Although microglial polarization and neuroinflammation are crucial cellular responses after traumatic brain injury,the fundamental regulatory and functional mechanisms remain insufficiently understood.As potent anti-i...Although microglial polarization and neuroinflammation are crucial cellular responses after traumatic brain injury,the fundamental regulatory and functional mechanisms remain insufficiently understood.As potent anti-inflammato ry agents,the use of glucoco rticoids in traumatic brain injury is still controversial,and their regulatory effects on microglial polarization are not yet known.In the present study,we sought to determine whether exacerbation of traumatic brain injury caused by high-dose dexamethasone is related to its regulatory effects on microglial polarization and its mechanisms of action.In vitro cultured BV2 cells and primary microglia and a controlled cortical impact mouse model were used to investigate the effects of dexamethasone on microglial polarization.Lipopolysaccharide,dexamethasone,RU486(a glucocorticoid receptor antagonist),and ruxolitinib(a Janus kinase 1 antagonist)were administered.RNA-sequencing data obtained from a C57BL/6 mouse model of traumatic brain injury were used to identify potential targets of dexamethasone.The Morris water maze,quantitative reverse transcription-polymerase chain reaction,western blotting,immunofluorescence and confocal microscopy analysis,and TUNEL,Nissl,and Golgi staining were performed to investigate our hypothesis.High-throughput sequencing results showed that arginase 1,a marker of M2 microglia,was significantly downregulated in the dexamethasone group compared with the traumatic brain injury group at3 days post-traumatic brain injury.Thus dexamethasone inhibited M1 and M2 microglia,with a more pronounced inhibitory effect on M2microglia in vitro and in vivo.Glucocorticoid receptor plays an indispensable role in microglial polarization after dexamethasone treatment following traumatic brain injury.Additionally,glucocorticoid receptor activation increased the number of apoptotic cells and neuronal death,and also decreased the density of dendritic spines.A possible downstream receptor signaling mechanism is the GR/JAK1/STAT3 pathway.Overactivation of glucocorticoid receptor by high-dose dexamethasone reduced the expression of M2 microglia,which plays an antiinflammatory role.In contrast,inhibiting the activation of glucocorticoid receptor reduced the number of apoptotic glia and neurons and decreased the loss of dendritic spines after traumatic brain injury.Dexamethasone may exe rt its neurotoxic effects by inhibiting M2 microglia through the GR/JAK1/STAT3 signaling pathway.展开更多
Acquiring accurate molecular-level information about petroleum is crucial for refining and chemical enterprises to implement the“selection of the optimal processing route”strategy.With the development of data predic...Acquiring accurate molecular-level information about petroleum is crucial for refining and chemical enterprises to implement the“selection of the optimal processing route”strategy.With the development of data prediction systems represented by machine learning,it has become possible for real-time prediction systems of petroleum fraction molecular information to replace analyses such as gas chromatography and mass spectrometry.However,the biggest difficulty lies in acquiring the data required for training the neural network.To address these issues,this work proposes an innovative method that utilizes the Aspen HYSYS and full two-dimensional gas chromatography-time-of-flight mass spectrometry to establish a comprehensive training database.Subsequently,a deep neural network prediction model is developed for heavy distillate oil to predict its composition in terms of molecular structure.After training,the model accurately predicts the molecular composition of catalytically cracked raw oil in a refinery.The validation and test sets exhibit R2 values of 0.99769 and 0.99807,respectively,and the average relative error of molecular composition prediction for raw materials of the catalytic cracking unit is less than 7%.Finally,the SHAP(SHapley Additive ExPlanation)interpretation method is used to disclose the relationship among different variables by performing global and local weight comparisons and correlation analyses.展开更多
BACKGROUND Stereotactic body radiotherapy(SBRT)and programmed cell death 1 inhibitors have shown potential in treating hepatocellular carcinoma(HCC)in retrospective studies.AIM To evaluate the efficacy of combining SB...BACKGROUND Stereotactic body radiotherapy(SBRT)and programmed cell death 1 inhibitors have shown potential in treating hepatocellular carcinoma(HCC)in retrospective studies.AIM To evaluate the efficacy of combining SBRT with sintilimab for patients with recurrent or oligometastatic HCC.METHODS This trial involved patients with recurrent or oligometastatic HCC intravenously treated with SBRT plus sintilimab every 3 wk for 12 mo or until disease progression.The primary endpoint was progression-free survival(PFS).RESULTS Twenty-five patients were enrolled from August 14,2019,to August 