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The concentration of polycyclic aromatic hydrocarbons (PAHs) in the air inside residential houses in Iran along with measuring the amount of 1-OHpyrene metabolite in the urine of the participants in the study was investigated by gas chromatography-mass spectrometry (GC-MS). Demographic characteristics (including age, gender, and body composition), equipment affecting air quality, and wealth index were also investigated. The mean ± standard error (SE) concentration of particulate matter 10 (PM 10 ) and ∑PAHs in the indoor environment was 43.2 ± 1.98 and 1.26 ± 0.15 μg/m 3 , respectively. The highest concentration of PAHs in the indoor environment in the gaseous and particulate phase related to Naphthalene was 1.1 ± 0.16 μg/m 3 and the lowest was 0.01 ± 0. 0.001 μg/m 3 Pyrene, while the most frequent compounds in the gas and particle phase were related to low molecular weight hydrocarbons. 30% of the samples in the indoor environment have BaP levels higher than the standards provided by WHO guidelines. 68% of low molecular weight hydrocarbons were in the gas phase and 73 and 75% of medium and high molecular weight hydrocarbons were in the particle phase. There was a significant relationship between the concentration of some PAH compounds with windows, evaporative coolers, printers, and copiers (p < 0.05). The concentration of PAHs in houses with low economic status was higher than in houses with higher economic status. The average concentration of 1-hydroxypyrene metabolite in the urine of people was 7.10 ± 0.76 μg/L, the concentration of this metabolite was higher in men than in women, and there was a direct relationship between the amount of this metabolite in urine and the amount of some hydrocarbon compounds in the air, PM 10 , visceral fat and body fat. This relationship was significant for age (p = 0.01). The concentration of hydrocarbons in the indoor environment has been above the standard in a significant number of non-smoking indoor environments, and the risk assessment of these compounds can be significant. Also, various factors have influenced the amount of these compounds in the indoor air, and paying attention to them can be effective in reducing these hydrocarbons in the air., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Ltd.)

Background: Emergency Medical Services (EMS) staff often encounter various safety incidents. Work-related factors can lead to unsafe behaviors and safety incidents. This study assessed unsafe behaviors and their relationship with work-related factors among EMS staff., Methods: This descriptive-correlational study used census sampling method to select 284 EMS staff in Ardabil Province, northwest of Iran, from April to June 2023. The data collection tools were demographic and occupational information form, Mearns Unsafe Behavior Scale, Cohen Perceived Stress Scale, Michielsen Fatigue Scale, and Patterson Teamwork Scale. The data were analyzed using the SPSSv-16, descriptive statistics, Pearson correlation, and multiple linear regression., Results: The mean of unsafe behavior, fatigue, perceived stress, non-conflict of teamwork, and conflict of teamwork were 15.80 (± 4.77), 20.57 (± 6.20), 16.10 (± 6.13), 117.89 (± 17.24), and 40.60 (± 9.59), respectively. Multiple linear regression analysis showed that "partner trust and shared mental models (PTSMM)," "physical fatigue," "age," "type of shift," "employment status," and "overtime hours per month" were predictors of general unsafe behavior (P < 0.001) and "mild task conflict (MTC)," "employment status," "partner trust and shared mental models (PTSMM)" were predictors of unsafe behavior under incentives EMS staff (P < 0.001)., Conclusion: The present study showed that some work-related factors were predictors of unsafe behaviors. The negative consequences of unsafe behaviors should be considered, and long-term planning should be done to reduce them. Developing specific guidelines for addressing unsafe behaviors, implementing measures to reduce fatigue, managing overtime hours in the workplace, and Establishing a system where novice staff work with experienced staff during their first year can be beneficial in reducing these behaviors among EMS staff., (© 2024. The Author(s).)

