The human pose paradigm is estimated using a transformer-based multi-branch multidimensional directed the three-dimensional(3D)method that takes into account self-occlusion,badly posedness,and a lack of depth data in ...The human pose paradigm is estimated using a transformer-based multi-branch multidimensional directed the three-dimensional(3D)method that takes into account self-occlusion,badly posedness,and a lack of depth data in the per-frame 3D posture estimation from two-dimensional(2D)mapping to 3D mapping.Firstly,by examining the relationship between the movements of different bones in the human body,four virtual skeletons are proposed to enhance the cyclic constraints of limb joints.Then,multiple parameters describing the skeleton are fused and projected into a high-dimensional space.Utilizing a multi-branch network,motion features between bones and overall motion features are extracted to mitigate the drift error in the estimation results.Furthermore,the estimated relative depth is projected into 3D space,and the error is calculated against real 3D data,forming a loss function along with the relative depth error.This article adopts the average joint pixel error as the primary performance metric.Compared to the benchmark approach,the estimation findings indicate an increase in average precision of 1.8 mm within the Human3.6M sample.展开更多
BACKGROUND Trastuzumab-targeted therapy is currently the standard of care for advanced human epidermal growth factor receptor 2(HER2)-positive gastric cancer.However,the emergence of resistance to trastuzumab poses si...BACKGROUND Trastuzumab-targeted therapy is currently the standard of care for advanced human epidermal growth factor receptor 2(HER2)-positive gastric cancer.However,the emergence of resistance to trastuzumab poses significant challenges.AIM To identify the key genes associated with trastuzumab resistance.These results provide a basis for the development of interventions to address drug resistance and improve patient outcomes.METHODS High-throughput sequencing and bioinformatics were used to identify the differentially expressed pivotal gene BIRC3 and delineate its potential function and pathway regulation.Tumor samples were collected from patients with HER2-positive gastric cancer to evaluate the correlation between BIRC3 expression and trastuzumab resistance.We established gastric cancer cell lines with both highly expressed and suppressed levels of BIRC3,followed by comprehensive in vitro and in vivo experiments to confirm the involvement of BIRC3 in trastuzumab resistance and to elucidate its underlying mechanisms.RESULTS In patients with HER2-positive gastric cancer,there is a significant correlation between elevated BIRC3 expression in tumor tissues and higher T stage,tumor node metastasis stage,as well as poor overall survival and progressionfree survival.BIRC3 is highly expressed in trastuzumab-resistant gastric cancer cell lines,where it inhibits tumor cell apoptosis and enhances trastuzumab resistance by promoting the phosphorylation and activation of the phosphoinositide 3-kinase-Akt(PI3K-AKT)pathway in HER2-positive gastric cancer cells,both in vivo and in vitro.CONCLUSION This study revealed a robust association between high BIRC3 expression and an unfavorable prognosis in patients with HER2-positive gastric cancer.Thus,the high expression of BIRC3 stimulated PI3K-AKT phosphorylation and activation,stimulating the proliferation of HER2-positive tumor cells and suppressing apoptosis,ultimately leading to trastuzumab resistance.展开更多
BACKGROUND Alveolar bone defects caused by inflammation are an urgent issue in oral implant surgery that must be solved.Regulating the various phenotypes of macrophages to enhance the inflammatory environment can sign...BACKGROUND Alveolar bone defects caused by inflammation are an urgent issue in oral implant surgery that must be solved.Regulating the various phenotypes of macrophages to enhance the inflammatory environment can significantly affect the progression of diseases and tissue engineering repair process.AIM To assess the influence of interleukin-10(IL-10)on the osteogenic differentiation of bone marrow mesenchymal stem cells(BMSCs)following their interaction with macrophages in an inflammatory environment.METHODS IL-10 modulates the differentiation of peritoneal macrophages in Wistar rats in an inflammatory environment.In this study,we investigated its impact on the proliferation,migration,and osteogenesis of BMSCs.The expression levels of signal transducer and activator of transcription 3(STAT3)and its activated form,phos-phorylated-STAT3,were examined in IL-10-stimulated macrophages.Subsequently,a specific STAT3 signaling inhibitor was used to impede STAT3 signal activation to further investigate the role of STAT3 signaling.RESULTS IL-10-stimulated macrophages underwent polarization to the M2 type through substitution,and these M2 macrophages actively facilitated the osteogenic differentiation of BMSCs.Mechanistically,STAT3 signaling plays a crucial role in the process by which IL-10 influences macrophages.Specifically,IL-10 stimulated the activation of the STAT3 signaling pathway and reduced the macrophage inflammatory response,as evidenced by its diminished impact on the osteogenic differentiation of BMSCs.CONCLUSION Stimulating macrophages with IL-10 proved effective in improving the inflammatory environment and promoting the osteogenic differentiation of BMSCs.The IL-10/STAT3 signaling pathway has emerged as a key regulator in the macrophage-mediated control of BMSCs’osteogenic differentiation.展开更多
