A novel green long afterglow material CaGeO_(3):Tb^(3+)was synthesized by high temperature solid phase method.According to the experimental results,CaGeO_(3):Tb^(3+)is a green long persistent luminescent(LPL)material ...A novel green long afterglow material CaGeO_(3):Tb^(3+)was synthesized by high temperature solid phase method.According to the experimental results,CaGeO_(3):Tb^(3+)is a green long persistent luminescent(LPL)material with excellent performance.CaGeO_(3):Tb^(3+)shows LPL emission peak at 442,490.9,548.7,588.5 and 623.8 nm,corresponding to ^(5)D_(3) to^(7)F_(4)and^(5)D_(4)to^(7)F_(J)(J=6,5,4,3)transitions of Tb^(3+).Its CIE coordinates are(0.27,0.50),indicating that the afterglow emission is close to the light of green region.In addition,after the excitation source is turned off,the afterglow time is longer than 90 min.After an interval of 7 h,the photo-stimulation luminescence(PSL)can be observed by using 980 nm laser cyclic excitation.The thermoluminescence(ThL)results show that there are three types of traps in the material and their depths are 0.666,0.774 and 0.892 eV respectively,which are caused by the doping of Tb^(3+).All the results show that CaGeO_(3):Tb^(3+)is an excellent long afterglow luminescent material.展开更多
Objective:To study data about SARS-CoV-2 virus shedding and clarify the risk factors for prolonged virus shedding.Methods:Data were retrospectively collected from adults hospitalized with laboratory-confirmed coronavi...Objective:To study data about SARS-CoV-2 virus shedding and clarify the risk factors for prolonged virus shedding.Methods:Data were retrospectively collected from adults hospitalized with laboratory-confirmed coronavirus disease-19(COVID-19)in Wuhan Union Hospital.We compared clinical features among patients with prolonged(a positive SARS-CoV-2 RNA on day 23 after illness onset)and short virus shedding and evaluated risk factors associated with prolonged virus shedding by multivariate regression analysis.Results:Among 238 patients,the median age was 55.5 years,57.1%were female,92.9%(221/238)were administered with arbidol,58.4%(139/238)were given arbidol in combination with interferon.The median duration of SARS-CoV-2 virus shedding was 23 days(IQR,17.8-30 days)with a longest one of 51 days.The patients with prolonged virus shedding had higher value of D-dimer(P=0.002),IL-6(P<0.001),CRP(P=0.005)and more lobes lung lesion(P=0.014)on admission,as well as older age(P=0.017)and more patients with hypertension(P=0.044)than in those the virus shedding less than 23 days.Multivariate regression analysis revealed that prolonged viral shedding was significantly associated with initiation arbidol≥8 days after symptom onset[OR:2.447,95%CI(1.351-4.431)],≥3 days from onset of symptoms to first medical visitation[OR:1.880,95%CI(1.035-3.416)],illness onset before Jan.31,2020[OR:3.289,95%CI(1.474-7.337)].Arbidol in combination with interferon was also significantly associated with shorter virus shedding[OR:0.363,95%CI(0.191-0.690)].Conclusion:Duration of SARS-CoV-2 virus shedding was long.Early initiation of arbidol and arbidol in combination with interferon as well as consulting doctor timely after illness onset were helpful for SARS-CoV-2 clearance.展开更多
Macrophages undergo dynamic transitions between M1 and M2 states,exerting profound influences on both inflammatory and regenerative processes.The biocompatible and wound-healing properties of decellularized amniotic m...Macrophages undergo dynamic transitions between M1 and M2 states,exerting profound influences on both inflammatory and regenerative processes.The biocompatible and wound-healing properties of decellularized amniotic membrane(d AM)make it a subject of exploration for its potential impact on the anti-inflammatory response of macrophages.Experimental findings unequivocally demonstrate that d AM promotes anti-inflammatory M2 polarization of macrophage,with its cytokine-rich content posited as a potential mediator.The application of RNA sequencing unveils differential gene expression,implicating the hypoxia inducible factor-1α(HIF-1α)signaling pathway in this intricate interplay.Subsequent investigation further demonstrates that d AM facilitates anti-inflammatory M2 polarization of macrophage through the upregulation of epidermal growth factor(EGF),which,in turn,activates the phosphatidylinositol 3-kinase(PI3K)/protein kinase B(AKT)pathway and stabilizes HIF-1α.This cascade results in a noteworthy augmentation of anti-inflammatory gene expression.This study significantly contributes to advancing our comprehension of d AM's immunomodulatory role in tissue repair,thereby suggesting promising therapeutic potential.展开更多