23,2021.The median treatment duration was 10.2(range,0.7-14.6)months.SBRT was delivered at a median dose of 54(range,48-60)Gy in 6(range,6-10)fractions.The median follow-up time was 21.9(range,10.3-39.7)mo,and 32 targeted lesions among 25 patients were evaluated for treatment response according to the Response Evaluation Criteria in Solid Tumors version 1.1.The median PFS was 19.7 mo[95%confidence interval(CI):16.9-NA],with PFS rates of 68%(95%CI:52-89)and 45.3%(95%CI:28-73.4)at 12 and 24 mo,respectively.The median overall survival(OS)was not reached,with OS rates of 91.5%(95%CI:80.8-100.0)and 83.2%(95%CI:66.5-100.0)at 12 and 24 mo,respectively.The 1-and 2-year local control rate were 100%and 90.9%(95%CI:75.4%-100.0%),respectively.The confirmed objective response rate and disease control rate was 96%,and 96%,respectively.Most adverse events were graded as 1 or 2,and grade 3 adverse events were observed in three patients.CONCLUSION SBRT plus sintilimab is an effective,well-tolerated treatment regimen for patients with recurrent or oligometastatic HCC.展开更多
After brain injury, infiltration and abnormal activation of neutrophils damages brain tissue and worsens inflammation, but the mediators that connect activated neutrophils with neuroinflammation have not yet been full...After brain injury, infiltration and abnormal activation of neutrophils damages brain tissue and worsens inflammation, but the mediators that connect activated neutrophils with neuroinflammation have not yet been fully clarified. To identify regulators of neutrophil-mediated neuroinflammation after traumatic brain injury, a mouse model of traumatic brain injury was established by controlled cortical impact. At 7 days post-injury(sub-acute phase), genome-wide transcriptomic data showed that interleukin 17 A-associated signaling pathways were markedly upregulated, suggesting that interleukin 17 A may be involved in neuroinflammation. Double immunofluorescence staining showed that interleukin 17 A was largely secreted by neutrophils rather than by glial cells and neurons. Furthermore, nuclear factor-kappaB and Stat3, both of which are important effectors in interleukin 17 A-mediated proinflammatory responses, were significantly activated. Collectively, our findings suggest that neutrophil-derived interleukin 17 A participates in neutrophil-mediated neuroinflammation during the subacute phase of traumatic brain injury. Therefore, interleukin 17 A may be a promising therapeutic target for traumatic brain injury.展开更多
Proteomics is a powerful tool that can be used to elucidate the underlying mechanisms of diseases and identify new biomarkers.Therefore,it may also be helpful for understanding the detailed pathological mechanism of t...Proteomics is a powerful tool that can be used to elucidate the underlying mechanisms of diseases and identify new biomarkers.Therefore,it may also be helpful for understanding the detailed pathological mechanism of traumatic brain injury(TBI).In this study,we performed Tandem Mass Tag-based quantitative analysis of cortical proteome profiles in a mouse model of TBI.Our results showed that there were 302 differentially expressed proteins in TBI mice compared with normal mice 7 days after injury.Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses showed that these differentially expressed proteins were predominantly involved in inflammatory responses,including complement and coagulation cascades,as well as chemokine signaling pathways.Subsequent transcription factor analysis revealed that the inflammation-related transcription factors NF-κB1,RelA,IRF1,STAT1,and Spi1 play pivotal roles in the secondary injury that occurs after TBI,which further corroborates the functional enrichment for inflammatory factors.Our results suggest that inflammation-related proteins and inflammatory responses are promising targets for the treatment of TBI.展开更多
Based on the high positioning accuracy,low cost and low-power consumption,the ultra-wide-band(UWB)is an ideal solution for indoor unmanned aerial vehicle(UAV)localization and navigation.However,the UWB signals are eas...Based on the high positioning accuracy,low cost and low-power consumption,the ultra-wide-band(UWB)is an ideal solution for indoor unmanned aerial vehicle(UAV)localization and navigation.However,the UWB signals are easy to be blocked or reflected by obstacles such as walls and furniture.A resilient tightly-coupled inertial navigation system(INS)/UWB integration is proposed and implemented for indoor UAV navigation in this paper.A factor graph optimization(FGO)method enhanced by resilient stochastic model is established to cope with the indoor challenging scenarios.To deal with the impact of UWB non-line-of-sight(NLOS)signals and noise uncertainty,the conventional neural net-works(CNNs)are introduced into the stochastic modelling to improve the resilience and reliability of the integration.Based on the status that the UWB features are limited,a‘two-phase'CNNs structure was designed and implemented:one for signal classification and the other one for measurement noise prediction.The proposed resilient FGO method is tested on flighting UAV platform under actual indoor challenging scenario.Compared to classical FGO method,the overall positioning errors can be decreased from about 0.60 m to centimeter-level under signal block and reflection scenarios.The superiority of resilient FGO which effectively verified in constrained environment is pretty important for positioning accuracy and integrity for indoor navigation task.展开更多