Background Intelligent garments,a burgeoning class of wearable devices,have extensive applications in domains such as sports training and medical rehabilitation.Nonetheless,existing research in the smart wearables dom...Background Intelligent garments,a burgeoning class of wearable devices,have extensive applications in domains such as sports training and medical rehabilitation.Nonetheless,existing research in the smart wearables domain predominantly emphasizes sensor functionality and quantity,often skipping crucial aspects related to user experience and interaction.Methods To address this gap,this study introduces a novel real-time 3D interactive system based on intelligent garments.The system utilizes lightweight sensor modules to collect human motion data and introduces a dual-stream fusion network based on pulsed neural units to classify and recognize human movements,thereby achieving real-time interaction between users and sensors.Additionally,the system incorporates 3D human visualization functionality,which visualizes sensor data and recognizes human actions as 3D models in real time,providing accurate and comprehensive visual feedback to help users better understand and analyze the details and features of human motion.This system has significant potential for applications in motion detection,medical monitoring,virtual reality,and other fields.The accurate classification of human actions contributes to the development of personalized training plans and injury prevention strategies.Conclusions This study has substantial implications in the domains of intelligent garments,human motion monitoring,and digital twin visualization.The advancement of this system is expected to propel the progress of wearable technology and foster a deeper comprehension of human motion.展开更多
Concentration-time profiles of ^(125)I-labeled recombinant human interleukin-3 (125IrhIL-3) were de-termined by reverse phase high performance liquid chromatography (RHPLC) after intravenous and subcutaneous ad-minist...Concentration-time profiles of ^(125)I-labeled recombinant human interleukin-3 (125IrhIL-3) were de-termined by reverse phase high performance liquid chromatography (RHPLC) after intravenous and subcutaneous ad-ministration of the drug in 16 rhesus monkeys. The initial and terminal T1/2 in plasma after intravenous of 30 μg/kg were (0. 15 ±0.13) and (2. 21 ± 0. 59) h, respectively. Terminal half-lives after 30, 90 and 180μg/kg subcutaneous (s.c.) injections were 2. 0-3.8 h. Area under concentration-time curves (AUC) following s. c. were roughly in-creased with dose, while CL5 were similar among different dosages. The absorption rates were dependent on concentra-tion at injected site. Bioavailability was about 0.7 after s.c. Rapid biodegradation was found in plasma. Distribution profiles of total radioactivity were as follows: the highest level was found in urinary system; levels in bile-enteric sys-tem, lymph nodes, bone marrow and spleen were near to that in plasma, and level in brain was the lowest. The RH-PLC analysis revealed that kidney was one of the major organs for biodegradation.展开更多
3D human pose estimation is a major focus area in the field of computer vision,which plays an important role in practical applications.This article summarizes the framework and research progress related to the estimat...3D human pose estimation is a major focus area in the field of computer vision,which plays an important role in practical applications.This article summarizes the framework and research progress related to the estimation of monocular RGB images and videos.An overall perspective ofmethods integrated with deep learning is introduced.Novel image-based and video-based inputs are proposed as the analysis framework.From this viewpoint,common problems are discussed.The diversity of human postures usually leads to problems such as occlusion and ambiguity,and the lack of training datasets often results in poor generalization ability of the model.Regression methods are crucial for solving such problems.Considering image-based input,the multi-view method is commonly used to solve occlusion problems.Here,the multi-view method is analyzed comprehensively.By referring to video-based input,the human prior knowledge of restricted motion is used to predict human postures.In addition,structural constraints are widely used as prior knowledge.Furthermore,weakly supervised learningmethods are studied and discussed for these two types of inputs to improve the model generalization ability.The problem of insufficient training datasets must also be considered,especially because 3D datasets are usually biased and limited.Finally,emerging and popular datasets and evaluation indicators are discussed.The characteristics of the datasets and the relationships of the indicators are explained and highlighted.Thus,this article can be useful and instructive for researchers who are lacking in experience and find this field confusing.In addition,by providing an overview of 3D human pose estimation,this article sorts and refines recent studies on 3D human pose estimation.It describes kernel problems and common useful methods,and discusses the scope for further research.展开更多
