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Kinesin family member 18B (KIF18B) is a new tumor-associated protein that contributes to the carcinogenesis of multiple malignancies. However, the detailed relevance of KIF18B in breast cancer has not been fully elucidated. This work aimed was to evaluate a possible relationship between KIF18B and breast cancer progression. Our findings show KIF18B is increased in breast cancer and demonstrate that high KIF18B level predicts a reduced survival rate. Cellular functional studies revealed that knockdown of KIF18B markedly reduces the proliferation, invasion, and epithelial-mesenchymal transition of breast cancer cells and enhances their chemosensitivity toward doxorubicin. Further studies showed that KIF18B modulates the level of phospho-Akt, phospho-glycogen synthase kinase-3β, and β-catenin. Notably, suppression of Akt abolished KIF18B-overexpression-induced increases in activation of Wnt/β-catenin pathway. In addition, re-expression of β-catenin reversed KIF18B-silencing-induced cancer-promoting effect. In vivo animal experiments elucidated that knockdown of KIF18B significantly weakened the tumorigenicity of breast cancer cells. Taken together, data of this study illustrate that KIF18B exerts a potential cancer-promoting function in breast cancer via enhancement of Wnt/β-catenin pathway through modulation of the Akt/GSK-3β axis.

Deregulation of v-myc avian myelocytomatosis viral oncogene homolog (MYC) occurs in a broad range of human cancers and often predicts poor prognosis and resistance to therapy. However, directly targeting oncogenic MYC remains unsuccessful, and indirectly inhibiting MYC emerges as a promising approach. Checkpoint kinase 1 (CHK1) is a protein kinase that coordinates the G2/M cell cycle checkpoint and protects cancer cells from excessive replicative stress. Using c-MYC-mediated T-cell acute lymphoblastic leukemia (T-acute lymphoblastic leukemia) and N-MYC-driven neuroblastoma as model systems, we reveal that both c-MYC and N-MYC directly bind to the CHK1 locus and activate its transcription. CHIR-124, a selective CHK1 inhibitor, impairs cell viability and induces remarkable synergistic lethality with mTOR inhibitor rapamycin in MYC-overexpressing cells. Mechanistically, rapamycin inactivates carbamoyl-phosphate synthetase 2, aspartate transcarbamoylase, and dihydroorotase (CAD), the essential enzyme for the first three steps of de novo pyrimidine synthesis, and deteriorates CHIR-124-induced replicative stress. We further demonstrate that dual treatments impede T-acute lymphoblastic leukemia and neuroblastoma progression in vivo. These results suggest simultaneous targeting of CHK1 and mTOR as a novel and powerful co-treatment modality for MYC-mediated tumors.

Purpose To evaluate pix2pix and CycleGAN and to assess the effects of multiple combination strategies on accuracy for patch-based synthetic computed tomography (sCT) generation for magnetic resonance (MR)-only treatment planning in head and neck (HN) cancer patients. Materials and methods Twenty-three deformably registered pairs of CT and mDixon FFE MR datasets from HN cancer patients treated at our institution were retrospectively analyzed to evaluate patch-based sCT accuracy via the pix2pix and CycleGAN models. To test effects of overlapping sCT patches on estimations, we (a) trained the models for three orthogonal views to observe the effects of spatial context, (b) we increased effective set size by using per-epoch data augmentation, and (c) we evaluated the performance of three different approaches for combining overlapping Hounsfield unit (HU) estimations for varied patch overlap parameters. Twelve of twenty-three cases corresponded to a curated dataset previously used for atlas-based sCT generation and were used for training with leave-two-out cross-validation. Eight cases were used for independent testing and included previously unseen image features such as fused vertebrae, a small protruding bone, and tumors large enough to deform normal body contours. We analyzed the impact of MR image preprocessing including histogram standardization and intensity clipping on sCT generation accuracy. Effects of mDixon contrast (in-phase vs water) differences were tested with three additional cases. The sCT generation accuracy was evaluated using mean absolute error (MAE) and mean error (ME) in HU between the plan CT and sCT images. Dosimetric accuracy was evaluated for all clinically relevant structures in the independent testing set and digitally reconstructed radiographs (DRRs) were evaluated with respect to the plan CT images. Results The cross-validated MAEs for the whole-HN region using pix2pix and CycleGAN were 66.9 ± 7.3 vs 82.3 ± 6.4 HU, respectively. On the independent testing set with additional artifacts and previously unseen image features, whole-HN region MAEs were 94.0 ± 10.6 and 102.9 ± 14.7 HU for pix2pix and CycleGAN, respectively. For patients with different tissue contrast (water mDixon MR images), the MAEs increased to 122.1 ± 6.3 and 132.8 ± 5.5 HU for pix2pix and CycleGAN, respectively. Our results suggest that combining overlapping sCT estimations at each voxel reduced both MAE and ME compared to single-view non-overlapping patch results. Absolute percent mean/max dose errors were 2% or less for the PTV and all clinically relevant structures in our independent testing set, including structures with image artifacts. Quantitative DRR comparison between planning CTs and sCTs showed agreement of bony region positions to Conclusions The dosimetric and MAE based accuracy, along with the similarity between DRRs from sCTs, indicate that pix2pix and CycleGAN are promising methods for MR-only treatment planning for HN cancer. Our methods investigated for overlapping patch-based HU estimations also indicate that combining transformation estimations of overlapping patches is a potential method to reduce generation errors while also providing a tool to potentially estimate the MR to CT aleatoric model transformation uncertainty. However, because of small patient sample sizes, further studies are required.

Emerging evidence has indicated that suppression of tumorigenicity 7-like (ST7L) is a tumour suppressor in multiple types of cancers. However, the functional involvement of ST7L has not been studied in breast cancer. In the present study, we aimed to investigate the potential biological function of ST7L in breast cancer. Herein, we found that ST7L expression was frequently downregulated in breast cancer cell lines. Functional assays revealed that ST7L overexpression significantly inhibited the proliferation and invasion of breast cancer cells, while ST7L silencing showed opposite effect. Notably, ST7L was found to decrease glycogen synthase kinase (GSK)-3β phosphorylation and downregulate active β-catenin protein expression, thereby leading to repression of β-catenin transcriptional activity. Activation of Wnt/β-catenin signalling by treatment of GSK-3β inhibitor significantly abrogated ST7L-mediated antitumour effect. Additionally, ST7L overexpression blunted the tumorigenicity of breast cancer cells in vivo in xenograft mice. Taken together, our results demonstrate that ST7L exerts antitumor function in breast cancer associated with the suppression of Wnt/β-catenin signalling, suggesting ST7L as a potential therapeutic target for breast cancer.

China’s first Anti-Domestic Violence Law that took effect in March 2016 is widely praised as progress against the malfunction of public power in the area of domestic violence allegations in an ingrained patriarchal society. However, empirical research points to the first contradiction in judicial practices, namely, the informal and flexible mechanism of judicial mediation that is the norm in domestic violence cases. The victims’ rights and safety are harmed under the political requirement of ‘saving and repairing the marital relationship’ or ‘maintaining social harmony.’ Facing this weakness of the judiciary and public power, Chinese civil society has been increasingly engaging in various forms of anti-domestic violence advocacy and has achieved considerable success inside and outside the courtroom. Nevertheless, in this context, the authorities have escalated repression and suppression of the anti-domestic violence advocacy amid its efforts of further suppressing civil society in general. Based on the analysis of these two contradictions, this article argues that the apparent contradictions reflect the inherent coherence of deploying law as an instrument serving for political ends in an authoritarian context. Alongside the intensifying tension in these two contradictions is the further setback to reform and the shrinking of the space for civil society activism.

