Health information spreads rapidly,which can effectively control epidemics.However,the swift dissemination of information also has potential negative impacts,which increasingly attracts attention.Message fatigue refer...Health information spreads rapidly,which can effectively control epidemics.However,the swift dissemination of information also has potential negative impacts,which increasingly attracts attention.Message fatigue refers to the psychological response characterized by feelings of boredom and anxiety that occur after receiving an excessive amount of similar information.This phenomenon can alter individual behaviors related to epidemic prevention.Additionally,recent studies indicate that pairwise interactions alone are insufficient to describe complex social transmission processes,and higher-order structures representing group interactions are crucial.To address this,we develop a novel epidemic model that investigates the interactions between information,behavioral responses,and epidemics.Our model incorporates the impact of message fatigue on the entire transmission system.The information layer is modeled using a static simplicial network to capture group interactions,while the disease layer uses a time-varying network based on activity-driven model with attractiveness to represent the self-protection behaviors of susceptible individuals and self-isolation behaviors of infected individuals.We theoretically describe the co-evolution equations using the microscopic Markov chain approach(MMCA)and get the epidemic threshold.Experimental results show that while the negative impact of message fatigue on epidemic transmission is limited,it significantly weakens the group interactions depicted by higher-order structures.Individual behavioral responses strongly inhibit the epidemic.Our simulations using the Monte Carlo(MC)method demonstrate that greater intensity in these responses leads to clustering of susceptible individuals in the disease layer.Finally,we apply the proposed model to real networks to verify its reliability.In summary,our research results enhance the understanding of the information-epidemic coupling dynamics,and we expect to provide valuable guidance for managing future emerging epidemics.展开更多
BACKGROUND Ileocecal laterally spreading tumors(LSTs)complicated by appendiceal tubular adenoma are rare and challenging to diagnose because of the absence of typical symptoms and specific diagnostic signs.Traditional...BACKGROUND Ileocecal laterally spreading tumors(LSTs)complicated by appendiceal tubular adenoma are rare and challenging to diagnose because of the absence of typical symptoms and specific diagnostic signs.Traditionally,the primary treatment has been laparoscopic appendectomy(LA).CASE SUMMARY A 63-year-old female presented with changes in bowel habits.Colonoscopy revealed an ileocecal LST.The patient underwent endoscopic submucosal dissection.Postoperative follow-up colonoscopy revealed mucosal elevation at the appendiceal orifice,with pathology confirming tubular adenoma.Abdominal computed tomography indicated a suspicious appendiceal tumor,leading to LA with partial cecectomy.The postoperative recovery was uneventful.At the 1-year follow-up,colonoscopy revealed no evidence of tumor recurrence.CONCLUSION Ileocecal LSTs with appendiceal tubular adenomas are traditionally treated with LA.endoscopic submucosal dissection can also yield favorable outcomes.展开更多
Objective:To predict the distribution of dengue vector Aedes(Ae.)albopictus and identify high-risk areas for dengue fever transmission.Methods:Data on Ae.albopictus occurrences were collected from electronic databases...Objective:To predict the distribution of dengue vector Aedes(Ae.)albopictus and identify high-risk areas for dengue fever transmission.Methods:Data on Ae.albopictus occurrences were collected from electronic databases.Ensemble models were developed to assess the impacts of climate,vegetation,and human activity on Ae.albopictus.The optimal ensemble model was then used to identify the distribution of suitable areas for Ae.albopictus.Results:After removing duplicate sites and retaining only one location per 100 m×100 m grid,189 Ae.albopictus breeding sites were identified.The optimal ensemble model revealed that Ae.albopictus exhibited higher breeding suitability in Shanghai under specific conditions:a normalized difference vegetation index of 0.1 to 0.6,maximum precipitation in the warmest month ranging from 400 mm to 470 mm,maximum temperature in the warmest month between 30.0℃and 31.0℃,and proximity to waterways within 0.5 km.The most suitable habitats for Ae.albopictus were primarily concentrated in Shanghai’s central urban areas and scattered across the inner suburban districts.Conclusions:The high-risk areas of Ae.albopictus are widely distributed throughout the central urban area and scattered across the inner suburban district of Shanghai,creating conditions conducive to the outbreak of dengue fever.It is essential to enhance targeted control measures for Ae.albopictus in the identified risk areas.展开更多
