Currently,the number of patients with myopia is increasing rapidly across the globe.Traditional Chinese medicine(TCM),with its long history and rich experience,has shown promise in effectively managing and treating th...Currently,the number of patients with myopia is increasing rapidly across the globe.Traditional Chinese medicine(TCM),with its long history and rich experience,has shown promise in effectively managing and treating this condition.Nevertheless,considering the vast amount of research that is currently being conducted,focusing on the utilization of TCM in the management of myopia,there is an urgent requirement for a thorough and comprehensive review.The review would serve to clarify the practical applications of TCM within this specific field,and it would also aim to elucidate the underlying mechanisms that are at play,providing a deeper understanding of how TCM principles can be effectively integrated into modern medical practices.Here,some modern medical pathogenesis of myopia and appropriate TCM techniques studies are summarized in the prevention and treatment of myopia.Further,we discussed the potential mechanisms and the future research directions of TCM against myopia.Identifying these mechanisms is crucial for understanding how TCM can be effectively utilized in this context.The combination of various TCM methods or the combination of traditional Chinese and Western medicine is of great significance for the prevention and control of myopia in the future.展开更多
Ganmai Dazao Decoction,originating from“Jin Gui Yao Lue”(Synopsis of the Golden Chamber),is a classical prescription for treating visceral agitation.Composed of three medicinal and edible substances-licorice(Gancao)...Ganmai Dazao Decoction,originating from“Jin Gui Yao Lue”(Synopsis of the Golden Chamber),is a classical prescription for treating visceral agitation.Composed of three medicinal and edible substances-licorice(Gancao),wheat(Xiaomai),and jujube(Dazao),it functions to nourish the heart and calm the mind,harmonize the middle burner and regulate Qi,and alleviate urgency and restlessness.As its clinical application has expanded from traditional emotional disorders to neurological,endocrine,and various psychosomatic diseases,establishing a scientifically precise quality control system and deeply elucidating its pharmacodynamic material basis and mechanism of action have become critical tasks.Modern analytical methods,typified by chromatography,spectroscopy,and their hyphenated techniques,with their high sensitivity,high resolution,and powerful substance characterization capabilities,have become the core driving force for standardizing the quality control and modernizing the clinical application research of this formula.This paper systematically reviews the progress of the aforementioned analytical techniques and chemometrics in interpreting the chemical composition,establishing fingerprint profiles,controlling process quality,and researching the pharmacodynamic material basis of Ganmai Dazao Decoction.Furthermore,it discusses integrated approaches combining analytical techniques with pharmacology and clinical medicine to reveal mechanisms of action and explore therapeutic biomarkers.Finally,it provides an outlook on future directions and challenges,including technological integration and innovation,standardization of whole-process quality control systems,and evidence-based research aimed at internationalization.展开更多
Synaptic pruning is a crucial process in synaptic refinement,eliminating unstable synaptic connections in neural circuits.This process is triggered and regulated primarily by spontaneous neural activity and experience...Synaptic pruning is a crucial process in synaptic refinement,eliminating unstable synaptic connections in neural circuits.This process is triggered and regulated primarily by spontaneous neural activity and experience-dependent mechanisms.The pruning process involves multiple molecular signals and a series of regulatory activities governing the“eat me”and“don't eat me”states.Under physiological conditions,the interaction between glial cells and neurons results in the clearance of unnecessary synapses,maintaining normal neural circuit functionality via synaptic pruning.Alterations in genetic and environmental factors can lead to imbalanced synaptic pruning,thus promoting the occurrence and development of autism spectrum disorder,schizophrenia,Alzheimer's disease,and other neurological disorders.In this review,we investigated the molecular mechanisms responsible for synaptic pruning during neural development.We focus on how synaptic pruning can regulate neural circuits and its association with neurological disorders.Furthermore,we discuss the application of emerging optical and imaging technologies to observe synaptic structure and function,as well as their potential for clinical translation.Our aim was to enhance our understanding of synaptic pruning during neural development,including the molecular basis underlying the regulation of synaptic function and the dynamic changes in synaptic density,and to investigate the potential role of these mechanisms in the pathophysiology of neurological diseases,thus providing a theoretical foundation for the treatment of neurological disorders.展开更多