BackgroundEndoscopic carpal tunnel release (ECTR) has been gradually adopted for the treatment of severe carpal tunnel syndrome (CTS). However, perioperative assessment of neuroanatomical parameters of median nerve, which are important determinant of median nerve recovery, has rarely been reported. This one-year prospective study aimed to investigate the natural history of the neuroanatomical morphology of the median nerve after ECTR in severe CTS patients by high-frequency ultrasonography and assess the ability of neuroanatomical measures to quantify morphological recovery of the median nerve after ECTR.MethodsThis study recruited 31 patients (44 wrists) with a definitive diagnosis of severe CTS and underwent ECTR operation. The edema length (EL) of median nerve from the inlet of the carpal tunnel to the proximal wrist was detected on long axis imaging plane and the anteroposterior diameter (D) and cross-sectional area (CSA) at the inlet of the carpal tunnel on short axis imaging plane were detected by high frequency ultrasound. All these metrics were detected at 3 days before surgery and at the 2nd week, 4th week, 3rd month, 6th month and 12th month after surgery separately.ResultsThere was no significant difference of each parameter between the 2-week postoperative (1.914 ± 0.598 cm in EL, 0.258 ± 0.039 cm in D and 0.138 ± 0.015 cm2in CSA) and 3-days preoperative time points (P-EL =0.250;P-D = 0.125;P-CSA =0.712). From the fourth week to the third month after surgery, the parameters quickly improved. The EL (0.715 ± 0.209 cm), D (0.225 ± 0.017 cm) and CSA (0.117 ± 0.012 cm2) at the 3- month postoperative time points were more reduced than at the fourth week after surgery (P-EL P-D = 0.038;P-CSA =0.014). Thereafter, the neurological anatomy parameters recovered slowly. By the 12-month postoperative time points, the three parameters were neuroanatomically close to normal. Compared to the control group in D (0.213 ± 0.005 cm), there was no difference at the 12-month time point (0.214 ± 0.009 cm,P = 0.939). However, the difference in EL (0.098 ± 0.030 cm vs. 0.016 ± 0.011 cm) and CSA (0.103 ± 0.008 cm2vs. 0.073 ± 0.005 cm2) between patients and healthy volunteers at the 12-month time point still existed (P-EL P-CSA ConclusionsNeuroanatomical parameters were gradually improved after ECTR surgery. The best time for US follow up is at 3-month postoperative time point for patients who do not show clinical improvement, since at this time the change is the greatest for most CTS patients. This study has been registered in Chinese Clinical Trial Registry: ChiCTR-ROC-17014068 (retrospectively registered 20-12-2017).

Purpose: Radiotherapy presents unique challenges and clinical requirements for longitudinal tumor and organ-at-risk (OAR) prediction during treatment. The challenges include tumor inflammation/edema and radiation-induced changes in organ geometry, whereas the clinical requirements demand flexibility in input/output sequence timepoints to update the predictions on rolling basis and the grounding of all predictions in relationship to the pre-treatment imaging information for response and toxicity assessment in adaptive radiotherapy. Methods: To deal with the aforementioned challenges and to comply with the clinical requirements, we present a novel 3D sequence-to-sequence model based on Convolution Long Short Term Memory (ConvLSTM) that makes use of series of deformation vector fields (DVF) between individual timepoints and reference pre-treatment/planning CTs to predict future anatomical deformations and changes in gross tumor volume as well as critical OARs. High-quality DVF training data is created by employing hyper-parameter optimization on the subset of the training data with DICE coefficient and mutual information metric. We validated our model on two radiotherapy datasets: a publicly available head-and-neck dataset (28 patients with manually contoured pre-, mid-, and post-treatment CTs), and an internal non-small cell lung cancer dataset (63 patients with manually contoured planning CT and 6 weekly CBCTs). Results: The use of DVF representation and skip connections overcomes the blurring issue of ConvLSTM prediction with the traditional image representation. The mean and standard deviation of DICE for predictions of lung GTV at week 4, 5, and 6 were 0.83$\pm$0.09, 0.82$\pm$0.08, and 0.81$\pm$0.10, respectively, and for post-treatment ipsilateral and contralateral parotids, were 0.81$\pm$0.06 and 0.85$\pm$0.02., Medical Physics 2021, Saad Nadeem and Yu-Chi Hu contributed equally

We propose a novel one-stage object detection network, called adaptively dense feature pyramid network (ADFPNet), to detect objects cross various scales. The proposed network is developed on single shot multibox detector (SSD) framework with a new proposed ADFP module, which is consisted of two components: a dense multi scales and receptive fields block (DMSRB) and an adaptively feature calibration block (AFCB). Specifically, DMSRB block extracts rich semantic information in a dense way through atrous convolutions with different atrous rates to extract dense features in multi scales and receptive fields; the AFCB block calibrate the dense features to retain features contributing more and depress features contributing less. The extensive experiments have been conducted on VOC 2007, VOC 2012, and MS COCO dataset to evaluate our method. In particular, we achieve the new state of the art accuracy with the mAP of 82.5 on VOC 2007 test set and the mAP of 36.4 on COCO test-dev set using a simple VGG-16 backbone. When testing with a lower resolution (300 × 300), we achieve an mAP of 81.1 on VOC 2007 test set with an FPS of 62.5 on an NVIDIA 1080ti GPU, which meets the requirement for real-time detection.

Despite the widespread availability of in-treatment room cone beam computed tomography (CBCT) imaging, due to the lack of reliable segmentation methods, CBCT is only used for gross set up corrections in lung radiotherapies. Accurate and reliable auto-segmentation tools could potentiate volumetric response assessment and geometry-guided adaptive radiation therapies. Therefore, we developed a new deep learning CBCT lung tumor segmentation method. Methods: The key idea of our approach called cross modality educed distillation (CMEDL) is to use magnetic resonance imaging (MRI) to guide a CBCT segmentation network training to extract more informative features during training. We accomplish this by training an end-to-end network comprised of unpaired domain adaptation (UDA) and cross-domain segmentation distillation networks (SDN) using unpaired CBCT and MRI datasets. Feature distillation regularizes the student network to extract CBCT features that match the statistical distribution of MRI features extracted by the teacher network and obtain better differentiation of tumor from background.} We also compared against an alternative framework that used UDA with MR segmentation network, whereby segmentation was done on the synthesized pseudo MRI representation. All networks were trained with 216 weekly CBCTs and 82 T2-weighted turbo spin echo MRI acquired from different patient cohorts. Validation was done on 20 weekly CBCTs from patients not used in training. Independent testing was done on 38 weekly CBCTs from patients not used in training or validation. Segmentation accuracy was measured using surface Dice similarity coefficient (SDSC) and Hausdroff distance at 95th percentile (HD95) metrics., The paper has been accepted to Medical Physics

Our contribution is a unified cross-modality feature disentagling approach for multi-domain image translation and multiple organ segmentation. Using CT as the labeled source domain, our approach learns to segment multi-modal (T1-weighted and T2-weighted) MRI having no labeled data. Our approach uses a variational auto-encoder (VAE) to disentangle the image content from style. The VAE constrains the style feature encoding to match a universal prior (Gaussian) that is assumed to span the styles of all the source and target modalities. The extracted image style is converted into a latent style scaling code, which modulates the generator to produce multi-modality images according to the target domain code from the image content features. Finally, we introduce a joint distribution matching discriminator that combines the translated images with task-relevant segmentation probability maps to further constrain and regularize image-to-image (I2I) translations. We performed extensive comparisons to multiple state-of-the-art I2I translation and segmentation methods. Our approach resulted in the lowest average multi-domain image reconstruction error of 1.34 ± 0.04. Our approach produced an average Dice similarity coefficient (DSC) of 0.85 for T1w and 0.90 for T2w MRI for multi-organ segmentation, which was highly comparable to a fully supervised MRI multi-organ segmentation network (DSC of 0.86 for T1w and 0.90 for T2w MRI) .