Information plays a crucial role in guiding behavioral decisions during public health emergencies. Individuals communicate to acquire relevant knowledge about an epidemic, which influences their decisions to adopt pro...Information plays a crucial role in guiding behavioral decisions during public health emergencies. Individuals communicate to acquire relevant knowledge about an epidemic, which influences their decisions to adopt protective measures.However, whether to disseminate specific information is also a behavioral decision. In light of this understanding, we develop a coupled information–vaccination–epidemic model to depict these co-evolutionary dynamics in a three-layer network. Negative information dissemination and vaccination are treated as separate decision-making processes. We then examine the combined effects of herd and risk motives on information dissemination and vaccination decisions through the lens of game theory. The microscopic Markov chain approach(MMCA) is used to describe the dynamic process and to derive the epidemic threshold. Simulation results indicate that increasing the cost of negative information dissemination and providing timely clarification can effectively control the epidemic. Furthermore, a phenomenon of diminishing marginal utility is observed as the cost of dissemination increases, suggesting that authorities do not need to overinvest in suppressing negative information. Conversely, reducing the cost of vaccination and increasing vaccine efficacy emerge as more effective strategies for outbreak control. In addition, we find that the scale of the epidemic is greater when the herd motive dominates behavioral decision-making. In conclusion, this study provides a new perspective for understanding the complexity of epidemic spreading by starting with the construction of different behavioral decisions.展开更多
This paper is devoted to investigating the spreading speed of a time-space periodic epidemic model with vital dynamics and standard incidence in discrete media. We establish the existence of the leftward and rightward...This paper is devoted to investigating the spreading speed of a time-space periodic epidemic model with vital dynamics and standard incidence in discrete media. We establish the existence of the leftward and rightward spreading speeds for the infective individuals, which can be used to estimate how fast the disease spreads. To overcome the difficulty arising from the lack of comparison principle for such time-space periodic nonmonotone systems, our proof is mainly based on constructing a series of scalar time-space periodic equations, establishing the spreading speeds for such auxiliary equations and using comparison methods. It may be the first work to study the spreading speed for time-space periodic non-monotone systems.展开更多
Botryosphaeria laricina(larch shoot blight)was first identified in 1973 in Jilin Province,China.The disease spread rapidly and caused considerable damage because its pathogenesis was unknown at the time and there were...Botryosphaeria laricina(larch shoot blight)was first identified in 1973 in Jilin Province,China.The disease spread rapidly and caused considerable damage because its pathogenesis was unknown at the time and there were no effective controls or quarantine methods.At present,it shows a spreading trend,but most research can only conduct physiological analyses within a relatively short period,combining individual influencing factors.Nevertheless,methods such as neural network models,ensemble learning algorithms,and Markov models are used in pest and disease prediction and forecasting.However,there may be fitting issues or inherent limitations associated with these methods.This study obtained B.laricina data at the county level from 2003 to 2021.The dataset was augmented using the SMOTE algorithm,and then algorithms such as XGBoost were used to select the significant features from a combined set of 12 features.A new stacking fusion model has been proposed to predict the status of B.laricina.The model is based on random forest,gradient boosted decision tree,CatBoost and logistic regression algorithms.The accuracy,recall,specificity,precision,F_(1) value and AUC of the model reached 90.9%,91.6%,90.4%,88.8%,90.2%and 96.2%.The results provide evidence of the strong performance and stability of the model.B.laricina is mainly found in the northeast and this study indicates that it is spreading northwest.Reasonable means should be used promptly to prevent further damage and spread.展开更多