BACKGROUND Diabetic cardiomyopathy(DCM),which is a complication of diabetes,poses a great threat to public health.Recent studies have confirmed the role of NLRP3(NOD-like receptor protein 3)activation in DCM developme...BACKGROUND Diabetic cardiomyopathy(DCM),which is a complication of diabetes,poses a great threat to public health.Recent studies have confirmed the role of NLRP3(NOD-like receptor protein 3)activation in DCM development through the inflammatory response.Teneligliptin is an oral hypoglycemic dipeptidyl peptidase-IV inhibitor used to treat diabetes.Teneligliptin has recently been reported to have anti-inflammatory and protective effects on myocardial cells.AIM To examine the therapeutic effects of teneligliptin on DCM in diabetic mice.METHODS Streptozotocin was administered to induce diabetes in mice,followed by treatment with 30 mg/kg teneligliptin.RESULTS Marked increases in cardiomyocyte area and cardiac hypertrophy indicator heart weight/tibia length reductions in fractional shortening,ejection fraction,and heart rate;increases in creatine kinase-MB(CK-MB),aspartate transaminase(AST),and lactate dehydrogenase(LDH)levels;and upregulated NADPH oxidase 4 were observed in diabetic mice,all of which were significantly reversed by teneligliptin.Moreover,NLRP3 inflammasome activation and increased release of interleukin-1βin diabetic mice were inhibited by teneligliptin.Primary mouse cardiomyocytes were treated with high glucose(30 mmol/L)with or without teneligliptin(2.5 or 5μM)for 24 h.NLRP3 inflammasome activation.Increases in CKMB,AST,and LDH levels in glucose-stimulated cardiomyocytes were markedly inhibited by teneligliptin,and AMP(p-adenosine 5‘-monophosphate)-p-AMPK(activated protein kinase)levels were increased.Furthermore,the beneficial effects of teneligliptin on hyperglycaemia-induced cardiomyocytes were abolished by the AMPK signaling inhibitor compound C.CONCLUSION Overall,teneligliptin mitigated DCM by mitigating activation of the NLRP3 inflammasome.展开更多
With the advancement of image sensing technology, estimating 3Dhuman pose frommonocular video has becomea hot research topic in computer vision. 3D human pose estimation is an essential prerequisite for subsequentacti...With the advancement of image sensing technology, estimating 3Dhuman pose frommonocular video has becomea hot research topic in computer vision. 3D human pose estimation is an essential prerequisite for subsequentaction analysis and understanding. It empowers a wide spectrum of potential applications in various areas, suchas intelligent transportation, human-computer interaction, and medical rehabilitation. Currently, some methodsfor 3D human pose estimation in monocular video employ temporal convolutional network (TCN) to extractinter-frame feature relationships, but the majority of them suffer from insufficient inter-frame feature relationshipextractions. In this paper, we decompose the 3D joint location regression into the bone direction and length, wepropose the TCG, a temporal convolutional network incorporating Gaussian error linear units (GELU), to solvebone direction. It enablesmore inter-frame features to be captured andmakes the utmost of the feature relationshipsbetween data. Furthermore, we adopt kinematic structural information to solve bone length enhancing the use ofintra-frame joint features. Finally, we design a loss function for joint training of the bone direction estimationnetwork with the bone length estimation network. The proposed method has extensively experimented on thepublic benchmark dataset Human3.6M. Both quantitative and qualitative experimental results showed that theproposed method can achieve more accurate 3D human pose estimations.展开更多
3-dimension(3-D)printing technology is growing strongly with many applications,one of which is the garment industry.The application of human body models to the garment industry is necessary to respond to the increasin...3-dimension(3-D)printing technology is growing strongly with many applications,one of which is the garment industry.The application of human body models to the garment industry is necessary to respond to the increasing personalization demand and still guarantee aesthetics.This paper proposes amethod to construct 3-D human models by applying deep learning.We calculate the location of the main slices of the human body,including the neck,chest,belly,buttocks,and the rings of the extremities,using pre-existing information.Then,on the positioning frame,we find the key points(fixed and unaltered)of these key slices and update these points tomatch the current parameters.To add points to a star slice,we use a deep learning model tomimic the form of the human body at that slice position.We use interpolation to produce sub-slices of different body sections based on the main slices to create complete body parts morphologically.We combine all slices to construct a full 3-D representation of the human body.展开更多
