Optimization problems are prevalent in various fields of science and engineering,with several real-world applications characterized by high dimensionality and complex search landscapes.Starfish optimization algorithm(...Optimization problems are prevalent in various fields of science and engineering,with several real-world applications characterized by high dimensionality and complex search landscapes.Starfish optimization algorithm(SFOA)is a recently optimizer inspired by swarm intelligence,which is effective for numerical optimization,but it may encounter premature and local convergence for complex optimization problems.To address these challenges,this paper proposes the multi-strategy enhanced crested porcupine-starfish optimization algorithm(MCPSFOA).The core innovation of MCPSFOA lies in employing a hybrid strategy to improve SFOA,which integrates the exploratory mechanisms of SFOA with the diverse search capacity of the Crested Porcupine Optimizer(CPO).This synergy enhances MCPSFOA’s ability to navigate complex and multimodal search spaces.To further prevent premature convergence,MCPSFOA incorporates Lévy flight,leveraging its characteristic long and short jump patterns to enable large-scale exploration and escape from local optima.Subsequently,Gaussian mutation is applied for precise solution tuning,introducing controlled perturbations that enhance accuracy and mitigate the risk of insufficient exploitation.Notably,the population diversity enhancement mechanism periodically identifies and resets stagnant individuals,thereby consistently revitalizing population variety throughout the optimization process.MCPSFOA is rigorously evaluated on 24 classical benchmark functions(including high-dimensional cases),the CEC2017 suite,and the CEC2022 suite.MCPSFOA achieves superior overall performance with Friedman mean ranks of 2.208,2.310 and 2.417 on these benchmark functions,outperforming 11 state-of-the-art algorithms.Furthermore,the practical applicability of MCPSFOA is confirmed through its successful application to five engineering optimization cases,where it also yields excellent results.In conclusion,MCPSFOA is not only a highly effective and reliable optimizer for benchmark functions,but also a practical tool for solving real-world optimization problems.展开更多
Peripheral nerve defect repair is a complex process that involves multiple cell types;perineurial cells play a pivotal role.Hair follicle neural crest stem cells promote perineurial cell proliferation and migration vi...Peripheral nerve defect repair is a complex process that involves multiple cell types;perineurial cells play a pivotal role.Hair follicle neural crest stem cells promote perineurial cell proliferation and migration via paracrine signaling;however,their clinical applications are limited by potential risks such as tumorigenesis and xenogeneic immune rejection,which are similar to the risks associated with other stem cell transplantations.The present study therefore focuses on small extracellular vesicles derived from hair follicle neural crest stem cells,which preserve the bioactive properties of the parent cells while avoiding the transplantation-associated risks.In vitro,small extracellular vesicles derived from hair follicle neural crest stem cells significantly enhanced the proliferation,migration,tube formation,and barrier function of perineurial cells,and subsequently upregulated the expression of tight junction proteins.Furthermore,in a rat model of sciatic nerve defects bridged with silicon tubes,treatment with small extracellular vesicles derived from hair follicle neural crest stem cells resulted in higher tight junction protein expression in perineurial cells,thus facilitating neural tissue regeneration.At 10 weeks post-surgery,rats treated with small extracellular vesicles derived from hair follicle neural crest stem cells exhibited improved nerve function recovery and reduced muscle atrophy.Transcriptomic and micro RNA analyses revealed that small extracellular vesicles derived from hair follicle neural crest stem cells deliver mi R-21-5p,which inhibits mothers against decapentaplegic homolog 7 expression,thereby activating the transforming growth factor-β/mothers against decapentaplegic homolog signaling pathway and upregulating hyaluronan synthase 2 expression,and further enhancing tight junction protein expression.Together,our findings indicate that small extracellular vesicles derived from hair follicle neural crest stem cells promote the proliferation,migration,and tight junction protein formation of perineurial cells.These results provide new insights into peripheral nerve regeneration from the perspective of perineurial cells,and present a novel approach for the clinical treatment of peripheral nerve defects.展开更多
Neural crest-derived mesenchymal stem cells(NC-MSCs)represent a unique population with remarkable regenerative potential,owing to their embryonic origin and exceptional differentiation capacity.These cells demonstrate...Neural crest-derived mesenchymal stem cells(NC-MSCs)represent a unique population with remarkable regenerative potential,owing to their embryonic origin and exceptional differentiation capacity.These cells demonstrate superior performance in neural and craniofacial tissue regeneration compared to conventional mesenchymal stem cells,with dental stem cells emerging as particularly promising candidates for clinical applications in periodontics and endodontics.Despite their therapeutic promise,adult NC-MSCs face significant challenges including donor site limitations,cellular heterogeneity,and scalability issues.Recent advances in pluripotent stem cell offer potential solutions through the generation of NC-MSCs in vitro,though safety concerns regarding tumorigenicity and long-term stability remain to be addressed through comprehensive preclinical studies.This review provides a comprehensive analysis of NC-MSC biology,highlighting their developmental origins,molecular characteristics,and current applications in regenerative medicine.We critically evaluate existing challenges and future directions,emphasizing the need for standardized protocols,improved characterization methods,and rigorous preclinical evaluation to facilitate clinical translation and therapeutic implementation.展开更多