Recently,security issues of smart contracts are arising great attention due to the enormous financial loss caused by vulnerability attacks.There is an increasing need to detect similar codes for hunting vulnerability ...Recently,security issues of smart contracts are arising great attention due to the enormous financial loss caused by vulnerability attacks.There is an increasing need to detect similar codes for hunting vulnerability with the increase of critical security issues in smart contracts.Binary similarity detection that quantitatively measures the given code diffing has been widely adopted to facilitate critical security analysis.However,due to the difference between common programs and smart contract,such as diversity of bytecode generation and highly code homogeneity,directly adopting existing graph matching and machine learning based techniques to smart contracts suffers from low accuracy,poor scalability and the limitation of binary similarity on function level.Therefore,this paper investigates graph neural network to detect smart contract binary code similarity at the program level,where we conduct instruction-level normalization to reduce the noise code for smart contract pre-processing and construct contract control flow graphs to represent smart contracts.In particular,two improved Graph Convolutional Network(GCN)and Message Passing Neural Network(MPNN)models are explored to encode the contract graphs into quantitatively vectors,which can capture the semantic information and the program-wide control flow information with temporal orders.Then we can efficiently accomplish the similarity detection by measuring the distance between two targeted contract embeddings.To evaluate the effectiveness and efficient of our proposed method,extensive experiments are performed on two real-world datasets,i.e.,smart contracts from Ethereum and Enterprise Operation System(EOS)blockchain-based platforms.The results show that our proposed approach outperforms three state-of-the-art methods by a large margin,achieving a great improvement up to 6.1%and 17.06%in accuracy.展开更多
基金supported by research grants from the Ningbo Science and Technology Plan Project,No.2022Z143hezuo(to BL)the National Natural Science Foundation of China,No.82201520(to XD)。
文摘Although microglial polarization and neuroinflammation are crucial cellular responses after traumatic brain injury,the fundamental regulatory and functional mechanisms remain insufficiently understood.As potent anti-inflammato ry agents,the use of glucoco rticoids in traumatic brain injury is still controversial,and their regulatory effects on microglial polarization are not yet known.In the present study,we sought to determine whether exacerbation of traumatic brain injury caused by high-dose dexamethasone is related to its regulatory effects on microglial polarization and its mechanisms of action.In vitro cultured BV2 cells and primary microglia and a controlled cortical impact mouse model were used to investigate the effects of dexamethasone on microglial polarization.Lipopolysaccharide,dexamethasone,RU486(a glucocorticoid receptor antagonist),and ruxolitinib(a Janus kinase 1 antagonist)were administered.RNA-sequencing data obtained from a C57BL/6 mouse model of traumatic brain injury were used to identify potential targets of dexamethasone.The Morris water maze,quantitative reverse transcription-polymerase chain reaction,western blotting,immunofluorescence and confocal microscopy analysis,and TUNEL,Nissl,and Golgi staining were performed to investigate our hypothesis.High-throughput sequencing results showed that arginase 1,a marker of M2 microglia,was significantly downregulated in the dexamethasone group compared with the traumatic brain injury group at3 days post-traumatic brain injury.Thus dexamethasone inhibited M1 and M2 microglia,with a more pronounced inhibitory effect on M2microglia in vitro and in vivo.Glucocorticoid receptor plays an indispensable role in microglial polarization after dexamethasone treatment following traumatic brain injury.Additionally,glucocorticoid receptor activation increased the number of apoptotic cells and neuronal death,and also decreased the density of dendritic spines.A possible downstream receptor signaling mechanism is the GR/JAK1/STAT3 pathway.Overactivation of glucocorticoid receptor by high-dose dexamethasone reduced the expression of M2 microglia,which plays an antiinflammatory role.In contrast,inhibiting the activation of glucocorticoid receptor reduced the number of apoptotic glia and neurons and decreased the loss of dendritic spines after traumatic brain injury.Dexamethasone may exe rt its neurotoxic effects by inhibiting M2 microglia through the GR/JAK1/STAT3 signaling pathway.
