Network pharmacology study of Citrus reticulata and Pinellia ternata in the treatment of non-small cell lung cancer.

It has been recognized that Citrus reticulata and Pinellia ternata have a good therapeutic effect on NSCLC. However, the potential mechanism of C. reticulata and P. ternata in the treatment of NSCLC based on network pharmacology analysis is not clear. The "Drug-Component-Target-Disease" network was constructed by Cytoscape, and the protein interaction (PPI) network was constructed by STRING. Our study indicated that 18 active ingredients of C. reticulata and P. Ternata were screened from the TCMSP database, and 56 target genes of C. reticulata and P. Ternata for the treatment of NSCLC were identified, and we constructed the "Drug-Component-Target-Disease" network. In this study, we screened 56 PPI core genes to establish a PPI network. We concluded that the network pharmacology mechanism of the effect of C. reticulata and P. Ternata  on NSCLC may be closely related to the protein expressed by TP53, ESR1, FOS, NCOA3 and MAPK8, and these may play the therapeutic roles by regulating the IL-17 signaling pathway, antigen processing and presentation, microRNAs in cancer and endocrine resistance.

DeepGene: an advanced cancer type classifier based on deep learning and somatic point mutations.

BACKGROUND: With the developments of DNA sequencing technology, large amounts of sequencing data have become available in recent years and provide unprecedented opportunities for advanced association studies between somatic point mutations and cancer types/subtypes, which may contribute to more accurate somatic point mutation based cancer classification (SMCC). However in existing SMCC methods, issues like high data sparsity, small volume of sample size, and the application of simple linear classifiers, are major obstacles in improving the classification performance. RESULTS: To address the obstacles in existing SMCC studies, we propose DeepGene, an advanced deep neural network (DNN) based classifier, that consists of three steps: firstly, the clustered gene filtering (CGF) concentrates the gene data by mutation occurrence frequency, filtering out the majority of irrelevant genes; secondly, the indexed sparsity reduction (ISR) converts the gene data into indexes of its non-zero elements, thereby significantly suppressing the impact of data sparsity; finally, the data after CGF and ISR is fed into a DNN classifier, which extracts high-level features for accurate classification. Experimental results on our curated TCGA-DeepGene dataset, which is a reformulated subset of the TCGA dataset containing 12 selected types of cancer, show that CGF, ISR and DNN all contribute in improving the overall classification performance. We further compare DeepGene with three widely adopted classifiers and demonstrate that DeepGene has at least 24% performance improvement in terms of testing accuracy. CONCLUSIONS: Based on deep learning and somatic point mutation data, we devise DeepGene, an advanced cancer type classifier, which addresses the obstacles in existing SMCC studies. Experiments indicate that DeepGene outperforms three widely adopted existing classifiers, which is mainly attributed to its deep learning module that is able to extract the high level features between combinatorial somatic point mutations and cancer types.

Adaptive noise estimation from highly textured hyperspectral images.

Accurate approximation of noise in hyperspectral (HS) images plays an important role in better visualization and image processing. Conventional algorithms often hypothesize the noise type to be either purely additive or of a mixed noise type for the signal-dependent (SD) noise component and the signal-independent (SI) noise component in HS images. This can result in application-driven algorithm design and limited use in different noise types. Moreover, as the highly textured HS images have abundant edges and textures, existing algorithms may fail to produce accurate noise estimation. To address these challenges, we propose a noise estimation algorithm that can adaptively estimate both purely additive noise and mixed noise in HS images with various complexities. First, homogeneous areas are automatically detected using a new region-growing-based approach, in which the similarity of two pixels is calculated by a robust spectral metric. Then, the mixed noise variance of each homogeneous region is estimated based on multiple linear regression technology. Finally, intensities of the SD and SI noise are obtained with a modified scatter plot approach. We quantitatively evaluated our algorithm on the synthetic HS data. Compared with the benchmarking and state-of-the-art algorithms, the proposed algorithm is more accurate and robust when facing images with different complexities. Experimental results with real Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) images further demonstrated the superiority of our algorithm.

Exploratory Training for Universal Lesion Detection: Enhancing Lesion Mining Quality Through Temporal Verification.