Due to batteries inconsistencies and potential faults in battery management systems,slight overcharging remains a common yet insufficiently understood safety risk,lacking effective warning methods.To illuminate the de...Due to batteries inconsistencies and potential faults in battery management systems,slight overcharging remains a common yet insufficiently understood safety risk,lacking effective warning methods.To illuminate the degradation behavior and failure mechanism of various overcharged states(100%SOC,105%SOC,110%SOC,and 115%SOC),multiple advanced in-situ characterization techniques(accelerating rate calorimeter,electrochemical impedance spectroscopy,ultrasonic scanning,and expansion instrument)were utilized.Additionally,re-overcharge-induced thermal runaway(TR)tests were conducted,with a specific emphasis on the evolution of the expansion force signal.Results indicated significant degradation at 110%SOC including conductivity loss,loss of lithium inventory,and loss of active material accompanied by internal gas generation.These failure behaviors slow down the expansion force rate during reovercharging,reducing the efficacy of active warnings that depend on rate thresholds of expansion force.Specifically,the warning time for 115%SOC battery is only 144 s,which is 740 s shorter than that for fresh battery,and the time to TR is advanced by 9 min.Moreover,the initial self-heating temperature(T1)is reduced by 62.4℃compared to that of fresh battery,reaching only 70.8℃.To address the low safety of overcharged batteries,a passive overcharge warning method utilizing relaxation expansion force was proposed,based on the continued gas generation after stopping charging,leading to a sustained increase in force.Compared to active methods that rely on thresholds of expansion force rate,the passive method can issue warnings 115 s earlier.By combining the passive and active warning methods,guaranteed effective overcharge warning can be issued 863-884 s before TR.This study introduces a novel perspective for enhancing the safety of batteries.展开更多
Transformer models have emerged as pivotal tools within the realm of drug discovery,distinguished by their unique architectural features and exceptional performance in managing intricate data landscapes.Leveraging the...Transformer models have emerged as pivotal tools within the realm of drug discovery,distinguished by their unique architectural features and exceptional performance in managing intricate data landscapes.Leveraging the innate capabilities of transformer architectures to comprehend intricate hierarchical dependencies inherent in sequential data,these models showcase remarkable efficacy across various tasks,including new drug design and drug target identification.The adaptability of pre-trained trans-former-based models renders them indispensable assets for driving data-centric advancements in drug discovery,chemistry,and biology,furnishing a robust framework that expedites innovation and dis-covery within these domains.Beyond their technical prowess,the success of transformer-based models in drug discovery,chemistry,and biology extends to their interdisciplinary potential,seamlessly combining biological,physical,chemical,and pharmacological insights to bridge gaps across diverse disciplines.This integrative approach not only enhances the depth and breadth of research endeavors but also fosters synergistic collaborations and exchange of ideas among disparate fields.In our review,we elucidate the myriad applications of transformers in drug discovery,as well as chemistry and biology,spanning from protein design and protein engineering,to molecular dynamics(MD),drug target iden-tification,transformer-enabled drug virtual screening(VS),drug lead optimization,drug addiction,small data set challenges,chemical and biological image analysis,chemical language understanding,and single cell data.Finally,we conclude the survey by deliberating on promising trends in transformer models within the context of drug discovery and other sciences.展开更多
