Endoscopic mucosal resection(EMR)and endoscopic submucosal dissection(ESD)are well-established therapeutics for gastrointestinal neoplasias,but complications after EMR/ESD,including bleeding and perforation,result in ...Endoscopic mucosal resection(EMR)and endoscopic submucosal dissection(ESD)are well-established therapeutics for gastrointestinal neoplasias,but complications after EMR/ESD,including bleeding and perforation,result in additional treatment morbidity and even threaten the lives of patients.Thus,designing biomaterials to treat gastric bleeding and wound healing after endoscopic treatment is highly desired and remains a challenge.Herein,a series of injectable pH-responsive selfhealing adhesive hydrogels based on acryloyl-6-aminocaproic acid(AA)and AA-g-N-hydroxysuccinimide(AA-NHS)were developed,and their great potential as endoscopic sprayable bioadhesive materials to efficiently stop hemorrhage and promote the wound healing process was further demonstrated in a swine gastric hemorrhage/wound model.The hydrogels showed a suitable gelation time,an autonomous and efficient self-healing capacity,hemostatic properties,and good biocompatibility.With the introduction of AA-NHS as a micro-cross-linker,the hydrogels exhibited enhanced adhesive strength.A swine gastric hemorrhage in vivo model demonstrated that the hydrogels showed good hemostatic performance by stopping acute arterial bleeding and preventing delayed bleeding.A gastric wound model indicated that the hydrogels showed excellent treatment effects with significantly enhanced wound healing with type I collagen deposition,α-SMA expression,and blood vessel formation.These injectable self-healing adhesive hydrogels exhibited great potential to treat gastric wounds after endoscopic treatment.展开更多
Synthetic biology aims to endow living cells with new functions by incorporating functional gene networks into them.By overexpressing,blocking and rewiring native gene pathways,synthetic biologists have harnessed this...Synthetic biology aims to endow living cells with new functions by incorporating functional gene networks into them.By overexpressing,blocking and rewiring native gene pathways,synthetic biologists have harnessed this promising technology to reprogram cells to perform diverse tasks such as drug discovery,biopharmaceutical manufacturing,gene therapy and tissue engineering,etc.In this review,we focus on current technologies of synthetic biosensors for disease detection.We start with the design principle of synthetic biosensors.Then we move towards the characteristics of simple synthetic biosensors,which can respond to a single input signal,and complex synthetic biosensors including Boolean gate biosensors,cascade biosensors,time-delay biosensors,oscillator biosensors and hysteretic biosensors,which can respond to more than two input signals and perform complex tasks.Synthetic biosensor has showed great potential in disease detection,but it is still in its infancy stage.More efforts should be made in identifying and constructing clinically relevant regulation systems.Computational tools are also needed in the design process in order to guarantee the precision of the synthetic biosensor.The ultimate goal of a synthetic biosensor is to act as a therapeutic sensor-effector device that connects diagnostic input with therapeutic output and therefore provides all-in-one diagnostic and therapeutic solutions for future gene-and cell-based therapies.展开更多
Background:In clinical datasets,the characteristics of an individual patient vary so much that data loss becomes a normal event,which may be a unignorable dilemma in clinical data analysis.Therefore,the construction o...Background:In clinical datasets,the characteristics of an individual patient vary so much that data loss becomes a normal event,which may be a unignorable dilemma in clinical data analysis.Therefore,the construction of a machine learning model aimed at missing clinical datasets(MCD)is of great clinical importance.Methods:All included patients were divided into two groups according to outcome within a period of up to 36 months or less.The following characteristics(variables)were collected:age,sex,Child-Pugh status,hepatitis status,cirrhosis status,treatment,tumor size,portal vein tumor thrombus,and alpha fetoprotein(μg/mL),and a missing dataset-independent support vector machine(MDI-SVM)independent of missing data was built for the analysis.Results:A MCD-independent SVM was developed based on clinical data from 1334 patients with hepatocellular carcinoma(HCC)at a single center,which had an accuracy of 84.43%in the survival analysis in the presence of 5%missing data.Based on the different combinations of features,our model calculated five features(tumor size,age,treatment,hepatitis status,and alpha fetoprotein)that had the greatest impact on survival in patients with HCC and extracted their weighting factors.Conclusions:A MCD-independent SVM was developed to achieve prognosis prediction for patients with HCC in the absence of first-visit data.展开更多
基金This work was jointly supported by the National Natural Science Foundation of China(grant Nos.:51973172,51673155,81201927,82002957 and 81672460)the National Key Research and Development Plan of China(No.2018YFC0115300)+5 种基金the State Key Laboratory for Mechanical Behavior of Materials,the World-Class Universities(Disciplines)the Characteristic Development Guidance Funds for the Central Universities,the Natural Science Foundation of Shaanxi Province(No.2020JC-03 and 2019TD-020)the Innovation Talent Promotion Plan of Shaanxi(No.2017KJXX-07)the Key Research and Development Program of Shaanxi Province(No.2019SF-012)the Opening Project of Key Laboratory of Shaanxi Province for Craniofacial Precision Medicine Research,College of Stomatology,Xi’an Jiaotong University(No.2019LHM-KFKT008)Fundamental Research Funds for the Central Universities of China(No.xjj2018090).
