Search

As a critical component of a train, the railway wagon bogie adapter has higher quality requirements. During the forging process, external loads can induce voids, cracks, and other defects in the forging, thereby reducing its service life. Hence, studying the damage behavior of the forging material, specifically AISI 1035 steel, becomes crucial. This study involved obtaining stress-strain curves for AISI 1035 steel through uniaxial tensile tests at temperatures of 900 °C, 1000 °C, and 1100 °C, with strain rates of 0.1 s -1 , 1 s -1 , and 10 s -1 . Subsequently, SEM was used to observe samples at various deformation stages. The damage parameters, q1, q2 and q3 in the GTN model "a computational model used to analyze and simulate material damage which can effectively capture the damage behavior of materials under different loading conditions" were then calibrated using the Ramberg-Osgood model and stress-strain curve fitting. Image Pro Plus software v11.1 quantified the sample porosity as f0, fn, fc and fF. A finite element model was established to simulate the tensile behavior of the AISI 1035 steel samples. By comparing the damage parameters of f0, fn, fc and fF obtained by the finite element method and experimental method, the validity of the damage parameters obtained by the finite element inverse method could be verified.

Background: Precise glioma segmentation from multi-parametric magnetic resonance (MR) images is essential for brain glioma diagnosis. However, due to the indistinct boundaries between tumor sub-regions and the heterogeneous appearances of gliomas in volumetric MR scans, designing a reliable and automated glioma segmentation method is still challenging. Although existing 3D Transformer-based or convolution-based segmentation networks have obtained promising results via multi-modal feature fusion strategies or contextual learning methods, they widely lack the capability of hierarchical interactions between different modalities and cannot effectively learn comprehensive feature representations related to all glioma sub-regions., Purpose: To overcome these problems, in this paper, we propose a 3D hierarchical cross-modality interaction network (HCMINet) using Transformers and convolutions for accurate multi-modal glioma segmentation, which leverages an effective hierarchical cross-modality interaction strategy to sufficiently learn modality-specific and modality-shared knowledge correlated to glioma sub-region segmentation from multi-parametric MR images., Methods: In the HCMINet, we first design a hierarchical cross-modality interaction Transformer (HCMITrans) encoder to hierarchically encode and fuse heterogeneous multi-modal features by Transformer-based intra-modal embeddings and inter-modal interactions in multiple encoding stages, which effectively captures complex cross-modality correlations while modeling global contexts. Then, we collaborate an HCMITrans encoder with a modality-shared convolutional encoder to construct the dual-encoder architecture in the encoding stage, which can learn the abundant contextual information from global and local perspectives. Finally, in the decoding stage, we present a progressive hybrid context fusion (PHCF) decoder to progressively fuse local and global features extracted by the dual-encoder architecture, which utilizes the local-global context fusion (LGCF) module to efficiently alleviate the contextual discrepancy among the decoding features., Results: Extensive experiments are conducted on two public and competitive glioma benchmark datasets, including the BraTS2020 dataset with 494 patients and the BraTS2021 dataset with 1251 patients. Results show that our proposed method outperforms existing Transformer-based and CNN-based methods using other multi-modal fusion strategies in our experiments. Specifically, the proposed HCMINet achieves state-of-the-art mean DSC values of 85.33% and 91.09% on the BraTS2020 online validation dataset and the BraTS2021 local testing dataset, respectively., Conclusions: Our proposed method can accurately and automatically segment glioma regions from multi-parametric MR images, which is beneficial for the quantitative analysis of brain gliomas and helpful for reducing the annotation burden of neuroradiologists., (© 2024 American Association of Physicists in Medicine.)

The classification problem concerning crisp-valued data has been well resolved. However, interval-valued data, where all of the observations' features are described by intervals, are also a common data type in real-world scenarios. For example, the data extracted by many measuring devices are not exact numbers but intervals. In this article, we focus on a highly challenging problem called learning from interval-valued data (LIND), where we aim to learn a classifier with high performance on interval-valued observations. First, we obtain the estimation error bound of the LIND problem based on the Rademacher complexity. Then, we give the theoretical analysis to show the strengths of multiview learning on classification problems, which inspires us to construct a new algorithm called multiview interval information extraction (Mv-IIE) approach for improving classification accuracy on interval-valued data. The experiment comparisons with several baselines on both synthetic and real-world datasets illustrate the superiority of the proposed framework in handling interval-valued data. Moreover, we describe an application of Mv-IIE that we can prevent data privacy leakage by transforming crisp-valued (raw) data into interval-valued data.

The theoretical analysis of multiclass classification has proved that the existing multiclass classification methods can train a classifier with high classification accuracy on the test set, when the instances are precise in the training and test sets with same distribution and enough instances can be collected in the training set. However, one limitation with multiclass classification has not been solved: how to improve the classification accuracy of multiclass classification problems when only imprecise observations are available. Hence, in this article, we propose a novel framework to address a new realistic problem called multiclass classification with imprecise observations (MCIMO), where we need to train a classifier with fuzzy-feature observations. First, we give the theoretical analysis of the MCIMO problem based on fuzzy Rademacher complexity. Then, two practical algorithms based on support vector machine and neural networks are constructed to solve the proposed new problem. The experiments on both synthetic and real-world datasets verify the rationality of our theoretical analysis and the efficacy of the proposed algorithms.

Background: Multi-modal learning is widely adopted to learn the latent complementary information between different modalities in multi-modal medical image segmentation tasks. Nevertheless, the traditional multi-modal learning methods require spatially well-aligned and paired multi-modal images for supervised training, which cannot leverage unpaired multi-modal images with spatial misalignment and modality discrepancy. For training accurate multi-modal segmentation networks using easily accessible and low-cost unpaired multi-modal images in clinical practice, unpaired multi-modal learning has received comprehensive attention recently., Purpose: Existing unpaired multi-modal learning methods usually focus on the intensity distribution gap but ignore the scale variation problem between different modalities. Besides, within existing methods, shared convolutional kernels are frequently employed to capture common patterns in all modalities, but they are typically inefficient at learning global contextual information. On the other hand, existing methods highly rely on a large number of labeled unpaired multi-modal scans for training, which ignores the practical scenario when labeled data is limited. To solve the above problems, we propose a modality-collaborative convolution and transformer hybrid network (MCTHNet) using semi-supervised learning for unpaired multi-modal segmentation with limited annotations, which not only collaboratively learns modality-specific and modality-invariant representations, but also could automatically leverage extensive unlabeled scans for improving performance., Methods: We make three main contributions to the proposed method. First, to alleviate the intensity distribution gap and scale variation problems across modalities, we develop a modality-specific scale-aware convolution (MSSC) module that can adaptively adjust the receptive field sizes and feature normalization parameters according to the input. Secondly, we propose a modality-invariant vision transformer (MIViT) module as the shared bottleneck layer for all modalities, which implicitly incorporates convolution-like local operations with the global processing of transformers for learning generalizable modality-invariant representations. Third, we design a multi-modal cross pseudo supervision (MCPS) method for semi-supervised learning, which enforces the consistency between the pseudo segmentation maps generated by two perturbed networks to acquire abundant annotation information from unlabeled unpaired multi-modal scans., Results: Extensive experiments are performed on two unpaired CT and MR segmentation datasets, including a cardiac substructure dataset derived from the MMWHS-2017 dataset and an abdominal multi-organ dataset consisting of the BTCV and CHAOS datasets. Experiment results show that our proposed method significantly outperforms other existing state-of-the-art methods under various labeling ratios, and achieves a comparable segmentation performance close to single-modal methods with fully labeled data by only leveraging a small portion of labeled data. Specifically, when the labeling ratio is 25%, our proposed method achieves overall mean DSC values of 78.56% and 76.18% in cardiac and abdominal segmentation, respectively, which significantly improves the average DSC value of two tasks by 12.84% compared to single-modal U-Net models., Conclusions: Our proposed method is beneficial for reducing the annotation burden of unpaired multi-modal medical images in clinical applications., (© 2023 American Association of Physicists in Medicine.)