Universal lesion detection (ULD) has great value in clinical practice as it can detect various lesions across multiple organs. Deep learning-based detectors have great potential but require high-quality annotated training data. In practice, due to cost, expertise requirements, and the diverse nature of lesions, incomplete annotations are encountered. Directly training ULD detectors under this condition can yield suboptimal results. Leading pseudo-label methods rely on a dynamic lesion-mining mechanism operating at the mini-batch level to address this issue. However, the quality of mined lesions is inconsistent across different iterations, potentially limiting performance enhancement. Inspired by the observation that deep models learn concepts with increasing complexity, we propose an exploratory-training-based ULD (ET-ULD) method to assess the reliability of mined lesions over time. Our approach uses a teacher-student detection model where the teacher mines suspicious lesions, which are then combined with incomplete annotations to train the student. On top of that, we design a bounding-box bank to record the mining timestamps. Each image is trained in several rounds, allowing us to get a sequence of timestamps for the mined lesions. If a mined lesion consistently appears, it is likely to be a true lesion, otherwise, it may just be a noise. This serves as a crucial criterion for selecting reliable mined lesions for retraining. Experimental results show that ET-ULD surpass existing state-of-the-art methods on two distinct lesion image datasets. Notably, on the DeepLesion dataset, ET-ULD achieved a 5.4% improvement in Average Precision (AP) over the previous methods, demonstrating its superior performance.

Multi-view attribute learning and context relationship encoding enhanced segmentation of lung tumors from CT images.

Graph convolutional neural networks (GCN) have shown the promise in medical image segmentation due to the flexibility of representing diverse range of image regions using graph nodes and propagating knowledge via graph edges. However, existing methods did not fully exploit the various attributes of image nodes and the context relationship among their attributes. We propose a new segmentation method with multi-similarity view enhancement and node attribute context learning (MNSeg). First, multiple views were formed by measuring the similarities among the image nodes, and MNSeg has a GCN based multi-view image node attribute learning (MAL) module to integrate various node attributes learnt from multiple similarity views. Each similarity view contains the specific similarities among all the image nodes, and it was integrated with the node attributes from all the channels to form the enhanced attributes of image nodes. Second, the context relationships among the attributes of image nodes are formulated by a transformer-based context relationship encoding (CRE) strategy to propagate these relationships across all the image nodes. During the transformer-based learning, the relationships were estimated based on the self-attention on all the image nodes, and then they were encoded into the learned node features. Finally, we design an attention at attribute category level (ACA) to discriminate and fuse the learnt diverse information from MAL, CRE, and the original node attributes. ACA identifies the more informative attribute categories by adaptively learn their importance. We validate the performance of MNSeg on a public lung tumor CT dataset and an in-house non-small cell lung cancer (NSCLC) dataset collected from the hospital. The segmentation results show that MNSeg outperformed the compared segmentation methods in terms of spatial overlap and the shape similarities. The ablation studies demonstrated the effectiveness of MAL, CRE, and ACA. The generalization ability of MNSeg was proved by the consistent improved segmentation performances using different 3D segmentation backbones.

Design and Strengthening of Superhydrophobic Coatings: The Influence of Intermediate Layers.

The employment of intermediate layer technology to improve the mechanical stability of superhydrophobic coatings (SHCs) is an acknowledged tool, but the mechanism by which intermediate layers, especially different ones, affect superhydrophobic composite coatings is not clear. In this work, a series of SHCs based on the strengthening of the intermediate layer were fabricated by employing polymers with different elastic moduli such as polydimethylsiloxane (PDMS), polyurethane (PU), epoxy (EP) resin, as well as graphite/SiO2 hydrophobic components. Following that, the effect of different elastic modulus polymers as an intermediate layer on the durability of SHCs was investigated. From the perspective of elastic buffering, the strengthening mechanism of elastic polymer-based SHCs was clarified. Furthermore, from the perspective of self-lubrication, the wear resistance mechanism of self-lubricating hydrophobic components in the SHCs was elucidated. Also, the prepared coatings exhibited excellent acid and alkali resistance, self-cleaning, anti-stain, and corrosion resistance. This work confirms that low-elastic-modulus polymers can also play the role of buffering external impact energy by elastic deformation even as an intermediate layer, and provides theoretical guidance for the development of SHCs with robustness.

