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Ferroptosis is a new form of regulated cell death and closely related to cancer. However, the mechanism underlying the regulation of ferroptosis in lung adenocarcinoma (LUAD) remains unclear. IB, IHC and ELISA were performed to analyze protein expression. RT-qPCR was used to analyze mRNA expression. Cell viability, 3D cell growth, MDA, the generation of lipid ROS and the Fe2+ concentration were measured to evaluate the responses to the induction of ferroptosis. Measurement of luciferase activity and ChIP were used to analyze the promoter activity regulated by the transcriptional regulator. Co-IP assays were performed to identify protein-protein interactions. In the present study, it was revealed that cAMP response element-binding protein (CREB) was highly expressed in LUAD, and knockdown of CREB inhibited cell viability and growth by promoting apoptosis- and ferroptosis-like cell death, concurrently. It was observed that CREB suppressed lipid peroxidation by binding the promoter region of glutathione peroxidase 4 (GPX4), and this binding could be enhanced by E1A binding protein P300 (EP300). The bZIP domain in CREB and the CBP/p300-HAT domain in EP300 were essential for CREB-EP300 binding in LUAD cells. Finally, it was revealed that CREB, GPX4, EP300 and 4-HNE were closely related to tumor size and stage, and tumors with a higher degree of malignancy were more likely to have a low degree of lipid peroxidation. Therefore, targeting this CREB/EP300/GPX4 axis may provide new strategies for treating LUAD.

Lung cancer remains the leading cause of cancer-related death worldwide, and the high incidence rates are worrisome. Exosomes are a class of extracellular vesicles secreted by most cells, including RNAs, proteins and lipids. Exosomes can mediate cell-to-cell communication in both physiologic and pathologic processes. Accumulated evidences show that cancer-derived exosomes aid in the recruitment and reprogramming of constituents correlated with tumor microenvironment. Furthermore, exosome-based clinical trials have been completed in advanced lung cancer patients. In this review, we discuss the roles of exosomes in a lung cancer microenvironment, such as its participation in lung cancer initiation, progression and metastasis as well as being involved in angiogenesis, epithelial–mesenchymal transition (EMT), immune escape, and drug resistance. In addition, we focus on the potential of exosomes as diagnostic and prognostic biomarkers in lung cancer, as well as the challenges faced by and advantages of exosomes as drug delivery vehicles and in exosome-based immunotherapy.

Industry 4.0 features intelligent manufacturing. Among them, the vision-based defect inspection algorithm is remarkable for quality control in parts manufacturing. With the help of AI and machine learning, auto-adaptive instead of manual operation is achievable in this field, and much progress has been made in recent years. In this study, considering the demand of inspection features in industrialization, we made further improvement in smart defect inspection. An efficient algorithm using Field Programmable Gate Array (FPGA)-accelerated You Only Look Once (YOLO) v3 based on an attention mechanism is proposed. First, because of the relatively fixed camera angle and defect features, an attention mechanism based on the concept of directing the focus of defect inspection is proposed. The attention mechanism consists of three improvements: (a) image preprocessing, which is to tailor images for selectively concentrating on the defect relevant things. Image preprocessing mainly includes cutting, zooming and splicing, named CZS operations. (b) Tailoring the YOLOv3 backbone network, which is to ignore invalid inspection regions in deep neural networks and optimize the network structure. (c) Data augmentation. First, two improvements can be made to efficiently reduce deep learning operations and accelerate the inspection speed, but the preprocessed images are similar and the lack of diversity will reduce network accuracy. So, (c) is added to mitigate the lack of considerable amounts of training data. Second, the algorithm is deployed on a PYNQ-Z2 FPGA board to meet the industrialization production requirements for accuracy, efficiency and extensibility. FPGA can provide a low-latency, low-cost, high-power-efficiency and flexible architecture that enables deep learning acceleration for industrial scenarios. A Xilinx Deep Neural Network Development Kit (DNNDK) converted the improved YOLOv3 to Programmable Logic (PL), which can be deployed on FPGA. The conversion process mainly consists of pruning, quantization and compilation. Experimental results showed that the algorithm had high efficiency, inspection accuracy reached 99.2%, processing speed reached 1.54 Frames per Second (FPS), and power consumption was only 10 W.