In-situ stress is a key parameter for underground mine design and rock stability analysis.The borehole overcoring technique is widely used for in-situ stress measurement,but the rheological recovery deformation of roc...In-situ stress is a key parameter for underground mine design and rock stability analysis.The borehole overcoring technique is widely used for in-situ stress measurement,but the rheological recovery deformation of rocks after stress relief introduces errors.To improve accuracy,this study proposes an in-situ stress solution theory that incorporates time-dependent stress relief effects.Triaxial stepwise loadingunloading rheological tests on granite and siltstone established quantitative relationships between instantaneous elastic recovery and viscoelastic recovery under different stress levels,confirming their impact on measurement accuracy.By integrating a dual-class elastic deformation recovery model,an improved in-situ stress solution theory was derived.Additionally,accounting for the nonlinear characteristics of rock masses,a determination method for time-dependent nonlinear mechanical parameters was proposed.Based on the CSIRO hollow inclusion strain cell,time-dependent strain correction equations and long-term confining pressure calibration equations were formulated.Finally,the proposed theory was successfully applied at one iron mine(736 m depth)in Xinjiang,China,and one coal mine(510 m depth)in Ningxia,China.Compared to classical theory,the calculated mean stress values showed accuracy improvements of 6.0%and 9.4%,respectively,validating the applicability and reliability of the proposed theory.展开更多
Lung cancer continues to be a leading cause of cancer-related deaths worldwide,emphasizing the critical need for improved diagnostic techniques.Early detection of lung tumors significantly increases the chances of suc...Lung cancer continues to be a leading cause of cancer-related deaths worldwide,emphasizing the critical need for improved diagnostic techniques.Early detection of lung tumors significantly increases the chances of successful treatment and survival.However,current diagnostic methods often fail to detect tumors at an early stage or to accurately pinpoint their location within the lung tissue.Single-model deep learning technologies for lung cancer detection,while beneficial,cannot capture the full range of features present in medical imaging data,leading to incomplete or inaccurate detection.Furthermore,it may not be robust enough to handle the wide variability in medical images due to different imaging conditions,patient anatomy,and tumor characteristics.To overcome these disadvantages,dual-model or multi-model approaches can be employed.This research focuses on enhancing the detection of lung cancer by utilizing a combination of two learning models:a Convolutional Neural Network(CNN)for categorization and the You Only Look Once(YOLOv8)architecture for real-time identification and pinpointing of tumors.CNNs automatically learn to extract hierarchical features from raw image data,capturing patterns such as edges,textures,and complex structures that are crucial for identifying lung cancer.YOLOv8 incorporates multiscale feature extraction,enabling the detection of tumors of varying sizes and scales within a single image.This is particularly beneficial for identifying small or irregularly shaped tumors that may be challenging to detect.Furthermore,through the utilization of cutting-edge data augmentation methods,such as Deep Convolutional Generative Adversarial Networks(DCGAN),the suggested approach can handle the issue of limited data and boost the models’ability to learn from diverse and comprehensive datasets.The combined method not only improved accuracy and localization but also ensured efficient real-time processing,which is crucial for practical clinical applications.The CNN achieved an accuracy of 97.67%in classifying lung tissues into healthy and cancerous categories.The YOLOv8 model achieved an Intersection over Union(IoU)score of 0.85 for tumor localization,reflecting high precision in detecting and marking tumor boundaries within the images.Finally,the incorporation of synthetic images generated by DCGAN led to a 10%improvement in both the CNN classification accuracy and YOLOv8 detection performance.展开更多
This paper presents a high-fidelity lumpedparameter(LP)thermal model(HF-LPTM)for permanent magnet synchronous machines(PMSMs)in electric vehicle(EV)applications,where various cooling techniques are considered,includin...This paper presents a high-fidelity lumpedparameter(LP)thermal model(HF-LPTM)for permanent magnet synchronous machines(PMSMs)in electric vehicle(EV)applications,where various cooling techniques are considered,including frame forced air/liquid cooling,oil jet cooling for endwinding,and rotor shaft cooling.To address the temperature misestimation in the LP thermal modelling due to assumptions of concentrated loss input and uniform heat flows,the developed HF-LPTM introduces two compensation thermal resistances for the winding and PM components,which are analytically derived from the multi-dimensional heat transfer equations and are robust against different load/thermal conditions.As validated by the finite element analysis method and experiments,the conventional LPTMs exhibit significant winding temperature deviations,while the proposed HF-LPTM can accurately predict both the midpoint and average temperatures.The developed HFLPTM is further used to assess the effectiveness of various cooling techniques under different scenarios,i.e.,steady-state thermal states under the rated load condition,and transient temperature profiles under city,freeway,and hybrid(city+freeway)driving cycles.Results indicate that no single cooling technique can maintain both winding and PM temperatures within safety limits.The combination of frame liquid cooling and oil jet cooling for end winding can sufficiently mitigate PMSM thermal stress in EV applications.展开更多
基金supported by Healthy China initiative of Traditional Chinese Medicine(No.889042).