To develop and evaluate a deep learning method to segment parotid glands from MRI using unannotated MRI and unpaired expert-segmented CT datasets. We introduced a new self-derived organ attention deep learning network for combined CT to MRI image-to-image translation (I2I) and MRI segmentation, all trained as an end-to-end network. The expert segmentations available on CT scans were combined with the I2I translated pseudo MR images to train the MRI segmentation network. Once trained, the MRI segmentation network alone is required. We introduced an organ attention discriminator that constrains the CT to MR generator to synthesize pseudo MR images that preserve organ geometry and appearance statistics as in real MRI. The I2I translation network training was regularized using the organ attention discriminator, global image-matching discriminator, and cycle consistency losses. MRI segmentation training was regularized by using cross-entropy loss. Segmentation performance was compared against multiple domain adaptation-based segmentation methods using the Dice similarity coefficient (DSC) and Hausdorff distance at the 95th percentile (HD95). All networks were trained using 85 unlabeled T2-weighted fat suppressed (T2wFS) MRIs and 96 expert-segmented CT scans. Performance upper-limit was based on fully supervised MRI training done using the 85 T2wFS MRI with expert segmentations. Independent evaluation was performed on 77 MRIs never used in training. The proposed approach achieved the highest accuracy (left parotid: DSC 0.82 ± 0.03, HD95 2.98 ± 1.01 mm; right parotid: 0.81 ± 0.05, HD95 3.14 ± 1.17 mm) compared to other methods. This accuracy was close to the reference fully supervised MRI segmentation (DSC of 0.84 ± 0.04, a HD95 of 2.24 ± 0.77 mm for the left parotid, and a DSC of 0.84 ± 0.06 and HD95 of 2.32 ± 1.37 mm for the right parotid glands).

During radiation therapy (RT) of head and neck (HN) cancer, the shape and volume of the parotid glands (PG) may change significantly, resulting in clinically relevant deviations of delivered dose from the planning dose. Early and accurate longitudinal prediction of PG anatomical changes during the RT can be valuable to inform decisions on plan adaptation. We developed a deep neural network for longitudinal predictions using the displacement fields (DFs) between the planning computed tomography (pCT) and weekly cone beam computed tomography (CBCT). Sixty-three HN patients treated with volumetric modulated arc were retrospectively studied. We calculated DFs between pCT and week 1–3 CBCT by B-spline and Demon deformable image registration (DIR). The resultant DFs were subsequently used as input to our novel network to predict the week 4 to 6 DFs for generating predicted weekly PG contours and weekly dose distributions. For evaluation, we measured dice similarity (DICE), and the uncertainty of accumulated dose. Moreover, we compared the detection accuracies of candidates for adaptive radiotherapy (ART) when the trigger criteria were mean dose difference more than 10%, 7.5%, and 5%, respectively. The DICE of ipsilateral/contralateral PG at week 4 to 6 using the prediction model trained with B-spline were 0.81 ± 0.07/0.81 ± 0.04 (week 4), 0.79 ± 0.06/0.81 ± 0.05 (week 5) and 0.78 ± 0.06/0.82 ± 0.02 (week 6). The DICE with the Demons model were 0.78 ± 0.08/0.82 ± 0.03 (week 4), 0.77 ± 0.07/0.82 ± 0.04 (week 5) and 0.75 ± 0.07/0.82 ± 0.02 (week 6). The dose volume histogram (DVH) analysis with the predicted accumulated dose showed the feasibility of predicting dose uncertainty due to the PG anatomical changes. The AUC of ART candidate detection with our predictive model was over 0.90. In conclusion, the proposed network was able to predict future anatomical changes and dose uncertainty of PGs with clinically acceptable accuracy, and hence can be readily integrated into the ART workflow.

We developed a new joint probabilistic segmentation and image distribution matching generative adversarial network (PSIGAN) for unsupervised domain adaptation (UDA) and multi-organ segmentation from magnetic resonance (MRI) images. Our UDA approach models the co-dependency between images and their segmentation as a joint probability distribution using a new structure discriminator. The structure discriminator computes structure of interest focused adversarial loss by combining the generated pseudo MRI with probabilistic segmentations produced by a simultaneously trained segmentation sub-network. The segmentation sub-network is trained using the pseudo MRI produced by the generator sub-network. This leads to a cyclical optimization of both the generator and segmentation sub-networks that are jointly trained as part of an end-to-end network. Extensive experiments and comparisons against multiple state-of-the-art methods were done on four different MRI sequences totalling 257 scans for generating multi-organ and tumor segmentation. The experiments included, (a) 20 T1-weighted (T1w) in-phase mdixon and (b) 20 T2-weighted (T2w) abdominal MRI for segmenting liver, spleen, left and right kidneys, (c) 162 T2-weighted fat suppressed head and neck MRI (T2wFS) for parotid gland segmentation, and (d) 75 T2w MRI for lung tumor segmentation. Our method achieved an overall average DSC of 0.87 on T1w and 0.90 on T2w for the abdominal organs, 0.82 on T2wFS for the parotid glands, and 0.77 on T2w MRI for lung tumors., Comment: This paper has been accepted by IEEE Transactions on Medical Imaging

Background and purpose: Magnetic resonance (MR) only radiation therapy for prostate treatment provides superior contrast for defining targets and organs-at-risk (OARs). This study aims to develop a deep learning model to leverage this advantage to automate the contouring process. Materials and methods: Six structures (bladder, rectum, urethra, penile bulb, rectal spacer, prostate and seminal vesicles) were contoured and reviewed by a radiation oncologist on axial T2-weighted MR image sets from 50 patients, which constituted expert delineations. The data was split into a 40/10 training and validation set to train a two-dimensional fully convolutional neural network, DeepLabV3+, using transfer learning. The T2-weighted image sets were pre-processed to 2D false color images to leverage pre-trained (from natural images) convolutional layers’ weights. Independent testing was performed on an additional 50 patient’s MR scans. Performance comparison was done against a U-Net deep learning method. Algorithms were evaluated using volumetric Dice similarity coefficient (VDSC) and surface Dice similarity coefficient (SDSC). Results: When comparing VDSC, DeepLabV3+ significantly outperformed U-Net for all structures except urethra (P

Background B-mode ultrasound is one of the most commonly used imaging techniques for evaluating thyroid nodules due to its noninvasive property and excellent performance in terms of discriminating between benign and malignant nodules. However, the accuracy of differential diagnosis strongly depends on the experience of ultrasonographers. In addition to B-mode ultrasound, the elastic mode and contrast-enhanced mode have shown complimentary value in the diagnosis of thyroid nodules. The combination of multiple modes in ultrasonic techniques may effectively undermine diagnostic subjectiveness and improve accuracy. In this study, we evaluated the diagnostic value of combining the three ultrasonic modes for differentiating thyroid cancers. Methods In this retrospective study, we analyzed a total of 196 thyroid nodules with suspected malignancies from 185 patients who gave informed consent. Xi’an Jiaotong University granted ethical approval (No. 2018200) to carry out the study within its facilities. All the patients received ultrasonic examinations with the B mode, elastic mode and contrast-enhanced mode, followed by histopathological confirmation by fine-need aspiration or surgery. A predictive multivariate logistic regression model was selected to integrate the variety of data obtained from the three modes. Results The combination of three ultrasonic techniques for differentiating malignant from benign thyroid nodules showed the highest diagnostic accuracy of 0.985 compared to the B mode alone (0.841) and the two-mode combination. The accuracy of the B mode combined with the elastic technique was 0.954, and the accuracy of the B mode combined with the contrast-enhanced technique was 0.960. Discussion Multimode ultrasonic techniques should be recommended to patients with suspected malignant thyroid nodules in routine clinical practice.

