Reducing The Risk Of Floods In Urban Areas With Combined Inland-River System

With the change of the water environment in accordance with climate change, the loss of lives and properties has increased due to urban flood. Although the importance of urban floods has been highlighted quickly, the construction of advancement technology of an urban drainage system combined with inland-river water and its relevant research has not been emphasized in Korea. In addition, without operation in consideration of combined inland-river water, it is difficult to prevent urban flooding effectively. This study, therefore, develops the uncertainty quantification technology of the risk-based water level and the assessment technology of a flood-risk region through a flooding analysis of the combination of inland-river. The study is also conducted to develop forecast technology of change in the water level of an urban region through the construction of very short-term/short-term flood forecast systems. This study is expected to be able to build an urban flood forecast system which makes it possible to support decision making for systematic disaster prevention which can cope actively with climate change

Editorial: Advances in deep learning methods for medical image analysis

The rapid development of artificial intelligence (AI) technology is leading many innovations in the medical field and is playing a major role in establishing objective, consistent, and efficient medical environments with large-scale data. Deep learning represented by convolutional neural networks has achieved remarkable performance improvement in medical image processing fields such as image segmentation, registration, and enhancement. Furthermore, AI technology with deep learning is pioneering medical applications, such as lesion detection, differential diagnosis, disease prognosis, and surgical planning. More advanced AI technologies, such as transformers with self-attention mechanisms, allowing for learning global dependencies, have been widely applied, which further enhanced the capability of deep learning to analyze medical images. However, despite the remarkable advances in deep learning, many challenges remain. For example, when training data are biased or incomplete, deep learning models may fail to achieve the good generalization capability required to solve real-world problems. In addition, the limitations of deep learning models in interpreting results, and misunderstandings of their intended uses and hypotheses make it difficult for AI to gain trust in healthcare settings. In this regard, disease-specific neural networks, generalized learning methods, high-quality training data, and external evaluation based on testable hypotheses can ensure the reliability of medical AI technologies for humans

Porphyra-334, a mycosporine-like amino acid, attenuates UV-induced apoptosis in HaCaT cells

The main aim of the current research was to study the effect of porphyra-334, one of mycosporine-like amino acids (MAAs), well known as UV-absorbing compounds, on UV-induced apoptosis in human immortalized keratinocyte (HaCaT) cells. Due to their UV-screening capacity and ability to prevent UV-induced DNA damage, MAAs have recently attracted considerable attention in both industry and research in pharmacology. Herein, human HaCaT cells were used to determine the biological activities of porphyra-334 by various in vitro assays, including proliferation, apoptosis and Western blot assays. The proliferation rate of UV-irradiated HaCaT cells was significantly decreased compared to the control group. Pretreatment with porphyra-334 markedly attenuated the inhibitory effect of UV and induced a dramatic decrease in the apoptotic rate. Expression of active caspase-3 protein was increased in response to UV irradiation, while caspase-3 levels were similar between cells treated with porphyra-334 and the non-irradiated control group. Taken together, our data suggest that porphyra-334 inhibits UV-induced apoptosis in HaCaT cells through attenuation of the caspase pathway

Effect of Direct Vertebral Rotation on the Uninstrumented Lumbar Curve in Thoracic Adolescent Idiopathic Scoliosis

Study DesignRetrospective study.PurposeTo determine the effect and direction of direct vertebral rotation (DVR) in the lowest instrumented vertebra (LIV) on the uninstrumented lumbar curve depending on the lumbar modifier used for the correction of thoracic adolescent idiopathic scoliosis.Overview of LiteratureDVR in the LIV should be implemented in a different direction to obtain better spontaneous lumbar correction depending on the preoperative lumbar spine modifier.MethodsWe retrospectively analyzed 160 patients with thoracic adolescent idiopathic scoliosis treated by pedicle screw instrumentation and rod derotation. Patients who had a distal fusion level between T11 and L1 were divided into two groups: the DVR group versus the No-DVR group. Each group was divided into subgroups depending on the lumbar modifier used: the DVR-A, B, and C groups versus the No-DVR-A, B, and C groups. The DVR-A group was subdivided into two subgroups depending on the direction of screw rotation in the LIV: the DVR-A-O group (opposite direction) and the DVR-A-S group (same direction).ResultsThere were no significant differences in the preoperative curve characteristics between the two groups. The preoperative lumbar curve was corrected in 70% of the patients in the DVR group and in 56% in the No-DVR group. Spontaneous coronal correction of the lumbar curve was better in the DVR-A-S group than that in the No-DVR-A group. However, the DVR-A-O group had the higher incidence of adding-on deformity. The DVR-B and C groups showed better spontaneous correction of lumbar coronal magnitude, apical vertebral translation, and rotation and the LIV tilting.ConclusionsIn lumbar modifiers B and C, screws in the LIV have to be rotated opposite to the direction of the screw rotation of the main thoracic curve; however, in modifier A, the screws have to be rotated in the same direction