Prostate cancer is one of the deadest cancers among human beings. To better diagnose the prostate cancer, prostate lesion segmentation becomes a very important work, but its progress is very slow due to the prostate lesions small in size, irregular in shape, and blurred in contour. Therefore, automatic prostate lesion segmentation from mp-MRI is a great significant work and a challenging task. However, the most existing multi-step segmentation methods based on voxel-level classification are time-consuming, may introduce errors in different steps and lead to error accumulation. To decrease the computation time, harness richer 3D spatial features, and fuse the multi-level contextual information of mp-MRI, we present an automatic segmentation method in which all steps are optimized conjointly as one step to form our end-to-end convolutional neural network. The proposed end-to-end network DMSA-V-Net consists of two parts: (1) a 3D V-Net is used as the backbone network, it is the first attempt in employing 3D convolutional neural network for CS prostate lesion segmentation, (2) a deep multi-scale attention mechanism is introduced into the 3D V-Net which can highly focus on the ROI while suppressing the redundant background. As a merit, the attention can adaptively re-align the context information between the feature maps at different scales and the saliency maps in high-levels. We performed experiments based on five cross-fold validation with data including 97 patients. The results show that the Dice and sensitivity are 0.7014 and 0.8652 respectively, which demonstrates that our segmentation approach is more significant and accurate compared to other methods., Competing Interests: Declaration of Competing Interest The authors declare that they have no conflict of interest., (Copyright © 2023. Published by Elsevier Inc.)

Using cine magnetic resonance imaging (cine MRI) images to track cardiac motion helps users to analyze the myocardial strain, and is of great importance in clinical applications. At present, most of the automatic deep learning-based motion tracking methods compare two images without considering temporal information between MRI frames, which easily leads to the lack of consistency of the generated motion fields. Even though a small number of works take into account the temporal factor, they are usually computationally intensive or have limitations on image length. To solve this problem, we propose a bidirectional convolution neural network for motion tracking of cardiac cine MRI images. This network leverages convolutional blocks to extract spatial features from three-dimensional (3D) image registration pairs, and models the temporal relations through a bidirectional recurrent neural network to obtain the Lagrange motion field between the reference image and other images. Compared with previous pairwise registration methods, the proposed method can automatically learn spatiotemporal information from multiple images with fewer parameters. We evaluated our model on three public cardiac cine MRI datasets. The experimental results demonstrated that the proposed method can significantly improve the motion tracking accuracy. The average Dice coefficient between estimated segmentation and manual segmentation has reached almost 0.85 on the widely used Automatic Cardiac Diagnostic Challenge (ACDC) dataset., Competing Interests: Declaration of Competing Interest We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work, there is no professional or other personal interest of any nature or kind in any product, service and/or company that could be construed as influencing the position presented in, or the review of, the manuscript., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Objective . Sliding motion may occur between organs in anatomical regions due to respiratory motion and heart beating. This issue is often neglected in previous studies, resulting in poor image registration performance. A new approach is proposed to handle discontinuity at the boundary and improve registration accuracy. Approach . The proposed discontinuity-preserving regularization (DPR) term can maintain local discontinuities. It leverages the segmentation mask to find organ boundaries and then relaxes the displacement field constraints in these boundary regions. A weakly supervised method using mask dissimilarity loss (MDL) is also proposed. It employs a simple formula to calculate the similarity between the fixed image mask and the deformed moving image mask. These two strategies are added to the loss function during network training to guide the model better to update parameters. Furthermore, during inference time, no segmentation mask information is needed. Main results . Adding the proposed DPR term increases the Dice coefficients by 0.005, 0.009, and 0.081 for three existing registration neural networks CRNet, VoxelMorph, and ViT-V-Net, respectively. It also shows significant improvements in other metrics, including Hausdorff Distance and Average Surface Distance. All quantitative indicator results with MDL have been slightly improved within 1%. After applying these two regularization terms, the generated displacement field is more reasonable at the boundary, and the deformed moving image is closer to the fixed image. Significance . This study demonstrates that the proposed regularization terms can effectively handle discontinuities at the boundaries of organs and improve the accuracy of deep learning-based cardiac image registration methods. Besides, they are generic to be extended to other networks., (© 2023 Institute of Physics and Engineering in Medicine.)

Purpose: Automatic liver segmentation from computed tomography (CT) images is an essential preprocessing step for computer-aided diagnosis of liver diseases. However, due to the large differences in liver shapes, low-contrast to adjacent tissues, and existence of tumors or other abnormalities, liver segmentation has been very challenging. This study presents an accurate and fast liver segmentation method based on a novel probabilistic active contour (PAC) model and its fast global minimization scheme (3D-FGMPAC), which is explainable as compared with deep learning methods., Methods: The proposed method first constructs a slice-indexed-histogram to localize the volume of interest (VOI) and estimate the probability that a voxel belongs to the liver according its intensity. The probabilistic image would be used to initialize the 3D PAC model. Secondly, a new contour indicator function, which is a component of the model, is produced by combining the gradient-based edge detection and Hessian-matrix-based surface detection. Then, a fast numerical scheme derived for the 3D PAC model is performed to evolve the initial probabilistic image into the global minimizer of the model, which is a smoothed probabilistic image showing a distinctly highlighted liver. Next, a simple region-growing strategy is applied to extract the whole liver mask from the smoothed probabilistic image. Finally, a B-spline surface is constructed to fit the patch of the rib cage to prevent possible leakage into adjacent intercostal tissues., Results: The proposed method is evaluated on two public datasets. The average Dice score, volume overlap error, volume difference, symmetric surface distance and volume processing time are 0.96, 7.35%, 0.02%, 1.17 mm and 19.8 s for the Sliver07 dataset, and 0.95, 8.89%, - 0.02 % $-0.02\%$ , 1.45 mm and 23.08 s for the 3Dircadb dataset, respectively., Conclusions: The proposed fully-automatic approach can effectively segment the liver from low-contrast and complex backgrounds. The quantitative and qualitative results demonstrate that the proposed segmentation method outperforms state-of-the-art traditional automatic liver segmentation algorithms and achieves very competitive performance compared with recent deep leaning-based methods., (© 2022 American Association of Physicists in Medicine.)

SHP2, a protein tyrosine phosphatase, plays a critical role in fully activating oncogenic signaling pathways such as Ras/MAPK downstream of cell surface tyrosine receptors (e.g., EGFR), which are often activated in human cancers, and thus has emerged as an attractive cancer therapeutic target. This study focused on evaluating the therapeutic potential of the novel SHP2 degrader, SHP2-D26 (D26), either alone or in combination, against non-small cell lung cancer (NSCLC) cells. While all tested NSCLC cell lines responded to D26 with IC 50 s of < 8 μM, a few cell lines (4/14) were much more sensitive than others with IC 50 s of ≤ 4 μM. There was no clear association between basal levels of SHP2 and cell sensitivities to D26. Moreover, D26 rapidly and potently decreased SHP2 levels in different NSCLC cell lines in a sustained way regardless of cell sensitivities to D26, suggesting that additional factors may impact cell response to D26. We noted that suppression of p70S6K/S6, but not ERK1/2, was associated with cell responses to D26. In the sensitive cell lines, D26 effectively increased Bim levels while decreasing Mcl-1 levels accompanied with the induction of apoptosis. When combined with the third generation EGFR inhibitor, osimertinib (AZD9291), synergistic effects on decreasing the survival of different osimertinib-resistant cell lines were observed with enhanced induction of apoptosis. Although D26 alone exerted moderate inhibition of the growth of NSCLC xenografts, the combination of osimertinib and D26 effectively inhibited the growth of osimertinib-resistant xenografts, suggesting promising efficacy in overcoming acquired resistance to osimertinib., (© 2022. The Author(s), under exclusive licence to Springer Nature America, Inc.)