OBJECTIVES To investigate the feasibility of using 2-dimensional ultrasound (US) and shear wave elastography (SWE) to evaluate infants with late-referral congenital muscular torticollis (CMT). METHODS A total of 46 infants with late-referral CMT were enrolled and divided into 4 groups according to the degree of the passive range of motion (PROM) deficit in neck rotation. We introduced 6 metrics to represent the US features of the sternocleidomastoid muscle detected by 2-dimensional US and SWE studies. RESULTS There were no significant differences in the age at referral, sex, weight, or side of involvement between the 4 groups (P > .05). The thickness and shear modulus of the involved sternocleidomastoid muscle were positively correlated with the degree of the PROM deficit in neck rotation in late-referral infants with CMT (r = 0.71 and 0.82, respectively; P

A major rise in the prevalence and influence of colorectal cancer (CRC) leads to substantially increasing healthcare costs and even death. It is widely accepted that early detection and removal of colonic polyps can prevent CRC. Detection of colonic polyps in colonoscopy videos is problematic because of complex environment of colon and various shapes of polyps. Currently, researchers indicate feasibility of Convolutional Neural Network (CNN)-based detection of polyps but better feature extractors are needed to improve detection performance. In this paper, we investigated the potential of the single shot detector (SSD) framework for detecting polyps in colonoscopy videos. SSD is a one-stage method, which uses a feed-forward CNN to produce a collection of fixed-size bounding boxes for each object from different feature maps. Three different feature extractors, including ResNet50, VGG16, and InceptionV3 were assessed. Multi-scale feature maps integrated into SSD were designed for ResNet50 and InceptionV3, respectively. We validated this method on the 2015 MICCAI polyp detection challenge datasets, compared it with teams attended the challenge, YOLOV3 and two-stage method, Faster-RCNN. Our results demonstrated that the proposed method surpassed all the teams in MICCAI challenge and YOLOV3 and was comparable with two-stage method. Especially in detection speed aspect, our proposed method outperformed all the methods, met real-time application requirement. Meanwhile, we also indicated that among all the feature extractors, InceptionV3 obtained the best result of precision and recall. In conclusion, SSD- based method achieved excellent detection performance in polyp detection and can potentially improve diagnostic accuracy and efficiency.

Volumetric lung tumor segmentation and accurate longitudinal tracking of tumor volume changes from computed tomography images are essential for monitoring tumor response to therapy. Hence, we developed two multiple resolution residually connected network (MRRN) formulations called incremental-MRRN and dense-MRRN. Our networks simultaneously combine features across multiple image resolution and feature levels through residual connections to detect and segment the lung tumors. We evaluated our method on a total of 1210 non-small cell (NSCLC) lung tumors and nodules from three data sets consisting of 377 tumors from the open-source Cancer Imaging Archive (TCIA), 304 advanced stage NSCLC treated with anti- PD-1 checkpoint immunotherapy from internal institution MSKCC data set, and 529 lung nodules from the Lung Image Database Consortium (LIDC). The algorithm was trained using 377 tumors from the TCIA data set and validated on the MSKCC and tested on LIDC data sets. The segmentation accuracy compared to expert delineations was evaluated by computing the dice similarity coefficient, Hausdorff distances, sensitivity, and precision metrics. Our best performing incremental-MRRN method produced the highest DSC of 0.74 ± 0.13 for TCIA, 0.75±0.12 for MSKCC, and 0.68±0.23 for the LIDC data sets. There was no significant difference in the estimations of volumetric tumor changes computed using the incremental-MRRN method compared with the expert segmentation. In summary, we have developed a multi-scale CNN approach for volumetrically segmenting lung tumors which enables accurate, automated identification of and serial measurement of tumor volumes in the lung.

PURPOSE Breast cancer is the most common cancer among women worldwide. Medical ultrasound imaging is one of the widely applied breast imaging methods for breast tumors. Automatic breast ultrasound (BUS) image segmentation can measure the size of tumors objectively. However, various ultrasound artifacts hinder segmentation. We proposed an attention-supervised full-resolution residual network (ASFRRN) to segment tumors from BUS images. METHODS In the proposed method, Global Attention Upsample (GAU) and deep supervision were introduced into a full-resolution residual network (FRRN), where GAU learns to merge features at different levels with attention for deep supervision. Two datasets were employed for evaluation. One (Dataset A) consisted of 163 BUS images with tumors (53 malignant and 110 benign) from UDIAT Centre Diagnostic, and the other (Dataset B) included 980 BUS images with tumors (595 malignant and 385 benign) from the Sun Yat-sen University Cancer Center. The tumors from both datasets were manually segmented by medical doctors. For evaluation, the Dice coefficient (Dice), Jaccard similarity coefficient (JSC), and F1 score were calculated. RESULTS For Dataset A, the proposed method achieved higher Dice (84.3 ± 10.0%), JSC (75.2 ± 10.7%), and F1 score (84.3 ± 10.0%) than the previous best method: FRRN. For Dataset B, the proposed method also achieved higher Dice (90.7 ± 13.0%), JSC (83.7 ± 14.8%), and F1 score (90.7 ± 13.0%) than the previous best methods: DeepLabv3 and dual attention network (DANet). For Dataset A + B, the proposed method achieved higher Dice (90.5 ± 13.1%), JSC (83.3 ± 14.8%), and F1 score (90.5 ± 13.1%) than the previous best method: DeepLabv3. Additionally, the parameter number of ASFRRN was only 10.6 M, which is less than those of DANet (71.4 M) and DeepLabv3 (41.3 M). CONCLUSIONS We proposed ASFRRN, which combined with FRRN, attention mechanism, and deep supervision to segment tumors from BUS images. It achieved high segmentation accuracy with a reduced parameter number.

MicroRNAs (miRNAs) contribute to tumorigenesis and progression via acting as tumor suppressors or oncogenes in human cancer. Aberrant expression of miR-302c has been reported in various types of cancer except colorectal cancer (CRC). Thus, our study was aimed to verify the expression of miR-302c and its functional role in CRC. We found a significant reduced expression of miR-302c in CRC tissues compared to tumor-adjacent tissues. Low miR-302c level was remarkably correlated with deeper tumor invasion, lymph node metastasis and advanced TNM stage. Importantly, low miR-302c expression was identified as an independent indicator for poor prognosis of CRC patients. Overexpression of miR-302c repressed migration and invasion capacities of SW620 and SW480 cells in vitro. Mechanistically, miR-302c inversely regulated transcription factor AP4 (TFAP4) abundance in both SW620 and SW480 cells, and it negatively correlated with TFAP4 mRNA expression in CRC samples. Herein, TFAP4, a regulator of epithelial-mesenchymal transition (EMT), was recognized as a direct target gene of miR-302c in CRC. Otherwise, miR-302c overexpression increased E-cadherin expression and reduced the levels of Vimentin and SNAI1, suggesting an inhibitory effect of miR-302c on EMT of CRC cells. Notably, our findings established that the EMT and metastasis of Caco-2 cells were enhanced by miR-302c knockdown, and subsequently reversed by TFAP4 silencing. Collectively, these data indicate that miR-302c represses EMT and CRC metastasis possibly by targeting TFAP4, and it may serve as a potential prognostic factor and therapeutic target for CRC.