Autism spectrum disorder(ASD)is a multifaceted neurological developmental condition that manifests in several ways.Nearly all autistic children remain undiagnosed before the age of three.Developmental problems affecti...Autism spectrum disorder(ASD)is a multifaceted neurological developmental condition that manifests in several ways.Nearly all autistic children remain undiagnosed before the age of three.Developmental problems affecting face features are often associated with fundamental brain disorders.The facial evolution of newborns with ASD is quite different from that of typically developing children.Early recognition is very significant to aid families and parents in superstition and denial.Distinguishing facial features from typically developing children is an evident manner to detect children analyzed with ASD.Presently,artificial intelligence(AI)significantly contributes to the emerging computer-aided diagnosis(CAD)of autism and to the evolving interactivemethods that aid in the treatment and reintegration of autistic patients.This study introduces an Ensemble of deep learning models based on the autism spectrum disorder detection in facial images(EDLM-ASDDFI)model.The overarching goal of the EDLM-ASDDFI model is to recognize the difference between facial images of individuals with ASD and normal controls.In the EDLM-ASDDFI method,the primary level of data pre-processing is involved by Gabor filtering(GF).Besides,the EDLM-ASDDFI technique applies the MobileNetV2 model to learn complex features from the pre-processed data.For the ASD detection process,the EDLM-ASDDFI method uses ensemble techniques for classification procedure that encompasses long short-term memory(LSTM),deep belief network(DBN),and hybrid kernel extreme learning machine(HKELM).Finally,the hyperparameter selection of the three deep learning(DL)models can be implemented by the design of the crested porcupine optimizer(CPO)technique.An extensive experiment was conducted to emphasize the improved ASD detection performance of the EDLM-ASDDFI method.The simulation outcomes indicated that the EDLM-ASDDFI technique highlighted betterment over other existing models in terms of numerous performance measures.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.12402139,No.52368070)supported by Hainan Provincial Natural Science Foundation of China(Grant No.524QN223)+3 种基金Scientific Research Startup Foundation of Hainan University(Grant No.RZ2300002710)State Key Laboratory of Structural Analysis,Optimization and CAE Software for Industrial Equipment,Dalian University of Technology(Grant No.GZ24107)the Horizontal Research Project(Grant No.HD-KYH-2024022)Innovative Research Projects for Postgraduate Students in Hainan Province(Grant No.Hys2025-217).
文摘Optimization problems are prevalent in various fields of science and engineering,with several real-world applications characterized by high dimensionality and complex search landscapes.Starfish optimization algorithm(SFOA)is a recently optimizer inspired by swarm intelligence,which is effective for numerical optimization,but it may encounter premature and local convergence for complex optimization problems.To address these challenges,this paper proposes the multi-strategy enhanced crested porcupine-starfish optimization algorithm(MCPSFOA).The core innovation of MCPSFOA lies in employing a hybrid strategy to improve SFOA,which integrates the exploratory mechanisms of SFOA with the diverse search capacity of the Crested Porcupine Optimizer(CPO).This synergy enhances MCPSFOA’s ability to navigate complex and multimodal search spaces.To further prevent premature convergence,MCPSFOA incorporates Lévy flight,leveraging its characteristic long and short jump patterns to enable large-scale exploration and escape from local optima.Subsequently,Gaussian mutation is applied for precise solution tuning,introducing controlled perturbations that enhance accuracy and mitigate the risk of insufficient exploitation.Notably,the population diversity enhancement mechanism periodically identifies and resets stagnant individuals,thereby consistently revitalizing population variety throughout the optimization process.MCPSFOA is rigorously evaluated on 24 classical benchmark functions(including high-dimensional cases),the CEC2017 suite,and the CEC2022 suite.MCPSFOA achieves superior overall performance with Friedman mean ranks of 2.208,2.310 and 2.417 on these benchmark functions,outperforming 11 state-of-the-art algorithms.Furthermore,the practical applicability of MCPSFOA is confirmed through its successful application to five engineering optimization cases,where it also yields excellent results.In conclusion,MCPSFOA is not only a highly effective and reliable optimizer for benchmark functions,but also a practical tool for solving real-world optimization problems.
