This study investigates the use of computational frameworks for sepsis.We consider two dimensions for investi-gation-early diagnosis of sepsis(EDS)and mortality prediction rate for sepsis patients(MPS).We concentrate ...This study investigates the use of computational frameworks for sepsis.We consider two dimensions for investi-gation-early diagnosis of sepsis(EDS)and mortality prediction rate for sepsis patients(MPS).We concentrate on the clinical parameters on which sepsis diagnosis and prognosis are currently done,including customized treatment plans based on historical data of the patient.We identify the most notable literature that uses com-putational models to address EDS and MPS based on those clinical parameters.In addition to the review of the computational models built upon the clinical parameters,we also provide details regarding the popular publicly available data sources.We provide brief reviews for each model in terms of prior art and present an analysis of their results,as claimed by the respective authors.With respect to the use of machine learning models,we have provided avenues for model analysis in terms of model selection,model validation,model interpretation,and model comparison.We further present the challenges and limitations of the use of computational models,providing future research directions.This study intends to serve as a benchmark for first-hand impressions on the use of computational models for EDS and MPS of sepsis,along with the details regarding which model has been the most promising to date.We have provided details regarding all the ML models that have been used to date for EDS and MPS of sepsis.展开更多
The aggregation ofα-synuclein(ɑ-syn)coupled with overexpressed neuroinflammation instigates the degeneration of dopaminergic neurons,thereby aggravating the progression of Parkinson’s disease(PD).Herein,we introduc...The aggregation ofα-synuclein(ɑ-syn)coupled with overexpressed neuroinflammation instigates the degeneration of dopaminergic neurons,thereby aggravating the progression of Parkinson’s disease(PD).Herein,we introduced a series of hydrophobic amino acid-based carbon dots(CDs)for inhibitingɑ-syn aggregation and mitigating the inflammation in PD neurons.Significantly,we show that phenylalanine CDs(Phe-CDs)could strongly bind withɑ-syn monomers and dimers via hydrophobic force,maintain their stability,and inhibit their further aggregation in situ and in vitro,finally conferring neuroprotection in PD by rescuing synaptic loss,ameliorating mitochondrial dysfunctions,and modulating Ca^(2+)flux.Importantly,Phe-CDs demonstrate the ability to penetrate the blood-brain barrier,significantly improving motor performance in PD mice.Our findings suggest that Phe-CDs hold great promise as a therapeutic agent for PD and the related neurodegenerative disease.展开更多
The aggregation ofα-synuclein(ɑ-syn)coupled with overexpressed neuroinflammation instigates the degeneration of dopaminer-gic neurons,thereby aggravating the progression of Parkinson’s disease(PD).Herein,we introdu...The aggregation ofα-synuclein(ɑ-syn)coupled with overexpressed neuroinflammation instigates the degeneration of dopaminer-gic neurons,thereby aggravating the progression of Parkinson’s disease(PD).Herein,we introduced a series of hydrophobic amino acid–based carbon dots(CDs)for inhibitingɑ-syn aggregation and mitigating the inflammation in PD neurons.Significantly,we show phenylalanine CDs(Phe-CDs)could strongly bind withɑ-syn monomers and dimers via hydrophobic force,maintain their stability,and inhibit their further aggregates in situ and in vitro,finally conferring neuroprotection in PD by rescuing synaptic loss,ameliorating mitochondrial dysfunctions,and modulating Ca^(2+) flux.Importantly,Phe-CDs demonstrate the ability to penetrate the blood–brain barrier(BBB),significantly improving motor performance in PD mice.Our findings suggest that Phe-CDs hold great promise as a therapeutic agent for PD and the relative neurodegenerative disease.展开更多
Reprogrammable soft matter brings flexibility to soft robots so that they can display various motions,which is flourishing in soft robotics.However,the reprogramming of photoresponsive materials used in soft robots is...Reprogrammable soft matter brings flexibility to soft robots so that they can display various motions,which is flourishing in soft robotics.However,the reprogramming of photoresponsive materials used in soft robots is time-consuming using existing methods.In this study,we promote a strategy for rapid reprogramming via switchable photothermal conversion efficiency(PCE).The liquid crystalline elastomers doped with semiconductor bismuth compounds(Bi-LCE)used in this work exhibited large photothermal actuation with over 35%shrinkage in 5 s at high PCE state,which demonstrated little deformation at low PCE state.Furthermore,the material was capable of being reprogrammed up to 10 times,with only 20 min required for one PCE reversible switch.Based on this switchable PCE effect,the same Bi-LCE film displayed various shape changes through different programmable pattern.Additionally,a reprogrammable hollow tube made of PCE reprogrammable materials could tune the diameter,cross-section configuration,and surface morphology,which was crucial for microfluidics field.Reprogrammable materials provide endless possibilities for reusability and sustainability in robotics.展开更多
文摘This study investigates the use of computational frameworks for sepsis.We consider two dimensions for investi-gation-early diagnosis of sepsis(EDS)and mortality prediction rate for sepsis patients(MPS).We concentrate on the clinical parameters on which sepsis diagnosis and prognosis are currently done,including customized treatment plans based on historical data of the patient.We identify the most notable literature that uses com-putational models to address EDS and MPS based on those clinical parameters.In addition to the review of the computational models built upon the clinical parameters,we also provide details regarding the popular publicly available data sources.We provide brief reviews for each model in terms of prior art and present an analysis of their results,as claimed by the respective authors.With respect to the use of machine learning models,we have provided avenues for model analysis in terms of model selection,model validation,model interpretation,and model comparison.We further present the challenges and limitations of the use of computational models,providing future research directions.This study intends to serve as a benchmark for first-hand impressions on the use of computational models for EDS and MPS of sepsis,along with the details regarding which model has been the most promising to date.We have provided details regarding all the ML models that have been used to date for EDS and MPS of sepsis.