Purpose: Patient-specific quality assurance (PSQA) requires manual operation of different workstations, which is time-consuming and error-prone. Therefore, developing automated solutions to improve efficiency and accu...Purpose: Patient-specific quality assurance (PSQA) requires manual operation of different workstations, which is time-consuming and error-prone. Therefore, developing automated solutions to improve efficiency and accuracy is a priority. The purpose of this study was to develop a general software interface with scripting on a human interactive device (HID) for improving the efficiency and accuracy of manual quality assurance (QA) procedures. Methods: As an initial application, we aimed to automate our PSQA workflow that involves Varian Eclipse treatment planning system, Elekta MOSAIQ oncology information system and PTW Verisoft application. A general platform, the AutoFrame interface with two imbedded subsystems—the AutoFlow and the PyFlow, was developed with a scripting language for automating human operations of aforementioned systems. The interface included three functional modules: GUI module, UDF script interpreter and TCP/IP communication module. All workstations in the PSQA process were connected, and most manual operations were automated by AutoFrame sequentially or in parallel. Results: More than 20 PSQA tasks were performed both manually and using the developed AutoFrame interface. On average, 175 (±12) manual operations of the PSQA procedure were eliminated and performed by the automated process. The time to complete a PSQA task was 8.23 (±0.78) minutes for the automated workflow, in comparison to 13.91 (±3.01) minutes needed for manual operations. Conclusion: We have developed the AutoFrame interface framework that successfully automated our PSQA procedure, and significantly reduced the time, human (control/clicking/typing) errors, and operators’ stress. Future work will focus on improving the system’s flexibility and stability and extending its operations to other QA procedures.展开更多
基金supported by the Medical Special Cultivation Project of Anhui University of Science and Technology(Grant No.YZ2023H2B013)the Anhui Provincial Key Research and Development Project(Grant No.2022i01020015)the Open Project of Key Laboratory of Conveyance Equipment(East China Jiaotong University),Ministry of Education(KLCE2022-01).
文摘The human pose paradigm is estimated using a transformer-based multi-branch multidimensional directed the three-dimensional(3D)method that takes into account self-occlusion,badly posedness,and a lack of depth data in the per-frame 3D posture estimation from two-dimensional(2D)mapping to 3D mapping.Firstly,by examining the relationship between the movements of different bones in the human body,four virtual skeletons are proposed to enhance the cyclic constraints of limb joints.Then,multiple parameters describing the skeleton are fused and projected into a high-dimensional space.Utilizing a multi-branch network,motion features between bones and overall motion features are extracted to mitigate the drift error in the estimation results.Furthermore,the estimated relative depth is projected into 3D space,and the error is calculated against real 3D data,forming a loss function along with the relative depth error.This article adopts the average joint pixel error as the primary performance metric.Compared to the benchmark approach,the estimation findings indicate an increase in average precision of 1.8 mm within the Human3.6M sample.
文摘BACKGROUND Trastuzumab-targeted therapy is currently the standard of care for advanced human epidermal growth factor receptor 2(HER2)-positive gastric cancer.However,the emergence of resistance to trastuzumab poses significant challenges.AIM To identify the key genes associated with trastuzumab resistance.These results provide a basis for the development of interventions to address drug resistance and improve patient outcomes.METHODS High-throughput sequencing and bioinformatics were used to identify the differentially expressed pivotal gene BIRC3 and delineate its potential function and pathway regulation.Tumor samples were collected from patients with HER2-positive gastric cancer to evaluate the correlation between BIRC3 expression and trastuzumab resistance.We established gastric cancer cell lines with both highly expressed and suppressed levels of BIRC3,followed by comprehensive in vitro and in vivo experiments to confirm the involvement of BIRC3 in trastuzumab resistance and to elucidate its underlying mechanisms.RESULTS In patients with HER2-positive gastric cancer,there is a significant correlation between elevated BIRC3 expression in tumor tissues and higher T stage,tumor node metastasis stage,as well as poor overall survival and progressionfree survival.BIRC3 is highly expressed in trastuzumab-resistant gastric cancer cell lines,where it inhibits tumor cell apoptosis and enhances trastuzumab resistance by promoting the phosphorylation and activation of the phosphoinositide 3-kinase-Akt(PI3K-AKT)pathway in HER2-positive gastric cancer cells,both in vivo and in vitro.CONCLUSION This study revealed a robust association between high BIRC3 expression and an unfavorable prognosis in patients with HER2-positive gastric cancer.Thus,the high expression of BIRC3 stimulated PI3K-AKT phosphorylation and activation,stimulating the proliferation of HER2-positive tumor cells and suppressing apoptosis,ultimately leading to trastuzumab resistance.
