NAMPT enhances LOX expression and promotes metastasis in human chondrosarcoma cells by inhibiting miR-26b-5p synthesis.

Chondrosarcoma is a malignant bone tumor that emerges from abnormalities in cartilaginous tissue and is related with lung metastases. Nicotinamide phosphoribosyltransferase (NAMPT) is an adipocytokine reported to enhance tumor metastasis. Our results from clinical samples and the Gene Expression Omnibus data set reveal that NAMPT levels are markedly higher in chondrosarcoma patients than in normal individuals. NAMPT stimulation significantly increased lysyl oxidase (LOX) production in chondrosarcoma cells. Additionally, NAMPT increased LOX-dependent cell migration and invasion in chondrosarcoma by suppressing miR-26b-5p generation through the c-Src and Akt signaling pathways. Overexpression of NAMPT promoted chondrosarcoma metastasis to the lung in vivo. Furthermore, knockdown of LOX counteracted NAMPT-facilitated metastasis. Thus, the NAMPT/LOX axis presents a novel target for treating the metastasis of chondrosarcoma.

The stimulation of exosome generation by visfatin polarizes M2 macrophages and enhances the motility of chondrosarcoma.

Chondrosarcoma is a malignant bone tumor that arises from abnormalities in cartilaginous tissue and is associated with lung metastases. Extracellular vesicles called exosomes are primarily used as mediators of intercellular signal transmission to control tumor metastasis. Visfatin is an adipokine reported to enhance tumor metastasis, but its relationship with exosome generation in chondrosarcoma motility remains undetermined. Our results found that overexpressing visfatin augments the production of exosomes from chondrosarcoma cells. Visfatin-treated chondrosarcoma exosomes educate macrophage polarization towards the M2 but not M1 phenotype. Interestingly, M2 macrophages polarized by exosomes return to chondrosarcoma cells to facilitate cell motility. According to these findings, chondrosarcoma cells emit more exosomes when treated with visfatin. The stimulation of exosome generation by visfatin polarizes M2 macrophages and enhances the motility of chondrosarcoma.

Visfatin Facilitates VEGF-D-Induced Lymphangiogenesis through Activating HIF-1α and Suppressing miR-2277-3p in Human Chondrosarcoma.

Chondrosarcoma is a malignant bone tumor that arises from abnormalities in cartilaginous tissue and is associated with lung metastases. Lymphangiogenesis plays an essential role in cancer metastasis. Visfatin is an adipokine reported to enhance tumor metastasis, but its relationship with VEGF-D generation and lymphangiogenesis in chondrosarcoma remains undetermined. Our results from clinical samples reveal that VEGF-D levels are markedly higher in chondrosarcoma patients than in normal individuals. Visfatin stimulation promotes VEGF-D-dependent lymphatic endothelial cell lymphangiogenesis. We also found that visfatin induces VEGF-D production by activating HIF-1α and reducing miR-2277-3p generation through the Raf/MEK/ERK signaling cascade. Importantly, visfatin controls chondrosarcoma-related lymphangiogenesis in vivo. Therefore, visfatin is a promising target in the treatment of chondrosarcoma lymphangiogenesis.

Melatonin inhibits chondrosarcoma cell proliferation and metastasis by enhancing miR-520f-3p production and suppressing MMP7 expression.

Chondrosarcoma has a high propensity to metastasize and responds poorly to chemotherapy and radiation treatment. The enzymatic activity of matrix metalloproteinases (MMPs) is very important in chondrosarcoma metastasis. Melatonin exhibits anticarcinogenic activity in many types of cancers by suppressing the expression of certain MMP family members, but this has not yet been clearly determined in chondrosarcoma. Our study demonstrates that MMP7 plays an essential role in chondrosarcoma cell proliferation, migration, and anoikis resistance. We also found that MMP7 is highly expressed in chondrosarcomas. Our in vitro and in vivo investigations show that melatonin strongly inhibits chondrosarcoma cell proliferation, migration, and anoikis resistance by directly suppressing MMP7 expression. Melatonin reduced MMP7 synthesis by promoting levels of miR-520f-3p expression, which were downregulated in human chondrosarcoma tissue samples. Pharmacological inhibition of miR-520f-3p markedly reversed the effects of melatonin upon chondrosarcoma proliferation and metastasis. Thus, our study suggests that melatonin has therapeutic potential for reducing the tumorigenesis and metastatic potential of chondrosarcoma via the miR-520f-3p/MMP7 axis.