BACKGROUND Non-small cell lung cancer (NSCLC) is one of the leading causes of cancer-related death in the world and its poor prognosis is a major concern. Periostin was found to be associated with the prognosis of NSCLC. However, the research results were inconsistent. This meta-analysis evaluated the correlation between periostin expression and the prognosis of NSCLC. MATERIAL AND METHODS A meta-analysis was performed on data acquired from PubMed, EMBASE, the Cochrane Library, China National Knowledge Infrastructure (CNKI), and Wanfang Database from inception to 18 June 2022. Published and unpublished studies investigating the correlation between periostin expression and the prognosis of NSCLC were included in this meta-analysis. Eligible studies reported at least 1 of the following clinical outcome measures: overall survival, progression-free survival, cancer-specific survival, relapse-free survival, disease-free survival, or other clinical parameters of prognosis. Pooled hazard ratios (HR) with 95% confidence interval (CI) were calculated using the random-effects model. Sensitivity and subgroup analyses and assessment of publication bias were also conducted. RESULTS This meta-analysis enrolled 2504 NSCLC cases from 12 eligible studies. The hazard ratio for the overall survival was 1.761 (95% CI: 1.022-3.033, P=0.041). Heterogeneity was significant among the studies, but publication bias was lacking. Subgroup analyses were performed based on different issues, such as districts, antibodies and methods for periostin detection. CONCLUSIONS Overexpression of periostin is a negative prognostic factor and is associated with worse overall survival (OS) in NSCLC patients. Periostin may serve as a prognostic biomarker for NSCLC patients.

This paper presents an automatic Couinaud segmentation method based on deep learning of key point detection. Assuming that the liver mask has been extracted, the proposed method can automatically divide the liver into eight anatomical segments according to Couinaud's definition. Firstly, an attentive residual hourglass-based cascaded network (ARH-CNet) is proposed to identify six key bifurcation points of the hepatic vascular system. Subsequently, the detected points are used to derive the planes that divide the liver into different functional units, and the caudate lobe is segmented slice-by-slice based on the circles defined by the detected points. We comprehensively evaluate our method on a public dataset from MICCAI 2018. Experiments firstly demonstrate the effectiveness of our landmark detection network ARH-CNet, which is superior to that of two baseline methods, also robust to noisy data. The average error distance of all predicted key points is 4.68 ± 3.17 mm, and the average accuracy of all points is 90% with the detection error distance of 7 mm. We also verify that summation of the corresponding heat-maps can improve the accuracy of point localization. Furthermore, the overlap-based accuracy and the Dice score of our landmark-derived Couinaud segmentation are respectively 91% and 84%, which are better than the performance of the direct segmentation approach and the traditional plane-based method, thus our method can be regarded as a good alternative for automatic Couinaud segmentation., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Treatment of EGFR-mutant non-small cell lung cancer (NSCLC) with mutation-selective third-generation EGFR-tyrosine kinase inhibitors (EGFR-TKIs) such as osimertinib has achieved remarkable success in the clinic. However, the immediate challenge is the emergence of acquired resistance, limiting the long-term remission of patients. This study suggests a novel strategy to overcome acquired resistance to osimertinib and other third-generation EGFR-TKIs through directly targeting the intrinsic apoptotic pathway. We found that osimertinib, when combined with Mcl-1 inhibition or Bax activation, synergistically decreased the survival of different osimertinib-resistant cell lines, enhanced the induction of intrinsic apoptosis, and inhibited the growth of osimertinib-resistant tumor in vivo. Interestingly, the triple-combination of osimertinib with Mcl-1 inhibition and Bax activation exhibited the most potent activity in decreasing the survival and inducing apoptosis of osimertinib-resistant cells and in suppressing the growth of osimertinib-resistant tumors. These effects were associated with increased activation of the intrinsic apoptotic pathway evidenced by augmented mitochondrial cytochrome C and Smac release. Hence, this study convincingly demonstrates a novel strategy for overcoming acquired resistance to osimertinib and other 3rd generation EGFR-TKIs by targeting activation of the intrinsic apoptotic pathway through Mcl-1 inhibition, Bax activation or both, warranting further clinical validation of this strategy., (© 2022. The Author(s), under exclusive licence to Springer Nature Limited.)

Lung cancer remains the leading cause of cancer deaths worldwide despite advances in knowledge in cancer biology and options of various targeted therapies. Efforts in identifying innovative and effective therapies are still highly appreciated. Targeting bromodomain and extra terminal (BET) proteins that function as epigenetic readers and master transcription coactivators is now a potential cancer therapeutic strategy. The current study evaluates the therapeutic efficacies of the novel BET degrader, QCA570, in lung cancer and explores its underlying mechanisms. QCA570 at low nanomolar ranges effectively decreased the survival of a panel of human lung cancer cell lines with induction of apoptosis in vitro . As expected, it potently induced degradation of BET proteins including BRD4, BRD3 and BRD2. Moreover, it potently decreased Mcl-1 levels due to transcriptional suppression and protein degradation; this event is critical for mediating apoptosis induced by QCA570. Moreover, QCA570 synergized with osimertinib in suppressing the growth of osimertinib-resistant cells in vitro and in vivo , suggesting potential in overcoming acquired resistance to osimertinib. These preclinical findings support the potential of QCA570 in treatment of lung cancer either as a single agent or in combination with others., Competing Interests: The University of Michigan has filed a patent on QCA570 and its analogues for which Shaomeng Wang is a co-inventor. The patent has been licensed by Roivant Sciences Inc/Proteovant Therapeutics Inc, for which Shaomeng Wang is a paid consultant. The University of Michigan has received a research contract from Proteovant Therapeutics Inc for which Shaomeng Wang is the principal investigator on the contract. Suresh Y. Ramalingam is on consulting/advisory board for AstraZeneca, BMS, Merck, Roche, Tesaro and Amgen. No potential conflicts of interest were disclosed for other authors., (AJCR Copyright © 2022.)

Brain tissue segmentation in multi-modal magnetic resonance (MR) images is significant for the clinical diagnosis of brain diseases. Due to blurred boundaries, low contrast, and intricate anatomical relationships between brain tissue regions, automatic brain tissue segmentation without prior knowledge is still challenging. This paper presents a novel 3D fully convolutional network (FCN) for brain tissue segmentation, called APRNet. In this network, we first propose a 3D anisotropic pyramidal convolutional reversible residual sequence (3DAPC-RRS) module to integrate the intra-slice information with the inter-slice information without significant memory consumption; secondly, we design a multi-modal cross-dimension attention (MCDA) module to automatically capture the effective information in each dimension of multi-modal images; then, we apply 3DAPC-RRS modules and MCDA modules to a 3D FCN with multiple encoded streams and one decoded stream for constituting the overall architecture of APRNet. We evaluated APRNet on two benchmark challenges, namely MRBrainS13 and iSeg-2017. The experimental results show that APRNet yields state-of-the-art segmentation results on both benchmark challenge datasets and achieves the best segmentation performance on the cerebrospinal fluid region. Compared with other methods, our proposed approach exploits the complementary information of different modalities to segment brain tissue regions in both adult and infant MR images, and it achieves the average Dice coefficient of 87.22% and 93.03% on the MRBrainS13 and iSeg-2017 testing data, respectively. The proposed method is beneficial for quantitative brain analysis in the clinical study, and our code is made publicly available.

Background: The tumor microenvironment evidently affects treatment response and clinical outcome. This study aims to construct a tumor microenvironment-based crosstalk between immunotherapy and epidermal growth factor receptor tyrosine kinase inhibitor (EGFR-TKI) in lung adenocarcinoma. Methods: We used ESTIMATE algorithm to calculate stromal and immune scores. Differentially expressed genes (DEGs) were extracted based on the comprehensive analysis of immune score groups and EGFR-TKI resistance samples. The independent prognostic value of the five selected genes was assessed by univariate/multivariate Cox regression analysis, survival analysis and the receiver operating characteristic (ROC) curve. Correlation analysis was performed using Spearman's rho value through TIMER 2.0. Results: The Kaplan-Meier survival curve show that patients with higher immune scores have significantly better overall survival. We identified 1328 DEGs from immune score groups and 806 DEGs from the EGFR-TKI resistance cohort GSE123066. A total of 19 co-regulated genes were found, and the Cox regression model produced a significant statistical prognosis for five genes ( CENPF , CYSLTR1 , GLDN , PIGR and SCGB3A1 ). Multivariate Cox regression analysis showed that the selected five gene signatures could be used as independent prognostic indicators. Furthermore, GSEA and correlation analysis demonstrated that CENPF was positively correlated to the signalling pathway which related to EGFR-TKI resistance and the well-known bypass gene. Conclusion: Our findings indicate that CENPF , CYSLTR1 , GLDN , PIGR and SCGB3A1 are independent prognostic biomarkers associated with acquired EGFR-TKI resistance and tumor immune cell infiltration in lung adenocarcinoma, and CENPF may be a potential target that can improve immunotherapy efficacy and overcome the acquired EGFR-TKI resistance., Competing Interests: Competing Interests: The authors have declared that no competing interest exists., (© The author(s).)

