.28, and 0.0005 µg/L, respectively. The mean carcinogenic risks of the mentioned metals were 1.23×10-4, 8.89×10-5, 2.07×10-4, and 2.23×10-4, respectively.

Conclusion:

 The average concentrations of heavy metals in drinking water of Bandar Abbas city and their risks, except for arsenic, were lower than the standard level. Although the concentrations and risk levels showed no significant threat for the consumers, more studies are needed especially on the arsenic concentration.

Diagnosing benign and malignant glands in thyroid ultrasound images is considered as a challenging issue. Recently, deep learning techniques have significantly resulted in extracting features from medical images and classifying them. Convolutional networks ignore the hierarchical structure of entities within images and do not pay attention to spatial information as well as the need for a large number of training samples. Capsule networks consist of different hierarchical capsules equivalent to the same layers in the CNN neural network. This study tried to extract textural features using a deep learning model based on a capsule network. Thyroid ultrasound images were given to the capsule network as input data, and finally the features learned in the capsule network were used to teach the Support Vector Machine classifier, in order to diagnose thyroid cancer. Experimental results showed that the proposed method with 98% accuracy has achieved better results compared to convolutional networks.

Microbial contamination of bread can endanger the health of consumers. The aim of this study was to determine the microbial contamination of bread and hands of workers directly supplying bread in selected bakeries in Bandar Abbas.

In this descriptive-analytical study conducted in 2020, 76 bakeries were randomly selected and bread samples were taken using relative stratified sampling method, based on the baking type, and transferred to the food laboratory to measure the microbial load. Also, from each bakery, a bread distributor was selected to investigate the hand microbial contamination. The microbial load of bread samples was measured in a food lLaboratory and the microbial load of workers' hands was measured using an SSP device. Statistical analysis was performed using SPSS software. Mean and standard deviation parameters were used to describe the data and Chi-square test was used to examine the relationship between variables at a significance level of 0.05.

Based on the results, out of a total of 76 samples taken from the workers, 51 samples (80%) were found to be infected with microorganisms. Having a health card, using face masks, and respecting personal hygiene, social distancing, and disinfection protocols had a significant relationship with workers' hand contamination (P-Value<0.05). The results of measuring the microbial load of bread samples showed that all samples were free of microbial contamination.

Despite no microbial contamination in the bread samples, contamination of workers' hands can be a source of microbial contamination for bread. Therefore, it is recommended that health officials consider strict controls over personal hygiene and the health of bakeries.

Following the outbreak of novel coronavirus disease 2019 (COVID-19), there have been debates among scientific communities, officials, and the public about the role of the mask in controlling and preventing this disease. Nonetheless, there are some ambiguities regarding various aspects of the mask which are still important to be addressed, including the type of the mask to be used, the situation in which the mask should be used, the correct way to use the mask, and the effectiveness of the mask in preventing the disease. Accordingly, addressing these ambiguities could change policy trends and public beliefs about the use of mask and help control the disease. To this end, this study discusses the role of the mask in preventing and controlling the COVID-19 pandemic.

Rice contamination with heavy metals is one of the most common cases of environmental pollution. The purpose of this study was to investigate the concentration of heavy metals (arsenic and cadmium) in the most widely consumed rice brands imported to southern Iran and to assess the health risk of exposure to them for consumers.

A total of 103 rice samples were selected from 10 brands imported from India during 2014-2018. Heavy metal concentrations were measured by dry ash method using atomic absorption spectrometer (GBC model SavantAA). After determining the concentration of heavy metals in rice samples, health risk assessment was performed using the Monte-Carlo simulation technique.

The concentrations of arsenic and cadmium were 94.3±34.1 and 11.3±6.5 mg/kg, respectively. The values of non-carcinogenic risk index (hazard quotient) of cadmium and arsenic were 0.017 and 0.489, respectively. The average carcinogenic risk index for arsenic was 1.7E10-4, which is higher than the standard range (10-4 to10-6).

Consumption of imported rice carries a risk of arsenic-induced carcinogenesis. Consumption of contaminated rice with heavy metals, especially arsenic, can pose potential health risks to the consumer population. Therefore, special attention should be paid to contaminated rice and special interventions should be made to reduce arsenic in imported rice.

Due to the pandemic, COVID-19 cases and deaths are increasing unexpectedly and precipitately. More importantly, asymptomatic carriers could infect healthy people through sneezing, coughing, and talking. Therefore, mask usage is required to prevent and control COVID-19.

This study was aimed at examining the knowledge, attitude, and performance levels (KAP) of the Iranian population regarding mask usage to prevent and control the COVID-19 outbreak during a one-month period from early April to early May 2020.

A researcher-made, validated, and reliable questionnaire consisting of 30 questions on knowledge, attitude, and performance regarding mask usage was utilized. Data collection was performed through the online method. To do so, the online questionnaire link was provided for the participants via social networks. The correct and wrong answers were scored 1 and 0 points, respectively. The final score of each dimension was calculated and classified as 0 - 50% (low), 51 - 75% (moderate), and 75% > (good).

A total of 1581 participants took part in the study. Although the participants had good attitude (74%) and performance (70.80%), but only 13.70% of them had good knowledge regarding mask usage. Also, significant differences were found between the participants’ knowledge and higher educational levels (P < 0.001). There were significant associations between the participants’ age, gender, educational level, and job and their knowledge and attitude levels (P < 0.05). Besides, female participants had higher performance scores than did male participants (P < 0.001). The participants with governmental jobs had significantly higher performance than unemployed ones (P < 0.001).

The participants obtained low KAP scores; therefore, it is required to train and inform the society and enhance their KAP levels via social media regarding the importance of mask usage, including proper donning, doffing, cleaning, disinfection, and safely and hygienic disposal procedures of masks to prevent and control COVID-19.

Classification is a machine learning method used to predict a particular sample’s label with the least error. The present study was conducted using label prediction ability with the help of a classifier to create a new feature. Today, there are several feature-extraction methods like principal component analysis (PCA) and independent component analysis (ICA) that are widely used in different fields; however, they all suffer from the high cost of transferring to another space. The purpose of the proposed method was to create a higher distinction between various classes using the new feature such that to make the data in the classes closer to each other. As a result, more differentiation is created between the data of various classes to increase the efficiency of classifiers. Firstly, the suggested labels for the primary data set were determined using one or more classifiers and added to the primary data set as a new feature. The model was created using a new data set. The new feature for training and testing data sets was provided separately. The tests were performed on 20 standard data sets and the results of the proposed method were compared with those of the two methods described in the related studies. The outputs indicated that the proposed method has significantly improved the classification accuracy. In the second part of the tests, the resolution of the new feature was examined according to two criteria, namely Information Gain and Gini Index, to examine the effectiveness of the proposed method. The results showed that the feature obtained in the proposed method has higher Information Gain and lower Gini Index in most cases, as it has less irregularity. To prevent the increase in data dimensions, the feature with the least Information Gain was replaced with the feature extracted with the most Information Gain. The results of this step showed an increase in efficiency as well.