文摘Currently,the number of patients with myopia is increasing rapidly across the globe.Traditional Chinese medicine(TCM),with its long history and rich experience,has shown promise in effectively managing and treating this condition.Nevertheless,considering the vast amount of research that is currently being conducted,focusing on the utilization of TCM in the management of myopia,there is an urgent requirement for a thorough and comprehensive review.The review would serve to clarify the practical applications of TCM within this specific field,and it would also aim to elucidate the underlying mechanisms that are at play,providing a deeper understanding of how TCM principles can be effectively integrated into modern medical practices.Here,some modern medical pathogenesis of myopia and appropriate TCM techniques studies are summarized in the prevention and treatment of myopia.Further,we discussed the potential mechanisms and the future research directions of TCM against myopia.Identifying these mechanisms is crucial for understanding how TCM can be effectively utilized in this context.The combination of various TCM methods or the combination of traditional Chinese and Western medicine is of great significance for the prevention and control of myopia in the future.
文摘Ganmai Dazao Decoction,originating from“Jin Gui Yao Lue”(Synopsis of the Golden Chamber),is a classical prescription for treating visceral agitation.Composed of three medicinal and edible substances-licorice(Gancao),wheat(Xiaomai),and jujube(Dazao),it functions to nourish the heart and calm the mind,harmonize the middle burner and regulate Qi,and alleviate urgency and restlessness.As its clinical application has expanded from traditional emotional disorders to neurological,endocrine,and various psychosomatic diseases,establishing a scientifically precise quality control system and deeply elucidating its pharmacodynamic material basis and mechanism of action have become critical tasks.Modern analytical methods,typified by chromatography,spectroscopy,and their hyphenated techniques,with their high sensitivity,high resolution,and powerful substance characterization capabilities,have become the core driving force for standardizing the quality control and modernizing the clinical application research of this formula.This paper systematically reviews the progress of the aforementioned analytical techniques and chemometrics in interpreting the chemical composition,establishing fingerprint profiles,controlling process quality,and researching the pharmacodynamic material basis of Ganmai Dazao Decoction.Furthermore,it discusses integrated approaches combining analytical techniques with pharmacology and clinical medicine to reveal mechanisms of action and explore therapeutic biomarkers.Finally,it provides an outlook on future directions and challenges,including technological integration and innovation,standardization of whole-process quality control systems,and evidence-based research aimed at internationalization.
基金supported by the National Natural Science Foundation of China,No.31760290,82160688the Key Development Areas Project of Ganzhou Science and Technology,No.2022B-SF9554(all to XL)。
文摘Synaptic pruning is a crucial process in synaptic refinement,eliminating unstable synaptic connections in neural circuits.This process is triggered and regulated primarily by spontaneous neural activity and experience-dependent mechanisms.The pruning process involves multiple molecular signals and a series of regulatory activities governing the“eat me”and“don't eat me”states.Under physiological conditions,the interaction between glial cells and neurons results in the clearance of unnecessary synapses,maintaining normal neural circuit functionality via synaptic pruning.Alterations in genetic and environmental factors can lead to imbalanced synaptic pruning,thus promoting the occurrence and development of autism spectrum disorder,schizophrenia,Alzheimer's disease,and other neurological disorders.In this review,we investigated the molecular mechanisms responsible for synaptic pruning during neural development.We focus on how synaptic pruning can regulate neural circuits and its association with neurological disorders.Furthermore,we discuss the application of emerging optical and imaging technologies to observe synaptic structure and function,as well as their potential for clinical translation.Our aim was to enhance our understanding of synaptic pruning during neural development,including the molecular basis underlying the regulation of synaptic function and the dynamic changes in synaptic density,and to investigate the potential role of these mechanisms in the pathophysiology of neurological diseases,thus providing a theoretical foundation for the treatment of neurological disorders.