Resection of Scar Tissue in Rats With Spinal Cord Injury Can Promote the Expression of ßâ ¢-tubulin in the Injured Area.

BACKGROUND: Previous research shows that scar tissue formed in the injured area after spinal cord injury blocks nerve regeneration and functional recovery. However, those researchers tried to prevent the formation of scar after spinal cord injury to promote nerve regeneration, but it ran counter to their desire, indicating that the formation of scar might play a role in functional recovery after spinal cord injury. METHODS: To investigate roles of scar formation on functional repair after spinal cord injury, we selected several different key time points to resect the scar tissue formed after spinal cord injury based on the rat models of the T8-T9 transection injury of spinal cord. First, the recovery of motor function was evaluated by Basso Beattie Bresnahan score and electrophysiologic examination; second, the pathologic features of functional recovery were analyzed mainly by immunofluorescence ßⅢ-tubulin staining; finally, the genes related to the recovery of motor function were predicted by high-throughput sequencing analysis. RESULTS: Immunofluorescence results showed that the resection of scar tissue promoted significantly the recovery of motor function and the expression of ßⅢ-tubulin in the injured area in the second week after spinal cord injury. Furthermore, RNA-seq studies showed that Tubb3 and Tubb6 gene expression and other neural regeneration pathways were significantly different in the tissue before and after early resection. CONCLUSIONS: Excision of scar tissue in the second week promoted nerve regeneration after spinal cord injury. Tubb3 and Tubb6 genes might be the potential targets for spinal cord injury therapy in our study.

Hierarchical cumulative network for unsupervised medical image registration.

Unsupervised deep learning techniques have gained increasing popularity in deformable medical image registration However, existing methods usually overlook the optimal similarity position between moving and fixed images To tackle this issue, we propose a novel hierarchical cumulative network (HCN), which explicitly considers the optimal similarity position with an effective Bidirectional Asymmetric Registration Module (BARM). The BARM simultaneously learns two asymmetric displacement vector fields (DVFs) to optimally warp both moving images and fixed images to their optimal similar shape along the geodesic path. Furthermore, we incorporate the BARM into a Laplacian pyramid network with hierarchical recursion, in which the moving image at the lowest level of the pyramid is warped successively for aligning to the fixed image at the lowest level of the pyramid to capture multiple DVFs. We then accumulate these DVFs and up-sample them to warp the moving images at higher levels of the pyramid to align to the fixed image of the top level. The entire system is end-to-end and jointly trained in an unsupervised manner. Extensive experiments were conducted on two public 3D Brain MRI datasets to demonstrate that our HCN outperforms both the traditional and state-of-the-art registration methods. To further evaluate the performance of our HCN, we tested it on the validation set of the MICCAI Learn2Reg 2021 challenge. Additionally, a cross-dataset evaluation was conducted to assess the generalization of our HCN. Experimental results showed that our HCN is an effective deformable registration method and achieves excellent generalization performance.

Topological structure and global features enhanced graph reasoning model for non-small cell lung cancer segmentation from CT.

Objective.Accurate and automated segmentation of lung tumors from computed tomography (CT) images is critical yet challenging. Lung tumors are of various sizes and locations and have indistinct boundaries adjacent to other normal tissues.Approach.We propose a new segmentation model that can integrate the topological structure and global features of image region nodes to address the challenges. Firstly, we construct a weighted graph with image region nodes. The graph topology reflects the complex spatial relationships among these nodes, and each node has its specific attributes. Secondly, we propose a node-wise topological feature learning module based on a new graph convolutional autoencoder (GCA). Meanwhile, a node information supplementation (GNIS) module is established by integrating specific features of each node extracted by a convolutional neural network (CNN) into each encoding layer of GCA. Afterwards, we construct a global feature extraction model based on multi-layer perceptron (MLP) to encode the features learnt from all the image region nodes which are crucial complementary information for tumor segmentation.Main results.Ablation study results over the public lung tumor segmentation dataset demonstrate the contributions of our major technical innovations. Compared with other segmentation methods, our new model improves the segmentation performance and has generalization ability on different 3D image segmentation backbones. Our model achieved Dice of 0.7827, IoU of 0.6981, and HD of 32.1743 mm on the public dataset 2018 Medical Segmentation Decathlon challenge, and Dice of 0.7004, IoU of 0.5704 and HD of 64.4661 mm on lung tumor dataset from Shandong Cancer Hospital.Significance. The novel model improves automated lung tumor segmentation performance especially the challenging and complex cases using topological structure and global features of image region nodes. It is of great potential to apply the model to other CT segmentation tasks.

