As a co-receptor for vascular endothelial growth factor (VEGF), Neuropilin-1 (NRP-1) plays an important role in angiogenesis and malignant progression of many human cancers. However, the role of NRP-1 in hepatocellular carcinoma (HCC) is not well understood. The study aimed to detected the expression of Neuropilin-1 in HCC and investigate the association between its expression and the clinicopathological characteristics and prognosis of HCC. Quantitative real-time PCR (qRT-PCR), Western blot, Immunofluorescence and immunohistochemistry (IHC) analyses were performed to characterize the expression of NRP-1 in HCC cell lines and tissues. The association of NRP-1 expression with the clinicopathological characteristics and the prognosis was subsequently assessed. qRT-PCR and Western blot assays revealed that the expression of NRP-1 in HCC was significantly increased relative to that of normal live cells and tissues (P < 0.05,and <0.001, respectively). In addition, high expression of NRP-1 was significantly associated with intrahepatic metastasis (P = 0.036), Edmondson grade (P = 0.007), TNM classification (P = 0.0031), and portal vein invasion (P = 0.004). Furthermore, the HCC patients with high NRP-1 expression had shorter overall survival (OS), and recurrence-free survival (RFS), whereas, patients with low NRP-1 expression had better OS and RFS (P = 0.0035, and 0.0048, respectively). These data indicate that NRP-1 expression may play an important role in the progression of HCC, and that high NRP-1 expression suggests unfavorable clinicopathological characteristics and survival in HCC patients.
Neuropilin-1 (NRP-1) is a nontyrosine kinase coreceptor for semaphorin 3A and the vascular endothelial growth factor involved in tumor angiogenesis, growth, and metastasis and is regarded as a promising target for cancer therapy. In the present study, we investigated the effects of an anti-NRP-1 monoclonal antibody (mAb) that we generated for MCF7 breast cancer cellular adhesion studies. MTT, colony formation, and adhesion assays showed that our anti-NRP-1 mAb dose-dependently inhibited MCF7 proliferation and fibronectin adhesion, leading to a rounded cellular morphology. Further, rhodamine phalloidin stain revealed that fibronectin-dependent formation of actin stress fibers was inhibited by anti-NRP-1 mAb. Immunoprecipitation and western blot showed that anti-NRP-1 mAb treatment inhibited the formation of NRP-1-α5β1 integrin complexes and suppressed the phosphorylation of focal adhesion kinase and p130cas in MCF7 cells. These findings contribute to further understanding the NRP-1 function in cell adhesion and tumor metastasis. Moreover, our anti-NRP-1 mAb is a prospective drug candidate for tumor treatment.
Purpose Epilepsy is one of the most disabling neurological disorders, which affects all age groups and often results in severe consequences. Since misdiagnoses are common, many pediatric patients fail to receive the correct treatment. Recently, 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) imaging has been used for the evaluation of pediatric epilepsy. However, the epileptic focus is very difficult to be identified by visual assessment since it may present either hypo- or hyper-metabolic abnormality with unclear boundary. This study aimed to develop a novel symmetricity-driven deep learning framework of PET imaging for the identification of epileptic foci in pediatric patients with temporal lobe epilepsy (TLE). Methods We retrospectively included 201 pediatric patients with TLE and 24 age-matched controls who underwent 18F-FDG PET-CT studies. 18F-FDG PET images were quantitatively investigated using 386 symmetricity features, and a pair-of-cube (PoC)-based Siamese convolutional neural network (CNN) was proposed for precise localization of epileptic focus, and then metabolic abnormality level of the predicted focus was calculated automatically by asymmetric index (AI). Performances of the proposed framework were compared with visual assessment, statistical parametric mapping (SPM) software, and Jensen-Shannon divergence-based logistic regression (JS-LR) analysis. Results The proposed deep learning framework could detect the epileptic foci accurately with the dice coefficient of 0.51, which was significantly higher than that of SPM (0.24, P < 0.01) and significantly (or marginally) higher than that of visual assessment (0.31–0.44, P = 0.005–0.27). The area under the curve (AUC) of the PoC classification was higher than that of the JS-LR (0.93 vs. 0.72). The metabolic level detection accuracy of the proposed method was significantly higher than that of visual assessment blinded or unblinded to clinical information (90% vs. 56% or 68%, P < 0.01). Conclusion The proposed deep learning framework for 18F-FDG PET imaging could identify epileptic foci accurately and efficiently, which might be applied as a computer-assisted approach for the future diagnosis of epilepsy patients. Trial registration NCT04169581. Registered November 13, 2019 Public site: https://clinicaltrials.gov/ct2/show/NCT04169581
Purpose Positron emission tomography (PET) with the first and only tau targeting radiotracer of 18F-flortaucipir approved by FDA has been increasingly used in depicting tau pathology deposition and distribution in patients with cognitive impairment. The goal of this international consensus is to help nuclear medicine practitioners procedurally perform 18F-flortaucipir PET imaging. Method A multidisciplinary task group formed by experts from various countries discussed and approved the consensus for 18F-flortaucipir PET imaging in Alzheimer’s disease (AD), focusing on clinical scenarios, patient preparation, and administered activities, as well as image acquisition, processing, interpretation, and reporting. Conclusion This international consensus and practice guideline will help to promote the standardized use of 18F-flortaucipir PET in patients with AD. It will become an international standard for this purpose in clinical practice.
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