Information spreading has been investigated for many years,but the mechanism of why the information explosively catches on overnight is still under debate.This explosive spreading phenomenon was usually considered dri...Information spreading has been investigated for many years,but the mechanism of why the information explosively catches on overnight is still under debate.This explosive spreading phenomenon was usually considered driven separately by social reinforcement or higher-order interactions.However,due to the limitations of empirical data and theoretical analysis,how the higher-order network structure affects the explosive information spreading under the role of social reinforcement has not been fully explored.In this work,we propose an information-spreading model by considering the social reinforcement in real and synthetic higher-order networks,describable as hypergraphs.Depending on the average group size(hyperedge cardinality)and node membership(hyperdegree),we observe two different spreading behaviors:(i)The spreading progress is not sensitive to social reinforcement,resulting in the information localized in a small part of nodes;(ii)a strong social reinforcement will promote the large-scale spread of information and induce an explosive transition.Moreover,a large average group size and membership would be beneficial to the appearance of the explosive transition.Further,we display that the heterogeneity of the node membership and group size distributions benefit the information spreading.Finally,we extend the group-based approximate master equations to verify the simulation results.Our findings may help us to comprehend the rapidly information-spreading phenomenon in modern society.展开更多
The significant threat of wildfires to forest ecology and biodiversity,particularly in tropical and subtropical regions,underscores the necessity for advanced predictive models amidst shifting climate patterns.There i...The significant threat of wildfires to forest ecology and biodiversity,particularly in tropical and subtropical regions,underscores the necessity for advanced predictive models amidst shifting climate patterns.There is a need to evaluate and enhance wildfire prediction methods,focusing on their application during extended periods of intense heat and drought.This study reviews various wildfire modelling approaches,including traditional physical,semi-empirical,numerical,and emerging machine learning(ML)-based models.We critically assess these models’capabilities in predicting fire susceptibility and post-ignition spread,highlighting their strengths and limitations.Our findings indicate that while traditional models provide foundational insights,they often fall short in dynamically estimating parameters and predicting ignition events.Cellular automata models,despite their potential,face challenges in data integration and computational demands.Conversely,ML models demonstrate superior efficiency and accuracy by leveraging diverse datasets,though they encounter interpretability issues.This review recommends hybrid modelling approaches that integrate multiple methods to harness their combined strengths.By incorporating data assimilation techniques with dynamic forecasting models,the predictive capabilities of ML-based predictions can be significantly enhanced.This review underscores the necessity for continued refinement of these models to ensure their reliability in real-world applications,ultimately contributing to more effective wildfire mitigation and management strategies.Future research should focus on improving hybrid models and exploring new data integration methods to advance predictive capabilities.展开更多
With the increasing demand for water in hydroponic systems and agricultural irrigation,viral diseases have seriously affected the yield and quality of crops.By removing plant viruses in water environments,virus transm...With the increasing demand for water in hydroponic systems and agricultural irrigation,viral diseases have seriously affected the yield and quality of crops.By removing plant viruses in water environments,virus transmission can be prevented and agricultural production and ecosystems can be protected.But so far,there have been few reports on the removal of plant viruses in water environments.Herein,in this study,easily recyclable biomass-based carbon nanotubes catalysts were synthesized with varying metal activities to activate peroxymonosulfate(PMS).Among them,the magnetic 0.125Fe@NCNTs-1/PMS system showed the best overall removal performance against pepper mild mottle virus,with a 5.9 log_(10)removal within 1 min.Notably,the key reactive species in the 0.125Fe@NCNTs-1/PMS system is^(1)O_(2),which can maintain good removal effect in real water matrices(river water and tap water).Through RNA fragment analyses and label free analysis,it was found that this system could effectively cleave virus particles,destroy viral proteins and expose their genome.The capsid protein of pepper mild mottle virus was effectively decomposed where serine may be the main attacking sites by^(1)O_(2).Long viral RNA fragments(3349 and 1642 nt)were cut into smaller fragments(∼160 nt)and caused their degradation.In summary,this study contributes to controlling the spread of plant viruses in real water environment,which will potentially help protect agricultural production and food safety,and improve the health and sustainability of ecosystems.展开更多