Hierarchical Image Transformation and Multi-Level Features for Anomaly Defect Detection.

Anomalies are a set of samples that do not follow the normal behavior of the majority of data. In an industrial dataset, anomalies appear in a very small number of samples. Currently, deep learning-based models have achieved important advances in image anomaly detection. However, with general models, real-world application data consisting of non-ideal images, also known as poison images, become a challenge. When the work environment is not conducive to consistently acquiring a good or ideal sample, an additional adaptive learning model is needed. In this work, we design a potential methodology to tackle poison or non-ideal images that commonly appear in industrial production lines by enhancing the existing training data. We propose Hierarchical Image Transformation and Multi-level Features (HIT-MiLF) modules for an anomaly detection network to adapt to perturbances from novelties in testing images. This approach provides a hierarchical process for image transformation during pre-processing and explores the most efficient layer of extracted features from a CNN backbone. The model generates new transformations of training samples that simulate the non-ideal condition and learn the normality in high-dimensional features before applying a Gaussian mixture model to detect the anomalies from new data that it has never seen before. Our experimental results show that hierarchical transformation and multi-level feature exploration improve the baseline performance on industrial metal datasets.

MicroRNA-631 Resensitizes Doxorubicin-Resistant Chondrosarcoma Cells by Targeting Apelin.

Chondrosarcoma is the second most common type of bone cancer. Surgical resection is the best choice for clinical treatment. High-grade chondrosarcoma is destructive and is more possible to metastasis, which is difficult to remove using surgery. Doxorubicin (Dox) is the most commonly used chemotherapy drug in the clinical setting; however, drug resistance is a major obstacle to effective treatment. In the present study, we compared Dox-resistant SW1353 cells to their parental cells using RNA sequencing (RNA-Seq). We found that the apelin (APLN) pathway was highly activated in resistant cells. In addition, tissue array analysis also showed that APLN was higher in high-grade tissues compared to low-grade tissues. APLN is a member of the adipokine family, which is a novel secreted peptide with multifunctional and biological activities. Previously, studies have shown that inhibition of the APLN axis may have a therapeutic benefit in cancers. However, the role of APLN in chondrosarcoma is completely unclear, and no related studies have been reported. During in vitro experiments, APLN was also observed to be highly expressed and secreted in Dox-resistant cells. Once APLN was knocked down, it could effectively improve its sensitivity to Dox. We also explored possible upstream regulatory microRNAs (miRNAs) of APLN through bioinformatics tools and the results disclosed that miR-631 was the most likely regulator of APLN. Furthermore, the expression of miR-631 was lower in the resistant cells, but overexpression of miR-631 in the Dox-resistant cell lines significantly increased the Dox sensitivity. These results were also observed in another chondrosarcoma cell line, JJ012 cells. Taken together, these findings will provide rationale for the development of drug resistance biomarkers and therapeutic strategies for APLN pathway inhibitors to improve the survival of patients with chondrosarcoma.

Enhancing Precision with an Ensemble Generative Adversarial Network for Steel Surface Defect Detectors (EnsGAN-SDD).

Defects are the primary problem affecting steel product quality in the steel industry. The specific challenges in developing detect defectors involve the vagueness and tiny size of defects. To solve these problems, we propose incorporating super-resolution technique, sequential feature pyramid network, and boundary localization. Initially, the ensemble of enhanced super-resolution generative adversarial networks (ESRGAN) was proposed for the preprocessing stage to generate a more detailed contour of the original steel image. Next, in the detector section, the latest state-of-the-art feature pyramid network, known as De-tectoRS, utilized the recursive feature pyramid network technique to extract deeper multi-scale steel features by learning the feedback from the sequential feature pyramid network. Finally, Side-Aware Boundary Localization was used to precisely generate the output prediction of the defect detectors. We named our approach EnsGAN-SDD. Extensive experimental studies showed that the proposed methods improved the defect detector's performance, which also surpassed the accuracy of state-of-the-art methods. Moreover, the proposed EnsGAN achieved better performance and effectiveness in processing time compared with the original ESRGAN. We believe our innovation could significantly contribute to improved production quality in the steel industry.

