Background Previous studies have explored the potential on radiomics features of primary breast cancer tumor to identify axillary lymph node (ALN) metastasis. However, the value of deep learning (DL) to identify ALN metastasis remains unclear. Purpose To investigate the potential of the proposed attention‐based DL model for the preoperative differentiation of ALN metastasis in breast cancer on dynamic contrast‐enhanced MRI (DCE‐MRI). Study Type Retrospective. Population A total of 941 breast cancer patients who underwent DCE‐MRI before surgery were included in the training (742 patients), internal test (83 patients), and external test (116 patients) cohorts. Field Strength/Sequence A 3.0 T MR scanner, DCE‐MRI sequence. Assessment A DL model containing a 3D deep residual network (ResNet) architecture and a convolutional block attention module, named RCNet, was proposed for ALN metastasis identification. Three RCNet models were established based on the tumor, ALN, and combined tumor‐ALN regions on the images. The performance of these models was compared with ResNet models, radiomics models, the Memorial Sloan‐Kettering Cancer Center (MSKCC) model, and three radiologists (W.L., H.S., and F. L.). Statistical Tests Dice similarity coefficient for breast tumor and ALN segmentation. Accuracy, sensitivity, specificity, intercorrelation and intracorrelation coefficients, area under the curve (AUC), and Delong test for ALN classification. Results The optimal RCNet model, that is, RCNet−tumor+ALN, achieved an AUC of 0.907, an accuracy of 0.831, a sensitivity of 0.824, and a specificity of 0.837 in the internal test cohort, as well as an AUC of 0.852, an accuracy of 0.828, a sensitivity of 0.792, and a specificity of 0.853 in the external test cohort. Additionally, with the assistance of RCNet−tumor+ALN, the radiologists' performance was improved (external test cohort, P < 0.05). Data Conclusion DCE‐MRI‐based RCNet model could provide a noninvasive auxiliary tool to identify ALN metastasis preoperatively in breast cancer, which may assist radiologists in conducting more accurate evaluation of ALN status. Evidence Level 3 Technical Efficacy Stage 2

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ObjectiveWe aim to establish and validate computed tomography (CT)-based radiomics model for predicting TP53 status in patients with laryngeal squamous cell carcinoma (LSCC).MethodsWe divided all patients into a training set 1 (n=66) and a testing set 1 (n=30) to establish and validate radiomics model to predict TP53. Radiomics features were selected by analysis of variance (ANOVA) and the least absolute shrinkage and selection operator (Lasso) regression analysis. Five radiomics models were established by using K-Nearest Neighbor, logistics regressive, linear-support vector machine (SVM), gaussian-SVM, and polynomial-SVM in training set 1. We also divided all patients into a training set 2 and a testing set 2 according to different CT equipment to establish and evaluate the stability of the radiomics models.ResultsAfter ANOVA and subsequent Lasso regression analysis, 22 radiomics features were selected to build the radiomics model in training set 1. The radiomics model based on linear-SVM has the best predictive performance of the five models, and the area under the receiver operating characteristic curve in training set 1 and testing set 1 were 0.831(95% confidence interval [CI] 0.692–0.970) and 0.797(95% CI 0.632–0.957) respectively. The specificity, sensitivity, and accuracy were 0.971(95% CI 0.834–0.999), 0.714(95% CI 0.535–0.848), and 0.843(95% CI 0.657–0.928) in training set 1 and 0.750(95% CI 0.500–0.938), 0.786(95% CI 0.571–1.000), and 0.667(95% CI 0.467–0.720) in testing set 1, respectively. In addition, the radiomics model also achieved stable prediction results even in different CT equipment. Decision curve analysis showed that the radiomics model for predicting TP53 status could benefit LSCC patients.ConclusionWe developed and validated a relatively optimal radiomics model for TP53 status prediction by trying five different machine learning methods in patients with LSCC. It shown great potential of radiomics features for predicting TP53 status preoperatively and guiding clinical treatment.

What is the central question of this study? We investigated the effects of oestrogen and Cimicifuga racemosa on the stellate ganglion, cardiac noradrenaline pathway and Ca -calmodulin-dependent protein kinase II in ovariectomized rats. What is the main finding and its importance? The right stellate ganglion, but not the left, may be associated with decreased left ventricular noradrenaline content in ovariectomized rats. Oestrogen can reverse all changes caused by ovariectomy. Cimicifuga racemosa did not affect left ventricular noradrenaline, but decreased protein expression of β -adrenergic receptor and Ca -calmodulin-dependent protein kinase II. The results might explain potential effects of C. racemosa on the cardiovascular system and provide new insights into cardiovascular protection. The aim of this study was to investigate the effects of oestrogen and Cimicifuga racemosa on the stellate ganglion, cardiac noradrenaline (NA) pathway and Ca -calmodulin-dependent protein kinase II (CaMK II). Forty adult female Sprague-Dawley rats were randomly divided into the following four groups: sham operated (SHAM); ovariectomized (OVX); ovariectomized with oestrogen treatment (E2); and ovariectomized with C. racemosa treatment (iCR). After 4 weeks of treatment, the NA content was determined by high-performance liquid chromatography, and dopamine β-hydroxylase (DBH) and noradrenaline transporter (NET) expression were detected by immunohistochemistry. Western blotting was used to determine NET, β -adrenergic receptor (β -AR) and CaMK II expression. Compared with the SHAM group, body weights, DBH and NET expression in the right stellate ganglia, and NET, β -AR and CaMK II expression in the left ventricles of the OVX group were increased, whereas left ventricular NA content was decreased; DBH and NET expression in the left stellate ganglion was not significantly different. The indexes of the E2 group were similar to those of the SHAM group. Moreover, in the iCR group, DBH, NET, β -AR and CaMK II expression was decreased; NET expression and NA content of the left ventricle remained unchanged. Our conclusions are as follows. First, the right stellate ganglion, but not the left, may be associated with decreased left ventricular NA content in OVX rats. Second, oestrogen increases the left ventricular NA content and adjusts the expression of DBH and NET in the right stellate ganglion and restores β -AR and CaMK II protein expression to normal levels. Third, C. racemosa does not affect left ventricular NA, but decreases the protein expression of β -AR and CaMK II.

