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,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.