基金supported by the National Science and Technology Major Project of the Ministry of Science and Technology of China(No.2024ZD1700201)the National Natural Science Foundation of China(Nos.U2034206,51974014 and 51574014)+1 种基金the Guangdong Basic and Applied Basic Research Foundation(No.2024A1515011631)the National Key Research and Development Project of China(No.2022YFC3004601)。
文摘In-situ stress is a key parameter for underground mine design and rock stability analysis.The borehole overcoring technique is widely used for in-situ stress measurement,but the rheological recovery deformation of rocks after stress relief introduces errors.To improve accuracy,this study proposes an in-situ stress solution theory that incorporates time-dependent stress relief effects.Triaxial stepwise loadingunloading rheological tests on granite and siltstone established quantitative relationships between instantaneous elastic recovery and viscoelastic recovery under different stress levels,confirming their impact on measurement accuracy.By integrating a dual-class elastic deformation recovery model,an improved in-situ stress solution theory was derived.Additionally,accounting for the nonlinear characteristics of rock masses,a determination method for time-dependent nonlinear mechanical parameters was proposed.Based on the CSIRO hollow inclusion strain cell,time-dependent strain correction equations and long-term confining pressure calibration equations were formulated.Finally,the proposed theory was successfully applied at one iron mine(736 m depth)in Xinjiang,China,and one coal mine(510 m depth)in Ningxia,China.Compared to classical theory,the calculated mean stress values showed accuracy improvements of 6.0%and 9.4%,respectively,validating the applicability and reliability of the proposed theory.
文摘Lung cancer continues to be a leading cause of cancer-related deaths worldwide,emphasizing the critical need for improved diagnostic techniques.Early detection of lung tumors significantly increases the chances of successful treatment and survival.However,current diagnostic methods often fail to detect tumors at an early stage or to accurately pinpoint their location within the lung tissue.Single-model deep learning technologies for lung cancer detection,while beneficial,cannot capture the full range of features present in medical imaging data,leading to incomplete or inaccurate detection.Furthermore,it may not be robust enough to handle the wide variability in medical images due to different imaging conditions,patient anatomy,and tumor characteristics.To overcome these disadvantages,dual-model or multi-model approaches can be employed.This research focuses on enhancing the detection of lung cancer by utilizing a combination of two learning models:a Convolutional Neural Network(CNN)for categorization and the You Only Look Once(YOLOv8)architecture for real-time identification and pinpointing of tumors.CNNs automatically learn to extract hierarchical features from raw image data,capturing patterns such as edges,textures,and complex structures that are crucial for identifying lung cancer.YOLOv8 incorporates multiscale feature extraction,enabling the detection of tumors of varying sizes and scales within a single image.This is particularly beneficial for identifying small or irregularly shaped tumors that may be challenging to detect.Furthermore,through the utilization of cutting-edge data augmentation methods,such as Deep Convolutional Generative Adversarial Networks(DCGAN),the suggested approach can handle the issue of limited data and boost the models’ability to learn from diverse and comprehensive datasets.The combined method not only improved accuracy and localization but also ensured efficient real-time processing,which is crucial for practical clinical applications.The CNN achieved an accuracy of 97.67%in classifying lung tissues into healthy and cancerous categories.The YOLOv8 model achieved an Intersection over Union(IoU)score of 0.85 for tumor localization,reflecting high precision in detecting and marking tumor boundaries within the images.Finally,the incorporation of synthetic images generated by DCGAN led to a 10%improvement in both the CNN classification accuracy and YOLOv8 detection performance.
文摘This paper presents a high-fidelity lumpedparameter(LP)thermal model(HF-LPTM)for permanent magnet synchronous machines(PMSMs)in electric vehicle(EV)applications,where various cooling techniques are considered,including frame forced air/liquid cooling,oil jet cooling for endwinding,and rotor shaft cooling.To address the temperature misestimation in the LP thermal modelling due to assumptions of concentrated loss input and uniform heat flows,the developed HF-LPTM introduces two compensation thermal resistances for the winding and PM components,which are analytically derived from the multi-dimensional heat transfer equations and are robust against different load/thermal conditions.As validated by the finite element analysis method and experiments,the conventional LPTMs exhibit significant winding temperature deviations,while the proposed HF-LPTM can accurately predict both the midpoint and average temperatures.The developed HFLPTM is further used to assess the effectiveness of various cooling techniques under different scenarios,i.e.,steady-state thermal states under the rated load condition,and transient temperature profiles under city,freeway,and hybrid(city+freeway)driving cycles.Results indicate that no single cooling technique can maintain both winding and PM temperatures within safety limits.The combination of frame liquid cooling and oil jet cooling for end winding can sufficiently mitigate PMSM thermal stress in EV applications.