Near-Infrared Responsive Smart Superhydrophobic Coating with Self-Healing and Robustness Enhanced by Disulfide-Bonded Polyurethane.

Synergistic self-healing materials and inorganic particles to create self-healing superhydrophobic surfaces for improving their robustness is a common technique, but the suitability between the two is rarely mentioned. In this work, we developed a multifunctional superhydrophobic coating with room-temperature stability, mechanical stability, self-healing, and NIR stimuli response, in which self-healing polyurethane (PU) serves as the interface reinforcement layer and poly(dopamine) (PDA)-coated flower-like ZnO composite particles serve as the hydrophobic layer. A series of temperature-dependent self-healing PU materials were designed and synthesized by regulating the ratio of hard and soft chain segments in PU, and the relationship between the healing temperature of PU and the hydrophobic stability of the composite coatings was investigated. Based on dynamic hydrogen and disulfide bonds, PUs displayed excellent self-healing performance. Thanks to the self-healing and interfacial strengthening effect of PU and the photothermal properties of PDA, the composite coating exhibits not only excellent mechanical stability but also rapid self-healing ability in response to NIR stimuli. Furthermore, the smart coating demonstrated superior self-cleaning and corrosion resistance. This work provides a reference for developing strong and stable water-repellent reversible superhydrophobic coatings with great potential and promising future.

The optimal neoadjuvant chemotherapy regimen for locally advanced gastric and gastroesophageal junction adenocarcinoma: a systematic review and Bayesian network meta-analysis.

BACKGROUND: Neoadjuvant chemotherapy (NAC) for locally advanced gastric and gastroesophageal junction adenocarcinoma (LAGC) has been recommended in several guidelines. However, there is no global consensus about the optimum of NAC regimens. We aimed to determine the optimal NAC regimen for LAGC. METHODS: A systematic review and Bayesian network meta-analysis was performed. The literature search was conducted from inception to June 2022. The odds ratio (OR) value and 95% confidence interval (95% CI) were used for assessment of R0 resection rate and pathological complete response rate (pCR) as primary outcomes. The hazard ratio (HR) value and 95% CI were interpreted for the assessment of overall survival (OS) and disease-free survival (DFS) as second outcomes. The risk ratio (RR) value and 95% CI were used for safety assessment. RESULTS: Twelve randomized controlled trials were identified with 3846 eligible participants. The network plots for R0 resectability, OS, and DFS constituted closed loops. The regimens of TPF (taxane and platinum plus fluoropyrimidine), ECF (epirubicin and cisplatin plus fluorouracil), and PF (platinum plus fluoropyrimidine) showed a meaningful improvement of R0 resectability, as well as OS and/or DFS, compared with surgery (including surgery-alone and surgery plus postoperative adjuvant chemotherapy). Importantly, among these regimens, TPF regimen showed significant superiority in R0 resection rate (versus ECF regimen), OS (versus ECF regimen), DFS (versus PF and ECF regimens), and pCR (versus PF regimen). CONCLUSIONS: The taxane-based triplet regimen of TPF is likely the optimal neoadjuvant chemotherapy regimen for LAGC patients.

Convolutional bi-directional learning and spatial enhanced attentions for lung tumor segmentation.