文摘BACKGROUND Alveolar bone defects caused by inflammation are an urgent issue in oral implant surgery that must be solved.Regulating the various phenotypes of macrophages to enhance the inflammatory environment can significantly affect the progression of diseases and tissue engineering repair process.AIM To assess the influence of interleukin-10(IL-10)on the osteogenic differentiation of bone marrow mesenchymal stem cells(BMSCs)following their interaction with macrophages in an inflammatory environment.METHODS IL-10 modulates the differentiation of peritoneal macrophages in Wistar rats in an inflammatory environment.In this study,we investigated its impact on the proliferation,migration,and osteogenesis of BMSCs.The expression levels of signal transducer and activator of transcription 3(STAT3)and its activated form,phos-phorylated-STAT3,were examined in IL-10-stimulated macrophages.Subsequently,a specific STAT3 signaling inhibitor was used to impede STAT3 signal activation to further investigate the role of STAT3 signaling.RESULTS IL-10-stimulated macrophages underwent polarization to the M2 type through substitution,and these M2 macrophages actively facilitated the osteogenic differentiation of BMSCs.Mechanistically,STAT3 signaling plays a crucial role in the process by which IL-10 influences macrophages.Specifically,IL-10 stimulated the activation of the STAT3 signaling pathway and reduced the macrophage inflammatory response,as evidenced by its diminished impact on the osteogenic differentiation of BMSCs.CONCLUSION Stimulating macrophages with IL-10 proved effective in improving the inflammatory environment and promoting the osteogenic differentiation of BMSCs.The IL-10/STAT3 signaling pathway has emerged as a key regulator in the macrophage-mediated control of BMSCs’osteogenic differentiation.
基金Supported by the National Natural Science Foundation of China (62202346)Hubei Key Research and Development Program (2021BAA042)+3 种基金Open project of Engineering Research Center of Hubei Province for Clothing Information (2022HBCI01)Wuhan Applied Basic Frontier Research Project (2022013988065212)MIIT′s AI Industry Innovation Task Unveils Flagship Projects (Key Technologies,Equipment,and Systems for Flexible Customized and Intelligent Manufacturing in the Clothing Industry)Hubei Science and Technology Project of Safe Production Special Fund (Scene Control Platform Based on Proprioception Information Computing of Artificial Intelligence)。
文摘Background Intelligent garments,a burgeoning class of wearable devices,have extensive applications in domains such as sports training and medical rehabilitation.Nonetheless,existing research in the smart wearables domain predominantly emphasizes sensor functionality and quantity,often skipping crucial aspects related to user experience and interaction.Methods To address this gap,this study introduces a novel real-time 3D interactive system based on intelligent garments.The system utilizes lightweight sensor modules to collect human motion data and introduces a dual-stream fusion network based on pulsed neural units to classify and recognize human movements,thereby achieving real-time interaction between users and sensors.Additionally,the system incorporates 3D human visualization functionality,which visualizes sensor data and recognizes human actions as 3D models in real time,providing accurate and comprehensive visual feedback to help users better understand and analyze the details and features of human motion.This system has significant potential for applications in motion detection,medical monitoring,virtual reality,and other fields.The accurate classification of human actions contributes to the development of personalized training plans and injury prevention strategies.Conclusions This study has substantial implications in the domains of intelligent garments,human motion monitoring,and digital twin visualization.The advancement of this system is expected to propel the progress of wearable technology and foster a deeper comprehension of human motion.