基金the National Natural Science Foundation of China(22108307)the Natural Science Foundation of Shandong Province(ZR2020KB006)the Outstanding Youth Fund of Shandong Provincial Natural Science Foundation(ZR2020YQ17).
文摘Acquiring accurate molecular-level information about petroleum is crucial for refining and chemical enterprises to implement the“selection of the optimal processing route”strategy.With the development of data prediction systems represented by machine learning,it has become possible for real-time prediction systems of petroleum fraction molecular information to replace analyses such as gas chromatography and mass spectrometry.However,the biggest difficulty lies in acquiring the data required for training the neural network.To address these issues,this work proposes an innovative method that utilizes the Aspen HYSYS and full two-dimensional gas chromatography-time-of-flight mass spectrometry to establish a comprehensive training database.Subsequently,a deep neural network prediction model is developed for heavy distillate oil to predict its composition in terms of molecular structure.After training,the model accurately predicts the molecular composition of catalytically cracked raw oil in a refinery.The validation and test sets exhibit R2 values of 0.99769 and 0.99807,respectively,and the average relative error of molecular composition prediction for raw materials of the catalytic cracking unit is less than 7%.Finally,the SHAP(SHapley Additive ExPlanation)interpretation method is used to disclose the relationship among different variables by performing global and local weight comparisons and correlation analyses.
基金The Ministry of Science and Technology of The People's Republic of China,No.2022YFC2503700,and No.2022YFC2503704.
文摘BACKGROUND Stereotactic body radiotherapy(SBRT)and programmed cell death 1 inhibitors have shown potential in treating hepatocellular carcinoma(HCC)in retrospective studies.AIM To evaluate the efficacy of combining SBRT with sintilimab for patients with recurrent or oligometastatic HCC.METHODS This trial involved patients with recurrent or oligometastatic HCC intravenously treated with SBRT plus sintilimab every 3 wk for 12 mo or until disease progression.The primary endpoint was progression-free survival(PFS).RESULTS Twenty-five patients were enrolled from August 14,2019,to August 23,2021.The median treatment duration was 10.2(range,0.7-14.6)months.SBRT was delivered at a median dose of 54(range,48-60)Gy in 6(range,6-10)fractions.The median follow-up time was 21.9(range,10.3-39.7)mo,and 32 targeted lesions among 25 patients were evaluated for treatment response according to the Response Evaluation Criteria in Solid Tumors version 1.1.The median PFS was 19.7 mo[95%confidence interval(CI):16.9-NA],with PFS rates of 68%(95%CI:52-89)and 45.3%(95%CI:28-73.4)at 12 and 24 mo,respectively.The median overall survival(OS)was not reached,with OS rates of 91.5%(95%CI:80.8-100.0)and 83.2%(95%CI:66.5-100.0)at 12 and 24 mo,respectively.The 1-and 2-year local control rate were 100%and 90.9%(95%CI:75.4%-100.0%),respectively.The confirmed objective response rate and disease control rate was 96%,and 96%,respectively.Most adverse events were graded as 1 or 2,and grade 3 adverse events were observed in three patients.CONCLUSION SBRT plus sintilimab is an effective,well-tolerated treatment regimen for patients with recurrent or oligometastatic HCC.
基金supported by the National Natural Science Foundation of China,No. 81771327 (to BYL)Construction of Central Nervous System Injury Basic Science and Clinical Translational Research PlatformBudget of Beijing Municipal Health Commission 2020, No. PXM2020_026280_000002 (BYL)。
文摘After brain injury, infiltration and abnormal activation of neutrophils damages brain tissue and worsens inflammation, but the mediators that connect activated neutrophils with neuroinflammation have not yet been fully clarified. To identify regulators of neutrophil-mediated neuroinflammation after traumatic brain injury, a mouse model of traumatic brain injury was established by controlled cortical impact. At 7 days post-injury(sub-acute phase), genome-wide transcriptomic data showed that interleukin 17 A-associated signaling pathways were markedly upregulated, suggesting that interleukin 17 A may be involved in neuroinflammation. Double immunofluorescence staining showed that interleukin 17 A was largely secreted by neutrophils rather than by glial cells and neurons. Furthermore, nuclear factor-kappaB and Stat3, both of which are important effectors in interleukin 17 A-mediated proinflammatory responses, were significantly activated. Collectively, our findings suggest that neutrophil-derived interleukin 17 A participates in neutrophil-mediated neuroinflammation during the subacute phase of traumatic brain injury. Therefore, interleukin 17 A may be a promising therapeutic target for traumatic brain injury.