The aim of this study was to explore the value of transcutaneous laryngeal ultrasonography in the diagnosis of vocal fold polyps. From December 2016 to June 2019, 87 patients with vocal fold polyps diagnosed pathologically in the Otolaryngology Head and Neck Surgery Department of the Second Affiliated Hospital of Xi'an Jiaotong University were enrolled and examined by electronic laryngoscopy and percutaneous laryngeal ultrasound on the same day before operation. To observe the effect of calcification length as a percentage of thyroid cartilage at the glottic level on vocal fold display, the characteristics of ultrasound images of vocal fold polyps and the value of transcutaneous laryngeal ultrasonography in the diagnosis of vocal fold polyps were assessed. Among 87 patients, the calcification rate of thyroid cartilage at the glottic level was 33.3%. The differences in calcification rate and percentage of calcification length between males and females were statistically significant. The rate of detection of vocal folds decreased gradually with an increase in calcification length percentage. Imaging features of vocal fold polyps were hypo-echoic with a clear boundary and regular shape. The detection rates for circular and non-circular polyps were 92.0% and 70.6%. Ultrasound was more likely to detect circular than non-circular polyps; however, the difference was not significant. Transcutaneous laryngeal ultrasonography can identify the morphology and location of vocal fold polyps and is non-invasive and highly accurate. Therefore, it has the potential to be an effective supplement to laryngoscopy for initial screening and post-operative review of vocal fold polyps.

An open-source library of implementations for deep-learning based image segmentation and radiotherapy outcomes models is presented in this work. As oncology treatment planning becomes increasingly driven by automation, such a library of model implementations is crucial to (i) validate existing models on datasets collected at different institutions, (ii) automate segmentation, (iii) create ensembles for improving performance and (iv) incorporate validated models in the clinical workflow. The library was developed with the Computational Environment for Radiological Research (CERR) software platform. Centralizing model implementations in CERR builds upon its rich set of radiotherapy and radiomics tools and caters to the world-wide user base. CERR provides well-validated feature extraction pipelines for radiotherapy dosimetry and radiomics with fine control over the calculation settings, allowing users to select the appropriate parameters used in model derivation. Models for automatic image segmentation are distributed via containers, with seamless i/o to and from CERR. Container technology allow segmentation models to be deployed with a variety of scientific computing architectures. The library includes implementations of popular radiotherapy models outlined in the Quantitative Analysis of Normal Tissue Effects in the Clinic effort and recently published literature. Radiomics models include features from the Image Biomarker Standardization Initiative and application-specific features found to be relevant across multiple sites and image modalities. Deep learning-based image segmentation models include state-of-the-art networks such as DeepLab and other problem-specific architectures. The source code is distributed at https://www.github.com/cerr/CERR under the GNU-GPL copyleft with additional restrictions on clinical and commercial use and provision to dual license in future.

Lipopolysaccharide (LPS) plays an important role in lung endothelial apoptosis which is crucial for lung fibrogenesis in ARDS progression. Reactive oxygen species (ROS) has been reported to be involved in LPS-induced lung epithelial cell apoptosis. Propofol is a commonly used intravenous anesthetic agent in clinic and it could attenuate LPS-induced epithelial cells oxidation and apoptosis. However, the mechanisms are still obscure. In this study, we examined whether and how propofol attenuates LPS-induced oxidation and apoptosis in BEAS-2B cells. Compared with control group, LPS up-regulated Pin-1, phosphatase A2 (PP2A) expression, induced p66Shc-Ser36 phosphorylation, and facilitated p66Shc mitochondrial translocation, thus leading to superoxide anion (O2-) generation, mitochondrial cytochrome c release, active caspase 3 over-expression and cell viability inhibition. Importantly, propofol was shown to down-regulate LPS-induced PP2A expression, limit p66Shc mitochondrial translocation, decrease O2- generation, inhibit mitochondrial cytochrome c release, reduce active caspase 3 expression, and recover cells viability, while propofol had no effects on LPS-induced Pin-1 expression and p66Shc-Ser36 phosphorylation. Moreover, the protective effects of propofol on LPS-induced BEAS-2B cells apoptosis were similar to that of calyculin A, which is an inhibitor of PP2A. We also found that FTY720, which is an activator of PP2A, can effectively reverse the protective function of propofol. Our data illustrated that propofol could alleviate LPS-induced BEAS-2B cells oxidation and apoptosis through down-regulating PP2A expression, limiting p66Shc-Ser36 dephosphorylation and p66Shc mitochondrial translocation, decreasing O2- generation, mitochondrial cytochrome c release, activating caspase 3 expression.

Insulin-like growth factor (IGF)-1 functions as a neuroprotective hormone and may protect against cognitive impairment, which may occur as a result of sevoflurane exposure. The aim of the present study was to assess the effect of sevoflurane on the production of IGF‑1 and investigate the molecular mechanisms underlying this regulation. The BRL rat hepatocyte cell line and adult mice were exposed to 1 or 2 minimal alveolar concentrations sevoflurane for 4 or 8 h. IGF‑1 and microRNA‑98 levels were quantified using an enzyme‑linked immunosorbent assay, western blotting and reverse transcription‑quantitative polymerase chain reaction analyses. The importance of microRNA‑98 in the regulation of IGF‑1 by sevoflurane was investigated using a microRNA‑98 inhibitor. Sevoflurane treatment reduced IGF‑1 levels and simultaneously upregulated microRNA‑98 expression levelsin rat hepatocytes and adult mice. Inhibition of microRNA‑98 attenuated this effect. Therefore, sevoflurane may reduce the synthesis of IGF‑1 by upregulating microRNA‑98 expression.

Forkhead box protein A1 (FOXA1) is a transcription factor; recent studies have reported that FOXA1 has an oncogenic or tumor suppressive role in human malignancies, and its expression is associated with the prognosis of patients with cancer. However, further studies are required to determine the clinical significance of FOXA1 and its role in colorectal cancer (CRC). In the present study, FOXA1 expression was detected in 90 samples of CRC tissues and matched noncancerous tissues using immunohistochemistry. In these cases, FOXA1 expression was detected in 57.8% (52/90) of the CRC samples, whereas only 37.8% (34/90) of the noncancerous specimens exhibited a positive FOXA1 signal. In addition, the present study demonstrated that the mRNA expression levels of FOXA1 were significantly increased in CRC tissues compared with in matched tumor-adjacent tissues. Furthermore, the positive expression of FOXA1 was associated with poor clinicopathological characteristics of CRC, including poor tumor differentiation, large tumor size, lymph node metastases and advanced tumor-node-metastasis tumor stage. Notably, patients with CRC with positive FOXA1 expression exhibited a significantly reduced 5-year survival rate compared with those with negative FOXA1 expression. Multivariate Cox regression analysis revealed that FOXA1 expression was an independent prognostic indicator for patients with CRC. In addition, FOXA1 knockdown evidently inhibited cell proliferation and induced apoptosis in SW480 and HCT116 CRC cells. Notably, FOXA1 knockdown also prominently reduced the expression of yes-associated protein (YAP) in SW480 and HCT116 cells. In conclusion, the results of the present study indicated that FOXA1 may be considered a potential prognostic marker, and may promote tumor growth of CRC by upregulating YAP expression.

