Deep learning (DL) has shown great potential in conversions between various imaging modalities. Similarly, DL can be applied to synthesize a high-kV computed tomography (CT) image from its corresponding low-kV CT image. This indicates the feasibility of obtaining dual-energy CT (DECT) images without purchasing a DECT scanner. In this study, we investigated whether a low-to-high kV mapping was better than a high-to-low kV mapping. We used a U-Net model to perform conversions between different kV CT images. Moreover, we proposed a double U-Net model to improve the quality of original single-energy CT images. Ninety-eight patients who underwent brain DECT scans were used to train, validate, and test the proposed DL-based model. The results showed that the low-to-high kV conversion was better than the high-to-low kV conversion. In addition, the DL-based DECT images had better signal-to-noise ratios (SNRs) than the true (original) DECT images, but at the expense of a slight loss in spatial resolution. The mean CT number differences between the true and DL-based DECT images were within [Formula see text] 1 HU. No statistically significant difference in CT number measurements was found between the true and DL-based DECT images (p > 0.05). The DL-based DECT images with improved SNR could produce low-noise virtual monoenergetic images. Our preliminary results indicate that DL has the potential to generate brain DECT images using single-energy brain CT images.Lumbar spondylolisthesis (LS) is the anterior shift of one of the lower vertebrae about the subjacent vertebrae. There are several symptoms to define LS, and these symptoms are not detected in the early stages of LS. This leads to disease progress further without being identified. Thus, advanced treatment mechanisms are required to implement for diagnosing LS, which is crucial in terms of early diagnosis, rehabilitation, and treatment planning. Herein, a transfer learning-based CNN model is developed that uses only lumbar X-rays. The model was trained with 1922 images, and 187 images were used for validation. Later, the model was tested with 598 images. During training, the model extracts the region of interests (ROIs) via Yolov3, and then the ROIs are split into training and validation sets. Later, the ROIs are fed into the fine-tuned MobileNet CNN to accomplish the training. However, during testing, the images enter the model, and then they are classified as spondylolisthesis or normal. The end-to-end transfer learning-based CNN model reached the test accuracy of 99%, whereas the test sensitivity was 98% and the test specificity 99%. The performance results are encouraging and state that the model can be used in outpatient clinics where any experts are not present.Although much deep learning research has focused on mammographic detection of breast cancer, relatively little attention has been paid to mammography triage for radiologist review. The purpose of this study was to develop and test DeepCAT, a deep learning system for mammography triage based on suspicion of cancer. Specifically, we evaluate DeepCAT's ability to provide two augmentations to radiologists (1) discarding images unlikely to have cancer from radiologist review and (2) prioritization of images likely to contain cancer.  https://www.selleckchem.com/products/necrosulfonamide.html  We used 1878 2D-mammographic images (CC & MLO) from the Digital Database for Screening Mammography to develop DeepCAT, a deep learning triage system composed of 2 components (1) mammogram classifier cascade and (2) mass detector, which are combined to generate an overall priority score. This priority score is used to order images for radiologist review. Of 595 testing images, DeepCAT recommended low priority for 315 images (53%), of which none contained a malignant mass. In evaluation of prioritizing images according to likelihood of containing cancer, DeepCAT's study ordering required an average of 26 adjacent swaps to obtain perfect review order. Our results suggest that DeepCAT could substantially increase efficiency for breast imagers and effectively triage review of mammograms with malignant masses.
  Oral health has been reported to have an impact on the activities of daily life such as chewing, eating, and laughing, while psychological factors such as depression and loneliness have been reported to affect oral health. Little is known, however, about the association between laughter and oral health in older adults. This study examined the bidirectional association between the frequency of daily laughter and oral health in community-dwelling older Japanese adults.
 
  Our cross-sectional study employed data from the 2013 Japan Gerontological Evaluation Study's self-reported survey, which included 11,239 male and 12,799 female community-dwelling independent individuals aged 65years or older. We defined the oral health status by the number of remaining teeth. The association between the self-reported frequency of laughter (almost every day, 1-5days per week, 1-3days per month, or almost never) and oral health was examined using logistic regression analysis.
 
  The participants with 10 or more teeth were significantly more likely to laugh compared with the edentulous participants, after adjusting for all covariates. Compared with those who almost never laughed, those who laughed 1-5days per week were significantly less likely to be edentulous. After stratifying by sex, similar results were found only in the men for both analyses.
 
  There was a significant bidirectional association between frequency of laughter and oral health that was independent of socioeconomic and lifestyle factors among older adults.
 There was a significant bidirectional association between frequency of laughter and oral health that was independent of socioeconomic and lifestyle factors among older adults.
  The purpose of this study was to determine whether deficiencies of water-soluble vitamin intake predicted health-related quality of life (HRQOL) and the composite end point of all-cause mortality or cardiac- or heart failure (HF)-related hospitalization in HF patients. Patients with HF may be at risk for inadequate consumption of water-soluble vitamins due to poor appetite and dietary sodium restriction. Because water-soluble vitamins are important in metabolic processes, inadequate dietary intake of these vitamins may negatively affect health outcomes.
 
  We consecutively recruited patients with HF from outpatient clinics affiliated with academic medical centers. Patients were referred by providers to investigators who verified their eligibility. Patients with HF completed a four-day food diary to determine dietary deficiencies of water-soluble vitamins and the Minnesota Living with HF questionnaire to assess HRQOL at baseline. Patients were followed to determine an event.
 
  A total of 216 patients were included.