RESUMEN
Background: Associations between trace elements and nasopharyngeal carcinoma (NPC) have been speculated but not thoroughly examined. Methods: This study registered a total of 225 newly diagnosed patients with NPC and 225 healthy controls matched by sex and age from three municipal hospitals in Guangdong Province, southern China between 2011 and 2015. Information was collected by questionnaire on the demographic characteristics and other possibly confounding lifestyle factors. Eight trace elements and the level of Epstein-Barr virus (EBV) antibody were measured in casual (spot) serum specimens by inductively coupled plasma-mass spectrometry (ICP-MS) and enzyme-linked immunosorbent assay (ELISA), respectively. Restricted cubic splines and conditional logistic regression were applied to assess the relationship between trace elements and NPC risk through single-and multiple-elements models. Results: Serum levels of chromium (Cr), cobalt (Co), nickel (Ni), arsenic (As), strontium (Sr) and molybdenum (Mo) were not associated with NPC risk. Manganese (Mn) and cadmium (Cd) were positively associated with NPC risk in both single-and multiple-element models, with ORs of the highest tertile compared with the reference categories 3.90 (95% CI, 1.27 to 7.34) for Mn and 2.30 (95% CI, 1.26 to 3.38) for Cd. Restricted cubic splines showed that there was a linear increasing trend between Mn and NPC risk, while for Cd there was a J-type correlation. Conclusion: Serum levels of Cd and Mn was positively related with NPC risk. Prospective researches on the associations of the two trace elements with NPC ought to be taken into account within the future.
RESUMEN
The computer-aided diagnosis (CAD) system can provide a reference basis for the clinical diagnosis of skin diseases. Convolutional neural networks (CNNs) can not only extract visual elements such as colors and shapes but also semantic features. As such they have made great improvements in many tasks of dermoscopy images. The imaging of dermoscopy has no principal orientation, indicating that there are a large number of skin lesion rotations in the datasets. However, CNNs lack rotation invariance, which is bound to affect the robustness of CNNs against rotations. To tackle this issue, we propose a rotation meanout (RM) network to extract rotation-invariant features from dermoscopy images. In RM, each set of rotated feature maps corresponds to a set of outputs of the weight-sharing convolutions and they are fused using meanout strategy to obtain the final feature maps. Through theoretical derivation, the proposed RM network is rotation-equivariant and can extract rotation-invariant features when followed by the global average pooling (GAP) operation. The extracted rotation-invariant features can better represent the original data in classification and retrieval tasks for dermoscopy images. The RM is a general operation, which does not change the network structure or increase any parameters, and can be flexibly embedded in any part of CNNs. Extensive experiments are conducted on a dermoscopy image dataset. The results show that our method outperforms other anti-rotation methods and achieves great improvements in skin disease classification and retrieval tasks, indicating the potential of rotation invariance in the field of dermoscopy images.