BACKGROUND Colorectal laterally spreading tumors(LSTs)are best treated with endoscopic submucosal dissection or endoscopic mucosal resection.AIM To analyze the clinicopathological and endoscopic profiles of colorectal...BACKGROUND Colorectal laterally spreading tumors(LSTs)are best treated with endoscopic submucosal dissection or endoscopic mucosal resection.AIM To analyze the clinicopathological and endoscopic profiles of colorectal LSTs,determine predictive factors for high-grade dysplasia(HGD)/carcinoma(CA),submucosal invasion,and complications.METHODS We retrospectively assessed the endoscopic and histological characteristics of 375 colorectal LSTs at our hospital between January 2016 and December 2023.We performed univariate and multivariate analysis to identify risk factors associated with HGD/CA,submucosal invasion and complications.RESULTS The numbers of granular(LST-G)and non-granular LST(LST-NG)were 260 and 115,respectively.The rates of low-grade dysplasia and HGD/CA were 60.3%and 39.7%,respectively.Multivariate analysis indicated that a tumor size≥30 mm[odds ratio(OR)=1.934,P=0.032],LST granular nodular mixed type(OR=2.100,P=0.005),and LST non-granular pseudo depressed type(NG-PD)(OR=3.016,P=0.015)were independent risk factors significantly associated with higher odds of HGD/CA.NG-PD(OR=6.506,P=0.001),tumor size(20-29 mm)(OR=2.631,P=0.036)and tumor size≥30 mm(OR=3.449,P=0.016)were associated with increased odds of submucosal invasion.Tumor size≥30 mm(OR=4.888,P=0.003)was a particularly important predictor of complications.A nomogram model demonstrated a satisfactory fit,with an area under the receiver operating characteristic curve of 0.716(95%confidence interval:0.653-0.780),indicating strong predictive performance.CONCLUSION The novel nomogram incorporating tumor size,location,and morphology predicted HGD/CA during endoscopic resection for LSTs.NG-PD lesions larger than 20 mm were more likely to invade the submucosa.Tumor size≥30 mm was an important predictor of complications.展开更多
基金Project supported by the National Natural Science Foundation of China(Grant Nos.72171136 and 72134004)Humanities and Social Science Research Project,Ministry of Education of China(Grant No.21YJC630157)+1 种基金the Natural Science Foundation of Shandong Province(Grant No.ZR2022MG008)Shandong Provincial Colleges and Universities Youth Innovation Technology of China(Grant No.2022RW066)。
文摘Health information spreads rapidly,which can effectively control epidemics.However,the swift dissemination of information also has potential negative impacts,which increasingly attracts attention.Message fatigue refers to the psychological response characterized by feelings of boredom and anxiety that occur after receiving an excessive amount of similar information.This phenomenon can alter individual behaviors related to epidemic prevention.Additionally,recent studies indicate that pairwise interactions alone are insufficient to describe complex social transmission processes,and higher-order structures representing group interactions are crucial.To address this,we develop a novel epidemic model that investigates the interactions between information,behavioral responses,and epidemics.Our model incorporates the impact of message fatigue on the entire transmission system.The information layer is modeled using a static simplicial network to capture group interactions,while the disease layer uses a time-varying network based on activity-driven model with attractiveness to represent the self-protection behaviors of susceptible individuals and self-isolation behaviors of infected individuals.We theoretically describe the co-evolution equations using the microscopic Markov chain approach(MMCA)and get the epidemic threshold.Experimental results show that while the negative impact of message fatigue on epidemic transmission is limited,it significantly weakens the group interactions depicted by higher-order structures.Individual behavioral responses strongly inhibit the epidemic.Our simulations using the Monte Carlo(MC)method demonstrate that greater intensity in these responses leads to clustering of susceptible individuals in the disease layer.Finally,we apply the proposed model to real networks to verify its reliability.In summary,our research results enhance the understanding of the information-epidemic coupling dynamics,and we expect to provide valuable guidance for managing future emerging epidemics.
