Intensive Care Nurses' Knowledge of Radiation Safety and Their Behaviors Towards Portable Radiological Examinations

Background: Radiological examinations for patients who are hospitalized at intensive care units are usually performed using portable radiography devices. However they may require knowledge and safety precautions of nurses. Objectives: The aim of the study was to investigate ICU nurses’ knowledge of radiation safety and their behaviors towards portable radiological examinations. Materials and Methods: In total, 44 intensive care nurses were recruited for this cross-sectional descriptive study using census sampling during April and May 2014. The study setting was at intensive care units of Shahid Beheshti Hospital of Kashan, Iran. An eleven-item questionnaire and a five-item checklist were used for evaluating nurses’ radiation protection knowledge and behaviors, respectively. An expert panel consisting of ten nursing and radiology faculty members confirmed the content validity of the questionnaire and the checklist. Moreover, a Geiger-Müller counter was used for measuring ionizing radiation during portable radiological examinations. Study data were analyzed using the SPSS software version 13.0. Mean, standard deviation, frequency and one-sample t test were used for description of the data. The level of significance was set at below 0.05. Results: The mean of participants’ radiation protection knowledge was 4.77 ± 1.38. The most prevalent radiation protection behavior of nurses was leaving the intensive care unit during portable radiological examinations. Only 6.8% of nurses stayed at the nursing station during radiological examinations. The highest dose of radiation was 0.11 micro Sievert per hour (μSv/h), which was much lower than the highest permitted level of radiation exposure i.e. 0.25 μSv/h. Conclusions: Portable radiological examinations did not expose healthcare providers to high doses of ionizing radiation. Nurses’ radiation protection knowledge was limited and hence, they require in-service education programs

Energy Efficiency Analysis and Optimization for Virtual-MIMO Systems

Virtual multiple-input-multiple-output (MIMO) systems using multiple antennas at the transmitter and a single antenna at each of the receivers have recently emerged as an alternative to point-to-point MIMO systems. This paper investigates the relationship between energy efficiency (EE) and spectral efficiency (SE) for a virtual-MIMO system that has one destination and one relay using compress-and-forward (CF) cooperation. To capture the cost of cooperation, the power allocation (between the transmitter and the relay) and the bandwidth allocation (between the data and cooperation channels) are studied. This paper derives a tight upper bound for the overall system EE as a function of SE, which exhibits good accuracy for a wide range of SE values. The EE upper bound is used to formulate an EE optimization problem. Given a target SE, the optimal power and bandwidth allocation can be derived such that the overall EE is maximized. Results indicate that the EE performance of virtual-MIMO is sensitive to many factors, including resource-allocation schemes and channel characteristics. When an out-of-band cooperation channel is considered, the performance of virtual-MIMO is close to that of the MIMO case in terms of EE. Considering a shared-band cooperation channel, virtual-MIMO with optimal power and bandwidth allocation is more energy efficient than the noncooperation case under most SE values

Investigation of Mutations of Exon 11-A of BRCA1 Gene in Women with Breast Cancer in the Northwest of Iran

BACKGROUND AND OBJECTIVE: Breast cancer is the most common cancer in women, which is associated with genetic changes such as mutations in carcinogenic genes and tumor suppressor genes. One of the most important tumor suppressor genes involved in breast cancer is the BRCA1 gene. The mutation in this gene is a common occurrence in human breast cancer. The purpose of this study is to investigate the mutations of exon 11-A of BRCA1 gene in women with breast cancer in the northwest of Iran. METHODS: In this descriptive study, blood sample were collected form 40 patients with breast cancer whose cancer was diagnosed before the age of 40 years and the exon 11-A of BRCA1 gene was examined using PCR and direct sequencing methods to detect mutations. Sequencing results were analyzed using Chromas software. FINDING: In the present study, a nonsynonymous mutation was reported as a new mutation of BRCA1 gene for the first time: Ala584Thr mutation was also observed in two samples. The mutations of codon 694 (Ser694Ser) showed a higher incidence (52.5%). Other mutations were observed in codons 693, 356, 486, 550 and 628. CONCLUSION: Based on the results of this study, mutations and polymorphisms of exon 11 of BRCA1 gene were observed for the first time in the northwestern population of Iran. One new case of mutation was observed in exon 11-A of BRCA1 gene