文摘The aggregation ofα-synuclein(ɑ-syn)coupled with overexpressed neuroinflammation instigates the degeneration of dopaminergic neurons,thereby aggravating the progression of Parkinson’s disease(PD).Herein,we introduced a series of hydrophobic amino acid-based carbon dots(CDs)for inhibitingɑ-syn aggregation and mitigating the inflammation in PD neurons.Significantly,we show that phenylalanine CDs(Phe-CDs)could strongly bind withɑ-syn monomers and dimers via hydrophobic force,maintain their stability,and inhibit their further aggregation in situ and in vitro,finally conferring neuroprotection in PD by rescuing synaptic loss,ameliorating mitochondrial dysfunctions,and modulating Ca^(2+)flux.Importantly,Phe-CDs demonstrate the ability to penetrate the blood-brain barrier,significantly improving motor performance in PD mice.Our findings suggest that Phe-CDs hold great promise as a therapeutic agent for PD and the related neurodegenerative disease.
基金supported by the National Natural Science Foundation of China(Grant 52002133).
文摘The aggregation ofα-synuclein(ɑ-syn)coupled with overexpressed neuroinflammation instigates the degeneration of dopaminer-gic neurons,thereby aggravating the progression of Parkinson’s disease(PD).Herein,we introduced a series of hydrophobic amino acid–based carbon dots(CDs)for inhibitingɑ-syn aggregation and mitigating the inflammation in PD neurons.Significantly,we show phenylalanine CDs(Phe-CDs)could strongly bind withɑ-syn monomers and dimers via hydrophobic force,maintain their stability,and inhibit their further aggregates in situ and in vitro,finally conferring neuroprotection in PD by rescuing synaptic loss,ameliorating mitochondrial dysfunctions,and modulating Ca^(2+) flux.Importantly,Phe-CDs demonstrate the ability to penetrate the blood–brain barrier(BBB),significantly improving motor performance in PD mice.Our findings suggest that Phe-CDs hold great promise as a therapeutic agent for PD and the relative neurodegenerative disease.
基金supported by the National Natural Science Foundation of China(62075064)the Key R&D Program of Guangzhou(202007020003)+1 种基金the Guangdong Basic and Applied Basic Research Foundation(2021B1515020095,2021A1515110919)the Fundamental Research Funds for the Central Universities(2022ZYGXZR003).
文摘Reprogrammable soft matter brings flexibility to soft robots so that they can display various motions,which is flourishing in soft robotics.However,the reprogramming of photoresponsive materials used in soft robots is time-consuming using existing methods.In this study,we promote a strategy for rapid reprogramming via switchable photothermal conversion efficiency(PCE).The liquid crystalline elastomers doped with semiconductor bismuth compounds(Bi-LCE)used in this work exhibited large photothermal actuation with over 35%shrinkage in 5 s at high PCE state,which demonstrated little deformation at low PCE state.Furthermore,the material was capable of being reprogrammed up to 10 times,with only 20 min required for one PCE reversible switch.Based on this switchable PCE effect,the same Bi-LCE film displayed various shape changes through different programmable pattern.Additionally,a reprogrammable hollow tube made of PCE reprogrammable materials could tune the diameter,cross-section configuration,and surface morphology,which was crucial for microfluidics field.Reprogrammable materials provide endless possibilities for reusability and sustainability in robotics.