The occurrence of ureteral metastasis from distant primary tumors is uncommon, and appears to be especially rare when it originates from the lungs. In the case presented here, a patient with lumbago and left hydronephrosis was diagnosed with left ureteral metastasis of pulmonary adenocarcinoma after a CT-guided percutaneous transthoracic needle biopsy of the lung and retroperitoneal laparoscopic left nephroureterectomy. He accepted the targeted therapy because the lung tumor epidermal growth factor receptor mutation (exon19 deletion) was positive, and preoperative staging of lung adenocarcinoma was stage IVA. After an 8-month follow-up, he is still alive and well, with no local recurrence or distant metastases. The therapy outcome assessment is stable disease. Although rare, our case has demonstrated that pulmonary adenocarcinoma has the possibility of metastasizing to the ureter, a risk that should be considered in some lung cancer patients., (© 2021 The Authors. Thoracic Cancer published by China Lung Oncology Group and John Wiley & Sons Australia, Ltd.)

Background: The preoperative systemic immune-inflammation index (SII) is correlated with prognosis in several malignancies. The aim of this study was to investigate the prognosis value of SII in patients with resected breast cancer., Materials and Methods: A total of 784 breast cancer patients who underwent surgical resection were consecutively investigated. The optimal cutoff value of SII was evaluated using the receiver operating characteristic (ROC) curve. The collection of SII with clinicopathological characteristic and prognosis was further evaluated., Results: The optimal cutoff value for SII in the prediction of survival was 514 according to ROC curve analysis. A high SII was significantly correlated with younger age ( P = 0.037), PR status ( P < 0.001), and HER2 status ( P = 0.035). Univariate analysis revealed that SII ( P < 0.001), T-stage ( P < 0.001), lymph node involvement post-surgery ( P = 0.024), and histological grade ( P < 0.001) were significantly related to DFS, and SII ( P < 0.001), T-stage ( P = 0.003), lymph node involvement post-surgery ( P = 0.006), and histological grade ( P < 0.001) were significantly associated with OS. In multivariate analysis, a high SII was an independent worse prognostic factor for DFS (HR, 4.530; 95% CI, 3.279-6.258; P < 0.001) and OS (HR, 3.825; 95% CI, 2.594-5.640; P < 0.001) in all the enrolled patients. Furthermore, subgroup analysis of molecular subtype revealed that SII was significantly associated with prognosis in all subtypes., Conclusion: Preoperative SII is a simple and useful prognostic factor for predicting long-term outcomes for breast cancer patients undergoing surgery., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Li, Ma, Deng, Chen, Liu, Chen and Wu.)

Purpose: Deformable image registration (DIR) of lung four-dimensional computed tomography (4DCT) plays a vital role in a wide range of clinical applications. Most of the existing deep learning-based lung 4DCT DIR methods focus on pairwise registration which aims to register two images with large deformation. However, the temporal continuities of deformation fields between phases are ignored. This paper proposes a fast and accurate deep learning-based lung 4DCT DIR approach that leverages the temporal component of 4DCT images., Methods: We present Lung-CRNet, an end-to-end convolutional recurrent registration neural network for lung 4DCT images and reformulate 4DCT DIR as a spatiotemporal sequence predicting problem in which the input is a sequence of three-dimensional computed tomography images from the inspiratory phase to the expiratory phase in a respiratory cycle. The first phase in the sequence is selected as the only reference image and the rest as moving images. Multiple convolutional gated recurrent units (ConvGRUs) are stacked to capture the temporal clues between images. The proposed network is trained in an unsupervised way using a spatial transformer layer. During inference, Lung-CRNet is able to yield the respective displacement field for each reference-moving image pair in the input sequence., Results: We have trained the proposed network using a publicly available lung 4DCT dataset and evaluated performance on the widely used the DIR-Lab dataset. The mean and standard deviation of target registration error are 1.56 ± 1.05 mm on the DIR-Lab dataset. The computation time for each forward prediction is less than 1 s on average., Conclusions: The proposed Lung-CRNet is comparable to the existing state-of-the-art deep learning-based 4DCT DIR methods in both accuracy and speed. Additionally, the architecture of Lung-CRNet can be generalized to suit other groupwise registration tasks which align multiple images simultaneously., (© 2021 American Association of Physicists in Medicine.)

To identify the prognostic biomarker of the competitive endogenous RNA (ceRNA) and explore the tumor infiltrating immune cells (TIICs) which might be the potential prognostic factors in lung adenocarcinoma. In addition, we also try to explain the crosstalk between the ceRNA and TIICs to explore the molecular mechanisms involved in lung adenocarcinoma. The transcriptome data of lung adenocarcinoma were obtained from The Cancer Genome Atlas (TCGA) database, and the hypergeometric correlation of the differently expressed miRNA-lncRNA and miRNA-mRNA were analyzed based on the starBase. In addition, the Kaplan-Meier survival and Cox regression model analysis were used to identify the prognostic ceRNA network and TIICs. Correlation analysis was performed to analysis the correlation between the ceRNA network and TIICs. In the differently expressed RNAs between tumor and normal tissue, a total of 190 miRNAs, 224 lncRNAs and 3024 mRNAs were detected, and the constructed ceRNA network contained 5 lncRNAs, 92 mRNAs and 10 miRNAs. Then, six prognostic RNAs (FKBP3, GPI, LOXL2, IL22RA1, GPR37, and has-miR-148a-3p) were viewed as the key members for constructing the prognostic prediction model in the ceRNA network, and three kinds of TIICs (Monocytes, Macrophages M1, activated mast cells) were identified to be significantly related with the prognosis in lung adenocarcinoma. Correlation analysis suggested that the FKBP3 was associated with Monocytes and Macrophages M1, and the GPI was obviously related with Monocytes and Macrophages M1. Besides, the LOXL2 was associated with Monocytes and Activated mast cells, and the IL22RA1 was significantly associated with Monocytes and Macrophages M1, while the GPR37 and Macrophages M1 was closely related. The constructed ceRNA network and identified Monocytes, Macrophages M1 and activated Mast cells are all prognostic factors for lung adenocarcinoma. Moreover, the crosstalk between the ceRNA network and TIICs might be a potential molecular mechanism involved.

Purpose: The segmentation accuracy of medical images was improved by increasing the number of training samples using a local image warping technique. The performance of the proposed method was evaluated in the segmentation of breast masses, prostate and brain tumors, and lung nodules., Methods: We propose a simple data augmentation method which is called stochastic evolution (SE). Specifically, the idea of SE stems from our thinking about the deterioration of the diseased tissue and the healing process. In order to simulate this natural process, we implement it according to the local distortion algorithm in image warping. In other words, the irregular deterioration and healing processes of the diseased tissue is simulated according to the direction of the local distortion, thereby producing a natural sample that is indistinguishable by humans., Results: The proposed method is evaluated on four segmentation tasks of breast masses, prostate, brain tumors, and lung nodules. Comparing the experimental results of four segmentation methods based on the UNet segmentation architecture without adding any expanded data during training, the accuracy and the Hausdorff distance obtained in our approach remain almost the same as other methods. However, the dice similarity coefficient (DSC) and sensitivity (SEN) have both improved to some extent. Among them, DSC is increased by 5.2%, 2.8%, 1.0%, and 3.2%, respectively; SEN is increased by 6.9%, 4.3%, 1.2%, and 4.5%, respectively., Conclusions: Experimental results show that the proposed SE data augmentation method could improve the segmentation accuracy of breast masses, prostate, brain tumors, and lung nodules. The method also shows the robustness with different image datasets and imaging modalities., (© 2020 American Association of Physicists in Medicine.)