Purpose To predict the spatial and temporal trajectories of lung tumor during radiotherapy monitored under a longitudinal magnetic resonance imaging (MRI) study via a deep learning algorithm for facilitating adaptive radiotherapy (ART). Methods We monitored 10 lung cancer patients by acquiring weekly MRI-T2w scans over a course of radiotherapy. Under an ART workflow, we developed a predictive neural network (P-net) to predict the spatial distributions of tumors in the coming weeks utilizing images acquired earlier in the course. The three-step P-net consisted of a convolutional neural network to extract relevant features of the tumor and its environment, followed by a recurrence neural network constructed with gated recurrent units to analyze trajectories of tumor evolution in response to radiotherapy, and finally an attention model to weight the importance of weekly observations and produce the predictions. The performance of P-net was measured with Dice and root mean square surface distance (RMSSD) between the algorithm-predicted and experts-contoured tumors under a leave-one-out scheme. Results Tumor shrinkage was 60% ± 27% (mean ± standard deviation) by the end of radiotherapy across nine patients. Using images from the first three weeks, P-net predicted tumors on future weeks (4, 5, 6) with a Dice and RMSSD of (0.78 ± 0.22, 0.69 ± 0.24, 0.69 ± 0.26), and (2.1 ± 1.1 mm, 2.3 ± 0.8 mm, 2.6 ± 1.4 mm), respectively. Conclusion The proposed deep learning algorithm can capture and predict spatial and temporal patterns of tumor regression in a longitudinal imaging study. It closely follows the clinical workflow, and could facilitate the decision-making of ART. A prospective study including more patients is warranted.

Lack of large expert annotated MR datasets makes training deep learning models difficult. Therefore, a cross-modality (MR-CT) deep learning segmentation approach that augments training data using pseudo MR images produced by transforming expert-segmented CT images was developed. Eighty-One T2-weighted MRI scans from 28 patients with non-small cell lung cancers were analyzed. Cross-modality prior encoding the transformation of CT to pseudo MR images resembling T2w MRI was learned as a generative adversarial deep learning model. This model augmented training data arising from 6 expert-segmented T2w MR patient scans with 377 pseudo MRI from non-small cell lung cancer CT patient scans with obtained from the Cancer Imaging Archive. A two-dimensional Unet implemented with batch normalization was trained to segment the tumors from T2w MRI. This method was benchmarked against (a) standard data augmentation and two state-of-the art cross-modality pseudo MR-based augmentation and (b) two segmentation networks. Segmentation accuracy was computed using Dice similarity coefficient (DSC), Hausdroff distance metrics, and volume ratio. The proposed approach produced the lowest statistical variability in the intensity distribution between pseudo and T2w MR images measured as Kullback-Leibler divergence of 0.069. This method produced the highest segmentation accuracy with a DSC of 0.75 and the lowest Hausdroff distance on the test dataset. This approach produced highly similar estimations of tumor growth as an expert (P = 0.37). A novel deep learning MR segmentation was developed that overcomes the limitation of learning robust models from small datasets by leveraging learned cross-modality priors to augment training. The results show the feasibility of the approach and the corresponding improvement over the state-of-the-art methods., Comment: Submitted to Medical Physics

Sperm-associated antigen 5 (SPAG5) has currently emerged as a novel oncogene in various cancers. High expression of SPAG5 has been frequently detected in breast cancer. However, the biological function and regulatory mechanism of SPAG5 in breast cancer remain unclear. In this study, we aimed to investigate the potential biological function of SPAG5 in breast cancer cells. Herein, we found that both the mRNA and protein expression of SPAG5 were significantly up-regulated in breast cancer cell lines. Functional experiments showed that silencing of SPAG5 inhibited the proliferation and invasion of breast cancer cells, while the overexpression of SPAG5 promoted the proliferation and invasion of breast cancer cells. Mechanistic investigation demonstrated that SPAG5 promoted the expression of Wnt3 and β-catenin, and increased the activation of β-catenin/TCF4 transcriptional activity. Notably, the inhibition of Wnt3 partially reversed the promotion effect of SPAG5 on breast cancer cell proliferation and invasion and β-catenin/TCF4 signalling. In addition, the inhibition of β-catenin also significantly abrogated SPAG5-mediated oncogenic effects in breast cancer. Taken together, these findings demonstrate that SPAG5 promotes the proliferation and invasion of breast cancer cells by activating Wnt/β-catenin signalling via up-regulating Wnt3 expression.

Accumulating evidence has suggested that microRNA-449b-5p (miR-449b-5p) plays an important role in the development and progression of multiple cancers. However, little is known about the role of miR-449b-5p in breast cancer. In this study, we aimed to investigate the expression level, biological function and underlying mechanism of miR-449b-5p in breast cancer. Our results showed that miR-449b-5p expression was frequently down-regulated in breast cancer cell lines and tissues. The overexpression of miR-449b-5p significantly inhibited growth and invasion, and induced the cell cycle arrest of breast cancer cells. In contrast, the inhibition of miR-449b-5p showed the opposite effect. Interestingly, bioinformatic analysis predicted that cell cycle-related and expression-elevated protein in tumor (CREPT), an important oncogene in breast cancer, was a potential target gene of miR-449b-5p. The overexpression of miR-449b-5p decreased CREPT expression while miR-449b-5p inhibition promoted CREPT expression in breast cancer cells. Restoration of CREPT expression in miR-449b-5p mimics transfected cells partially reversed the suppressive effect of miR-449b-5p on breast cancer cell growth and invasion. Notably, our results showed that miR-449b-5p overexpression decreased the expression of β-catenin and suppressed the activation of Wnt/β-catenin/TCF-4 signaling via targeting CREPT. In addition, blocking Wnt/β-catenin partially reversed the promotion effect of miR-449b-5p inhibition on breast cancer cell growth and invasion. Overall, these results reveal a tumor suppressive role of miR-449b-5p that restricts the growth and invasion of breast cancer cells through targeting CREPT and inhibiting CREPT-mediated activation of Wnt/β-catenin signaling. Our study suggests that the miR-449b-5p/CREPT/Wnt/β-catenin axis may play an important role in the pathogenesis of breast cancer and miR-449-5p may serve as a potential therapeutic target for breast cancer.

We present an adversarial domain adaptation based deep learning approach for automatic tumor segmentation from T2-weighted MRI. Our approach is composed of two steps: (i) a tumor-aware unsupervised cross-domain adaptation (CT to MRI), followed by (ii) semi-supervised tumor segmentation using Unet trained with synthesized and limited number of original MRIs. We introduced a novel target specific loss, called tumor-aware loss, for unsupervised cross-domain adaptation that helps to preserve tumors on synthesized MRIs produced from CT images. In comparison, state-of-the art adversarial networks trained without our tumor-aware loss produced MRIs with ill-preserved or missing tumors. All networks were trained using labeled CT images from 377 patients with non-small cell lung cancer obtained from the Cancer Imaging Archive and unlabeled T2w MRIs from a completely unrelated cohort of 6 patients with pre-treatment and 36 on-treatment scans. Next, we combined 6 labeled pre-treatment MRI scans with the synthesized MRIs to boost tumor segmentation accuracy through semi-supervised learning. Semi-supervised training of cycle-GAN produced a segmentation accuracy of 0.66 computed using Dice Score Coefficient (DSC). Our method trained with only synthesized MRIs produced an accuracy of 0.74 while the same method trained in semi-supervised setting produced the best accuracy of 0.80 on test. Our results show that tumor-aware adversarial domain adaptation helps to achieve reasonably accurate cancer segmentation from limited MRI data by leveraging large CT datasets.