Lung cancer is one of the most common and deadly malignancies worldwide, in spite of advances in targeted therapy in recent years. An effective strategy for lung cancer prevention remains a major problem. Advances in next-generation sequencing have helped in understanding the RNA and identifying novel circular RNAs (circRNAs) that may have a broad impact on the early diagnosis and treatment of lung cancer. The circRNAs, exhibiting spatiotemporal-specific expression, are stable and conserved and present diverse biological functions in the normal and diseased states, including cancer. In this review, we summarize the recent advances in elucidating the functional role of circRNAs in lung cancer pathogenesis and discuss their potential mechanisms.

After regular rehabilitation training, paralysis sequelae can be significantly reduced in patients with limb movement disorders caused by stroke. Rehabilitation assessment is the basis for the formulation of rehabilitation training programs and the objective standard for evaluating the effectiveness of training. However, the quantitative rehabilitation assessment is still in the experimental stage and has not been put into clinical practice. In this work, we propose improved spatial-temporal graph convolutional networks based on precise posture measurement for upper limb rehabilitation assessment. Two Azure Kinect are used to enlarge the angle range of the visual field. The rigid body model of the upper limb with multiple degrees of freedom is established. And the inverse kinematics is optimized based on the hybrid particle swarm optimization algorithm. The self-attention mechanism map is calculated to analyze the role of each upper limb joint in rehabilitation assessment, to improve the spatial-temporal graph convolution neural network model. Long short-term memory is built to explore the sequence dependence in spatial-temporal feature vectors. An exercise protocol for detecting the distal reachable workspace and proximal self-care ability of the upper limb is designed, and a virtual environment is built. The experimental results indicate that the proposed posture measurement method can reduce position jumps caused by occlusion, improve measurement accuracy and stability, and increase Signal Noise Ratio. By comparing with other models, our rehabilitation assessment model achieved the lowest mean absolute deviation, root mean square error, and mean absolute percentage error. The proposed method can effectively quantitatively evaluate the upper limb motor function of stroke patients.

Hydrogen energy is developing rapidly, and the hydrogen-based integrated energy system (HIES) offers improved economic performance, flexibility, and environmental benefits compared with conventional power systems. However, the increasing frequency of natural disasters caused by climate change introduces significant vulnerabilities that threaten system security. Preventive scheduling provides a proactive and economical means to enhance system resilience against such uncertainties. This paper proposes a preventive scheduling model for HIES based on adaptive robust optimization (ARO) to address the uncertain impacts of natural disasters on transmission lines, pipelines, and roads. The model incorporates the operational constraints and interdependencies among multiple energy subsystems and integrates flexible scheduling strategies such as power-to-hydrogen-and-heat (P2HH) and hydrogen transportation (HT). A hybrid algorithm is developed to efficiently solve the large-scale ARO problem with numerous integer variables. Case studies performed on two test systems demonstrate that the proposed preventive scheduling model effectively reduces operational costs and load curtailments. Simulation results show that coordinating P2HH and HT reduces power, heat, hydrogen, and gas load curtailments by 14.35%, 43.39%, 49.97%, and 40.32%, respectively, as well as operational costs by 14.60%. Moreover, the proposed hybrid algorithm enhances computational efficiency, reducing solution time by 21% with only a 2% deviation from the solution obtained by the conventional C&CG–AOP algorithm.

Diabetes is an important risk factor for liver cancer, but its mechanism is unknown. Corosolic acid (CA) has been proven to have both hypoglycemic and antitumor effects, so revealing the function of CA can help us understand the relationship between diabetes and liver cancer. In previous studies, we confirmed that CA can effectively inhibit the expression of YAP, an important oncoprotein in HCC cells, and the proliferation of HCC cells. In addition, we also found that O -GlcNAcylation plays an indispensable role in HCC tumorigenesis. However, it is not clear whether CA can inhibit the effect of O -GlcNAcylation on HCC cells. In this study, the antitumor ability of CA was investigated by inhibiting the O -GlcNAcylation level and its corresponding mechanism. The results showed that HG (high glucose) could promote the proliferation of liver cancer cells, while CA could inhibit cell growth under HG conditions and tumor growth in a xenotransplantation model. CA can inhibit the activation of the HBP pathway and reduce the expression of YAP and OGT under HG conditions. Importantly, we found that CA can reduce YAP expression and O -GlcNAcylation by inhibiting the activity of CDK19. Overexpression of CDK19 partially reversed the CA-induced decrease in YAP and O -GlcNAcylation. This is the first evidence that CA can reduce the proliferative capacity of cells with high glucose levels and further inhibit tumor growth by inactivating the CDK19/YAP/ O -GlcNAcylation pathway, suggesting that CA is a candidate drug for the development of treatments against diabetes-associated liver cancer.