Abstract: This study aims to analyze the relationship between clinicopathological characteristics and survival in young patients (≤35 years old) with resected breast cancer.A total of 173 cases were included in this study. The clinicopathological factors potentially associated with prognosis were evaluated. Furthermore, we categorized patients into different groups to evaluate the prognosis according to hormone receptor status or important risk factors.Younger age (≤30 years) was an independent predictor for poor disease-free survival (DFS) and overall survival (OS). Besides, PR negative status, tumor grade, and advanced lymph nodes postsurgery were independent prognostic factors of DFS, while PR negative status and advanced lymph nodes postsurgery were independent prognostic factors of OS. For hormone receptor-positive patients, people with ER+ or PR+ and HER2-/+ showed poorer prognosis than the other 2 levels. Risk factor grouping based on the ER, PR, HER2, Ki-67 status, tumor grade, and lymph nodes postsurgery showed that patients in highest score group received the poorest prognosis. Grading system based on the hormone status or the risk factor grouping may offer a useful approach to assess which subgroups of young breast cancer present poorer prognosis., Competing Interests: The authors have no conflicts of interests to disclose., (Copyright © 2021 the Author(s). Published by Wolters Kluwer Health, Inc.)

Background: Artesunate (ART) has been used extensively as anti-malarial drugs worldwide. Besides, it has also been reported to have anti-cancer activities. This study was aimed to explore the anti-cancer activity of ART in combination with cisplatin (CIS) on A549 cells., Methods: Cells were cultured with different concentrations of ART and/or CIS for 24, 48, or 72 h to test the anti-proliferative effects by CCK-8 assay. Colony formation assay and EdU staining were also performed. TUNEL staining was used to illustrate the morphologic changes. Cell cycle and apoptosis were determined by flow cytometry assay, and Western blot analysis was conducted to detect the expression of apoptosis- and proliferation-related proteins. Caspase activities were determined by colorimetric assay kit. Moreover, the synergistic effect of ART with CIS in A549 cell xenograft model was also determined., Results: ART significantly inhibited cell proliferation in dose- and time-dependent manners. Collectively, the combination treatment remarkably decreased colony formation rates and increased the rates of TUNEL-positive cells compared with mono-treatment. Mechanistically, the combination treatment influenced the expression of Bcl-2, Bax, p-P53, Caspase-3/7, Caspase-9, CyclinB1, P34, P21, and synergistically regulated the activity of P38/JNK/ERK1/2 MAPK pathway. In mice A549 xenograft tumors, the combination strategy significantly increased the anti-cancer efficacy of ART and CIS alone, consistent with the in vitro observations., Conclusions: ART exhibited significant anti-tumor effect on A549 cells and this efficiency could be enhanced by combination with CIS., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Classification of benign and malignant in lung nodules using chest CT images is a key step in the diagnosis of early-stage lung cancer, as well as an effective way to improve the patients' survival rate. However, due to the diversity of lung nodules and the visual similarity of lung nodules to their surrounding tissues, it is difficult to construct a robust classification model with conventional deep learning-based diagnostic methods. To address this problem, we propose a multi-model ensemble learning architecture based on 3D convolutional neural network (MMEL-3DCNN). This approach incorporates three key ideas: (1) Constructed multi-model network architecture can be well adapted to the heterogeneity of lung nodules. (2) The input that concatenated of the intensity image corresponding to the nodule mask, the original image, and the enhanced image corresponding to which can help training model to extract advanced feature with more discriminative capacity. (3) Select the corresponding model to different nodule size dynamically for prediction, which can improve the generalization ability of the model effectively. In addition, ensemble learning is applied in this paper to further improve the robustness of the nodule classification model. The proposed method has been experimentally verified on the public dataset, LIDC-IDRI. The experimental results show that the proposed MMEL-3DCNN architecture can obtain satisfactory classification results.

The prognostic value of microvessel density (MVD) in head and neck squamous cell carcinoma (HNSCC) remains disputable. The purpose of this study was to comprehensively determine the prognostic value of MVD in HNSCC. Relevant literatures were identified using PubMed, Embase, and Cochrane Library. A meta-analysis was performed to clarify the prognostic role of MVD in HNSCC patients and different subgroups. A total of 14 eligible articles were included in this meta-analysis. The combined hazard ratio (HR) and 95% confidence interval (95% CI) for overall survival (OS) of 11 studies was 1.663 (1.236-2.237, P = 0.001), and the pooled HR and 95% CI for progression-free survival (PFS) of 7 studies was 2.069 (1.281-3.343, P = 0.003). Subgroup analyses were also performed on different issues, such as regional distribution of patients, age, tumor location, antibody, and treatment strategy. To conclude, high MVD is associated with worse OS and PFS in patients with HNSCC., Competing Interests: The authors declare that there are no competing interests., (Copyright © 2020 Yanbo Dong et al.)

Early detection of lung cancer is an effective way to improve the survival rate of patients. It is a critical step to have accurate detection of lung nodules in computed tomography (CT) images for the diagnosis of lung cancer. However, due to the heterogeneity of the lung nodules and the complexity of the surrounding environment, it is a challenge to develop a robust nodule detection method. In this study, we propose a two-stage convolutional neural networks (TSCNN) for lung nodule detection. The first stage based on the improved U-Net segmentation network is to establish an initial detection of lung nodules. During this stage, in order to obtain a high recall rate without introducing excessive false positive nodules, we propose a new sampling strategy for training. Simultaneously, a two-phase prediction method is also proposed in this stage. The second stage in the TSCNN architecture based on the proposed dual pooling structure is built into three 3D-CNN classification networks for false positive reduction. Since the network training requires a significant amount of training data, we designed a random mask as the data augmentation method in this study. Furthermore, we have improved the generalization ability of the false positive reduction model by means of ensemble learning. We verified the proposed architecture on the LUNA dataset in our experiments, which showed that the proposed TSCNN architecture did obtain competitive detection performance.

Many inflammation indicators have been reported to be related with patient outcomes in various cancers. Previous studies have evaluated the combination of platelet (PLT) and lymphocyte to monocyte ratio (COP-LMR) as a systemic inflammatory marker for prognostication in lung cancer, yet its prognostic role among breast cancer patients remains unclear.In the present study, a total of 409 breast cancer patients with surgical resection were retrospectively investigated. The receiver operating characteristic (ROC) curve was used to choose the optimal cut-off value of PLT and lymphocyte to monocyte ratio (LMR). Patients were classified into 3 groups according to the score of COP-LMR, and its relationship with various clinicopathological factors and breast cancer prognosis were further evaluated.The ROC curve analysis showed that COP-LMR had a higher area under the ROC curve for the prediction of 5-year disease-free survival and overall survival than PLT or LMR alone. Multivariable analysis showed that an elevated COP-LMR was an independent predictor of poor disease-free survival (P = .032) and overall survival (P = .005). Subgroup analysis revealed that COP-LMR was still significantly associated with prognosis in both luminal A and luminal B subtypes.Preoperative COP-LMR is a potential prognostic factor in breast cancer patients who underwent surgery.

Determining the nutrient factors influencing food choice provides important insight into the feeding strategy of animals, which is crucial for understanding their behavioral response to environmental changes. A bamboo-leaf-based diet is rare among mammals. Animals' food choice and nutritional goals have been explained by several frameworks; however, the influence of nutrients on food choice in bamboo-leaf-based macaques is not yet available. Assamese macaques ( Macaca assamensis ) inhabiting limestone forests are characterized by such a bamboo-leaf-based diet, predominantly consuming young leaves of Bonia saxatilis , a shrubby, karst-endemic bamboo. We studied the feeding behavior of one group of Assamese macaques using instantaneous scan sampling in limestone forests of the Guangxi Nonggang National Nature Reserve in southwest Guangxi, China. We compared the nutrient content of staple food and nonfood items and examine the role of key nutrients in the food selection of macaques. Our results showed that young leaves of bamboo B. saxatilis contained more water, crude protein, phosphorus, and less tannin than nonfood items. Furthermore, staple foods contained a higher content of water and less content of calcium than nonfood items. More specifically, quantities of water, crude protein, calcium, and phosphorus in food items were critical factors affecting feeding time on a specific plant item. Our results suggest that young bamboo leaves could meet macaques' required protein and water intake, while enabling them to maintain their mineral balance, consequently facilitating to maintain the primates' bamboo-leaf-diet in the limestone forest. Our findings confirm the effects of nutrient contents in food choice of Assamese macaques, highlighting the importance of the nutrient contents in maintaining their bamboo-based diet and the need to increase the knowledge on their nutritional strategy adapted to the bamboo-dominated diet inhabiting the unique limestone habitat., Competing Interests: None declared., (© 2020 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.)