文摘Concentration-time profiles of ^(125)I-labeled recombinant human interleukin-3 (125IrhIL-3) were de-termined by reverse phase high performance liquid chromatography (RHPLC) after intravenous and subcutaneous ad-ministration of the drug in 16 rhesus monkeys. The initial and terminal T1/2 in plasma after intravenous of 30 μg/kg were (0. 15 ±0.13) and (2. 21 ± 0. 59) h, respectively. Terminal half-lives after 30, 90 and 180μg/kg subcutaneous (s.c.) injections were 2. 0-3.8 h. Area under concentration-time curves (AUC) following s. c. were roughly in-creased with dose, while CL5 were similar among different dosages. The absorption rates were dependent on concentra-tion at injected site. Bioavailability was about 0.7 after s.c. Rapid biodegradation was found in plasma. Distribution profiles of total radioactivity were as follows: the highest level was found in urinary system; levels in bile-enteric sys-tem, lymph nodes, bone marrow and spleen were near to that in plasma, and level in brain was the lowest. The RH-PLC analysis revealed that kidney was one of the major organs for biodegradation.
基金supported by the Program of Entrepreneurship and Innovation Ph.D.in Jiangsu Province(JSSCBS20211175)the School Ph.D.Talent Funding(Z301B2055)the Natural Science Foundation of the Jiangsu Higher Education Institutions of China(21KJB520002).
文摘3D human pose estimation is a major focus area in the field of computer vision,which plays an important role in practical applications.This article summarizes the framework and research progress related to the estimation of monocular RGB images and videos.An overall perspective ofmethods integrated with deep learning is introduced.Novel image-based and video-based inputs are proposed as the analysis framework.From this viewpoint,common problems are discussed.The diversity of human postures usually leads to problems such as occlusion and ambiguity,and the lack of training datasets often results in poor generalization ability of the model.Regression methods are crucial for solving such problems.Considering image-based input,the multi-view method is commonly used to solve occlusion problems.Here,the multi-view method is analyzed comprehensively.By referring to video-based input,the human prior knowledge of restricted motion is used to predict human postures.In addition,structural constraints are widely used as prior knowledge.Furthermore,weakly supervised learningmethods are studied and discussed for these two types of inputs to improve the model generalization ability.The problem of insufficient training datasets must also be considered,especially because 3D datasets are usually biased and limited.Finally,emerging and popular datasets and evaluation indicators are discussed.The characteristics of the datasets and the relationships of the indicators are explained and highlighted.Thus,this article can be useful and instructive for researchers who are lacking in experience and find this field confusing.In addition,by providing an overview of 3D human pose estimation,this article sorts and refines recent studies on 3D human pose estimation.It describes kernel problems and common useful methods,and discusses the scope for further research.
基金Supported by National Natural Science Foundation of China,No.82000276the Science and Technology Project of Jiangxi Provincial Health Commission,No.202310005.
文摘BACKGROUND Diabetic cardiomyopathy(DCM),which is a complication of diabetes,poses a great threat to public health.Recent studies have confirmed the role of NLRP3(NOD-like receptor protein 3)activation in DCM development through the inflammatory response.Teneligliptin is an oral hypoglycemic dipeptidyl peptidase-IV inhibitor used to treat diabetes.Teneligliptin has recently been reported to have anti-inflammatory and protective effects on myocardial cells.AIM To examine the therapeutic effects of teneligliptin on DCM in diabetic mice.METHODS Streptozotocin was administered to induce diabetes in mice,followed by treatment with 30 mg/kg teneligliptin.RESULTS Marked increases in cardiomyocyte area and cardiac hypertrophy indicator heart weight/tibia length reductions in fractional shortening,ejection fraction,and heart rate;increases in creatine kinase-MB(CK-MB),aspartate transaminase(AST),and lactate dehydrogenase(LDH)levels;and upregulated NADPH oxidase 4 were observed in diabetic mice,all of which were significantly reversed by teneligliptin.Moreover,NLRP3 inflammasome activation and increased release of interleukin-1βin diabetic mice were inhibited by teneligliptin.Primary mouse cardiomyocytes were treated with high glucose(30 mmol/L)with or without teneligliptin(2.5 or 5μM)for 24 h.NLRP3 inflammasome activation.Increases in CKMB,AST,and LDH levels in glucose-stimulated cardiomyocytes were markedly inhibited by teneligliptin,and AMP(p-adenosine 5‘-monophosphate)-p-AMPK(activated protein kinase)levels were increased.Furthermore,the beneficial effects of teneligliptin on hyperglycaemia-induced cardiomyocytes were abolished by the AMPK signaling inhibitor compound C.CONCLUSION Overall,teneligliptin mitigated DCM by mitigating activation of the NLRP3 inflammasome.