基金supported by the National Natural Science Foundation of China,No. 81771327a grant for the Platform Construction of Basic Research and Clinical Translation of Nervous System Injury,China,No. PXM2020_026280_000002 (both to BYL)
文摘Proteomics is a powerful tool that can be used to elucidate the underlying mechanisms of diseases and identify new biomarkers.Therefore,it may also be helpful for understanding the detailed pathological mechanism of traumatic brain injury(TBI).In this study,we performed Tandem Mass Tag-based quantitative analysis of cortical proteome profiles in a mouse model of TBI.Our results showed that there were 302 differentially expressed proteins in TBI mice compared with normal mice 7 days after injury.Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses showed that these differentially expressed proteins were predominantly involved in inflammatory responses,including complement and coagulation cascades,as well as chemokine signaling pathways.Subsequent transcription factor analysis revealed that the inflammation-related transcription factors NF-κB1,RelA,IRF1,STAT1,and Spi1 play pivotal roles in the secondary injury that occurs after TBI,which further corroborates the functional enrichment for inflammatory factors.Our results suggest that inflammation-related proteins and inflammatory responses are promising targets for the treatment of TBI.
基金National Natural Science Foundation of China(Grant No.62203111)the Open Research Fund of State Key Laboratory of Information Engineering in Surveying,Mapping and Remote Sensing,Wuhan University(Grant No.21P01)the Foundation of Key Laboratory of Micro-Inertial Instrument and Advanced Navigation Technology,Ministry of Education,China(Grant No.SEU-MIAN-202101)to provide fund for conducting experiments。
文摘Based on the high positioning accuracy,low cost and low-power consumption,the ultra-wide-band(UWB)is an ideal solution for indoor unmanned aerial vehicle(UAV)localization and navigation.However,the UWB signals are easy to be blocked or reflected by obstacles such as walls and furniture.A resilient tightly-coupled inertial navigation system(INS)/UWB integration is proposed and implemented for indoor UAV navigation in this paper.A factor graph optimization(FGO)method enhanced by resilient stochastic model is established to cope with the indoor challenging scenarios.To deal with the impact of UWB non-line-of-sight(NLOS)signals and noise uncertainty,the conventional neural net-works(CNNs)are introduced into the stochastic modelling to improve the resilience and reliability of the integration.Based on the status that the UWB features are limited,a‘two-phase'CNNs structure was designed and implemented:one for signal classification and the other one for measurement noise prediction.The proposed resilient FGO method is tested on flighting UAV platform under actual indoor challenging scenario.Compared to classical FGO method,the overall positioning errors can be decreased from about 0.60 m to centimeter-level under signal block and reflection scenarios.The superiority of resilient FGO which effectively verified in constrained environment is pretty important for positioning accuracy and integrity for indoor navigation task.
基金supported by the Basic Research Program(No.JCKY2019210B029)Network threat depth analysis software(KY10800210013).
文摘Recently,security issues of smart contracts are arising great attention due to the enormous financial loss caused by vulnerability attacks.There is an increasing need to detect similar codes for hunting vulnerability with the increase of critical security issues in smart contracts.Binary similarity detection that quantitatively measures the given code diffing has been widely adopted to facilitate critical security analysis.However,due to the difference between common programs and smart contract,such as diversity of bytecode generation and highly code homogeneity,directly adopting existing graph matching and machine learning based techniques to smart contracts suffers from low accuracy,poor scalability and the limitation of binary similarity on function level.Therefore,this paper investigates graph neural network to detect smart contract binary code similarity at the program level,where we conduct instruction-level normalization to reduce the noise code for smart contract pre-processing and construct contract control flow graphs to represent smart contracts.In particular,two improved Graph Convolutional Network(GCN)and Message Passing Neural Network(MPNN)models are explored to encode the contract graphs into quantitatively vectors,which can capture the semantic information and the program-wide control flow information with temporal orders.Then we can efficiently accomplish the similarity detection by measuring the distance between two targeted contract embeddings.To evaluate the effectiveness and efficient of our proposed method,extensive experiments are performed on two real-world datasets,i.e.,smart contracts from Ethereum and Enterprise Operation System(EOS)blockchain-based platforms.The results show that our proposed approach outperforms three state-of-the-art methods by a large margin,achieving a great improvement up to 6.1%and 17.06%in accuracy.