T 2 -weighted magnetic resonance imaging (MRI) and diffusion-weighted imaging (DWI) are essential components of cervical cancer diagnosis. However, combining these channels for the training of deep learning models is challenging due to image misalignment. Here, we propose a novel multi-head framework that uses dilated convolutions and shared residual connections for the separate encoding of multiparametric MRI images. We employ a residual U-Net model as a baseline, and perform a series of architectural experiments to evaluate the tumor segmentation performance based on multiparametric input channels and different feature encoding configurations. All experiments were performed on a cohort of 207 patients with locally advanced cervical cancer. Our proposed multi-head model using separate dilated encoding for T 2 W MRI and combined b1000 DWI and apparent diffusion coefficient (ADC) maps achieved the best median Dice similarity coefficient (DSC) score, 0.823 (confidence interval (CI), 0.595-0.797), outperforming the conventional multi-channel model, DSC 0.788 (95% CI, 0.568-0.776), although the difference was not statistically significant ( p > 0.05). We investigated channel sensitivity using 3D GRAD-CAM and channel dropout, and highlighted the critical importance of T 2 W and ADC channels for accurate tumor segmentation. However, our results showed that b1000 DWI had a minor impact on the overall segmentation performance. We demonstrated that the use of separate dilated feature extractors and independent contextual learning improved the model's ability to reduce the boundary effects and distortion of DWI, leading to improved segmentation performance. Our findings could have significant implications for the development of robust and generalizable models that can extend to other multi-modal segmentation applications.

Handcrafted and deep learning (DL) radiomics are popular techniques used to develop computed tomography (CT) imaging-based artificial intelligence models for COVID-19 research. However, contrast heterogeneity from real-world datasets may impair model performance. Contrast-homogenous datasets present a potential solution. We developed a 3D patch-based cycle-consistent generative adversarial network (cycle-GAN) to synthesize non-contrast images from contrast CTs, as a data homogenization tool. We used a multi-centre dataset of 2078 scans from 1,650 patients with COVID-19. Few studies have previously evaluated GAN-generated images with handcrafted radiomics, DL and human assessment tasks. We evaluated the performance of our cycle-GAN with these three approaches. In a modified Turing-test, human experts identified synthetic vs acquired images, with a false positive rate of 67% and Fleiss' Kappa 0.06, attesting to the photorealism of the synthetic images. However, on testing performance of machine learning classifiers with radiomic features, performance decreased with use of synthetic images. Marked percentage difference was noted in feature values between pre- and post-GAN non-contrast images. With DL classification, deterioration in performance was observed with synthetic images. Our results show that whilst GANs can produce images sufficient to pass human assessment, caution is advised before GAN-synthesized images are used in medical imaging applications., (© 2023. The Author(s).)

Purpose: Low bone mineral density (BMD) including low bone mass and osteoporosis is a bone state that carries the risk of fractures and the consequent burden. Since Iran has an aging population and is considered a high-risk country regarding fracture, the objective of this study was to report the low BMD attributable burden in Iran from 1990 to 2019 at national and subnational levels., Materials and Methods: In this study, the Global Burden of Disease (GBD) study 2019 estimates of exposure value and attributable burden were used. For each risk-outcome pair, following the estimation of relative risk, exposure level, and the Theoretical Minimum Risk Exposure Level (TMREL), the Population Attributable Fractions (PAFs) and attributable burden were computed. The Summary Exposure Value (SEV) index was also computed., Results: Although the age-standardized DALYs and deaths decreased (- 41.0 [95% uncertainty interval: - 45.7 to - 33.2] and - 43.3 [- 48.9 to - 32.5]), attributable all age numbers in Iran increased from 1990 to 2019 (64.3 [50.6 to 89.1] and 66.8 [49.7 to 102.0]). The male gender had a higher low BMD attributed burden in Iran at national and subnational levels except for Tehran. Among low BMD-associated outcomes, motor vehicle road injuries and falls accounted for most of the low BMD-attributed burden in Iran. The SEV for low BMD remained constant from 1990 to 2019 in the country and females had higher SEVs., Conclusion: Low BMD and the associated outcomes has to gain attention in Iran's health system due to an aging population. Hence, timely interventions by health systems and the population at stake might assist in reducing the burden attributed to low BMD., (© 2022. International Osteoporosis Foundation and Bone Health and Osteoporosis Foundation.)