Novel Spatio-Temporal Continuous Sign Language Recognition Using an Attentive Multi-Feature Network.

Given video streams, we aim to correctly detect unsegmented signs related to continuous sign language recognition (CSLR). Despite the increase in proposed deep learning methods in this area, most of them mainly focus on using only an RGB feature, either the full-frame image or details of hands and face. The scarcity of information for the CSLR training process heavily constrains the capability to learn multiple features using the video input frames. Moreover, exploiting all frames in a video for the CSLR task could lead to suboptimal performance since each frame contains a different level of information, including main features in the inferencing of noise. Therefore, we propose novel spatio-temporal continuous sign language recognition using the attentive multi-feature network to enhance CSLR by providing extra keypoint features. In addition, we exploit the attention layer in the spatial and temporal modules to simultaneously emphasize multiple important features. Experimental results from both CSLR datasets demonstrate that the proposed method achieves superior performance in comparison with current state-of-the-art methods by 0.76 and 20.56 for the WER score on CSL and PHOENIX datasets, respectively.

A Deep Learning Method for Foot Progression Angle Detection in Plantar Pressure Images.

Foot progression angle (FPA) analysis is one of the core methods to detect gait pathologies as basic information to prevent foot injury from excessive in-toeing and out-toeing. Deep learning-based object detection can assist in measuring the FPA through plantar pressure images. This study aims to establish a precision model for determining the FPA. The precision detection of FPA can provide information with in-toeing, out-toeing, and rearfoot kinematics to evaluate the effect of physical therapy programs on knee pain and knee osteoarthritis. We analyzed a total of 1424 plantar images with three different You Only Look Once (YOLO) networks: YOLO v3, v4, and v5x, to obtain a suitable model for FPA detection. YOLOv4 showed higher performance of the profile-box, with average precision in the left foot of 100.00% and the right foot of 99.78%, respectively. Besides, in detecting the foot angle-box, the ground-truth has similar results with YOLOv4 (5.58 ± 0.10° vs. 5.86 ± 0.09°, p = 0.013). In contrast, there was a significant difference in FPA between ground-truth vs. YOLOv3 (5.58 ± 0.10° vs. 6.07 ± 0.06°, p < 0.001), and ground-truth vs. YOLOv5x (5.58 ± 0.10° vs. 6.75 ± 0.06°, p < 0.001). This result implies that deep learning with YOLOv4 can enhance the detection of FPA.

Mini Review: Molecular Interpretation of the IGF/IGF-1R Axis in Cancer Treatment and Stem Cells-Based Therapy in Regenerative Medicine.

In addition to the fundamental role of insulin-like growth factor (IGF)/IGF-1 receptor (IGF-1R) signaling dysregulation in cancer initiation and proliferation, the IGF/IGF-1R signaling also plays an important role in the maintenance of stem cell characteristics and enhancement of stem cell-based therapeutic efficacy. This review focused on the role of IGF/IGF-1R signaling in preclinical IGF-targeted therapies, including IGF-1R monoclonal antibodies, IGF-1R tyrosine kinase inhibitors, and neutralizing antibodies of IGFs in multiple tumors and endocrine disorders. On the other hand, the function of IGF/IGF-1R signaling in stem cell self-renewal, pluripotency and therapeutic efficacy in regenerative medicine was outlined. Finally, the review summarized ongoing studies on IGF/IGF-1R signaling blockade in multiple cancers and highlighted the IGF-1R signaling modifications in stem cells as a potential strategy to improve stem cell-based therapeutics in regenerative medicine.