基金supported by the National Natural Science Foundation of China,No.81571211(to FL)the Natural Science Foundation of Shanghai,No.22ZR1476800(to CH)。
文摘Peripheral nerve defect repair is a complex process that involves multiple cell types;perineurial cells play a pivotal role.Hair follicle neural crest stem cells promote perineurial cell proliferation and migration via paracrine signaling;however,their clinical applications are limited by potential risks such as tumorigenesis and xenogeneic immune rejection,which are similar to the risks associated with other stem cell transplantations.The present study therefore focuses on small extracellular vesicles derived from hair follicle neural crest stem cells,which preserve the bioactive properties of the parent cells while avoiding the transplantation-associated risks.In vitro,small extracellular vesicles derived from hair follicle neural crest stem cells significantly enhanced the proliferation,migration,tube formation,and barrier function of perineurial cells,and subsequently upregulated the expression of tight junction proteins.Furthermore,in a rat model of sciatic nerve defects bridged with silicon tubes,treatment with small extracellular vesicles derived from hair follicle neural crest stem cells resulted in higher tight junction protein expression in perineurial cells,thus facilitating neural tissue regeneration.At 10 weeks post-surgery,rats treated with small extracellular vesicles derived from hair follicle neural crest stem cells exhibited improved nerve function recovery and reduced muscle atrophy.Transcriptomic and micro RNA analyses revealed that small extracellular vesicles derived from hair follicle neural crest stem cells deliver mi R-21-5p,which inhibits mothers against decapentaplegic homolog 7 expression,thereby activating the transforming growth factor-β/mothers against decapentaplegic homolog signaling pathway and upregulating hyaluronan synthase 2 expression,and further enhancing tight junction protein expression.Together,our findings indicate that small extracellular vesicles derived from hair follicle neural crest stem cells promote the proliferation,migration,and tight junction protein formation of perineurial cells.These results provide new insights into peripheral nerve regeneration from the perspective of perineurial cells,and present a novel approach for the clinical treatment of peripheral nerve defects.
基金Supported by the National Natural Science Foundation of China,No.82270951.
文摘Neural crest-derived mesenchymal stem cells(NC-MSCs)represent a unique population with remarkable regenerative potential,owing to their embryonic origin and exceptional differentiation capacity.These cells demonstrate superior performance in neural and craniofacial tissue regeneration compared to conventional mesenchymal stem cells,with dental stem cells emerging as particularly promising candidates for clinical applications in periodontics and endodontics.Despite their therapeutic promise,adult NC-MSCs face significant challenges including donor site limitations,cellular heterogeneity,and scalability issues.Recent advances in pluripotent stem cell offer potential solutions through the generation of NC-MSCs in vitro,though safety concerns regarding tumorigenicity and long-term stability remain to be addressed through comprehensive preclinical studies.This review provides a comprehensive analysis of NC-MSC biology,highlighting their developmental origins,molecular characteristics,and current applications in regenerative medicine.We critically evaluate existing challenges and future directions,emphasizing the need for standardized protocols,improved characterization methods,and rigorous preclinical evaluation to facilitate clinical translation and therapeutic implementation.
基金Researchers supporting Project number(RSPD2025R1107),King Saud University,Riyadh,Saudi Arabia.
文摘Autism spectrum disorder(ASD)is a multifaceted neurological developmental condition that manifests in several ways.Nearly all autistic children remain undiagnosed before the age of three.Developmental problems affecting face features are often associated with fundamental brain disorders.The facial evolution of newborns with ASD is quite different from that of typically developing children.Early recognition is very significant to aid families and parents in superstition and denial.Distinguishing facial features from typically developing children is an evident manner to detect children analyzed with ASD.Presently,artificial intelligence(AI)significantly contributes to the emerging computer-aided diagnosis(CAD)of autism and to the evolving interactivemethods that aid in the treatment and reintegration of autistic patients.This study introduces an Ensemble of deep learning models based on the autism spectrum disorder detection in facial images(EDLM-ASDDFI)model.The overarching goal of the EDLM-ASDDFI model is to recognize the difference between facial images of individuals with ASD and normal controls.In the EDLM-ASDDFI method,the primary level of data pre-processing is involved by Gabor filtering(GF).Besides,the EDLM-ASDDFI technique applies the MobileNetV2 model to learn complex features from the pre-processed data.For the ASD detection process,the EDLM-ASDDFI method uses ensemble techniques for classification procedure that encompasses long short-term memory(LSTM),deep belief network(DBN),and hybrid kernel extreme learning machine(HKELM).Finally,the hyperparameter selection of the three deep learning(DL)models can be implemented by the design of the crested porcupine optimizer(CPO)technique.An extensive experiment was conducted to emphasize the improved ASD detection performance of the EDLM-ASDDFI method.The simulation outcomes indicated that the EDLM-ASDDFI technique highlighted betterment over other existing models in terms of numerous performance measures.