Background: The COVID-19 pandemic has affected all health care systems. During these critical times, radiology personnel and nurses have been heavily involved in the diagnosis and management of patients with COVID-19., Purpose: This study investigates the experiences of radiology personnel about the COVID-19 crisis., Methods: This qualitative content analysis was conducted on seven radiology personnel. In-depth semistructured interviews were used to collect data. Purposive sampling was carried out to select the participants., Findings: The data analysis led to the emergence of six categories, including psychological-emotional reactions, knowledge-related challenges, humaneness, workplace conditions, hopefulness, and support., Conclusion: Learning from the experiences of radiology personnel and nurses during the COVID-19 crisis can help better manage any subsequent health crises., (© 2022 Association for Radiologic & Imaging Nursing. Published by Elsevier Inc. All rights reserved.)

Background: The outbreak of coronavirus disease 2019 (COVID-19) dates back to December 2019 in China. Iran has been among the most prone countries to the virus. The aim of this study was to report demographics, clinical data, and their association with death and CFR., Methods: This observational cohort study was performed from 20th March 2020 to 18th March 2021 in three tertiary educational hospitals in Tehran, Iran. All patients were admitted based on the WHO, CDC, and Iran's National Guidelines. Their information was recorded in their medical files. Multivariable analysis was performed to assess demographics, clinical profile, outcomes of disease, and finding the predictors of death due to COVID-19., Results: Of all 5318 participants, the median age was 60.0 years, and 57.2% of patients were male. The most significant comorbidities were hypertension and diabetes mellitus. Cough, dyspnea, and fever were the most dominant symptoms. Results showed that ICU admission, elderly age, decreased consciousness, low BMI, HTN, IHD, CVA, dialysis, intubation, Alzheimer disease, blood injection, injection of platelets or FFP, and high number of comorbidities were associated with a higher risk of death related to COVID-19. The trend of CFR was increasing (WPC: 1.86) during weeks 25 to 51., Conclusions: Accurate detection of predictors of poor outcomes helps healthcare providers in stratifying patients, based on their risk factors and healthcare requirements to improve their survival chance., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2022 Hamidreza Hatamabadi et al.)

Objective: The coronavirus disease 2019 (COVID-19) outbreak is the most threatening public health challenge in the 21th century, and more than 200 countries are affected. Considering that Iran was one of the first countries influenced by the COVID-19 pandemic, this study aimed to explain the crisis management strategies during the COVID-19 pandemic in Ardabil province., Methods: This study used a qualitative method using content analysis in which 12 health-care managers or decision-makers involved in the management of the COVID-19 crisis were recruited through purposeful sampling. In-depth, semi-structured interviews were used to collect data, which continued until data saturation., Results: Data analysis led to nine categories, including prior preparation for the COVID-19 crisis; challenges and management of workforce shortages; benefiting from the participation of volunteer staff; challenges and strategies for physical space, supplies, and personal protective equipment (PPE); designation of referral centers for COVID-19; protocolized patient transport; benefiting from donations and charity support; management of information about COVID-19; and learning from the prior stages of crisis., Conclusion: This study revealed that, in critical situations, managers use multiple and, to some extent, unique strategies for decision-making and crisis control. Therefore, the health system can use the findings of the current study for proper response to similar crises and training of future managers.

The recent rise of deep learning (DL) and its promising capabilities in capturing non-explicit detail from large datasets have attracted substantial research attention in the field of medical image processing. DL provides grounds for technological development of computer-aided diagnosis and segmentation in radiology and radiation oncology. Amongst the anatomical locations where recent auto-segmentation algorithms have been employed, the pelvis remains one of the most challenging due to large intra- and inter-patient soft-tissue variabilities. This review provides a comprehensive, non-systematic and clinically-oriented overview of 74 DL-based segmentation studies, published between January 2016 and December 2020, for bladder, prostate, cervical and rectal cancers on computed tomography (CT) and magnetic resonance imaging (MRI), highlighting the key findings, challenges and limitations.