IL-17 Facilitates VCAM-1 Production and Monocyte Adhesion in Osteoarthritis Synovial Fibroblasts by Suppressing miR-5701 Synthesis.

Osteoarthritis (OA) is characterized by the infiltration and adhesion of monocytes into the inflamed joint synovium. Interleukin (IL)-17 is a critical inflammatory mediator that participates in the progression of OA, although the mechanisms linking IL-17 and monocyte infiltration are not well understood. Our analysis of synovial tissue samples retrieved from the Gene Expression Omnibus (GEO) dataset exhibited higher monocyte marker (CD11b) and vascular cell adhesion molecule 1 (VCAM-1) levels in OA samples than in normal, healthy samples. The stimulation of human OA synovial fibroblasts (OASFs) with IL-17 increased VCAM-1 production and subsequently enhanced monocyte adhesion. IL-17 affected VCAM-1-dependent monocyte adhesion by reducing miR-5701 expression through the protein kinase C (PKC)-α and c-Jun N-terminal kinase (JNK) signaling cascades. Our findings improve our understanding about the effect of IL-17 on OA progression and, in particular, VCAM-1 production and monocyte adhesion, which may help with the design of more effective OA treatments.

DPP4 reduces proinflammatory cytokine production in human rheumatoid arthritis synovial fibroblasts.

Rheumatoid arthritis (RA) is an autoimmune disorder that is characterized by increasing levels of proinflammatory cytokines. The ubiquitous enzyme dipeptidyl peptidase-4 (DPP4, also known as CD26) regulates different immune disorders, although the effects of DPP4 in RA are uncertain. Here, we found lower levels of DPP4 in RA synovial tissues compared with normal tissues. DPP4 levels were also lower in a rat collagen-induced arthritis model than in control (healthy) rats. Overexpression of DPP4 or exogenous treatment of RA synovial fibroblasts with DPP4 reduced levels of proinflammatory interleukin (IL)-1ß, IL-6, and IL-13, and increased anti-inflammatory IL-10 synthesis, while DPP4 inhibitors sitagliptin and vildagliptin increased proinflammatory cytokine production, indicating an enhanced risk of RA development. The evidence suggests that increasing DPP4 expression is a novel strategy for RA disease.

PM2.5 facilitates IL-6 production in human osteoarthritis synovial fibroblasts via ASK1 activation.

Osteoarthritis (OA) is a progressive degenerative joint disorder characterized by synovial inflammation. Interleukin-6 (IL-6) is a key proinflammatory cytokine in OA progression. Particulate matter 2.5 (PM2.5) exposure increases the risk of different diseases, including OA. Up until now, no studies have described any association between PM2.5 and IL-6 expression in human OA synovial fibroblasts (OASFs). Here, our data show that PM2.5 concentration- and time-dependently promoted IL-6 synthesis in human OASFs. We also found that reactive oxygen species (ROS) generation potentiated the effects of PM2.5 on IL-6 production. ASK1, ERK, p38, and JNK inhibitors reduced PM2.5-induced increases of IL-6 expression. Treatment of OASFs with PM2.5 promoted phosphorylation of these signaling cascades. We also found that PM2.5 enhanced c-Jun phosphorylation and its translocation into the nucleus. Thus, PM2.5 increases IL-6 production in human OASFs via the ROS, ASK1, ERK, p38, JNK, and AP-1 signaling pathways. Our evidence links PM2.5 with OA progression.

Resistin enhances IL-1ß and TNF-α expression in human osteoarthritis synovial fibroblasts by inhibiting miR-149 expression via the MEK and ERK pathways.

Resistin is a cysteine-rich adipokine that promotes the release of inflammatory cytokines, particularly interleukin 1 beta (IL-1ß) and tumor necrosis factor alpha (TNF-α), which are critical pro-inflammatory mediators in osteoarthritis (OA) pathogenesis. We describe evidence of significantly higher levels of resistin, IL-1ß, and TNF-α expression in OA knee synovial tissue compared with that from non-OA knees. Resistin-induced enhancement of IL-1ß and TNF-α expression in human OA synovial fibroblasts (OASFs) were attenuated by MEK and ERK inhibitors, as well as their respective siRNAs. Our data reveal that resistin enhances the expression of TNF-α and IL-1ß in OASFs by inhibiting miR-149 expression via MEK and ERK signaling. Our findings elucidate the inter-relationships between resistin and pro-inflammatory mediators during OA pathogenesis and could help to facilitate the development of synovium-targeted therapy in OA.