Aminopeptidase N (APN) was closely associated with cancer invasion, metastasis, and angiogenesis. Therefore, APN inhibitors have attracted more and more attention of scientists as antitumor agents. In the current study, we designed, synthesized, and evaluated one new series of pyrazoline-based hydroxamate derivatives as APN inhibitors. Moreover, the structure–activity relationships of those were discussed in detail. 2,6-Dichloro substituted compound 14o with R1 = CH3, showed the best capacity for inhibiting APN with an IC50 value of 0.0062 ± 0.0004 μM, which was three orders of magnitude better than that of the positive control bestatin. Compound 14o possessed both potent anti-proliferative activities against tumor cells and potent anti-angiogenic activity. At the same concentration of 50 μM, compound 14o exhibited much better capacity for inhibiting the micro-vessel growth relative to bestatin in the rat thoracic aorta ring model. Additionally, the putative interactions of 14o with the active site of APN are also discussed. The hydroxamate moiety chelated the zinc ion and formed four hydrogen bonds with His297, Glu298 and His301. Meanwhile, the terminal phenyl group and another phenyl group of 14o interacted with S2′ and S1 pockets via hydrophobic effects, respectively.

Specific neural coding (SNC) forms the basis of information processing in bio-brain, which generates distinct patterns of neural coding in response to corresponding exterior forms of stimulus. The performance of SNC is extremely dependent on brain-inspired models. However, the bio-rationality of a brain-inspired model remains inadequate. The purpose of this paper is to investigate a more bio-rational brain-inspired model and the SNC of this brain-inspired model. In this study, we construct a complex spiking neural network (CSNN) in which its topology has the small-word property and the scale-free property. Then, we investigated the SNC of CSNN under various strengths of various stimuli and discussed its mechanism. Our results indicate that (1) CSNN has similar neural time coding under same kind of stimulus; (2) CSNN has significant SNC based on time coding under various exterior stimuli; (3) our discussion implies that the inherent factor of SNC is synaptic plasticity.

Background Combining immune checkpoint inhibitors with chemotherapy can synergistically improve antitumor activity and are generally well tolerated. Recently, the efficacy and safety of combination therapy has been demonstrated for many cancers, including urothelial carcinomas. The aim of this retrospective pilot study was to evaluate the efficacy and safety of tislelizumab plus chemotherapy as first-line adjuvant treatment for locally advanced or metastatic bladder cancer. Methods We conducted a retrospective analysis of 31 patients with locally advanced or metastatic bladder cancer from December 2020 to January 2022 with an Eastern Cooperative Oncology Group performance status of 0/1. Of the 31 patients, 14 patients received tislelizumab (200 mg i.v. every 3 weeks, Q3W) plus 21 days cycles of chemotherapy (gemcitabine, 1000 mg/m 2 i.v. on days 1 and 8 of each cycle + cisplatin, 70 mg/m 2 i.v. on day 2 of each cycle) (TGC) treatment and 17 patients received gemcitabine plus cisplatin chemotherapy (GC) treatment. All patients treated with bladder cytoreductive surgery and were treated for four 21 days cycles until disease progression or intolerable treatment-related adverse events (TRAEs). The objective progression-free survival (PFS), overall survival (OS), overall response rate (ORR), disease control rate (DCR), clinical benefit rate (CBR) and TRAEs were recorded and reviewed. Results As of the cut-off date (March 25, 2022), PFS, OS, ORR, DCR, CBR and TRAEs were evaluated in 14 patients receiving combination therapy and 17 patients in the chemotherapy alone group. The median PFS was 36.0 [95% confidence interval (CI) 33.1–38.9] weeks in the TGC group and 29.0 (95% CI 25.4–32.6) weeks in the GC group [hazard ratio (HR) 0.15 (95% CI 0.04–0.55)]. In the GC group, the median OS was 48.0 (95% CI 39.7–56.3) weeks; the median OS was not yet mature for the TGC group [HR 0.26 (95% CI 0.07–0.94)]. Treatment with TGC resulted in improved DCR (TGC 71.4%; GC 65.0%) and CBR (TGC 64.3%; GC 52.9%) compared with GC. However, although higher incidences of grade ≥ 3 TRAEs were observed with TGC compared with GC (35.7% vs 23.5%), the difference was not statistically significant ( p  = 0.47). Conclusion This study suggested that TGC provided survivors of locally advanced or metastatic bladder cancer with encouraging antitumor activity and was generally well tolerated.