文摘Endoscopic mucosal resection(EMR)and endoscopic submucosal dissection(ESD)are well-established therapeutics for gastrointestinal neoplasias,but complications after EMR/ESD,including bleeding and perforation,result in additional treatment morbidity and even threaten the lives of patients.Thus,designing biomaterials to treat gastric bleeding and wound healing after endoscopic treatment is highly desired and remains a challenge.Herein,a series of injectable pH-responsive selfhealing adhesive hydrogels based on acryloyl-6-aminocaproic acid(AA)and AA-g-N-hydroxysuccinimide(AA-NHS)were developed,and their great potential as endoscopic sprayable bioadhesive materials to efficiently stop hemorrhage and promote the wound healing process was further demonstrated in a swine gastric hemorrhage/wound model.The hydrogels showed a suitable gelation time,an autonomous and efficient self-healing capacity,hemostatic properties,and good biocompatibility.With the introduction of AA-NHS as a micro-cross-linker,the hydrogels exhibited enhanced adhesive strength.A swine gastric hemorrhage in vivo model demonstrated that the hydrogels showed good hemostatic performance by stopping acute arterial bleeding and preventing delayed bleeding.A gastric wound model indicated that the hydrogels showed excellent treatment effects with significantly enhanced wound healing with type I collagen deposition,α-SMA expression,and blood vessel formation.These injectable self-healing adhesive hydrogels exhibited great potential to treat gastric wounds after endoscopic treatment.
基金Fund supported by the National Natural Science Foundation of China (81770491) and the Ministry of Education Innovation TeamDevelopment Program of China (IRT16R57).
文摘Synthetic biology aims to endow living cells with new functions by incorporating functional gene networks into them.By overexpressing,blocking and rewiring native gene pathways,synthetic biologists have harnessed this promising technology to reprogram cells to perform diverse tasks such as drug discovery,biopharmaceutical manufacturing,gene therapy and tissue engineering,etc.In this review,we focus on current technologies of synthetic biosensors for disease detection.We start with the design principle of synthetic biosensors.Then we move towards the characteristics of simple synthetic biosensors,which can respond to a single input signal,and complex synthetic biosensors including Boolean gate biosensors,cascade biosensors,time-delay biosensors,oscillator biosensors and hysteretic biosensors,which can respond to more than two input signals and perform complex tasks.Synthetic biosensor has showed great potential in disease detection,but it is still in its infancy stage.More efforts should be made in identifying and constructing clinically relevant regulation systems.Computational tools are also needed in the design process in order to guarantee the precision of the synthetic biosensor.The ultimate goal of a synthetic biosensor is to act as a therapeutic sensor-effector device that connects diagnostic input with therapeutic output and therefore provides all-in-one diagnostic and therapeutic solutions for future gene-and cell-based therapies.
基金supported by the National Nature Science Foundation of China(grant nos.11534008,11804271,and 91736104)the Ministry of Science and Technology of China(2016YFA0301404)+3 种基金the China Postdoctoral Science Foundation via project no.2020M673366the foundation of the First Affiliated Hospital of Xi'an Jiaotong University no.2021QN-15.In addition,Yi Lv acknowledges support from the National Key R&D Project of China(nos.2018YFC0115300 and 2018YFC0115305,YL)the National Natural Science Foundation of China(no.81727802)the Innovation Capacity Support Plan of Shaanxi Province(no.2020TD-040,RW).
文摘Background:In clinical datasets,the characteristics of an individual patient vary so much that data loss becomes a normal event,which may be a unignorable dilemma in clinical data analysis.Therefore,the construction of a machine learning model aimed at missing clinical datasets(MCD)is of great clinical importance.Methods:All included patients were divided into two groups according to outcome within a period of up to 36 months or less.The following characteristics(variables)were collected:age,sex,Child-Pugh status,hepatitis status,cirrhosis status,treatment,tumor size,portal vein tumor thrombus,and alpha fetoprotein(μg/mL),and a missing dataset-independent support vector machine(MDI-SVM)independent of missing data was built for the analysis.Results:A MCD-independent SVM was developed based on clinical data from 1334 patients with hepatocellular carcinoma(HCC)at a single center,which had an accuracy of 84.43%in the survival analysis in the presence of 5%missing data.Based on the different combinations of features,our model calculated five features(tumor size,age,treatment,hepatitis status,and alpha fetoprotein)that had the greatest impact on survival in patients with HCC and extracted their weighting factors.Conclusions:A MCD-independent SVM was developed to achieve prognosis prediction for patients with HCC in the absence of first-visit data.