The present study aimed to determine the correlation between controlling nutritional status (CONUT) and prognosis in resected breast cancer patients. Totally, 861 breast cancer patients with surgical resection in West China Hospital of Sichuan University between 2007 and 2010 were included. The relationship between CONUT and various clinicopathological factors as well as prognosis was evaluated. The results showed that the optimal cutoff value for CONUT to predict the 5-year survival was 3 and CONUT had a higher area under the ROC curve (AUC) for 5-year disease free survival (DFS) and overall survival (OS) prediction compared with the neutrophil lymphocyte ratio (NLR) and prognostic nutritional index (PNI). High CONUT was significantly correlated with older age, lymph node involvement, advanced T-stage, and surgery type. In the multivariate analysis, CONUT-high patients had worse DFS and OS, when compared with CONUT-low patients. In conclusion, preoperative CONUT is a useful marker for predicting long term outcomes in breast cancer patients after curative resection.

Background: Nm23-H1 gene has been found to be an inhibitor of tumor metastasis in lung cancer. MicroRNAs (miRNAs) play key roles in tumor metastasis through multiple signaling pathways. This study explored whether the nm23-H1 gene could inhibit invasion and metastasis of lung cancer cells by regulating miRNA-660-5p targets., Methods: Quantitative real-time PCR (qRT-PCR) and western blots were used to measure the expression of nm23-H1 and miR-660-5p of various human lung cancer cell lines. Cell counting kit-8 (CCK-8), wound-healing and transwell assay were carried out to assess cell proliferation, migration and invasion of each cell line. Xenograft were applied to determine in vivo effects of miR-660-5p among nude mice. Luciferase assay and western blot were performed to determine the target gene of miR-660-5p., Results: We found that high expression of nm23-H1 correlated with decreased miRNA-660-5p expression. Inhibiting miR-660-5p suppressed lung cancer cells progression significantly in vitro, whereas overexpression of miR-660-5p facilitated tumor growth and bone metastasis in vivo. In addition, as the potential target gene of miR-660-5p, SMARCA5 overexpression in vitro suppressed tumor progression and osteolytic metastasis associated RANKL signaling, which is congruent with the effect of nm23-H1 on the lung cancer cells., Conclusion: Nm23-H1 inhibits tumor progression and bone-specific metastasis of lung cancer by regulating miR-660-5p/SMARCA5/RANKL axis, which indicates the related genes may serve as potential targets for the treatment of human lung cancer., Key Points: Significant findings of the study High expression of nm23-H1 correlated with decreased miRNA-660-5p expression. Further, downregulation of miR-660-5p significantly suppressed the tumor progression and bone-specific metastasis of lung cancer cells. What this study adds This is the first study to show an inverse association between nm23-H1 and miR-660-5p, and confirm that nm23-H1 inhibits tumor progression and bone-specific metastasis of lung cancer by regulating miR-660-5p/SMARCA5/RANKL axis., (© 2020 The Authors. Thoracic Cancer published by China Lung Oncology Group and John Wiley & Sons Australia, Ltd.)

The activity budget is important for understanding behavioural variability and adaptation in primates. Assamese macaques (Macaca assamensis) found in the limestone forest of Guangxi Nonggang National Nature Reserve, Southwest China, primarily feed on young leaves of Bonia saxatilis (a shrubby, karst-endemic bamboo). To understand how a specific bamboo leaf-based diet and ecological factors affect activity budget, one group of Assamese macaques was studied using instantaneous scan sampling for 1 year. The macaques spent most of their time feeding (32.7 ± 5.4%), followed by resting (28.6 ± 6.3%), moving (28.6 ± 5.3%), grooming (8.0 ± 3.0%), playing (1.7 ± 1.6%) and other activities (0.4 ± 0.2%). Their activity budget was similar to that of typical frugivorous primates and bamboo-dominated primates, which spent more time on active behaviours (feeding and moving, 61.3 ± 6.0%) than on inactive behaviours (resting and grooming, 36.6 ± 6.4%). The macaques spent significantly more time resting during the fruit-lean season and more time moving during the fruit-rich season. Their activity budget was significantly affected by diet. Resting time increased with decreased fruit consumption, whereas moving time decreased with the increasing mature leaf consumption. Playing time decreased when the macaques consumed more young bamboo leaves. The activity budget was also influenced by food availability and climatic factors. Resting time increased with decreasing temperature, whereas moving time increased with increasing day length and young leaf availability. Grooming time increased with decreasing day length and increasing temperature, and playing time increased with increasing day length. Our findings provide evidence of the importance of diet, food availability, temperature and day length in coping with seasonal variation in ecological factors, highlighting the need to increase knowledge of the behavioural ecology of the Assamese macaques living in the unique limestone forest and to understand the influence of a bamboo-dominated diet and ecological factors on their survival., (© 2020 S. Karger AG, Basel.)

It is difficult to obtain an accurate segmentation due to the variety of lung nodules in computed tomography (CT) images. In this study, we propose a data-driven model, called the Cascaded Dual-Pathway Residual Network (CDP-ResNet) to improve the segmentation of lung nodules in the CT images. Our approach incorporates the multi-view and multi-scale features of different nodules from CT images. The proposed residual block based dual-path network extracts local features and rich contextual information of lung nodules. In addition, we designed an improved weighted sampling strategy to select training samples based on the edge. The proposed method was extensively evaluated on an LIDC dataset, which contains 986 nodules. Experimental results show that the CDP-ResNet achieves superior segmentation performance with an average DICE score (standard deviation) of 81.58% (11.05) on the LIDC dataset. Moreover, we compared our results with those of four radiologists on the same dataset. The comparison shows that the CDP-ResNet is slightly better than human experts in terms of segmentation accuracy. Meanwhile, the proposed segmentation method outperforms existing methods., (Copyright © 2019 Associazione Italiana di Fisica Medica. All rights reserved.)

Background: Nuclear Yes-associated protein 1 (YAP1) has often been regarded as an adverse prognostic indicator in various tumors. Recent studies have associated YAP1 with unfavorable prognosis in nonsmall cell lung cancer (NSCLC). However, due to small sample sizes, the prognostic value of nuclear YAP1 in NSCLC patients is not well understood. In the present study, we evaluated the prognostic role of nuclear YAP1 in NSCLC patients via a systematic review and meta-analysis., Methods: We searched the PubMed, EMBASE, Cochrane, Web of Science, China National Knowledge Infrastructure (CNKI), and Wanfang Databases for papers investigating the prognostic significance of nuclear YAP1 expression in NSCLC patients. Hazard ratios (HRs) and the corresponding 95% confidence intervals (CIs) were calculated with reference to overall survival (OS) and progression-free survival (PFS) of NSCLC patients to provide synthesized estimates of the effects of nuclear YAP1 expression., Results: Among 414 cases, higher nuclear YAP1 expression presented as a predictive factor of poorer OS (HR = 1.52; 95% CI: 1.11-2.08; P = .01; I = 0.0%) and decreased PFS (HR = 2.11; 95% CI: 1.52-2.93; P < .001; I = 44.2%) in NSCLC patients. Subgroup analysis revealed shortened OS (HR = 1.63; 95% CI: 1.14-2.34; P = .007; I = 0.0%) and worse PFS (HR = 2.25; 95% CI: 1.53-3.30; P < .001; I = 0.0%) in patients from Asia with higher nuclear YAP1 expression. Prognosis was also worse in patients with III-IV stage cancer (PFSHR = 2.09; 95% CI: 1.45-3.01; P < .001; I = 58.1%) and in patients treated with epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs) (OS HR = 1.59; 95% CI: 1.00-2.51; P = .048; I = 15.5%, and PFS HR = 2.35, 95% CI: 1.62-3.42; P < .001; I = 0.0%)., Conclusion: High expression of nuclear YAP1 was associated with shorter survival outcome in patients with NSCLC.

