This editorial discusses an article recently published in the World Journal of Clinical Cases,focusing on risk factors associated with intensive care unit-acquired weak-ness(ICU-AW).ICU-AW is a serious neuromuscular c...This editorial discusses an article recently published in the World Journal of Clinical Cases,focusing on risk factors associated with intensive care unit-acquired weak-ness(ICU-AW).ICU-AW is a serious neuromuscular complication seen in criti-cally ill patients,characterized by muscle dysfunction,weakness,and sensory impairments.Post-discharge,patients may encounter various obstacles impacting their quality of life.The pathogenesis involves intricate changes in muscle and nerve function,potentially leading to significant disabilities.Given its global significance,ICU-AW has become a key research area.The study identified critical risk factors using a multilayer perceptron neural network model,highlighting the impact of intensive care unit stay duration and mechanical ventilation duration on ICU-AW.Recommendations were provided for preventing ICU-AW,empha-sizing comprehensive interventions and risk factor mitigation.This editorial stresses the importance of external validation,cross-validation,and model tran-sparency to enhance model reliability.Moreover,the application of machine learning in clinical medicine has demonstrated clear benefits in improving disease understanding and treatment decisions.While machine learning presents oppor-tunities,challenges such as model reliability and data management necessitate thorough validation and ethical considerations.In conclusion,integrating ma-chine learning into healthcare offers significant potential and challenges.Enhan-cing data management,validating models,and upholding ethical standards are crucial for maximizing the benefits of machine learning in clinical practice.展开更多
Aggregate-induced emission luminogens(AIEgens) have been widely used in biological imaging, chemical sensing, and disease treatments. The rational design and construction of AIEgens have received considerable research...Aggregate-induced emission luminogens(AIEgens) have been widely used in biological imaging, chemical sensing, and disease treatments. The rational design and construction of AIEgens have received considerable research interests during the last few years. Herein, molecular docking-aided AIEgen design has been reasonably proposed and AIEgen TBQZY with excellent ~1O_(2) generation ability has been synthesized. The newly developed TBQZY could efficiently kill S. epidermidis and methicillinresistant S. epidermidis(MRSE) by tightly binding to bacteria and triggering the accumulation of ~1O_(2) in bacteria. TBQZY specifically regulated the immune system and polarized macrophages from M1 to M2 to accelerate the elimination of biofilm in vivo. In addition, healing acceleration was observed in chronic wounds treated with TBQZY, and side effects were negligible.Meanwhile, TBQZY had extraordinary potential for combating drug-resistant bacteria in the clinical setting. This research not only provided new concepts for the design of AIEgens, but also shed some lights on the discovery of drugs against drug-resistant bacteria.展开更多
Dynamic changes of traffic features in unstructured road networks challenge the scene-cognitive abilities of drivers,which brings various heterogeneous traffic behaviors.Modeling traffic with these heterogeneous behav...Dynamic changes of traffic features in unstructured road networks challenge the scene-cognitive abilities of drivers,which brings various heterogeneous traffic behaviors.Modeling traffic with these heterogeneous behaviors would have significant impact on realistic traffic simulation.Most existing traffic methods generate traffic behaviors by adjust-ing parameters and cannot describe those heterogeneous traffic flows in detail.In this paper,a cognition-driven traffic-simulation method inspired by the theory of cognitive psychology is introduced.We first present a visual-filtering model and a perceptual-information fusion model to describe drivers'heterogeneous cognitive processes.Then,logistic regression is used to model drivers'heuristic decision-making processes based on the above cognitive results.Lastly,we apply the high-level cognitive decision-making results to low-level traffic simulation.The experimental results show that our method can provide realistic simulations for the traffic with those heterogeneous behaviors in unstructured road networks and has nearly the same efficiency as that of existing methods.展开更多
文摘This editorial discusses an article recently published in the World Journal of Clinical Cases,focusing on risk factors associated with intensive care unit-acquired weak-ness(ICU-AW).ICU-AW is a serious neuromuscular complication seen in criti-cally ill patients,characterized by muscle dysfunction,weakness,and sensory impairments.Post-discharge,patients may encounter various obstacles impacting their quality of life.The pathogenesis involves intricate changes in muscle and nerve function,potentially leading to significant disabilities.Given its global significance,ICU-AW has become a key research area.The study identified critical risk factors using a multilayer perceptron neural network model,highlighting the impact of intensive care unit stay duration and mechanical ventilation duration on ICU-AW.Recommendations were provided for preventing ICU-AW,empha-sizing comprehensive interventions and risk factor mitigation.This editorial stresses the importance of external validation,cross-validation,and model tran-sparency to enhance model reliability.Moreover,the application of machine learning in clinical medicine has demonstrated clear benefits in improving disease understanding and treatment decisions.While machine learning presents oppor-tunities,challenges such as model reliability and data management necessitate thorough validation and ethical considerations.In conclusion,integrating ma-chine learning into healthcare offers significant potential and challenges.Enhan-cing data management,validating models,and upholding ethical standards are crucial for maximizing the benefits of machine learning in clinical practice.
基金supported by the start-up funding from Wuhan University (691000002, 600460026)Tai Kang Center for Life and Medical Sciences (692000007)Wuhan University large instrument and equipment open subsidies。
文摘Aggregate-induced emission luminogens(AIEgens) have been widely used in biological imaging, chemical sensing, and disease treatments. The rational design and construction of AIEgens have received considerable research interests during the last few years. Herein, molecular docking-aided AIEgen design has been reasonably proposed and AIEgen TBQZY with excellent ~1O_(2) generation ability has been synthesized. The newly developed TBQZY could efficiently kill S. epidermidis and methicillinresistant S. epidermidis(MRSE) by tightly binding to bacteria and triggering the accumulation of ~1O_(2) in bacteria. TBQZY specifically regulated the immune system and polarized macrophages from M1 to M2 to accelerate the elimination of biofilm in vivo. In addition, healing acceleration was observed in chronic wounds treated with TBQZY, and side effects were negligible.Meanwhile, TBQZY had extraordinary potential for combating drug-resistant bacteria in the clinical setting. This research not only provided new concepts for the design of AIEgens, but also shed some lights on the discovery of drugs against drug-resistant bacteria.
基金supported by the National Natural Science Foundation of China under Grant Nos.61602425,61572445 and 61822701the Doctor Fund of Zhengzhou University of Light Industry of China under Grant No.2015BSJJ007。
文摘Dynamic changes of traffic features in unstructured road networks challenge the scene-cognitive abilities of drivers,which brings various heterogeneous traffic behaviors.Modeling traffic with these heterogeneous behaviors would have significant impact on realistic traffic simulation.Most existing traffic methods generate traffic behaviors by adjust-ing parameters and cannot describe those heterogeneous traffic flows in detail.In this paper,a cognition-driven traffic-simulation method inspired by the theory of cognitive psychology is introduced.We first present a visual-filtering model and a perceptual-information fusion model to describe drivers'heterogeneous cognitive processes.Then,logistic regression is used to model drivers'heuristic decision-making processes based on the above cognitive results.Lastly,we apply the high-level cognitive decision-making results to low-level traffic simulation.The experimental results show that our method can provide realistic simulations for the traffic with those heterogeneous behaviors in unstructured road networks and has nearly the same efficiency as that of existing methods.