Complex Urban Systems for Sustainability and Health: A structured approach to support the development and implementation of city policies for population and planetary health

Context: The multi-disciplinary and multi-partner CUSSH project (Complex Urban Systems for Sustainability and Health) seeks to support cities to take transformative action towards population and planetary health goals. Rationale: As all cities are complex systems with unique contexts and priorities, our approach is to engage with partner cities in a participatory process to build a shared understanding of relevant systems that will inform the development and implementation of new city policies. Description: Six partner cities were selected to represent larger and smaller cities from across the global spectrum of income: London (UK) and Rennes (France) in Europe, Nairobi and Kisumu in Kenya, and Beijing and Ningbo in China. In each setting we are engaging stakeholders in a broadly similar structured approach that integrates evidence gathering and modelling, participatory engagement framework, and ongoing tracking and evaluation. In addition, we are developing a working theory of change in each setting to explain how and why the chosen policies may work. Achievements: Our city engagement to date has focused on indoor air pollution (Nairobi), green infrastructure (London) and GHG emissions (Rennes), where findings highlighted not only multiple pathways by which policy interventions could affect health, but also potential counter-intuitive influences and tensions, and synergistic opportunities for solving both sustainability and health problems

A system dynamics-based scenario analysis of residential solid waste management in Kisumu, Kenya

The problem of solid waste management presents an issue of increasing importance in many low-income settings, including the progressively urbanised context of Kenya. Kisumu County is one such setting with an estimated 500 t of waste generated per day and with less than half of it regularly collected. The open burning and natural decay of solid waste is an important source of greenhouse gas (GHG) emissions and atmospheric pollutants with adverse health consequences. In this paper, we use system dynamics modelling to investigate the expected impact on GHG and PM_{2.5} emissions of (i) a waste-to-biogas initiative and (ii) a regulatory ban on the open burning of waste in landfill. We use life tables to estimate the impact on mortality of the reduction in PM_{2.5} exposure. Our results indicate that combining these two interventions can generate over 1.1 million tonnes of cumulative savings in GHG emissions by 2035, of which the largest contribution (42%) results from the biogas produced replacing unclean fuels in household cooking. Combining the two interventions is expected to reduce PM_{2.5} emissions from the waste and residential sectors by over 30% compared to our baseline scenario by 2035, resulting in at least around 1150 cumulative life years saved over 2021–2035. The contribution and novelty of this study lies in the quantification of a potential waste-to-biogas scenario and its environmental and health impact in Kisumu for the first time

Applications of different machine learning approaches in prediction of breast cancer diagnosis delay

Background: The increasing rate of breast cancer (BC) incidence and mortality in Iran has turned this disease into a challenge. A delay in diagnosis leads to more advanced stages of BC and a lower chance of survival, which makes this cancer even more fatal. Objectives: The present study was aimed at identifying the predicting factors for delayed BC diagnosis in women in Iran. Methods: In this study, four machine learning methods, including extreme gradient boosting (XGBoost), random forest (RF), neural networks (NNs), and logistic regression (LR), were applied to analyze the data of 630 women with confirmed BC. Also, different statistical methods, including chi-square, p-value, sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC), were utilized in different steps of the survey. Results: Thirty percent of patients had a delayed BC diagnosis. Of all the patients with delayed diagnoses, 88.5% were married, 72.1% had an urban residency, and 84.8% had health insurance. The top three important factors in the RF model were urban residency (12.04), breast disease history (11.58), and other comorbidities (10.72). In the XGBoost, urban residency (17.54), having other comorbidities (17.14), and age at first childbirth (>30) (13.13) were the top factors; in the LR model, having other comorbidities (49.41), older age at first childbirth (82.57), and being nulliparous (44.19) were the top factors. Finally, in the NN, it was found that being married (50.05), having a marriage age above 30 (18.03), and having other breast disease history (15.83) were the main predicting factors for a delayed BC diagnosis. Conclusion: Machine learning techniques suggest that women with an urban residency who got married or had their first child at an age older than 30 and those without children are at a higher risk of diagnosis delay. It is necessary to educate them about BC risk factors, symptoms, and self-breast examination to shorten the delay in diagnosis. 2023 Dehdar, Salimifard, Mohammadi, Marzban, Saadatmand, Fararouei and Dianati-Nasab

