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Qinghao Liu,* Weiwen Zhao,* Hao Yin Department of Orthopedic, The First Affiliated Hospital of Hunan Normal University Hunan Provincial People’s Hospital, Changsha, China Mainland (PRC)*These authors contributed equally to this workCorrespondence: Hao Yin, Email ehour@hunnu.edu.cnBackground: Spontaneous spinal epidural hematoma (SSEH) presenting in the context of JAK2 V617F-positive myeloproliferative neoplasms is a rare condition, characterized by the compression of the spinal cord leading to various symptoms. The etiology, pathogenesis, and optimal treatment strategies for this condition remain undetermined. The occurrence of spontaneous spinal epidural hematoma (SSEH) in the context of JAK2 V617F-positive myeloproliferative neoplasms (MPNs) represents a rare manifestation. Magnetic Resonance Imaging (MRI) plays a crucial role in the definitive diagnosis of this condition. With a good understanding of the pathogenic characteristics and clinical presentations of this disease, a diagnosis can be reasonably made, even in the absence of MRI, based on physical examinations indicating the affected area. Once diagnosed, immediate surgery is recommended to attempt the restoration of spinal cord function. Postoperatively, the use of hydroxyurea has proven effective in disease control.Case Presentation: We report a case of a 65-year-old male patient who presented with progressive lumbar back pain and bilateral lower limb paralysis lasting for 36 hours. CT imaging revealed an intraspinal lesion at the L1-3 level, and genetic testing confirmed the presence of the JAK2V617F mutation. Following surgery, there was a significant recovery of sensory and motor function in the lower limbs. At one-year follow-up, the patient demonstrated good functional status, and blood tests indicated a platelet count within the normal range.Conclusion: The presented case adds to the existing literature on SSEH by highlighting the association with myeloproliferative neopla

Jiahe Hu,1,* Jialan Shi,2,3 Yingqian Gao,1 Wei Yang,1 Ping Liu,1 Qinghao Liu,1 Fei He,4 Chunxu Wang,2 Tao Li,2 Rui Xie,1 Jiuxin Zhu,5 Piaoping Yang4,* 1Department of Digestive Internal Medicine, Harbin Medical University Cancer Hospital, Harbin 150081, People’s Republic of China; 2Department of Hematology, The First Affiliated Hospital, Harbin Medical University, Harbin 150001, People’s Republic of China; 3Department of Surgery, VA Boston Healthcare System, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 12132, USA; 4Key Laboratory of Superlight Materials and Surface Technology, Ministry of Education, College of Material Sciences and Chemical Engineering, Harbin Engineering University, Harbin 150001, People’s Republic of China; 5Department of Pharmacology (State-Province Key Laboratories of Biomedicine-Pharmaceutics of China, Key Laboratories of Cardiovascular Medicine Research, Ministry of Education), College of Pharmacy, Harbin Medical University, Harbin 150081, People’s Republic of China*These authors contributed equally to this workCorrespondence: Jiuxin ZhuDepartment of Pharmacology (State-Province Key Laboratories of Biomedicine-Pharmaceutics of China Key Laboratories of Cardiovascular Medicine Research, Ministry of Education), College of Pharmacy, Harbin Medical University, Harbin 150081, People’s Republic of ChinaTel +86 15104557900Email zhujiuxin@hrbmu.edu.cnRui XieDepartment of Digestive Internal Medicine Harbin Medical University Cancer Hospital, 150 Haping Road, Nangang District, Heilongjiang, Harbin 150081, People’s Republic of ChinaTel +86 13845098117Email rxie@hrbmu.edu.cnBackground: It is important to explore effective treatment for liver cancer. Photodynamic therapy (PDT) is a novel technique to treat liver cancer, but its clinical application is obstructed by limited depth of visible light penetration into tissue. The near-infrared (NIR) photosensitizer

Jiahe Hu,1,* Jialan Shi,2,3 Yingqian Gao,1 Wei Yang,1 Ping Liu,1 Qinghao Liu,1 Fei He,4 Chunxu Wang,2 Tao Li,2 Rui Xie,1 Jiuxin Zhu,5 Piaoping Yang4,* 1Department of Digestive Internal Medicine, Harbin Medical University Cancer Hospital, Harbin 150081, People’s Republic of China; 2Department of Hematology, The First Affiliated Hospital, Harbin Medical University, Harbin 150001, People’s Republic of China; 3Department of Surgery, VA Boston Healthcare System, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 12132, USA; 4Key Laboratory of Superlight Materials and Surface Technology, Ministry of Education, College of Material Sciences and Chemical Engineering, Harbin Engineering University, Harbin 150001, People’s Republic of China; 5Department of Pharmacology (State-Province Key Laboratories of Biomedicine-Pharmaceutics of China, Key Laboratories of Cardiovascular Medicine Research, Ministry of Education), College of Pharmacy, Harbin Medical University, Harbin 150081, People’s Republic of China*These authors contributed equally to this workCorrespondence: Jiuxin ZhuDepartment of Pharmacology (State-Province Key Laboratories of Biomedicine-Pharmaceutics of China Key Laboratories of Cardiovascular Medicine Research, Ministry of Education), College of Pharmacy, Harbin Medical University, Harbin 150081, People’s Republic of ChinaTel +86 15104557900Email zhujiuxin@hrbmu.edu.cnRui XieDepartment of Digestive Internal Medicine Harbin Medical University Cancer Hospital, 150 Haping Road, Nangang District, Heilongjiang, Harbin 150081, People’s Republic of ChinaTel +86 13845098117Email rxie@hrbmu.edu.cnBackground: It is important to explore effective treatment for liver cancer. Photodynamic therapy (PDT) is a novel technique to treat liver cancer, but its clinical application is obstructed by limited depth of visible light penetration into tissue. The near-infrared (NIR) photosensitizer

Both accuracy and efficiency are of significant importance to the task of visual object tracking. In recent years, as the surge of deep learning, Deep Convolutional NeuralNetwork (DCNN) becomes a very popular choice among the tracking community. However, due to the high computational complexity, end-to-end visual object trackers can hardly achieve an acceptable inference time and therefore can difficult to be utilized in many real-world applications. In this paper, we revisit a hierarchical deep feature-based visual tracker and found that both the performance and efficiency of the deep tracker are limited by the poor feature quality. Therefore, we propose a feature selection module to select more discriminative features for the trackers. After removing redundant features, our proposed tracker achieves significant improvements in both performance and efficiency. Finally, comparisons with state-of-the-art trackers are provided.