Similarities between embryonic development and tumorigenesis are reflected in biological behavior and gene expression. Although the gene signature during development and the clinical phenotype of different cancers show certain correlation pattern, the correlation between early embryo development and cancer remains largely unexplored. To compare the gene expression profile between development and cancer, our study analyzed the gene expression of chorionic villi samples at different gestational ages (6, 7, 8, 9, 10, 40 weeks) obtained from gene expression omnibus (GEO) datasets using correlation test. Then the villi development-related genes that gradually showed a positive correlation (upregulated) (n = 394) or negative correlation (downregulated) (n = 325) with time were used to construct protein-protein interaction (PPI) networks. Three subnetworks among the gradually upregulated genes and 3 subnetworks among the downregulated genes were identified using the molecular complex detection (MCODE) plugin in Cytoscape software. The most significant GO terms for villi-correlated genes were immune, inflammatory response and cell division. These gene clusters were also dysregulated in lung squamous cell carcinoma (SCC). Moreover the prognostic value of the gene clusters was then analyzed with TCGA lung SCC data, which showed 4 clusters that were associated with prognosis. Our results demonstrate the gene expression similarity between development and lung SCC and identified development-associated gene clusters that could contain prognostic information for lung SCC patients.

Background: This meta-analysis of randomized controlled trials aimed to comprehensively assess the efficacy and toxicity of cyclin-dependent kinase (CDK) 4/6 inhibitors in advanced breast cancer (ABC) with hormone-receptor positive (HR + ) and human epidermal growth factor receptor 2 negative (HER2 - ) disease., Methods: We performed a systematical search using Cochrane Library, PubMed, Embase, and Web of Science up to March 2018. Only phase 2 and 3 randomized clinical trials assessing the efficacy and toxicity of the combination regimen of CDK4/6 inhibitors plus hormone therapy compared with hormone therapy alone were eligible for this meta-analysis. The pooled analyses of relative risk (RR) and hazard ratio were carried out by Stata software., Results: A total of 7 randomized controlled trials including 3854 patients with HR + /HER2 - ABC were included in this meta-analysis. The pooled hazard ratio for progression-free survival was 0.54 (95% confidence interval, 0.49-0.59; P < .001), and the pooled RR for the objective response rate in all intent-to-treat patients was 1.51 (95% confidence interval, 1.26-1.81; P < .001). The pooled RRs for all grade adverse events (AEs) and grade 3/4 AEs were 1.07 (95% confidence interval, 1.03-1.11; P < .001) and 2.81 (95% confidence interval, 2.54-3.11; P < .001), respectively. However, to investigate the influence of CDK4/6 inhibitors on overall survival, sufficient follow-up is still needed., Conclusion: CDK4/6 inhibitors plus hormone therapy can significantly prolong the progression-free survival of patients with HR + /HER2 - ABC and improve the objective response rate compared to conventional hormone therapy alone. The combined regimen results in a higher risk of AEs, especially grade 3/4 AEs., (Copyright © 2018 Elsevier Inc. All rights reserved.)

Background: Incidences of graft rupture are associated with postoperative knee laxity after anterior cruciate ligament (ACL) reconstruction. Reports of postoperative knee laxity after ACL reconstruction using different femoral fixation techniques in several studies are controversial., Purpose: To compare, via meta-analysis of randomized controlled trials (RCTs), the clinical outcomes and postoperative knee laxity of autogenous hamstring ACL reconstruction using cortical button versus cross-pin femoral fixation., Study Design: Meta-analysis., Methods: This study followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. The online PubMed, EMBASE, and Cochrane Central Register of Controlled Trials databases were searched from inception to April 1, 2017. The study included only level 1 or 2 RCTs that compared cortical button and cross-pin femoral fixation for ACL reconstruction with hamstring autografts and that reported clinical outcomes or postoperative knee laxity. The Cochrane Collaboration's risk of bias tool was used to assess the risk of bias for all included studies. For the meta-analysis, the investigators extracted data on clinical outcomes measured by postoperative International Knee Documentation Committee (IKDC) score or Lysholm score and postoperative knee laxity defined as >5 mm side-to-side difference by the arthrometric measurement, Lachman test ≥2+, and pivot-shift test ≥2+. The risk ratio (RR) and its corresponding 95% confidence interval (CI) were computed for dichotomous data. Heterogeneity was assessed by I 2 tests., Results: A total of 6 RCTs with 445 patients were included. Statistical analysis of pooled data showed no significant difference between the cortical button and cross-pin groups on postoperative IKDC score (RR, 0.94; 95% CI, 0.88-1.02; P = .13; I 2 = 4%) and Lysholm score (RR, 0.97; 95% CI, 0.91-1.04; P = .45; I 2 = 0%). Postoperative knee laxity was reported in 5 studies, and no significant difference was found between the 2 groups (RR, 1.49; 95% CI, 0.83-2.68; P = .18; I 2 = 37%)., Conclusion: Cortical button femoral fixation for autogenous hamstring ACL reconstruction had no significant difference in terms of clinical outcomes and postoperative knee laxity compared with cross-pin femoral fixation.

Background: Programmed cell death-ligand 1 (PD-L1) seemed to be associated with the outcomes of non-small cell lung cancer. However the prognostic role of PD-L1 expression among NSCLC remained unclear and inconsistent. The aim of the study set out to evaluate the correlation between PD-L1 expression and the prognosis of patients that developed NSCLC., Methods: Identified literatures were extracted of various electronic databases and a meta-analysis was performed to evaluate the prognostic role of PD-L1 among NSCLC patients., Results: Totally 25 studies from 11 countries containing 5861 patients were included in the meta-analysis. The pooled hazard ratios (HRs) for overall survival (OS) and progression-free survival (PFS) were 1.176 (95% CI: 1.016-1.361, P = 0.029) and 1.170 (95% CI: 0.984-1.392, P = 0.076), respectively. High PD-L1 expression on NSCLC tissue was also related with worse OS among Asian patients (HR = 1.381, 95% CI: 1.127-1.629, P = 0.002), adenocarcinomas (HR = 1.899, 95% CI: 1.306-2.762, P = 0.001) and poor PFS in non-Asian patients (HR = 1.695, 95% CI: 1.158-2.480, P = 0.002). Sensitivity analysis indicated that removal of any particular included literature won't affect the pooled results. Publication bias among the studies was not significant neither., Conclusions: PD-L1 expression is a prognostic factor related with poor survival among patients that developed NSCLC., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Purpose: Numerous studies have reported the prognostic significance of lymphocyte-to-monocyte ratio (LMR) in malignancies, but its prognostic value among lung cancer remains controversial. This meta-analysis aimed to explore the prognostic significance of LMR in lung cancer patients., Results: Eight studies including 3954 patients were included in this meta-analysis. Pooled results indicated that low LMR was significantly associated with poorer progression-free survival (hazard ratio (HR): 1.431, 95% confidence interval (CI): 1.294-1.582, p < 0.001) and overall survival (OS) (HR: 1.651, 95% CI: 1.306-2.086, p < 0.001), compared with high LMR. Similar results were observed in subgroups regardless of treatment, LMR cut-off value, or districts. However, no significant correlation between the LMR and OS was observed in the small cell lung cancer (SCLC) subgroup (HR = 1.262, 95% CI: 0.864-1.841, p = 0.229)., Materials and Methods: Identified literatures were extracted and retrieved from PubMed, Embase, Web of Science, and the Cochrane Library databases; All eligible studies focused on the association between LMR and the prognosis of lung cancer., Conclusions: Low LMR is associated with poor outcomes among lung cancer patients. Further studies are needed to discuss the correlation between LMR and lung cancer prognosis., Competing Interests: CONFLICTS OF INTEREST None.