Estimation of Various Walking Intensities Based on Wearable Plantar Pressure Sensors Using Artificial Neural Networks.

Walking has been demonstrated to improve health in people with diabetes and peripheral arterial disease. However, continuous walking can produce repeated stress on the plantar foot and cause a high risk of foot ulcers. In addition, a higher walking intensity (i.e., including different speeds and durations) will increase the risk. Therefore, quantifying the walking intensity is essential for rehabilitation interventions to indicate suitable walking exercise. This study proposed a machine learning model to classify the walking speed and duration using plantar region pressure images. A wearable plantar pressure measurement system was used to measure plantar pressures during walking. An Artificial Neural Network (ANN) was adopted to develop a model for walking intensity classification using different plantar region pressure images, including the first toe (T1), the first metatarsal head (M1), the second metatarsal head (M2), and the heel (HL). The classification consisted of three walking speeds (i.e., slow at 0.8 m/s, moderate at 1.6 m/s, and fast at 2.4 m/s) and two walking durations (i.e., 10 min and 20 min). Of the 12 participants, 10 participants (720 images) were randomly selected to train the classification model, and 2 participants (144 images) were utilized to evaluate the model performance. Experimental evaluation indicated that the ANN model effectively classified different walking speeds and durations based on the plantar region pressure images. Each plantar region pressure image (i.e., T1, M1, M2, and HL) generates different accuracies of the classification model. Higher performance was achieved when classifying walking speeds (0.8 m/s, 1.6 m/s, and 2.4 m/s) and 10 min walking duration in the T1 region, evidenced by an F1-score of 0.94. The dataset T1 could be an essential variable in machine learning to classify the walking intensity at different speeds and durations.

Visfatin Promotes the Metastatic Potential of Chondrosarcoma Cells by Stimulating AP-1-Dependent MMP-2 Production in the MAPK Pathway.

Chondrosarcoma is a malignant bone tumor that is characterized by high metastatic potential and marked resistance to radiation and chemotherapy. The knowledge that adipokines facilitate the initiation, progression, metastasis, and treatment resistance of various tumors has driven several in vitro and in vivo investigations into the effects of adipokines resistin, leptin, and adiponectin upon the development and progression of chondrosarcomas. Another adipokine, visfatin, is known to regulate tumor progression and metastasis, although how this molecule may affect chondrosarcoma metastasis is unclear. Here, we found that visfatin facilitated cellular migration via matrix metalloproteinase-2 (MMP-2) production in human chondrosarcoma cells and overexpression of visfatin enhanced lung metastasis in a mouse model of chondrosarcoma. Visfatin-induced stimulation of MMP-2 synthesis and activation of the AP-1 transcription factor facilitated chondrosarcoma cell migration via the ERK, p38, and JNK signaling pathways. This evidence suggests that visfatin is worth targeting in the treatment of metastatic chondrosarcoma.

Adaptive Synaptic Memory via Lithium Ion Modulation in RRAM Devices.

Biologically plausible computing systems require fine-grain tuning of analog synaptic characteristics. In this study, lithium-doped silicate resistive random access memory with a titanium nitride (TiN) electrode mimicking biological synapses is demonstrated. Biological plausibility of this RRAM device is thought to occur due to the low ionization energy of lithium ions, which enables controllable forming and filamentary retraction spontaneously or under an applied voltage. The TiN electrode can effectively store lithium ions, a principle widely adopted from battery construction, and allows state-dependent decay to be reliably achieved. As a result, this device offers multi-bit functionality and synaptic plasticity for simulating various strengths in neuronal connections. Both short-term memory and long-term memory are emulated across dynamical timescales. Spike-timing-dependent plasticity and paired-pulse facilitation are also demonstrated. These mechanisms are capable of self-pruning to generate efficient neural networks. Time-dependent resistance decay is observed for different conductance values, which mimics both biological and artificial memory pruning and conforms to the trend of the biological brain that prunes weak synaptic connections. By faithfully emulating learning rules that exist in human's higher cortical areas from STDP to synaptic pruning, the device has the capacity to drive forward the development of highly efficient neuromorphic computing systems.

