Although innovative and impactful interventions are necessary for the primary prevention of breast cancer, the factors influencing program adoption, implementation, and sustainment are key, yet remain poorly understood. Insufficient attention has been paid to the primary prevention of breast cancer in state and national cancer plans, limiting the impact of evidence-based interventions on population health. This commentary highlights the state of primary prevention of breast cancer and gaps in the current literature.  https://www.selleckchem.com/products/MLN8237.html  As a way to enhance the reach and adoption of cancer prevention policies and programs, the utility of dissemination and implementation (D&I) science is highlighted. Examples of how D&I could be applied to study policies and programs for chronic disease prevention are described, in addition to needs for future research. Through application of D&I science and a strong focus on health equity, a clearer understanding of contextual factors influencing the success of prevention programs will be achieved, ultimately impacting population health.Several pathologies have a direct impact on society, causing public health problems. Pulmonary diseases such as Chronic obstructive pulmonary disease (COPD) are already the third leading cause of death in the world, leaving tuberculosis at ninth with 1.7 million deaths and over 10.4 million new occurrences. The detection of lung regions in images is a classic medical challenge. Studies show that computational methods contribute significantly to the medical diagnosis of lung pathologies by Computerized Tomography (CT), as well as through Internet of Things (IoT) methods based in the context on the health of things. The present work proposes a new model based on IoT for classification and segmentation of pulmonary CT images, applying the transfer learning technique in deep learning methods combined with Parzen's probability density. The proposed model uses an Application Programming Interface (API) based on the Internet of Medical Things to classify lung images. The approach was very effective, with results above 98% accuracy for classification in pulmonary images. Then the model proceeds to the lung segmentation stage using the Mask R-CNN network to create a pulmonary map and use fine-tuning to find the pulmonary borders on the CT image. The experiment was a success, the proposed method performed better than other works in the literature, reaching high segmentation metrics values such as accuracy of 98.34%. Besides reaching 5.43 s in segmentation time and overcoming other transfer learning models, our methodology stands out among the others because it is fully automatic. The proposed approach has simplified the segmentation process using transfer learning. It has introduced a faster and more effective method for better-performing lung segmentation, making our model fully automatic and robust.The interventional cardiac magnetic resonance imaging (iCMR) catheterization procedure is feasible and safe for children and adults with pulmonary hypertension and congenital heart defects (CHD). With iCMR, the calculation of pulmonary vascular resistance (PVR) in children with complex CHD with multilevel shunt lesions is accurate. In this paper, we describe the role of the MRI-guided right-sided cardiac catheterization procedure to accurately estimate PVR in the setting of multiple shunt lesions (ventricular septal defect and patent ductus arteriosus) and to address the clinical question of operability in an adolescent with trisomy 21 and severe pulmonary hypertension.The aim of this work was the examination of biological activity of three selected racemic cis-β-aryl-δ-iodo-γ-lactones. Tested iodolactones differed in the structure of the aromatic fragment of molecule, bearing isopropyl (1), methyl (2), or no substituent (3) on the para position of the benzene ring. A broad spectrum of biological activity as antimicrobial, antiviral, antitumor, cytotoxic, antioxidant, and hemolytic activity was examined. All iodolactones showed bactericidal activity against Proteus mirabilis, and lactones 1,2 were active against Bacillus cereus. The highest cytotoxic activity towards HeLa and MCF7 cancer cell lines and NHDF normal cell line was found for lactone 1. All assessed lactones significantly disrupted antioxidative/oxidative balance of the NHDF, and the most harmful effect was determined by lactone 1. Contrary to lactone 1, lactones 2 and 3 did not induce the hemolysis of erythrocytes after 48 h of incubation. The differences in activity of iodolactones 1-3 in biological tests may be explained by their different impact on physicochemical properties of membrane as the packing order in the hydrophilic area and fluidity of hydrocarbon chains. This was dependent on the presence and type of alkyl substituent. The highest effect on the membrane organization was observed for lactone 1 due to the presence of bulky isopropyl group on the benzene ring.Clostridioides difficile infection (CDI) is an enteric bacterial disease that is increasing in incidence worldwide. Symptoms of CDI range from mild diarrhea to severe life-threatening inflammation of the colon. While antibiotics are standard-of-care treatments for CDI, they are also the biggest risk factor for development of CDI and recurrence. Therefore, novel therapies that successfully treat CDI and protect against recurrence are an unmet clinical need. Screening for novel drug leads is often tested by manual image analysis. The process is slow, tedious and is subject to human error and bias. So far, little work has focused on computer-aided screening for drug leads based on fluorescence images. Here, we propose a novel method to identify characteristic morphological changes in human fibroblast cells exposed to C. difficile toxins based on computer vision algorithms supported by deep learning methods. Classical image processing algorithms for the pre-processing stage are used together with an adjusted pre-trained deep convolutional neural network responsible for cell classification. In this study, we take advantage of transfer learning methodology by examining pre-trained VGG-19, ResNet50, Xception, and DenseNet121 convolutional neural network (CNN) models with adjusted, densely connected classifiers. We compare the obtained results with those of other machine learning algorithms and also visualize and interpret them. The proposed models have been evaluated on a dataset containing 369 images with 6112 cases. DenseNet121 achieved the highest results with a 93.5% accuracy, 92% sensitivity, and 95% specificity, respectively.