Colorectal cancer (CRC) is a highly invasive tumor that is frequently associated with distant metastasis, which is the primary cause of poor prognosis. However, the mechanisms of metastasis remain poorly understood. MicroRNAs (miRNAs/miRs) have been considered to be implicated in CRC progression. In particular, miR-517a is proposed as a novel tumor-associated miRNA and has a potential role in tumor metastasis. The expression of miR-517a in CRC specimens was detected by reverse transcription-quantitative polymerase chain reaction. Transwell assays were performed to determine the migration and invasion of CRC cells. The putative target genes of miR-517a were disclosed using publicly available databases and western blot analysis. The present study identified that the expression of miR-517a was significantly higher in CRC tissues as compared with adjacent non-tumor tissues. Clinical analysis indicated that increased expression of miR-517a was correlated with poor prognostic features and poor long-term survival of CRC patients. In vitro evidences demonstrated that downregulation of miR-517a inhibited cell migration and invasion in HCT-116 cells. By contrast, upregulation of miR-517a increased the number of migrated and invaded SW480 cells. Notably, miR-517a expression was inversely regulated by forkhead box J3 (FOXJ3) abundance in CRC cells. Furthermore, an inverse correlation between miR-517a and FOXJ3 expression was observed in CRC tissues. In conclusion, miR-517a appears to be an independent prognostic marker for predicting survival of CRC patients, and may promote cell migration and invasion by inhibiting FOXJ3.

Purpose To evaluate the discriminatory value of carotid artery wall thickness and elasticity for thyroid dysfunction in Hashimoto's thyroiditis (HT) patients. Methods A total of 180 female HT patients were assigned to three groups on the basis of laboratory testing: HT hyperthyroidism group (group A), HT hypothyroidism group (group B), and HT euthyroid group (group C). We used radiofrequency sonographic signal analysis for the measurement of intima-media thickness and arterial stiffness. Results Intima-media thickness was significantly higher in group A than in other groups. After stepwise variable selection, distensibility coefficient (DC), compliance coefficient, stiffness index (β), and pulse wave velocity (PWV) were selected to discriminate different thyroid hormone levels, resulting in the following discriminant function: Z1 = −0.398DC + 0.803PWV. The receiver-operator characteristic curve analysis showed that the critical points were DC = 0.022 kPa−1 and PWV = 5.36 m/s for HT hyperthyroidism, and DC = 0.016 kPa−1 and PWV = 6.875 m/s for HT hypothyroidism. Conclusions DC and PWV may be useful for the evaluation of thyroid function in HT patients. © 2016 Wiley Periodicals, Inc. J Clin Ultrasound, 2016

Sevoflurane, an inhalational anesthetic, is extensively used during oral cancer surgery. However, the effect of sevoflurane on head and neck squamous cell carcinoma (HNSCC) remains unclear. The objective of the present study was to investigate the effects of sevoflurane on the proliferation, apoptosis and invasion in HNSCC cell lines and the underlying molecular mechanism. The Cell Counting Kit-8 assay was used to evaluate cell proliferation. Apoptosis was analyzed by flow cytometry. Cell invasion was evaluated using the Transwell invasion assay. The expression levels of Akt, p-Akt (Ser473), hypoxia‑inducible factor-1α (HIF-1α), Fas and Bcl-2 were measured by western blotting. Significant inhibition of cell proliferation and induction of apoptosis were observed in FaDu and CAL-27 cells following sevoflurane treatment. The expression of Akt, p-Akt (Ser473) and Bcl-2 was supressed, while that of Fas was significantly increased, which was partly associated with the activation of the HIF-1α pathway. In addition, the results revealed a statistically significant inhibition of cell invasion in the FaDu cell line following exposure to 2 and 4% sevoflurane for 2, 4, 6 and 8 h. Therefore, the present study demonstrated that sevoflurane decreased the malignant behavior of HNSCC cell lines in vitro, which was associated with activation of the HIF-1α pathway.

Autoimmune thyroid disease (AITD) is characterized by immune attacks on the person's own thyroid. Hashimoto's thyroiditis (HT) is a subtype of AITD and is a common cause of hypothyroidism and related symptoms. Regulatory B (Breg) cells can express interleukin 10 (IL-10) and have recently emerged as a critical participant in suppression pathogenic inflammation and promoting peripheral tolerance. The role of Breg cells in HT is not yet clear. In this study, we first examined the IL-10 production by B cells in healthy controls and HT patients, and found that the healthy control B cells demonstrated significantly higher IL-10 expression than HT B cells after CpG stimulation. In both groups, the IL-10-producing B cells were highly enriched in the CD24+CD38+ compartment. However, compared to healthy controls, HT patients presented higher levels of circulating CD24+CD38+ B cells, but lower percentage of IL-10+ cells in the CD24+CD38+ B cell compartment. In healthy controls, we performed coculture experiments of T cells with autologous total B cells, CD24+CD38+ B cells, and non-CD24+CD38+ B cells, and found significantly lower T cell proliferation as well as tumor necrosis factor (TNF) and interferon gamma (IFN-γ) production in cell cultures containing CD24+CD38+ B cells. In contrast, the HT CD24+CD38+ B cells demonstrated reduced capacity in suppressing T cell proliferation and did not suppress TNF and IFN-γ production. This lack of inhibitory activity in HT CD24+CD38+ B cells was related to a lack of IL-10, since addition of exogenous IL-10 in CD24+CD38+ B cell-T cell coculture significantly suppressed the proliferation of T cells and reduced proinflammatory cytokine secretion. Together, our study identified an upregulation of CD24+CD38+ B cells but a downregulation in their regulatory activity in HT patients.

Summary Deregulation of MYC plays an essential role in T cell acute lymphoblastic leukemia (T-ALL), yet the mechanisms underlying its deregulation remain elusive. Herein, we identify a molecular mechanism responsible for reciprocal activation between Aurora B kinase (AURKB) and MYC. AURKB directly phosphorylates MYC at serine 67, counteracting GSK3β-directed threonine 58 phosphorylation and subsequent FBXW7-mediated proteasomal degradation. Stabilized MYC, in concert with T cell acute lymphoblastic leukemia 1 (TAL1), directly activates AURKB transcription, constituting a positive feedforward loop that reinforces MYC-regulated oncogenic programs. Therefore, inhibitors of AURKB induce prominent MYC degradation concomitant with robust leukemia cell death. These findings reveal an AURKB-MYC regulatory circuit that underlies T cell leukemogenesis, and provide a rationale for therapeutic targeting of oncogenic MYC via AURKB inhibition.