BackgroundThe use of an interbody fusion device (cage) to assist fusion and increase intervertebral stability is widely supported. We applied the morselized impacted bone graft method without using a cage in a single level interbody fusion with encouraging medium-term clinical results. The purpose of this paper is to compare the clinical and radiological results of local bone grafts with a cage to morselized impacted bone grafts without cage, in patients undergoing transforaminal lumbar interbody fusion (TLIF) surgery.MethodsOne hundred eighty-nine consecutive patients who underwent TLIF in our hospital were evaluated from July 2009 to July 2012. Eighty-four patients received TLIF and local bone graft with one polyetheretherketone (PEEK) cage, while 96 patients received the TLIF with local morselized impacted bone grafts without a cage. The clinical data and perioperative parameters of the patients in the two groups were recorded and compared.ResultsThe mean follow-up time was 35 months. There were no significant differences in operation time and blood loss between the two groups. Single-level fusion was performed in all patients. There were no statistically significant differences between the two groups, according to the preoperative or postoperative Oswestry Disability Index (ODI) score. No statistically significant differences in fusion rate were observed between the two groups. At the final follow-up, the ratio of the disc height to vertebral height (HR) was not significantly different between the two groups.ConclusionMorselized impacted bone graft is as beneficial as local bone grafts with a cage for TLIF. Since the no cage procedure is less expensive, the morselized impacted bone graft is an affordable choice for single level TLIF, especially in less developed regions.

We herein present the case report of a 83-year-old female patient who had undergone right colon resection for adenocarcinoma 2 years earlier, and developed osteolytic lesions of the right femur 6 months ago. A roentgenogram of the right thigh, technetium-99m phosphate bone scintigraphy and combined 18F-fluoro-2-deoxy-D-glucose positron emission tomography/computed tomography imaging were performed, and the results revealed multiple osteolytic lesions in the humerus bilaterally, the right scapula and the right femur. The lesions were suspected to be colon cancer metastases. To improve the quality of life of the patient, palliative surgery was performed. However, the intraoperative biopsy of the focal lesions and immunohistochemical evaluation revealed multiple myeloma (MM). Chemotherapy was administered 2 weeks after surgery and the patient recovered uneventfully. The manifestations of MM and bone metastases are occasionally similar. Although the coexistence of the two diseases is rare, both conditions should be considered in the differential diagnosis of osteolytic lesions.

A novel nonsense mutation was found in a LQTS family. The mutated transcript was subjected to NMD mechanism according to the NMD rule. NMD might contribute to the mild phenotype presented in the pore surrounding mutation carriers.

Functional connectivity (FC) network based on resting-state functional magnetic resonance imaging (rs-fMRI) has become an important tool to explore and understand the brain, which can provide objective basis for the diagnosis of neurodegenerative diseases, such as autism spectrum disorder (ASD). However, most functional connectivity (FC) networks only consider the unilateral features of nodes or edges, and the interaction between them is ignored. In fact, their integration can provide more comprehensive and crucial information in the diagnosis. To address this issue, a new multi-view brain network feature enhancement method based on self-attention mechanism graph convolutional network (SA-GCN) is proposed in this article, which can enhance node features through the connection relationship among different nodes, and then extract deep-seated and more discriminative features. Specifically, we first plug the pooling operation of self-attention mechanism into graph convolutional network (GCN), which can consider the node features and topology of graph network at the same time and then capture more discriminative features. In addition, the sample size is augmented by a “sliding window” strategy, which is beneficial to avoid overfitting and enhance the generalization ability. Furthermore, to fully explore the complex connection relationship among brain regions, we constructed the low-order functional graph network (Lo-FGN) and the high-order functional graph network (Ho-FGN) and enhance the features of the two functional graph networks (FGNs) based on SA-GCN. The experimental results on benchmark datasets show that: (1) SA-GCN can play a role in feature enhancement and can effectively extract more discriminative features, and (2) the integration of Lo-FGN and Ho-FGN can achieve the best ASD classification accuracy (79.9%), which reveals the information complementarity between them.