Purpose: The novel coronavirus disease 2019 (COVID-19) has imposed a great global burden on public health. As one of the most affected countries, Iran has tackled emerging challenges in the path to overcoming the epidemic, with three peaks of the disease propagation as of February 19, 2020. To flatten the curve of the COVID-19 pandemic, most countries have implemented bundles of intrusive, sometimes extremely stringent non-pharmaceutical interventions (NPIs). In this communication, we have dissected the effectiveness of NPIs and compared the strategies implemented by Iran, Turkey, and South Korea to mitigate the disease's spread., Methods: We searched online databases via PubMed, Web of Knowledge, and Scopus. Titles/abstracts and full-texts were screened by two reviewers and discrepancies were resolved upon discussion., Results: Our results provide insights into five domains: prevention, screening, in-patient and out-patient facilities, governance, and management of diabetes mellitus. Analysis of previous efforts put in place illustrates that by fostering efficient social distancing measures, increasing the capability to perform prompt polymerase chain reaction tests, applying smart contact tracing, and supplying adequate personal protective equipment, Turkey and South Korea have brought the epidemic sub-optimally under control., Conclusion: From the perspective of policymakers, these achievements are of utmost importance given that attaining the aspirational goals in the management of the COVID-19 necessities a suitable adjustment of previous successful strategies. Hence, policymakers should be noticed that a suitable combination of NPIs is necessary to stem the disease's propagation., Competing Interests: Conflicts of interest/Competing interestsThe authors have no relevant financial or non-financial interests to disclose., (© Springer Nature Switzerland AG 2021.)

Background: Computed tomography (CT) and magnetic resonance imaging (MRI) are the mainstay imaging modalities in radiotherapy planning. In MR-Linac treatment, manual annotation of organs-at-risk (OARs) and clinical volumes requires a significant clinician interaction and is a major challenge. Currently, there is a lack of available pre-annotated MRI data for training supervised segmentation algorithms. This study aimed to develop a deep learning (DL)-based framework to synthesize pelvic T 1 -weighted MRI from a pre-existing repository of clinical planning CTs., Methods: MRI synthesis was performed using UNet++ and cycle-consistent generative adversarial network (Cycle-GAN), and the predictions were compared qualitatively and quantitatively against a baseline UNet model using pixel-wise and perceptual loss functions. Additionally, the Cycle-GAN predictions were evaluated through qualitative expert testing (4 radiologists), and a pelvic bone segmentation routine based on a UNet architecture was trained on synthetic MRI using CT-propagated contours and subsequently tested on real pelvic T 1 weighted MRI scans., Results: In our experiments, Cycle-GAN generated sharp images for all pelvic slices whilst UNet and UNet++ predictions suffered from poorer spatial resolution within deformable soft-tissues (e.g. bladder, bowel). Qualitative radiologist assessment showed inter-expert variabilities in the test scores; each of the four radiologists correctly identified images as acquired/synthetic with 67%, 100%, 86% and 94% accuracy. Unsupervised segmentation of pelvic bone on T1-weighted images was successful in a number of test cases., Conclusion: Pelvic MRI synthesis is a challenging task due to the absence of soft-tissue contrast on CT. Our study showed the potential of deep learning models for synthesizing realistic MR images from CT, and transferring cross-domain knowledge which may help to expand training datasets for 21 development of MR-only segmentation models., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Kalantar, Messiou, Winfield, Renn, Latifoltojar, Downey, Sohaib, Lalondrelle, Koh and Blackledge.)

Background: Detection of COVID-19 cases' accuracy is posing a conundrum for scientists, physicians, and policy-makers. As of April 23, 2020, 2.7 million cases have been confirmed, over 190,000 people are dead, and about 750,000 people are reported recovered. Yet, there is no publicly available data on tests that could be missing infections. Complicating matters and furthering anxiety are specific instances of false-negative tests., Methods: We developed a deep learning model to improve accuracy of reported cases and to precisely predict the disease from chest X-ray scans. Our model relied on convolutional neural networks (CNNs) to detect structural abnormalities and disease categorization that were keys to uncovering hidden patterns. To do so, a transfer learning approach was deployed to perform detections from the chest anterior-posterior radiographs of patients. We used publicly available datasets to achieve this., Results: Our results offer very high accuracy (96.3%) and loss (0.151 binary cross-entropy) using the public dataset consisting of patients from different countries worldwide. As the confusion matrix indicates, our model is able to accurately identify true negatives (74) and true positives (32); this deep learning model identified three cases of false-positive and one false-negative finding from the healthy patient scans., Conclusions: Our COVID-19 detection model minimizes manual interaction dependent on radiologists as it automates identification of structural abnormalities in patient's CXRs, and our deep learning model is likely to detect true positives and true negatives and weed out false positive and false negatives with > 96.3% accuracy.