文摘BACKGROUND Ileocecal laterally spreading tumors(LSTs)complicated by appendiceal tubular adenoma are rare and challenging to diagnose because of the absence of typical symptoms and specific diagnostic signs.Traditionally,the primary treatment has been laparoscopic appendectomy(LA).CASE SUMMARY A 63-year-old female presented with changes in bowel habits.Colonoscopy revealed an ileocecal LST.The patient underwent endoscopic submucosal dissection.Postoperative follow-up colonoscopy revealed mucosal elevation at the appendiceal orifice,with pathology confirming tubular adenoma.Abdominal computed tomography indicated a suspicious appendiceal tumor,leading to LA with partial cecectomy.The postoperative recovery was uneventful.At the 1-year follow-up,colonoscopy revealed no evidence of tumor recurrence.CONCLUSION Ileocecal LSTs with appendiceal tubular adenomas are traditionally treated with LA.endoscopic submucosal dissection can also yield favorable outcomes.
基金supported by Three-Year Initiative Plan for Strengthening Public Health System Construction in Shanghai(2023-2025)Key Discipline Project(No.GWVI-11.1-12).
文摘Objective:To predict the distribution of dengue vector Aedes(Ae.)albopictus and identify high-risk areas for dengue fever transmission.Methods:Data on Ae.albopictus occurrences were collected from electronic databases.Ensemble models were developed to assess the impacts of climate,vegetation,and human activity on Ae.albopictus.The optimal ensemble model was then used to identify the distribution of suitable areas for Ae.albopictus.Results:After removing duplicate sites and retaining only one location per 100 m×100 m grid,189 Ae.albopictus breeding sites were identified.The optimal ensemble model revealed that Ae.albopictus exhibited higher breeding suitability in Shanghai under specific conditions:a normalized difference vegetation index of 0.1 to 0.6,maximum precipitation in the warmest month ranging from 400 mm to 470 mm,maximum temperature in the warmest month between 30.0℃and 31.0℃,and proximity to waterways within 0.5 km.The most suitable habitats for Ae.albopictus were primarily concentrated in Shanghai’s central urban areas and scattered across the inner suburban districts.Conclusions:The high-risk areas of Ae.albopictus are widely distributed throughout the central urban area and scattered across the inner suburban district of Shanghai,creating conditions conducive to the outbreak of dengue fever.It is essential to enhance targeted control measures for Ae.albopictus in the identified risk areas.
基金Project supported by the National Natural Science Foundation of China (Grant No. 72174121)the Program for Professor of Special Appointment (Eastern Scholar) at Shanghai Institutions of Higher Learning, and the Soft Science Research Project of Shanghai (Grant No. 22692112600)。
文摘Information plays a crucial role in guiding behavioral decisions during public health emergencies. Individuals communicate to acquire relevant knowledge about an epidemic, which influences their decisions to adopt protective measures.However, whether to disseminate specific information is also a behavioral decision. In light of this understanding, we develop a coupled information–vaccination–epidemic model to depict these co-evolutionary dynamics in a three-layer network. Negative information dissemination and vaccination are treated as separate decision-making processes. We then examine the combined effects of herd and risk motives on information dissemination and vaccination decisions through the lens of game theory. The microscopic Markov chain approach(MMCA) is used to describe the dynamic process and to derive the epidemic threshold. Simulation results indicate that increasing the cost of negative information dissemination and providing timely clarification can effectively control the epidemic. Furthermore, a phenomenon of diminishing marginal utility is observed as the cost of dissemination increases, suggesting that authorities do not need to overinvest in suppressing negative information. Conversely, reducing the cost of vaccination and increasing vaccine efficacy emerge as more effective strategies for outbreak control. In addition, we find that the scale of the epidemic is greater when the herd motive dominates behavioral decision-making. In conclusion, this study provides a new perspective for understanding the complexity of epidemic spreading by starting with the construction of different behavioral decisions.