As the global population ages,osteoporotic bone fractures leading to bone defects are increasingly becoming a significant challenge in the field of public health.Treating this disease faces many challenges,especially ...As the global population ages,osteoporotic bone fractures leading to bone defects are increasingly becoming a significant challenge in the field of public health.Treating this disease faces many challenges,especially in the context of an imbalance between osteoblast and osteoclast activities.Therefore,the development of new biomaterials has become the key.This article reviews various design strategies and their advantages and disadvantages for biomaterials aimed at osteoporotic bone defects.Overall,current research progress indicates that innovative design,functionalization,and targeting of materials can significantly enhance bone regeneration under osteoporotic conditions.By comprehensively considering biocompatibility,mechanical properties,and bioactivity,these biomaterials can be further optimized,offering a range of choices and strategies for the repair of osteoporotic bone defects.展开更多
Accurate prediction of drug-target interactions(DTIs)plays a pivotal role in drug discovery,facilitating optimization of lead compounds,drug repurposing and elucidation of drug side effects.However,traditional DTI pre...Accurate prediction of drug-target interactions(DTIs)plays a pivotal role in drug discovery,facilitating optimization of lead compounds,drug repurposing and elucidation of drug side effects.However,traditional DTI prediction methods are often limited by incomplete biological data and insufficient representation of protein features.In this study,we proposed KG-CNNDTI,a novel knowledge graph-enhanced framework for DTI prediction,which integrates heterogeneous biological information to improve model generalizability and predictive performance.The proposed model utilized protein embeddings derived from a biomedical knowledge graph via the Node2Vec algorithm,which were further enriched with contextualized sequence representations obtained from ProteinBERT.For compound representation,multiple molecular fingerprint schemes alongside the Uni-Mol pre-trained model were evaluated.The fused representations served as inputs to both classical machine learning models and a convolutional neural network-based predictor.Experimental evaluations across benchmark datasets demonstrated that KG-CNNDTI achieved superior performance compared to state-of-the-art methods,particularly in terms of Precision,Recall,F1-Score and area under the precision-recall curve(AUPR).Ablation analysis highlighted the substantial contribution of knowledge graph-derived features.Moreover,KG-CNNDTI was employed for virtual screening of natural products against Alzheimer's disease,resulting in 40 candidate compounds.5 were supported by literature evidence,among which 3 were further validated in vitro assays.展开更多
Activity cliffs(ACs)are generally defined as pairs of similar compounds that only differ by a minor structural modification but exhibit a large difference in their binding affinity for a given target.ACs offer crucial...Activity cliffs(ACs)are generally defined as pairs of similar compounds that only differ by a minor structural modification but exhibit a large difference in their binding affinity for a given target.ACs offer crucial insights that aid medicinal chemists in optimizing molecular structures.Nonetheless,they also form a major source of prediction error in structure-activity relationship(SAR)models.To date,several studies have demonstrated that deep neural networks based on molecular images or graphs might need to be improved further in predicting the potency of ACs.In this paper,we integrated the triplet loss in face recognition with pre-training strategy to develop a prediction model ACtriplet,tailored for ACs.Through extensive comparison with multiple baseline models on 30 benchmark datasets,the results showed that ACtriplet was significantly better than those deep learning(DL)models without pretraining.In addition,we explored the effect of pre-training on data representation.Finally,the case study demonstrated that our model's interpretability module could explain the prediction results reasonably.In the dilemma that the amount of data could not be increased rapidly,this innovative framework would better make use of the existing data,which would propel the potential of DL in the early stage of drug discovery and optimization.展开更多