Applications of different machine learning approaches in prediction of breast cancer diagnosis delay

Background: The increasing rate of breast cancer (BC) incidence and mortality in Iran has turned this disease into a challenge. A delay in diagnosis leads to more advanced stages of BC and a lower chance of survival, which makes this cancer even more fatal. Objectives: The present study was aimed at identifying the predicting factors for delayed BC diagnosis in women in Iran. Methods: In this study, four machine learning methods, including extreme gradient boosting (XGBoost), random forest (RF), neural networks (NNs), and logistic regression (LR), were applied to analyze the data of 630 women with confirmed BC. Also, different statistical methods, including chi-square, p-value, sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC), were utilized in different steps of the survey. Results: Thirty percent of patients had a delayed BC diagnosis. Of all the patients with delayed diagnoses, 88.5% were married, 72.1% had an urban residency, and 84.8% had health insurance. The top three important factors in the RF model were urban residency (12.04), breast disease history (11.58), and other comorbidities (10.72). In the XGBoost, urban residency (17.54), having other comorbidities (17.14), and age at first childbirth (>30) (13.13) were the top factors; in the LR model, having other comorbidities (49.41), older age at first childbirth (82.57), and being nulliparous (44.19) were the top factors. Finally, in the NN, it was found that being married (50.05), having a marriage age above 30 (18.03), and having other breast disease history (15.83) were the main predicting factors for a delayed BC diagnosis. Conclusion: Machine learning techniques suggest that women with an urban residency who got married or had their first child at an age older than 30 and those without children are at a higher risk of diagnosis delay. It is necessary to educate them about BC risk factors, symptoms, and self-breast examination to shorten the delay in diagnosis. Copyright © 2023 Dehdar, Salimifard, Mohammadi, Marzban, Saadatmand, Fararouei and Dianati-Nasab

The value of serum uric acid as a mortality prediction in critically ill children

Objective: The role of initial serum uric acid on admission in critically ill patients is controversial; we presumed that uric acid level can predict the mortality of the admitted patients to intensive care unit as a simple test. Methods: Totally, 220 consecutively admitted children (96 girls, 124 boys) with mean age 3.5 years, who were at least 24 hours in pediatric intensive care unit (PICU), were enrolled in a prospective cohort study during January 2006 to December 2007. The subsequent PICU admission in the same hospitalization, those who were discharged from the hospital and then re-admitted to the PICU during the observation period, and the patients with chronic renal failure were excluded. Serum uric acid level was measured during the first day of PICU admission. Death or transfer from PICU was considered as final outcome. The statistical analysis was done by using linear regression analysis, ROC curve, Student t-test, and Chisquare. P value less than 0.05 was considered significant. Findings: From 44 patients who had serum uric acid level more than 8 mg/dl, 17 cases died showing with a higher relative risk of 1.88, higher mortality (P8 mg/dl and needed vasopressor was 1.04, and in those under mechanical ventilation 1.33. In patients who scored pediatric risk of mortality of >38 it was 1.4, and in septic cases 4 (P<0.05). Stepwise linear regression analysis showed that mainly the need for mechanical ventilation (P=0.001) and vasopressor had statistically significant correlation with the poor outcome (P=0.001). Conclusion: Uric acid level during the first day of intensive critical care admission is not an independent risk of mortality in PICU. Need for mechanical ventilation or inotropic agents was associated with poor outcome and only higher uric acid level in sepsis played an additive risk factor role. © 2010 by Pediatrics Center of Excellence, Children's Medical Center, Tehran University of Medical Sciences