Acute lower respiratory infection is the leading cause of child death in developing countries. Current strategies to reduce this problem include early detection and appropriate treatment. Better diagnostic and therapeutic strategies are still needed in poor countries. Artificial-intelligence chest X-ray scheme has the potential to become a screening tool for lower respiratory infection in child. Artificial-intelligence chest X-ray schemes for children are rare and limited to a single lung disease. We need a powerful system as a diagnostic tool for most common lung diseases in children. To address this, we present a computer-aided diagnostic scheme for the chest X-ray images of several common pulmonary diseases of children, including bronchiolitis/bronchitis, bronchopneumonia/interstitial pneumonitis, lobar pneumonia, and pneumothorax. The study consists of two main approaches: first, we trained a model based on YOLOv3 architecture for cropping the appropriate location of the lung field automatically. Second, we compared three different methods for multi-classification, included the one-versus-one scheme, the one-versus-all scheme and training a classifier model based on convolutional neural network. Our model demonstrated a good distinguishing ability for these common lung problems in children. Among the three methods, the one-versus-one scheme has the best performance. We could detect whether a chest X-ray image is abnormal with 92.47% accuracy and bronchiolitis/bronchitis, bronchopneumonia, lobar pneumonia, pneumothorax, or normal with 71.94%, 72.19%, 85.42%, 85.71%, and 80.00% accuracy, respectively. In conclusion, we provide a computer-aided diagnostic scheme by deep learning for common pulmonary diseases in children. This scheme is mostly useful as a screening for normal versus most of lower respiratory problems in children. It can also help review the chest X-ray images interpreted by clinicians and may remind possible negligence. This system can be a good diagnostic assistance under limited medical resources.
Introduction: Phthalates are substances that are added to plastic products to increase their plasticity. These substances are released easily into the environment and can act as endocrine disruptors. Epidemiological studies in children have showed inconsistent findings regarding the relationship between prenatal or postnatal exposure to phthalates and the risk of allergic disease. Our hypothesis is that prenatal exposure to phthalates may contribute to the development of allergies in children. Material and methods: The objective of this study was to determine the associations between urinary phthalate metabolite concentrations in pregnant women, maternal atopic diathesis, maternal lifestyle, and cord blood IgE. Pregnant mothers and paired newborns (n = 101) were enrolled from an antenatal clinic. The epidemiologic data and the clinical information were collected using standard questionnaires and medical records. The maternal blood and urine samples were collected at 24–28 weeks gestation, and cord blood IgE, IL-12p70, IL-4, and IL-10 levels were determined from the newborns at birth. The link between phthalates and maternal IgE was also assessed. To investigate the effects of phthalates on neonatal immunity, cord blood mononuclear cells (MNCs) were used for cytokine induction in another in vitro experiment. Results: We found that maternal urine monoethyl phthalate (MEP) (a metabolite of di-ethyl phthalate (DEP)) concentrations are positively correlated with the cord blood IgE of the corresponding newborns. The cord blood IL-12p70 levels of mothers with higher maternal urine MEP groups (high DEP exposure) were lower than mothers with low DEP exposure. In vitro experiments demonstrated that DEP could enhance IL-4 production of cord blood MNCs rather than adult MNCs. Conclusion: Prenatal DEP exposure is related to neonatal IgE level and alternation of cytokines relevant to Th1/Th2 polarization. This suggests the existence of a link between prenatal exposure to specific plasticizers and the future development of allergies.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.