Comparison of knowledge, attitude and practice of Urban and rural households toward iron deficiency anemia in three provinces of Iran

Background: Lack of nutritional knowledge is one of the most important reasons of nutritional problems and consequently improper practice, which can lead to several complications. This study has been designed in order to compare knowledge, attitude and practices of the urban and rural households regarding iron deficiency anemia (IDA) in Boushehr, Golestan and Sistan & Balouchestan provinces in 2004. Methods: The sampling method at household's level in each province was the single-stage cluster sampling with equal size clusters. The necessary data were gathered with a structured questionnaire and via the interviews between the questioners and the eligible people in each household. Comparison of frequency of variables between urban and rural areas were tested by chi square test. Results: A total of 2306 households were selected as overall sample size. In urban areas, people recognized iron food sources better than rural areas. Knowledge level of respondents about vulnerable group for IDA and the favorite attitude of households toward IDA were better in urban areas of Sistan & Blouchestan and Golestan provinces. In Sistan & Balouchestan and Golestan, rural households who drank tea immediately before or after meal was more than urban ones. The majority of pregnant and lactating mothers (except for rural areas of Bushehr) did not take iron supplement regularly. Less than 60 percent of children used iron drop regularly. Conclusion: Knowledge, attitude, and practice levels of households toward IDA were not acceptable. One of the best ways of improving nutritional practice is nutritional education with focus on applying available food resources

Evaluation of post operative analgesic efficacy of intramuscular pethidine, compared to indometacin and diclofenac Na suppositories in unilateral inguinal hernioplasty patients

We compared analgesic effects of intramuscular pethidine to diclofenac sodium and indometacin suppositories. This study is a semiexperimental clinical trial study over 55 patients of 17 to 60 years old who had undergone unilateral inguinal hernioplasty. These patients divided into 3 groups incidentally the first group including 17 patients who received 100 mg indometacin suppository every 8 h to relief postoperative pain. The second group of 18 patients who received 100 mg Diclofenac Na suppository every 8 h and the third group including 20 patients who received 0.5 mg kg-1 body weight pethidine intramuscularly every 8 h and the first dose of each drug started 2 h after termination of operation. The severity of pain was checked by Visual Analogue Scale (VSA) method every 2 h for 24 h. Mean pain severity checked and compared in 6 h intervals. Mean pain severity and standard deviation in the first 24 h were 23±12 for indometacin and 27±12 for pethidine and 31±9 for diclofenac Na groups respectively. There is no meaningful difference in pain relief during the first post op day. We concluded that Indometacin and diclofenac Na suppositories are good substitutes of intramuscular pethidine to relief post op pain during the first post op day

Phenotypic and genotypic evaluation of fluoroquinolone resistance in clinical isolates of Staphylococcus aureus in Tehran

Background: Fluoroquinolones are broad-spectrum antibiotics widely used in the treatment of bacterial infections such as Staphylococcus aureus isolates. Resistance to these antibiotics is increasing. Material/Methods: The occurrence of mutations in the grlA and gyrA loci were evaluated in 69 fluoroquinolone-resistant S. aureus isolates from 2 teaching hospitals of Tehran University of Medical Sciences. Results: Out of the 165 S. aureus isolates, 87 (52.7) were resistant to methicillin and 69 (41.8) were resistant to fluoroquinolone. Fluoroquinolone-resistant S. atoms isolates had a mutation at codon 80 in the grlA gene and different mutational combinations in the gyrA gene. These mutational combinations included 45 isolates at codons 84 and 86,23 isolates at codons 84,86 and 106 and 1 isolate at codons 84, 86 and 90. Fluoroquinolone-resistant S. aureus isolates were clustered into 33 PFGE types. Conclusions: The findings of this study show that the fluoroquinolone-resistant S. aureus strains isolated in the teaching hospitals in Tehran had multiple mutations in the QRDRs region of both grlA and gyrA genes

Reduction–oxidation (redox) system in radiation-induced normal tissue injury: molecular mechanisms and implications in radiation therapeutics

Every year, millions of cancer patients undergo radiation therapy for treating and destroying abnormal cell growths within normal cell environmental conditions. Thus, ionizing radiation can have positive therapeutic effects on cancer cells as well as post-detrimental effects on surrounding normal tissues. Previous studies in the past years have proposed that the reduction and oxidation metabolism in cells changes in response to ionizing radiation and has a key role in radiation toxicity to normal tissue. Free radicals generated from ionizing radiation result in upregulation of cyclooxygenases (COXs), nitric oxide synthase (NOSs), lipoxygenases (LOXs) as well as nicotinamide adenine dinucleotide phosphate oxidase (NADPH oxidase), and their effected changes in mitochondrial functions are markedly noticeable. Each of these enzymes is diversely expressed in multiple cells, tissues and organs in a specific manner. Overproduction of reactive oxygen radicals (ROS), reactive hydroxyl radical (ROH) and reactive nitrogen radicals (RNS) in multiple cellular environments in the affected nucleus, cell membranes, cytosol and mitochondria, and other organelles, can specifically affect the sensitive and modifying enzymes of the redox system and repair proteins that play a pivotal role in both early and late effects of radiation. In recent years, ionizing radiation has been known to affect the redox functions and metabolism of NADPH oxidases (NOXs) as well as having destabilizing and detrimental effects on directly and indirectly affected cells, tissues and organs. More noteworthy, chronic free radical production may continue for years, increasing the risk of carcinogenesis and other oxidative stress-driven degenerative diseases as well as pathologies, in addition to late effect complications of organ fibrosis. Hence, knowledge about the mechanisms of chronic oxidative damage and injury in affected cells, tissues and organs following exposure to ionizing radiation may help in the development of treatment and management strategies of complications associated with radiotherapy (RT) or radiation accident victims. Thus, this medically relevant phenomenon may lead to the discovery of potential antioxidants and inhibitors with promising results in targeting and modulating the ROS/NO-sensitive enzymes in irradiated tissues and organ injury systems