Ovarian cancer remains a leading cause of gynecological cancer mortality1,and patients with advanced stage ovarian cancer frequently develop malignant ascites that foster immunosuppressive microenvironments and therap...Ovarian cancer remains a leading cause of gynecological cancer mortality1,and patients with advanced stage ovarian cancer frequently develop malignant ascites that foster immunosuppressive microenvironments and therapeutic resistance2,3.Although ascites have traditionally been considered detrimental,we report a paradoxical role in which they enhance the cytotoxicity ofγδT cells—a unique T cell subset that can be allogenically transferred for cancer treatment4,5—toward ovarian cancer.展开更多
Polychlorinated naphthalenes(PCNs)are detrimental to human health and the environment.With the commercial production of PCNs banned,unintentional releases have emerged as a significant environmental source.However,rel...Polychlorinated naphthalenes(PCNs)are detrimental to human health and the environment.With the commercial production of PCNs banned,unintentional releases have emerged as a significant environmental source.However,relevant information is still scarce.In this study,provincial emissions for eight PCNs homologues from 37 sources in the Chinese mainland during the period of 1960-2019 were estimated based on a source-specific and time-varying emission factor database.The results showed that the total PCNs emissions in 2019 reached 757.0 kg with Hebei ranked at the top among all the provinces and iron&steel industry as the biggest source.Low-chlorinated PCNs comprised 90%of emissions by mass,while highly chlorinated PCNs dominated in terms of toxicity,highlighting divergent priorities for mitigating emissions and safeguarding human health.The emissions showed an overall upward trend from 1960 to 2019 driven by emission increase from iron&steel industry in terms of source,and from North China and East China in terms of geographic area.Per-capita emissions followed an inverted U-shaped environmental Kuznets curvewhile emission intensities decreased with increasing per-capita Gross Domestic Product(GDP)following a nearly linear pattern when log-transformed.展开更多
In the Acknowledgements section of this article,the grant number"2020YFE0205900"relating to"National Key R&D Program of China"was missing,it has been included and the corrected Acknowledgements...In the Acknowledgements section of this article,the grant number"2020YFE0205900"relating to"National Key R&D Program of China"was missing,it has been included and the corrected Acknowledgements section is given below.展开更多
基金Project supported by the State Key Research Projects of Shandong Natural Science Foundation(ZR2020KB019)the fund of"Two-Hundred Talent"Plan of Yantai City+1 种基金the National Natural Science Foundation of China(11974013)the Natural Science Foundation of Fujian Province(2022J011270)。
文摘A novel green long afterglow material CaGeO_(3):Tb^(3+)was synthesized by high temperature solid phase method.According to the experimental results,CaGeO_(3):Tb^(3+)is a green long persistent luminescent(LPL)material with excellent performance.CaGeO_(3):Tb^(3+)shows LPL emission peak at 442,490.9,548.7,588.5 and 623.8 nm,corresponding to ^(5)D_(3) to^(7)F_(4)and^(5)D_(4)to^(7)F_(J)(J=6,5,4,3)transitions of Tb^(3+).Its CIE coordinates are(0.27,0.50),indicating that the afterglow emission is close to the light of green region.In addition,after the excitation source is turned off,the afterglow time is longer than 90 min.After an interval of 7 h,the photo-stimulation luminescence(PSL)can be observed by using 980 nm laser cyclic excitation.The thermoluminescence(ThL)results show that there are three types of traps in the material and their depths are 0.666,0.774 and 0.892 eV respectively,which are caused by the doping of Tb^(3+).All the results show that CaGeO_(3):Tb^(3+)is an excellent long afterglow luminescent material.
基金supported by Fundamental Research Funds for the Central Universities(No.2020kfyXGYJ034,No.2020kfyXGYJ009).