BACKGROUND AND OBJECTIVE: Accurate lung tumor segmentation from computed tomography (CT) is complex due to variations in tumor sizes, shapes, patterns and growing locations. Learning semantic and spatial relations between different feature channels, image regions and positions is critical yet challenging. METHODS: We propose a new segmentation method, PRCS, by learning and integrating multi-channel contextual relations, and spatial and position dependencies across image regions. Firstly, to extract contextual relationships between different deep image feature tensor channels, we propose a new convolutional bi-directional gated recurrent unit based module for forward and backward learning. Secondly, a novel cross-channel region-level attention mechanism is proposed to discriminate the contributions of different local regions and associated features in the global learning process. Finally, spatial and position dependencies are formulated by a new position-enhanced self-attention mechanism. The new attention can measure the diverse contributions of other positions to a target position and obtain an enhanced adaptive feature vector for the target position. RESULTS: Our model outperformed seven state-of-the-art segmentation methods on both public and in-house lung tumor datasets in terms of spatial overlapping and shape similarity. Ablation study results proved the effectiveness of three technical innovations and generalization ability on different 3D CNN segmentation backbones. CONCLUSION: The proposed model enhanced the learning and propagation of contextual, spatial and position relations in 3D volumes, improving lung tumours' segmentation performance with large variations and indistinct boundaries. PRCS provides an effective automated approach to support precision diagnosis and treatment planning of lung cancer.

Graph based multi-scale neighboring topology deep learning for kidney and tumor segmentation.

Objective.Effective learning and modelling of spatial and semantic relations between image regions in various ranges are critical yet challenging in image segmentation tasks.Approach.We propose a novel deep graph reasoning model to learn from multi-order neighborhood topologies for volumetric image segmentation. A graph is first constructed with nodes representing image regions and graph topology to derive spatial dependencies and semantic connections across image regions. We propose a new node attribute embedding mechanism to formulate topological attributes for each image region node by performing multi-order random walks (RW) on the graph and updating neighboring topologies at different neighborhood ranges. Afterwards, multi-scale graph convolutional autoencoders are developed to extract deep multi-scale topological representations of nodes and propagate learnt knowledge along graph edges during the convolutional and optimization process. We also propose a scale-level attention module to learn the adaptive weights of topological representations at multiple scales for enhanced fusion. Finally, the enhanced topological representation and knowledge from graph reasoning are integrated with content features before feeding into the segmentation decoder.Main results.The evaluation results over public kidney and tumor CT segmentation dataset show that our model outperforms other state-of-the-art segmentation methods. Ablation studies and experiments using different convolutional neural networks backbones show the contributions of major technical innovations and generalization ability.Significance.We propose for the first time an RW-driven MCG with scale-level attention to extract semantic connections and spatial dependencies between a diverse range of regions for accurate kidney and tumor segmentation in CT volumes.

Association of 25-hydroxyvitamin D level with survival outcomes in female breast cancer patients: A meta-analysis.

Conflicting results have been reported on the association of blood vitamin D level with prognosis in women with breast cancer. This meta-analysis aimed to evaluate the association between blood 25-hydroxyvitamin D level and survival outcomes in female breast cancer patients. Two authors independently searched PubMed and Embase databases from their inception to August 25, 2020. Prospective or retrospective cohort studies evaluating the association between blood 25-hydroxyvitamin D level and survival outcomes in women with breast cancer were included. Outcome measures included overall survival (OS), breast cancer-specific survival (BCSS), and disease-free survival (DFS). Twelve studies involving 8574 female breast cancer patients were identified and analyzed. When compared the lowest with the highest category of 25-hydroxyvitamin D level, the pooled adjusted hazard ratio (HR) was 1.57 (95 % confidence interval [CI] 1.35-1.83) for OS, 1.98 (95 % CI 1.55-2.53) for DFS, and 1.44 (95 % CI 1.14-1.81) for BCSS. This meta-analysis indicates that lower blood 25-hydroxyvitamin D level is significantly associated with reduced survival among female breast cancer patients. Additional clinical trials are required to investigate whether vitamin D supplement can improve survival outcomes in these patients.

Nano-realgar suppresses lung cancer stem cell growth by repressing metabolic reprogramming.