Групповая эргатическая совместимость авиационных операторов в процессе эксплуатации авионики

Рассмотрены проблемы групповой эргатической совместимости авиационных операторов (пилотов, авиадиспетчеров, технического персонала) и использования технических средств для оценки групповой эргатической совместимости операторов как средства повышения авиационной безопасности за счет более тщательного отбора кандидатов для совместной работы в составе лeтных и космических экипажей.Розглянуто питання проблеми групової ергатичної сумісності авіаційних операторів (пілотів, авіадиспетчерів, технічного персоналу) і використання технічних пристроїв для оцінки групової ергатичної сумісності операторів як засобу підвищення авіаційної безпеки за рахунок більш кращого відбору кандидатів для спільної роботи у складі льотних і космічних екіпажів.We issued the problems of group ergatic aircraft operators (pilots, air traffic controllers, technicians) and the use of technical devices to assess the compatibility of the group ergatic operators as means of improving aviation safety through a better selection of candidates to work together as part of flight and space crews. In this research article to better selection of candidates for collaboration in the space flight crews and the authors propose to use modern computers with appropriate software. This computer complex will answer a number of important issues related to ensuring: compatibility Soames, rational distribution of functions between components ergatic systems, proper interaction with the machine operators as well as each other in normal and special situations professional selection, preparation and training of aviation operators

Urban Vegetation Mapping from Aerial Imagery Using Explainable AI (XAI)

Urban vegetation mapping is critical in many applications, i.e., preserving biodiversity, maintaining ecological balance, and minimizing the urban heat island effect. It is still challenging to extract accurate vegetation covers from aerial imagery using traditional classification approaches, because urban vegetation categories have complex spatial structures and similar spectral properties. Deep neural networks (DNNs) have shown a significant improvement in remote sensing image classification outcomes during the last few years. These methods are promising in this domain, yet unreliable for various reasons, such as the use of irrelevant descriptor features in the building of the models and lack of quality in the labeled image. Explainable AI (XAI) can help us gain insight into these limits and, as a result, adjust the training dataset and model as needed. Thus, in this work, we explain how an explanation model called Shapley additive explanations (SHAP) can be utilized for interpreting the output of the DNN model that is designed for classifying vegetation covers. We want to not only produce high-quality vegetation maps, but also rank the input parameters and select appropriate features for classification. Therefore, we test our method on vegetation mapping from aerial imagery based on spectral and textural features. Texture features can help overcome the limitations of poor spectral resolution in aerial imagery for vegetation mapping. The model was capable of obtaining an overall accuracy (OA) of 94.44% for vegetation cover mapping. The conclusions derived from SHAP plots demonstrate the high contribution of features, such as Hue, Brightness, GLCM_Dissimilarity, GLCM_Homogeneity, and GLCM_Mean to the output of the proposed model for vegetation mapping. Therefore, the study indicates that existing vegetation mapping strategies based only on spectral characteristics are insufficient to appropriately classify vegetation covers

Explainable artificial intelligence (XAI) for interpreting the contributing factors feed into the wildfire susceptibility prediction model.

One of the worst environmental catastrophes that endanger the Australian community is wildfire. To lessen potential fire threats, it is helpful to recognize fire occurrence patterns and identify fire susceptibility in wildfire-prone regions. The use of machine learning (ML) algorithms is acknowledged as one of the most well-known methods for addressing non-linear issues like wildfire hazards. It has always been difficult to analyze these multivariate environmental disasters because modeling can be influenced by a variety of sources of uncertainty, including the quantity and quality of training procedures and input variables. Moreover, although ML techniques show promise in this field, they are unstable for a number of reasons, including the usage of irrelevant descriptor characteristics when developing the models. Explainable AI (XAI) can assist us in acquiring insights into these constraints and, consequently, modifying the modeling approach and training data necessary. In this research, we describe how a Shapley additive explanations (SHAP) model can be utilized to interpret the results of a deep learning (DL) model that is developed for wildfire susceptibility prediction. Different contributing factors such as topographical, landcover/vegetation, and meteorological factors are fed into the model and various SHAP plots are used to identify which parameters are impacting the prediction model, their relative importance, and the reasoning behind specific decisions. The findings drawn from SHAP plots show the significant contributions made by factors such as humidity, wind speed, rainfall, elevation, slope, and normalized difference moisture index (NDMI) to the suggested model's output for wildfire susceptibility mapping. We infer that developing an explainable model would aid in comprehending the model's decision to map wildfire susceptibility, pinpoint high-contributing components in the prediction model, and consequently control fire hazards effectively