文摘Objective:To study data about SARS-CoV-2 virus shedding and clarify the risk factors for prolonged virus shedding.Methods:Data were retrospectively collected from adults hospitalized with laboratory-confirmed coronavirus disease-19(COVID-19)in Wuhan Union Hospital.We compared clinical features among patients with prolonged(a positive SARS-CoV-2 RNA on day 23 after illness onset)and short virus shedding and evaluated risk factors associated with prolonged virus shedding by multivariate regression analysis.Results:Among 238 patients,the median age was 55.5 years,57.1%were female,92.9%(221/238)were administered with arbidol,58.4%(139/238)were given arbidol in combination with interferon.The median duration of SARS-CoV-2 virus shedding was 23 days(IQR,17.8-30 days)with a longest one of 51 days.The patients with prolonged virus shedding had higher value of D-dimer(P=0.002),IL-6(P<0.001),CRP(P=0.005)and more lobes lung lesion(P=0.014)on admission,as well as older age(P=0.017)and more patients with hypertension(P=0.044)than in those the virus shedding less than 23 days.Multivariate regression analysis revealed that prolonged viral shedding was significantly associated with initiation arbidol≥8 days after symptom onset[OR:2.447,95%CI(1.351-4.431)],≥3 days from onset of symptoms to first medical visitation[OR:1.880,95%CI(1.035-3.416)],illness onset before Jan.31,2020[OR:3.289,95%CI(1.474-7.337)].Arbidol in combination with interferon was also significantly associated with shorter virus shedding[OR:0.363,95%CI(0.191-0.690)].Conclusion:Duration of SARS-CoV-2 virus shedding was long.Early initiation of arbidol and arbidol in combination with interferon as well as consulting doctor timely after illness onset were helpful for SARS-CoV-2 clearance.
基金supported by the National Natural Science Foundation of China(No.82302772)Guizhou Basic Research Project(No.ZK[2023]General 201)partially supported by Wuhan Kangchuang Biotechnology Co.,Ltd。
文摘Macrophages undergo dynamic transitions between M1 and M2 states,exerting profound influences on both inflammatory and regenerative processes.The biocompatible and wound-healing properties of decellularized amniotic membrane(d AM)make it a subject of exploration for its potential impact on the anti-inflammatory response of macrophages.Experimental findings unequivocally demonstrate that d AM promotes anti-inflammatory M2 polarization of macrophage,with its cytokine-rich content posited as a potential mediator.The application of RNA sequencing unveils differential gene expression,implicating the hypoxia inducible factor-1α(HIF-1α)signaling pathway in this intricate interplay.Subsequent investigation further demonstrates that d AM facilitates anti-inflammatory M2 polarization of macrophage through the upregulation of epidermal growth factor(EGF),which,in turn,activates the phosphatidylinositol 3-kinase(PI3K)/protein kinase B(AKT)pathway and stabilizes HIF-1α.This cascade results in a noteworthy augmentation of anti-inflammatory gene expression.This study significantly contributes to advancing our comprehension of d AM's immunomodulatory role in tissue repair,thereby suggesting promising therapeutic potential.
基金supported by the National Natural Science Foundation of China(52476200,52106244)the Guangdong Basic and Applied Basic Research Foundation(2024A1515030124)+1 种基金the Science and Technology Project of China Southern Power Grid under Grant GDKJXM20230246(030100KC23020017)the Fundamental Research Funds for the Central Universities。
文摘Due to batteries inconsistencies and potential faults in battery management systems,slight overcharging remains a common yet insufficiently understood safety risk,lacking effective warning methods.To illuminate the degradation behavior and failure mechanism of various overcharged states(100%SOC,105%SOC,110%SOC,and 115%SOC),multiple advanced in-situ characterization techniques(accelerating rate calorimeter,electrochemical impedance spectroscopy,ultrasonic scanning,and expansion instrument)were utilized.Additionally,re-overcharge-induced thermal runaway(TR)tests were conducted,with a specific emphasis on the evolution of the expansion force signal.Results indicated significant degradation at 110%SOC including conductivity loss,loss of lithium inventory,and loss of active material accompanied by internal gas generation.These failure behaviors slow down the expansion force rate during reovercharging,reducing the efficacy of active warnings that depend on rate thresholds of expansion force.Specifically,the warning time for 115%SOC battery is only 144 s,which is 740 s shorter than that for fresh battery,and the time to TR is advanced by 9 min.Moreover,the initial self-heating temperature(T1)is reduced by 62.4℃compared to that of fresh battery,reaching only 70.8℃.To address the low safety of overcharged batteries,a passive overcharge warning method utilizing relaxation expansion force was proposed,based on the continued gas generation after stopping charging,leading to a sustained increase in force.Compared to active methods that rely on thresholds of expansion force rate,the passive method can issue warnings 115 s earlier.By combining the passive and active warning methods,guaranteed effective overcharge warning can be issued 863-884 s before TR.This study introduces a novel perspective for enhancing the safety of batteries.