Sevoflurane is the most widely used anaesthetic administered by inhalation. Exposure to sevoflurane in neonatal mice can induce learning deficits and abnormal social behaviours. MicroRNA (miR)-27a-3p, a short, non-coding RNA that functions as a tumour suppressor, is up-regulated after inhalation of anaesthetic, and peroxisome proliferator-activated receptor γ (PPAR-γ) is one of its target genes. The objective of this study was to investigate how the miR-27a-3p-PPAR-γ interaction affects sevoflurane-induced neurotoxicity. A luciferase reporter assay was employed to identify the interaction between miR-27a-3p and PPAR-γ. Primary hippocampal neuron cultures prepared from embryonic day 0 C57BL/6 mice were treated with miR-27a-3p inhibitor or a PPAR-γ agonist to determine the effect of miR-27a-3p and PPAR-γ on sevoflurane-induced cellular damage. Cellular damage was assessed by a flow cytometry assay to detect apoptotic cells, immunofluorescence to detect reactive oxygen species, western blotting to detect NADPH oxidase 1/4 and ELISA to measure inflammatory cytokine levels. In vivo experiments were performed using a sevoflurane-induced anaesthetic mouse model to analyse the effects of miR-27a-3p on neurotoxicity by measuring the number of apoptotic neurons using the Terminal-deoxynucleoitidyl Transferase Mediated Nick End Labeling (TUNEL) method and learning and memory function by employing the Morris water maze test. Our results revealed that PPAR-γ expression was down-regulated by miR-27a-3p following sevoflurane treatment in hippocampal neurons. Down-regulation of miR-27a-3p expression decreased sevoflurane-induced hippocampal neuron apoptosis by decreasing inflammation and oxidative stress-related protein expression through the up-regulation of PPAR-γ. In vivo tests further confirmed that inhibition of miR-27a-3p expression attenuated sevoflurane-induced neuronal apoptosis and learning and memory impairment. Our findings suggest that down-regulation of miR-27a-3p expression ameliorated sevoflurane-induced neurotoxicity and learning and memory impairment through the PPAR-γ signalling pathway. MicroRNA-27a-3p may, therefore, be a potential therapeutic target for preventing or treating sevoflurane-induced neurotoxicity.

Insulin-like growth factor 1 (IGF-1) is a neuroprotective hormone and a decrease in levels of circulating IGF-1 contributes toward cognitive decline. The aim of this study was to investigate the effect of sevoflurane on the level of circulating IGF-1 and cognitive function in aged mice and the role of circulating IGF-1 in the cognitive dysfunction induced by sevoflurane. Aged mice were exposed to 1 or 2 minimal alveolar concentrations of sevoflurane for 4 or 8 h. Before and after the exposure, blood was collected from the tail vein and serum IGF-1 was measured by an enzyme-linked immunosorbent assay. After exposure, spatial learning and memory were tested in the Morris water maze. An intraperitoneal injection of IGF-1 was used to study the role of IGF-1 in the cognitive impairment induced by sevoflurane. Sevoflurane dose dependently decreased the serum IGF-1 concentration, and resulted in aged mice taking significantly longer and traveling significantly further to find the platform. Sevoflurane significantly decreased the times crossing the platform and %time spent in target quadrant relative to the control group. IGF-1 attenuated this effect, but could not completely reverse it. We conclude that downregulation of circulating IGF-1 contributes toward the cognitive impairment induced by sevoflurane.

Insulin-like growth factor binding protein-related protein 1 (IGFBP-rP1) is a potential tumor‑suppressor gene in various cancers. However, its biological role and underlying mechanism has not been well investigated in endometrial cancer yet. The aim of the present study aimed to investigate the role and underlying molecular mechanisms of IGFBP‑rP1 in endometrial cancer cells in vitro. The authors used transfection of IGFBP‑rP1 or small interfering (si)RNA in endometrial cancer HEC‑1A or Ishikawa cells, respectively. Biological functional alterations, such as cell growth and cell cycle were analyzed in endometrial cancer cells, combined with the use of PD98059. A panel of proteins including phospho‑retinoblastoma (p‑RB) and p‑extracellular signal‑regulated kinase (ERK)/ERK were detected by western blot analysis. It was observed that IGFBP‑rP1 transfection inhibited cell growth, and induced G1 phase arrest and cellular senescence in HEC‑1A cells while gene silencing presented the adverse functional changes. Moreover, p‑RB and p‑ERK were significantly downregulated or upregulated in HEC‑1A‑IGFBP‑rP1 cells or Ishikawa‑siRNA (IGFBP‑rP1) compared with control cells, respectively. These observations were reinforced in endometrial cancer cells by PD98059 treatment. The authors conclude that IGFBP‑rP1 acts as a potential tumor suppressor via the suppression of the ERK signaling pathway in endometrial cancer cells. These findings suggested that IGFBP‑rP1 may serve as a potential therapeutic target for cancer intervention in the future.

Signaling pathways of tumor necrosis factor-α (TNF-α) and insulin-like growth factor-I (IGF-I) are found to be functionally interrelated in some experimental studies. IGF-I may be involved in the pathogenesis of postoperative cognitive dysfunction (POCD). In order to investigate the possible interaction of TNF-α and IGF-I in POCD, the plasma levels of IGF-I and TNF-α were determined in 44 patients under general anesthesia. As compared with non-POCD patients, POCD patients showed increased TNF-α and decreased IGF-I levels in plasma, as well as a significant negative correlation between TNF-α and IGF-I values. The present results suggest that interaction of increased TNF-α levels and decreased IGF-1 levels might lead to a vicious circle, which may contribute to POCD.

The study aimed to assess the utility of ultrafast ultrasound imaging for evaluation of carotid pulse wave velocity (PWV) in newly diagnosed hypertension patients. This prospective non-randomized study enrolled 90 hypertensive patients in our hospital from September to December 2013 as a hypertension group. An age- and sex-matched cohort of 50 healthy adults in our hospital from September to December 2013 was also included in the study as a control group. Carotid PWV at the beginning and at the end of systole (PWV-BS and PWV-ES, respectively) and intima-media thickness (IMT) were measured by ultrafast ultrasound imaging technology. The associations of PWV-BS, PWV-ES, and IMT with hypertension stage were evaluated by Spearman correlation analysis. PWV-BS and PWV-ES in the hypertension group were significantly elevated compared with those in control group. Different hypertension stages significantly differed in PWV-BS and PWV-ES. PWV-BS and PWV-ES appeared to increase with the hypertension stage. Moreover, IMT, PWV-BS, and PWV-ES were positively correlated with the hypertension stage in hypertensive patients. Ultrafast ultrasound imaging was a valid and convenient method for the measurement of carotid PWV in hypertensive patients. Ultrafast ultrasound imaging might be recommended as a promising alternative method for early detection of arterial abnormality in clinical practice.

To compensate for brain shift in image-guided neurosurgery, we propose a new non-rigid registration method that integrates surface and vessel/sulci feature to noninvasively track the brain surface. Textured brain surfaces were acquired using phase-shift three-dimensional (3D) shape measurement, which offers 2D image pixels and their corresponding 3D points directly. Measured brain surfaces were noninvasively tracked using the proposed method by minimizing a new energy function, which is a weighted combination of 3D point corresponding estimation and surface deformation constraints. Initially, the measured surfaces were divided into featured and non-featured parts using a Frangi filter. The corresponding feature/non-feature points between intraoperative brain surfaces were estimated using the closest point algorithm. Subsequently, smoothness and rigidity constraints were introduced in the energy function for a smooth surface deformation and local surface detail conservation, respectively. Our 3D shape measurement accuracy was evaluated using 20 spheres for bias and precision errors. In addition, the proposed method was evaluated based on root mean square error (RMSE) and target registration error (TRE) with five porcine brains for which deformations were produced by gravity and pushing with different displacements in both the vertical and horizontal directions. The minimum and maximum bias errors were 0.32 and 0.61 mm, respectively. The minimum and maximum precision errors were 0.025 and 0.30 mm, respectively. Quantitative validation with porcine brains showed that the average RMSE and TRE were 0.1 and 0.9 mm, respectively. The proposed method appeared to be advantageous in integrating vessels/sulci feature, robust to changes in deformation magnitude and integrated feature numbers, and feasible in compensating for brain shift deformation in surgeries.