BACKGROUND: Recent studies in cancer biology suggest that metabolic glucose reprogramming is a potential target for cancer treatment. However, little is known about drug intervention in the glucose metabolism of cancer stem cells (CSCs) and its related underlying mechanisms. METHODS: The crude realgar powder was Nano-grinded to meets the requirements of Nano-pharmaceutical preparations, and Nano-realgar solution (NRS) was prepared for subsequent experiments. Isolation and characterization of lung cancer stem cells (LCSCs) was performed by magnetic cell sorting (MACS) and immunocytochemistry, respectively. Cell viability and intracellular glucose concentration were detected by MTT assay and glucose oxidase (GOD) kit. Protein expressions related to metabolic reprogramming was detected by ELISA assay. Determination of the expression of HIF-1α and PI3K/Akt/mTOR pathways was carried out by RT-PCR and western blotting analysis. A subcutaneous tumor model in BALB/c-nu mice was successfully established to evaluate the effects of Nano-realgar on tumor growth and histological structure, and the expression of HIF-1α in tumor tissues was measured by immunofluorescence. RESULTS: Nano-realgar inhibits cell viability and induces glucose metabolism in LCSCs, and inhibits protein expression related to metabolic reprogramming in a time- and dose-dependent manner. Nano-realgar downregulated the expression of HIF-1α and PI3K/Akt/mTOR pathways in vitro and in vivo. Nano-realgar inhibits tumor growth and changes the histological structure of tumors through in vivo experiments and consequently inhibits the constitutive activation of HIF-1α signaling. CONCLUSIONS: These results reveal that Nano-realgar inhibits tumor growth in vitro and in vivo by repressing metabolic reprogramming. This inhibitory effect potentially related to the downregulation HIF-1α expression via PI3K/Akt/mTOR pathway.

In vitro corrosion resistance of a Ta2O5 nanofilm on MAO coated magnesium alloy AZ31 by atomic layer deposition.

Micro-arc oxidation (MAO) coating with outstanding adhesion strength to Mg alloys has attracted more and more attention. However, owing to the porous structure, aggressive ions easily invaded the MAO/substrate interface through the through pores, limiting long-term corrosion resistance. Therefore, a dense and biocompatible tantalum oxide (Ta2O5) nanofilm was deposited on MAO coated Mg alloy AZ31 through atomic layer deposition (ALD) technique to seal the micropores and regulate the degradation rate. Surface micrography, chemical compositions and crystallographic structure were characterized using FE-SEM, EDS, XPS and XRD. The corrosion resistance of all samples was evaluated through electrochemical and hydrogen evolution tests. Results revealed that the Ta2O5 film mainly existed in the form of amorphousness. Moreover, uniform deposition of Ta2O5 film and effective sealing of micropores and microcracks in MAO coating were achieved. The current density (i corr) of the composite coating decreased three orders of magnitude than that of the substrate and MAO coating, improving corrosion resistance. Besides, the formation and corrosion resistance mechanisms of the composite coating were proposed.

In vitro degradation and cytocompatibility of a low temperature in-situ grown self-healing Mg-Al LDH coating on MAO-coated magnesium alloy AZ31.

Basically, Mg-Al layered double hydroxide (LDH) coatings are prepared on the surface of micro-arc oxidation (MAO) coated magnesium (Mg) alloys at a high temperature or a low pH value. This scenario leads to the growth rate of LDH coating inferior to the dissolution rate of the MAO coating. This in turn results in limited corrosion resistance of the composite coating. In this study, a Mg-Al LDH coating on MAO-coated Mg alloy AZ31 is prepared through a water bath with a higher pH (13.76) at a lower temperature (60 °C). FE-SEM, EDS, XRD, XPS, and FT-IR are applied to analyze the surface morphology, chemical compositions, and growth process. Electrochemical polarization, electrochemical impedance spectroscopy (EIS) and hydrogen evolution tests are employed to evaluate the corrosion resistance of the samples. The results disclose that the MAO coating is completely covered by the nanosheet-structured LDH coating with a thickness of approximately 3.8 µm. The corrosion current density of the MAO-LDH composite coating is decreased four orders of magnitude in comparison to its substrate; the presence of a wide passivation region in anodic polarization branch demonstrates its strong self-healing ability, indicating the hybrid coating possesses excellent corrosion resistance. The formation mechanism of the LDH coating on the MAO-coated Mg alloy is proposed. Furthermore, the cytocompatibility is assessed via an indirect extraction test for MC3T3-E1 pre-osteoblasts, which indicates an acceptable cytocompatibility of osteoblasts for the composite coating.