基金supported in part by National Institute of Health(NIH),USA(Grant Nos.:R01GM126189,R01AI164266,and R35GM148196)the National Science Foundation,USA(Grant Nos.DMS2052983,DMS-1761320,and IIS-1900473)+3 种基金National Aero-nautics and Space Administration(NASA),USA(Grant No.:80NSSC21M0023)Michigan State University(MSU)Foundation,USA,Bristol-Myers Squibb(Grant No.:65109)USA,and Pfizer,USAsupported by the National Natural Science Foundation of China(Grant Nos.:11971367,12271416,and 11972266).
文摘Transformer models have emerged as pivotal tools within the realm of drug discovery,distinguished by their unique architectural features and exceptional performance in managing intricate data landscapes.Leveraging the innate capabilities of transformer architectures to comprehend intricate hierarchical dependencies inherent in sequential data,these models showcase remarkable efficacy across various tasks,including new drug design and drug target identification.The adaptability of pre-trained trans-former-based models renders them indispensable assets for driving data-centric advancements in drug discovery,chemistry,and biology,furnishing a robust framework that expedites innovation and dis-covery within these domains.Beyond their technical prowess,the success of transformer-based models in drug discovery,chemistry,and biology extends to their interdisciplinary potential,seamlessly combining biological,physical,chemical,and pharmacological insights to bridge gaps across diverse disciplines.This integrative approach not only enhances the depth and breadth of research endeavors but also fosters synergistic collaborations and exchange of ideas among disparate fields.In our review,we elucidate the myriad applications of transformers in drug discovery,as well as chemistry and biology,spanning from protein design and protein engineering,to molecular dynamics(MD),drug target iden-tification,transformer-enabled drug virtual screening(VS),drug lead optimization,drug addiction,small data set challenges,chemical and biological image analysis,chemical language understanding,and single cell data.Finally,we conclude the survey by deliberating on promising trends in transformer models within the context of drug discovery and other sciences.
基金supported by the National Natural Science Foundation of China(Nos.82160419 and 82302772)Guizhou Basic Research Project(No.ZK[2023]General 201)。
文摘As the global population ages,osteoporotic bone fractures leading to bone defects are increasingly becoming a significant challenge in the field of public health.Treating this disease faces many challenges,especially in the context of an imbalance between osteoblast and osteoclast activities.Therefore,the development of new biomaterials has become the key.This article reviews various design strategies and their advantages and disadvantages for biomaterials aimed at osteoporotic bone defects.Overall,current research progress indicates that innovative design,functionalization,and targeting of materials can significantly enhance bone regeneration under osteoporotic conditions.By comprehensively considering biocompatibility,mechanical properties,and bioactivity,these biomaterials can be further optimized,offering a range of choices and strategies for the repair of osteoporotic bone defects.
基金supported by the National Natural Science Foundation of China(Nos.82173746 and U23A20530)Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism(Shanghai Municipal Education Commission)。
文摘Accurate prediction of drug-target interactions(DTIs)plays a pivotal role in drug discovery,facilitating optimization of lead compounds,drug repurposing and elucidation of drug side effects.However,traditional DTI prediction methods are often limited by incomplete biological data and insufficient representation of protein features.In this study,we proposed KG-CNNDTI,a novel knowledge graph-enhanced framework for DTI prediction,which integrates heterogeneous biological information to improve model generalizability and predictive performance.The proposed model utilized protein embeddings derived from a biomedical knowledge graph via the Node2Vec algorithm,which were further enriched with contextualized sequence representations obtained from ProteinBERT.For compound representation,multiple molecular fingerprint schemes alongside the Uni-Mol pre-trained model were evaluated.The fused representations served as inputs to both classical machine learning models and a convolutional neural network-based predictor.Experimental evaluations across benchmark datasets demonstrated that KG-CNNDTI achieved superior performance compared to state-of-the-art methods,particularly in terms of Precision,Recall,F1-Score and area under the precision-recall curve(AUPR).Ablation analysis highlighted the substantial contribution of knowledge graph-derived features.Moreover,KG-CNNDTI was employed for virtual screening of natural products against Alzheimer's disease,resulting in 40 candidate compounds.5 were supported by literature evidence,among which 3 were further validated in vitro assays.