基金supported by the Natural Science Basic Research Program of Shanxi(Grant No.2024JC-YBMS-025)the Innovation Capability Support Program of Shanxi(Grant No.2024RS-CXTD-88)。
文摘This paper is devoted to investigating the spreading speed of a time-space periodic epidemic model with vital dynamics and standard incidence in discrete media. We establish the existence of the leftward and rightward spreading speeds for the infective individuals, which can be used to estimate how fast the disease spreads. To overcome the difficulty arising from the lack of comparison principle for such time-space periodic nonmonotone systems, our proof is mainly based on constructing a series of scalar time-space periodic equations, establishing the spreading speeds for such auxiliary equations and using comparison methods. It may be the first work to study the spreading speed for time-space periodic non-monotone systems.
基金supported by the National Key R&D Program of China(Grant No.2021YFD1400300).
文摘Botryosphaeria laricina(larch shoot blight)was first identified in 1973 in Jilin Province,China.The disease spread rapidly and caused considerable damage because its pathogenesis was unknown at the time and there were no effective controls or quarantine methods.At present,it shows a spreading trend,but most research can only conduct physiological analyses within a relatively short period,combining individual influencing factors.Nevertheless,methods such as neural network models,ensemble learning algorithms,and Markov models are used in pest and disease prediction and forecasting.However,there may be fitting issues or inherent limitations associated with these methods.This study obtained B.laricina data at the county level from 2003 to 2021.The dataset was augmented using the SMOTE algorithm,and then algorithms such as XGBoost were used to select the significant features from a combined set of 12 features.A new stacking fusion model has been proposed to predict the status of B.laricina.The model is based on random forest,gradient boosted decision tree,CatBoost and logistic regression algorithms.The accuracy,recall,specificity,precision,F_(1) value and AUC of the model reached 90.9%,91.6%,90.4%,88.8%,90.2%and 96.2%.The results provide evidence of the strong performance and stability of the model.B.laricina is mainly found in the northeast and this study indicates that it is spreading northwest.Reasonable means should be used promptly to prevent further damage and spread.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.12305043 and 12165016)the Natural Science Foundation of Jiangsu Province(Grant No.BK20220511)+1 种基金the Project of Undergraduate Scientific Research(Grant No.22A684)the support from the Jiangsu Specially-Appointed Professor Program。
文摘Information spreading has been investigated for many years,but the mechanism of why the information explosively catches on overnight is still under debate.This explosive spreading phenomenon was usually considered driven separately by social reinforcement or higher-order interactions.However,due to the limitations of empirical data and theoretical analysis,how the higher-order network structure affects the explosive information spreading under the role of social reinforcement has not been fully explored.In this work,we propose an information-spreading model by considering the social reinforcement in real and synthetic higher-order networks,describable as hypergraphs.Depending on the average group size(hyperedge cardinality)and node membership(hyperdegree),we observe two different spreading behaviors:(i)The spreading progress is not sensitive to social reinforcement,resulting in the information localized in a small part of nodes;(ii)a strong social reinforcement will promote the large-scale spread of information and induce an explosive transition.Moreover,a large average group size and membership would be beneficial to the appearance of the explosive transition.Further,we display that the heterogeneity of the node membership and group size distributions benefit the information spreading.Finally,we extend the group-based approximate master equations to verify the simulation results.Our findings may help us to comprehend the rapidly information-spreading phenomenon in modern society.
基金funding enabled and organized by CAUL and its Member Institutions.
文摘The significant threat of wildfires to forest ecology and biodiversity,particularly in tropical and subtropical regions,underscores the necessity for advanced predictive models amidst shifting climate patterns.There is a need to evaluate and enhance wildfire prediction methods,focusing on their application during extended periods of intense heat and drought.This study reviews various wildfire modelling approaches,including traditional physical,semi-empirical,numerical,and emerging machine learning(ML)-based models.We critically assess these models’capabilities in predicting fire susceptibility and post-ignition spread,highlighting their strengths and limitations.Our findings indicate that while traditional models provide foundational insights,they often fall short in dynamically estimating parameters and predicting ignition events.Cellular automata models,despite their potential,face challenges in data integration and computational demands.Conversely,ML models demonstrate superior efficiency and accuracy by leveraging diverse datasets,though they encounter interpretability issues.This review recommends hybrid modelling approaches that integrate multiple methods to harness their combined strengths.By incorporating data assimilation techniques with dynamic forecasting models,the predictive capabilities of ML-based predictions can be significantly enhanced.This review underscores the necessity for continued refinement of these models to ensure their reliability in real-world applications,ultimately contributing to more effective wildfire mitigation and management strategies.Future research should focus on improving hybrid models and exploring new data integration methods to advance predictive capabilities.