Apoptosis induction by alantolactone in breast cancer MDA-MB-231 cells through reactive oxygen species-mediated mitochondrion-dependent pathway.

Alantolactone is a sesquiterpene lactone isolated from Inula helenium L. Although alantolactone possesses anti-inflammation and apoptosis-induction activities, the underlying mechanism of anti-cancer effect on human breast cancer cells remains largely unknown. In this study, we explored the possibility of alantolactone as an apoptosis-inducing cytotoxic agent using MDA-MB-231 cells as in vitro model. Alantolactone significantly induced its apoptosis, demonstrated by cell cycle analysis, annexin V-APC/7-AAD double staining and dUTP nick end labeling. Additionally, alantolactone triggered the mitochondrial-mediated caspase cascade apoptotic pathway, which was confirmed by increased Bax/Bcl-2 ratio, loss of MMP, release of cytc from mitochondria to cytoplasm, activation of caspase 9/3, and subsequent cleavage of PARP. Z-VAD-FMK partially prevented apoptosis induced by alantolactone. Alantolactone provoked the production of ROS, while NAC (a scavenger of ROS) reversed alantolactone-mediated depolarization of MMP and apoptosis. Alantolactone modulated the activities of MAPKs. As expected, cotreatment with SB203580, SP600125 or U0126 could reduced the apoptotic rate. Furthermore, alantolactone decreased the protein expressions of p-NF-kB p65 and p-STAT3, increased p-c-Jun level in a dose-dependent manner. These findings suggested that alantolactone possessed anticancer activity via ROS-mediated mitochondrial dysfunction involving MAPK pathway, and had an effect on the transcription factors of NF-kB, AP-1 and STAT3.

Earthquake Safety Training through Virtual Drills.

Recent popularity of consumer-grade virtual reality devices, such as the Oculus Rift and the HTC Vive, has enabled household users to experience highly immersive virtual environments. We take advantage of the commercial availability of these devices to provide an immersive and novel virtual reality training approach, designed to teach individuals how to survive earthquakes, in common indoor environments. Our approach makes use of virtual environments realistically populated with furniture objects for training. During a training, a virtual earthquake is simulated. The user navigates in, and manipulates with, the virtual environments to avoid getting hurt, while learning the observation and self-protection skills to survive an earthquake. We demonstrated our approach for common scene types such as offices, living rooms and dining rooms. To test the effectiveness of our approach, we conducted an evaluation by asking users to train in several rooms of a given scene type and then test in a new room of the same type. Evaluation results show that our virtual reality training approach is effective, with the participants who are trained by our approach performing better, on average, than those trained by alternative approaches in terms of the capabilities to avoid physical damage and to detect potentially dangerous objects.

Saliency-Based Lesion Segmentation Via Background Detection in Dermoscopic Images.

The segmentation of skin lesions in dermoscopic images is a fundamental step in automated computer-aided diagnosis of melanoma. Conventional segmentation methods, however, have difficulties when the lesion borders are indistinct and when contrast between the lesion and the surrounding skin is low. They also perform poorly when there is a heterogeneous background or a lesion that touches the image boundaries; this then results in under- and oversegmentation of the skin lesion. We suggest that saliency detection using the reconstruction errors derived from a sparse representation model coupled with a novel background detection can more accurately discriminate the lesion from surrounding regions. We further propose a Bayesian framework that better delineates the shape and boundaries of the lesion. We also evaluated our approach on two public datasets comprising 1100 dermoscopic images and compared it to other conventional and state-of-the-art unsupervised (i.e., no training required) lesion segmentation methods, as well as the state-of-the-art unsupervised saliency detection methods. Our results show that our approach is more accurate and robust in segmenting lesions compared to other methods. We also discuss the general extension of our framework as a saliency optimization algorithm for lesion segmentation.