基金supported by the National Natural Science Foundation of China(Grant Nos.:U23A20530,82273858,and 82173746)the National Key Research and Development Programof China(Grant No.:2023YFF1204904)Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism(Shanghai Municipal Education Commission,China).
文摘Activity cliffs(ACs)are generally defined as pairs of similar compounds that only differ by a minor structural modification but exhibit a large difference in their binding affinity for a given target.ACs offer crucial insights that aid medicinal chemists in optimizing molecular structures.Nonetheless,they also form a major source of prediction error in structure-activity relationship(SAR)models.To date,several studies have demonstrated that deep neural networks based on molecular images or graphs might need to be improved further in predicting the potency of ACs.In this paper,we integrated the triplet loss in face recognition with pre-training strategy to develop a prediction model ACtriplet,tailored for ACs.Through extensive comparison with multiple baseline models on 30 benchmark datasets,the results showed that ACtriplet was significantly better than those deep learning(DL)models without pretraining.In addition,we explored the effect of pre-training on data representation.Finally,the case study demonstrated that our model's interpretability module could explain the prediction results reasonably.In the dilemma that the amount of data could not be increased rapidly,this innovative framework would better make use of the existing data,which would propel the potential of DL in the early stage of drug discovery and optimization.
基金supported by the National Natural Science Foundation of China(Grant No.82274034)the Peking University Medicine plus X Pilot Program-Platform Construction Project(Grant No.2024YXXLHPT004).
文摘Ovarian cancer remains a leading cause of gynecological cancer mortality1,and patients with advanced stage ovarian cancer frequently develop malignant ascites that foster immunosuppressive microenvironments and therapeutic resistance2,3.Although ascites have traditionally been considered detrimental,we report a paradoxical role in which they enhance the cytotoxicity ofγδT cells—a unique T cell subset that can be allogenically transferred for cancer treatment4,5—toward ovarian cancer.
基金supported by National Key R&D Program of China (No.2022YFC3105800)the National Natural Science Foundation of China (Nos.42277388,42230505,42206148,and 41907313)the Science and Technology Commission of Shanghai Municipality (No.19ZR1415100).
文摘Polychlorinated naphthalenes(PCNs)are detrimental to human health and the environment.With the commercial production of PCNs banned,unintentional releases have emerged as a significant environmental source.However,relevant information is still scarce.In this study,provincial emissions for eight PCNs homologues from 37 sources in the Chinese mainland during the period of 1960-2019 were estimated based on a source-specific and time-varying emission factor database.The results showed that the total PCNs emissions in 2019 reached 757.0 kg with Hebei ranked at the top among all the provinces and iron&steel industry as the biggest source.Low-chlorinated PCNs comprised 90%of emissions by mass,while highly chlorinated PCNs dominated in terms of toxicity,highlighting divergent priorities for mitigating emissions and safeguarding human health.The emissions showed an overall upward trend from 1960 to 2019 driven by emission increase from iron&steel industry in terms of source,and from North China and East China in terms of geographic area.Per-capita emissions followed an inverted U-shaped environmental Kuznets curvewhile emission intensities decreased with increasing per-capita Gross Domestic Product(GDP)following a nearly linear pattern when log-transformed.
文摘In the Acknowledgements section of this article,the grant number"2020YFE0205900"relating to"National Key R&D Program of China"was missing,it has been included and the corrected Acknowledgements section is given below.