Basic fibroblast growth factor promotes doxorubicin resistance in chondrosarcoma cells by affecting XRCC5 expression.

Chondrosarcoma is the second most common form of bone cancer and is characterized by its ability to produce an extracellular matrix of the cartilage. High-grade chondrosarcoma is highly aggressive and can metastasize to other parts of the body. Chondrosarcoma is resistant to both conventional chemotherapy and radiotherapy; hence, the current main treatment is still surgical resection. Doxorubicin (Dox) has been shown to significantly improve patient survival compared with untreated chondrosarcoma. However, for patients with metastasis, surgical resection alone can hardly treat them. In addition, drug resistance is one of the leading causes of death in patients with chondrosarcoma. Secreted proteins can mediate cell-cell interactions in the cancer microenvironment, which may be associated with the development of drug resistance. In the present study, chondrosarcoma cells were treated with Dox, the conditioned medium was then collected and changes in secreted proteins were analyzed using the antibody array. Results showed that the Dox-treated group had the highest secretion of basic fibroblast growth factor (bFGF), indicating the effect of bFGF on Dox sensitivity in chondrosarcoma. Furthermore, lentiviral-mediated knockdown and treatment of exogenous recombinant protein were employed to further investigate the effect of bFGF on Dox resistance. Results demonstrated that bFGF can promote the expression of X-ray repair cross-complementing protein 5 (XRCC5), leading to Dox resistance. Secreted bFGF is likely to be detected in serum, in addition to being a biomarker for predicting Dox resistance, the combination of Dox and bFGF/XRCC5 blockers may be a new therapeutic strategy to improve the efficacy of Dox in future.

Global-and-Local Context Network for Semantic Segmentation of Street View Images.

Semantic segmentation of street view images is an important step in scene understanding for autonomous vehicle systems. Recent works have made significant progress in pixel-level labeling using Fully Convolutional Network (FCN) framework and local multi-scale context information. Rich global context information is also essential in the segmentation process. However, a systematic way to utilize both global and local contextual information in a single network has not been fully investigated. In this paper, we propose a global-and-local network architecture (GLNet) which incorporates global spatial information and dense local multi-scale context information to model the relationship between objects in a scene, thus reducing segmentation errors. A channel attention module is designed to further refine the segmentation results using low-level features from the feature map. Experimental results demonstrate that our proposed GLNet achieves 80.8% test accuracy on the Cityscapes test dataset, comparing favorably with existing state-of-the-art methods.

CXCL13/CXCR5 Interaction Facilitates VCAM-1-Dependent Migration in Human Osteosarcoma.

Osteosarcoma is the most common primary tumor of the skeletal system and is well-known to have an aggressive clinical outcome and high metastatic potential. The chemokine (C-X-C motif) ligand 13 (CXCL13) plays a vital role in the development of several cancers. However, the effect of CXCL13 in the motility of osteosarcoma cells remains uncertain. Here, we found that CXCL13 increases the migration and invasion potential of three osteosarcoma cell lines. In addition, CXCL13 expression was upregulated in migration-prone MG-63 cells. Vascular cell adhesion molecule 1 (VCAM-1) siRNA and antibody demonstrated that CXCL13 promotes migration via increasing VCAM-1 production. We also show that CXCR5 receptor controls CXCL13-mediated VCAM-1 expression and cell migration. Our study identified that CXCL13/CXCR5 axis facilitate VCAM-1 production and cell migration in human osteosarcoma via the phospholipase C beta (PLCß), protein kinase C α (PKCα), c-Src, and nuclear factor-κB (NF-κB) signaling pathways. CXCL13 and CXCR5 appear to be a novel therapeutic target in metastatic osteosarcoma.