Epidermal growth factor receptor (EGFR) T790M mutation accounted for over half of drug resistance cases in EGFR-mutant non-small cell lung cancer (NSCLC) patients treated with EGFR tyrosine kinase inhibitors (TKIs) and led to different outcomes. This study aimed to assess the prognostic role of T790M in NSCLC patients treated with EGFR-TKIs that developed drug resistance. Eligible literatures were reviewed from various databases and a meta-analysis was performed to evaluate the prognostic role of T790M mutation in EGFR-TKIs treated patients that went progression. Three studies containing 192 patients were included in the meta-analysis. The pooled hazard ratios (HRs) for overall survival (OS) and progression-free survival (PFS) were 0.66 (95% CI 0.49-0.89, P = 0.007) and 0.53 (95% CI 0.35-0.79, P = 0.002) respectively. Subgroups analyses were also performed on OS and PFS according to patients' districts, gender and histological type. In conclusion, T790M as a common mutation to cause drug-resistance in EGFR-TKIs treated NSCLC patients may be a favorable prognostic factor on OS and PFS both. Further studies are necessary to demonstrate the prognostic role of secondary T790M in NSCLC patients., Competing Interests: CONFLICTS OF INTEREST None.

Background: To date, literature has emerged that shows contradictory results about the prognostic role of microvessel density (MVD) in esophageal squamous cell cancer (ESCC). The aim of the study set out to evaluate the correlation between MVD and the prognosis of ESCC., Methods: Identified publications from various databases were obtained and reviewed. A meta-analysis was performed to evaluate the prognostic role of MVD among ESCC patients., Results: A total of 11 eligible studies containing 891 ESCC cases were included in the meta-analysis. The pooled hazard ratio for overall survival was 2.39 (95% confidence interval 1.92-2.96, P < .001). Heterogeneity among the studies was not significant, and publication bias was not found. Subgroup analyses were also performed on different issues, such as districts, antibodies, and median age., Conclusion: High MVD is a prognostic factor among ESCC that indicated worse prognosis in these patients. More studies are needed, and through abundant evidence, the topic could be re-evaluated by then.

The study of the past, present, and future state and dynamics of the tropical natural forest cover (NFC) might help to better understand the pattern of deforestation and fragmentation as well as the influence of social and natural processes. The obtained information will support the development of effective conservation policies and strategies. In the present study, we used historical data of the road network, topography, and climatic productivity to reconstruct NFC maps of Hainan Island, China, from the 1950s to the 2010s, using the random forest algorithm. We investigated the spatial and temporal patterns of NFC change from the 1950s to the 2010s and found that it was highly dynamic in both space and time. Our data showed that grid cells with low NFC were more vulnerable to NFC decrease, suggesting that conservation actions regarding natural forests need to focus on regions with low NFC and high ecological value. We also identified the hot spots of NFC change, which provides insights into the dynamic changes of natural forests over time., Competing Interests: The authors declare there are no competing interests.

Purpose: The outcome of pretreatment epidermal growth factor receptor (EGFR) T790M mutation in EGFR mutant non-small cell lung cancer (NSCLC) patients treated with EGFR tyrosine kinase inhibitors (TKIs) is controversial, this study aimed to evaluate the prognostic role of pretreatment T790M in advanced NSCLC patients treated with EGFR TKIs., Results: A total of 7 eligible studies containing 179 cases and 281 controls were included in the meta-analysis. The pooled hazard ratios (HRs) for progression-free survival (PFS) and overall survival (OS) were 2.21 (95% CI 1.49-3.29, P<0.001) and 1.24 (95% CI 0.90-1.71, P=0.186), respectively. We also did subgroup analyses on OS and PFS according to patients from various districts., Methods: Identified literatures from various databases were reviewed. A meta-analysis was performed to evaluate the prognostic role of pretreatment EGFR T790M in advanced EGFR mutant patients treated with EGFR TKIs., Conclusions: Pretreatment T790M may be a poor prognostic factor for PFS in advanced NSCLC patients treated with EGFR TKIs. However, no significant prognostic effect was found between pretreatment T790M mutation and OS. More studies are needed to demonstrate the prognostic role of pretreatment T790M mutation in advanced NSCLC patients., Competing Interests: CONFLICTS OF INTEREST We declare no conflict of interest.

Background: The association between facial flushing after alcohol consumption and the risk of cancer remains controversial. The aim of this study was to evaluate the relation between facial flushing and cancer risk., Methods: PubMed, EMBASE, and Cochrane Library were searched for relevant literature. The patients' baseline characteristics and estimated risks were extracted. Odds ratios (ORs) with 95% confidence intervals (CIs) were pooled to estimate the risk of facial flushing in cancer, and subgroup analysis was performed., Results: Ten studies with 89,376 participants from East Asia were included. The pooled OR of facial flushing in all cancers was 1.43 (95% CI, 1.08-1.91), with the pooled ORs of 1.94 (95% CI, 1.33-2.83) and 0.95 (95% CI, 0.80-1.12) in men and women, respectively. The pooled ORs were also estimated in different cancer types., Conclusion: Our results showed that facial flushing response to alcohol was associated with higher cancer risk in men in East Asia, especially in esophageal squamous cell carcinoma, yet facial flushing was not significantly associated with cancer risk among women.

This study investigated the in vitro and in vivo antioxidant activities of the flavonoids rich extract from Rhodomyrtus tomentosa Hassk (R. tomentosa) berries. The in vitro antioxidant assay demonstrated that the flavonoids rich extract (62.09% rutin equivalent) extracted by ethanol and purified by AB-8 macroporous resin was strong in reducing power, superoxide radical, hydroxyl radical and DPPH radical scavenging activity, as well as inhibiting lipid peroxidation. In the in vivo assays, the flavonoids rich extract significantly enhanced the activities of antioxidant enzymes in serums of mice after they were administered with the extract. The results suggested that the flavonoids rich extract from R. tomentosa fruits possesses potent antioxidant properties. In addition, the chemical compositions of flavonoids rich extract were identified by UPLC-TOF-MS/MS. Six flavonoids were tentatively identified as myricetin, quercetin, dihydromyricetin, kaempferol, quercetin 7,4'-diglucoside and vitexin. Therefore, R. tomentosa berries could be used as a new source of antioxidant ingredient., (Copyright © 2014 Elsevier Ltd. All rights reserved.)

Limestone hills are an unusual habitat for primates, prompting them to evolve specific behavioral adaptations to the component karst habitat. From September 2012 to August 2013, we collected data on the diet of one group of Assamese macaques living in limestone forests at Nonggang National Nature Reserve, Guangxi Province, China, using instantaneous scan sampling. Assamese macaques were primarily folivorous, young leaves accounting for 75.5% and mature leaves an additional 1.8% of their diet. In contrast, fruit accounted for only 20.1%. The young leaves of Bonia saxatilis, a shrubby, karst-endemic bamboo that is superabundant in limestone hills, comprised the bulk of the average monthly diet. Moreover, macaques consumed significantly more bamboo leaves during the season when the availability of fruit declined, suggesting that bamboo leaves are an important fallback food for Assamese macaques in limestone forests. In addition, diet composition varied seasonally. The monkeys consumed significantly more fruit and fewer young leaves in the fruit-rich season than in the fruit-lean season. Fruit consumption was positively correlated with fruit availability, indicating that fruit is a preferred food for Assamese macaques. Of seventy-eight food species, only nine contributed >0.5% of the annual diet, and together these nine foods accounted for 90.7% of the annual diet. Our results suggest that bamboo consumption represents a key factor in the Assamese macaque's dietary adaptation to limestone habitat., (© 2014 Wiley Periodicals, Inc.)

The optimal condition of spray drying purified flavonoids extract from R. tomentosa berries was studied by response surface methodology. The optimized condition for microencapsulation was of maltodextrin to gum Arabic ratio 1 : 1.3, total solid content 27.4%, glycerol monostearate content 0.25%, and core to coating material ratio 3 : 7, resulting in EE 91.75%. Prepared at the optimized condition, the flavonoids extract microcapsules (FEMs) were irregularly spherical particles with low moisture content (3.27%), high solubility (92.35%), and high bulk density (0.346 g/cm(3)). DPPH radical scavenging activity of FEMs was not decreased after spray drying (P > 0.05) and higher than those in citric acid and rutin at the same concentration. Moreover, FEMs effectively retarded the oxidation of fresh lard during the 10-day storage period compared with vitamin C, nonencapsulated flavonoids extract, and rutin. Therefore, FEMs produced at the optimized condition could be used as powder ingredients with antioxidant capacities.