基金supported by the Key Project of NSFC(Grant No.U1908214)Special Project of Central Government Guiding Local Science and Technology Development(Grant No.2021JH6/10500140)+5 种基金the Program for Innovative Research Team in University of Liaoning Province(LT2020015)the Support Plan for Key Field Innovation Team of Dalian(2021RT06)the Support Plan for Leading Innovation Team of Dalian University(XLJ202010)the Science and Technology Innovation Fund of Dalian(Grant No.2020JJ25CY001)in part by the National Natural Science Foundation of China under Grant 61906032the FundamentalResearch Funds for the Central Universities under Grant DUT21TD107.
文摘With the advancement of image sensing technology, estimating 3Dhuman pose frommonocular video has becomea hot research topic in computer vision. 3D human pose estimation is an essential prerequisite for subsequentaction analysis and understanding. It empowers a wide spectrum of potential applications in various areas, suchas intelligent transportation, human-computer interaction, and medical rehabilitation. Currently, some methodsfor 3D human pose estimation in monocular video employ temporal convolutional network (TCN) to extractinter-frame feature relationships, but the majority of them suffer from insufficient inter-frame feature relationshipextractions. In this paper, we decompose the 3D joint location regression into the bone direction and length, wepropose the TCG, a temporal convolutional network incorporating Gaussian error linear units (GELU), to solvebone direction. It enablesmore inter-frame features to be captured andmakes the utmost of the feature relationshipsbetween data. Furthermore, we adopt kinematic structural information to solve bone length enhancing the use ofintra-frame joint features. Finally, we design a loss function for joint training of the bone direction estimationnetwork with the bone length estimation network. The proposed method has extensively experimented on thepublic benchmark dataset Human3.6M. Both quantitative and qualitative experimental results showed that theproposed method can achieve more accurate 3D human pose estimations.
基金Funding for this study from Sai Gon University(Grant No.CSA2021–08).
文摘3-dimension(3-D)printing technology is growing strongly with many applications,one of which is the garment industry.The application of human body models to the garment industry is necessary to respond to the increasing personalization demand and still guarantee aesthetics.This paper proposes amethod to construct 3-D human models by applying deep learning.We calculate the location of the main slices of the human body,including the neck,chest,belly,buttocks,and the rings of the extremities,using pre-existing information.Then,on the positioning frame,we find the key points(fixed and unaltered)of these key slices and update these points tomatch the current parameters.To add points to a star slice,we use a deep learning model tomimic the form of the human body at that slice position.We use interpolation to produce sub-slices of different body sections based on the main slices to create complete body parts morphologically.We combine all slices to construct a full 3-D representation of the human body.
文摘Purpose: Patient-specific quality assurance (PSQA) requires manual operation of different workstations, which is time-consuming and error-prone. Therefore, developing automated solutions to improve efficiency and accuracy is a priority. The purpose of this study was to develop a general software interface with scripting on a human interactive device (HID) for improving the efficiency and accuracy of manual quality assurance (QA) procedures. Methods: As an initial application, we aimed to automate our PSQA workflow that involves Varian Eclipse treatment planning system, Elekta MOSAIQ oncology information system and PTW Verisoft application. A general platform, the AutoFrame interface with two imbedded subsystems—the AutoFlow and the PyFlow, was developed with a scripting language for automating human operations of aforementioned systems. The interface included three functional modules: GUI module, UDF script interpreter and TCP/IP communication module. All workstations in the PSQA process were connected, and most manual operations were automated by AutoFrame sequentially or in parallel. Results: More than 20 PSQA tasks were performed both manually and using the developed AutoFrame interface. On average, 175 (±12) manual operations of the PSQA procedure were eliminated and performed by the automated process. The time to complete a PSQA task was 8.23 (±0.78) minutes for the automated workflow, in comparison to 13.91 (±3.01) minutes needed for manual operations. Conclusion: We have developed the AutoFrame interface framework that successfully automated our PSQA procedure, and significantly reduced the time, human (control/clicking/typing) errors, and operators’ stress. Future work will focus on improving the system’s flexibility and stability and extending its operations to other QA procedures.