基金supported by the National Natural Science Foundation of China(No.52170060)the Major Science and Technology Project from the Ministry of Water Resources(No.SKS-2022069)the Science and Technology Program of Inner Mongolia Autonomous Region(No.2021GG0089).
文摘With the increasing demand for water in hydroponic systems and agricultural irrigation,viral diseases have seriously affected the yield and quality of crops.By removing plant viruses in water environments,virus transmission can be prevented and agricultural production and ecosystems can be protected.But so far,there have been few reports on the removal of plant viruses in water environments.Herein,in this study,easily recyclable biomass-based carbon nanotubes catalysts were synthesized with varying metal activities to activate peroxymonosulfate(PMS).Among them,the magnetic 0.125Fe@NCNTs-1/PMS system showed the best overall removal performance against pepper mild mottle virus,with a 5.9 log_(10)removal within 1 min.Notably,the key reactive species in the 0.125Fe@NCNTs-1/PMS system is^(1)O_(2),which can maintain good removal effect in real water matrices(river water and tap water).Through RNA fragment analyses and label free analysis,it was found that this system could effectively cleave virus particles,destroy viral proteins and expose their genome.The capsid protein of pepper mild mottle virus was effectively decomposed where serine may be the main attacking sites by^(1)O_(2).Long viral RNA fragments(3349 and 1642 nt)were cut into smaller fragments(∼160 nt)and caused their degradation.In summary,this study contributes to controlling the spread of plant viruses in real water environment,which will potentially help protect agricultural production and food safety,and improve the health and sustainability of ecosystems.
文摘BACKGROUND Colorectal laterally spreading tumors(LSTs)are best treated with endoscopic submucosal dissection or endoscopic mucosal resection.AIM To analyze the clinicopathological and endoscopic profiles of colorectal LSTs,determine predictive factors for high-grade dysplasia(HGD)/carcinoma(CA),submucosal invasion,and complications.METHODS We retrospectively assessed the endoscopic and histological characteristics of 375 colorectal LSTs at our hospital between January 2016 and December 2023.We performed univariate and multivariate analysis to identify risk factors associated with HGD/CA,submucosal invasion and complications.RESULTS The numbers of granular(LST-G)and non-granular LST(LST-NG)were 260 and 115,respectively.The rates of low-grade dysplasia and HGD/CA were 60.3%and 39.7%,respectively.Multivariate analysis indicated that a tumor size≥30 mm[odds ratio(OR)=1.934,P=0.032],LST granular nodular mixed type(OR=2.100,P=0.005),and LST non-granular pseudo depressed type(NG-PD)(OR=3.016,P=0.015)were independent risk factors significantly associated with higher odds of HGD/CA.NG-PD(OR=6.506,P=0.001),tumor size(20-29 mm)(OR=2.631,P=0.036)and tumor size≥30 mm(OR=3.449,P=0.016)were associated with increased odds of submucosal invasion.Tumor size≥30 mm(OR=4.888,P=0.003)was a particularly important predictor of complications.A nomogram model demonstrated a satisfactory fit,with an area under the receiver operating characteristic curve of 0.716(95%confidence interval:0.653-0.780),indicating strong predictive performance.CONCLUSION The novel nomogram incorporating tumor size,location,and morphology predicted HGD/CA during endoscopic resection for LSTs.NG-PD lesions larger than 20 mm were more likely to invade the submucosa.Tumor size≥30 mm was an important predictor of complications.
基金Supported by the Natural Science Foundation of Shanghai(No11ZR1425100)the Innovation Program of Shanghai Municipal Education Commission(No11YZ241)Mathematics and Applied Mathematics by Special Fund of Shanghai(No1130IA15)
