A Hybrid Free-Form Deformation(HFFD)method is developed to improve shape preservation in mesh deformation for perforated surfaces,which traditional Free-Form Deformation(FFD)techniques struggle to handle effectively.T...A Hybrid Free-Form Deformation(HFFD)method is developed to improve shape preservation in mesh deformation for perforated surfaces,which traditional Free-Form Deformation(FFD)techniques struggle to handle effectively.The proposed method enables high-fidelity parameterized deformation for both flat and curved perforated surfaces while maintaining mesh quality with minimal geometric distortion.To evaluate its effectiveness,comparative studies between HFFD and conventional FFD methods are conducted,demonstrating superior performance in mesh quality and geometric fidelity.The HFFD-based framework is further applied to the Multidisciplinary Design Optimization(MDO)of a double-wall turbine blade leading edge.Results indicate an 11.6%increase in cooling efficiency and a 16.21%reduction in maximum stress.Additionally,compared to traditional geometry-based parameterization in MDO,the HFFD approach improves model processing efficiency by 84.15%and overall optimization efficiency by20.05%.These findings demonstrate HFFD's potential to significantly improve complex engineering design optimization by achieving precise shape preservation and improving computational efficiency.展开更多
Understanding the metabolism of endogenous and exogenous substances in the human body is essential for elucidating disease mechanisms and evaluating the safety and efficacy of drug candidates during the drug developme...Understanding the metabolism of endogenous and exogenous substances in the human body is essential for elucidating disease mechanisms and evaluating the safety and efficacy of drug candidates during the drug development process.Recent advancements in artificial intelligence(AI),particularly in machine learning(ML)and deep learning(DL)techniques,have introduced innovative approaches to metabolism research,enabling more accurate predictions and insights.This paper emphasizes computational and AI-driven methodologies,highlighting how ML enhances predictive modeling for human metabolism at the molecular level and facilitates integration into genome-scale metabolic models(GEMs)at the omics level.Challenges still remain,including data heterogeneity and model interpretability.This work aims to provide valuable insights and references for researchers in drug discovery and development,ultimately contributing to the advancement of precision medicine.展开更多
Drug research and development(R&D)plays a crucial role in supporting public health.However,the traditional drug-discovery paradigm is hindered by significant drawbacks,including high costs,lengthy development time...Drug research and development(R&D)plays a crucial role in supporting public health.However,the traditional drug-discovery paradigm is hindered by significant drawbacks,including high costs,lengthy development timelines,high failure rates,and limited output of new drugs.Recent advances in micro/nanotechnology,along with progress in computer science,have positioned microfluidics and artificial intelligence(AI)as promising transformative tools for drug development.Microfluidics offers miniaturized,multiplexed,and versatile platforms for high-dimensional data acquisition,while AI enables the rapid processing of complex,large-scale microfluidic data;together,they are accelerating a paradigm shift in the drug-discovery process.This paper first outlines the mainstream microfluidic strategies and AI models used in drug R&D.It then summarizes and discusses real-world applications of the integrated use of these technologies across various stages of drug discovery,including early drug discovery,drug screening,drug evaluation,drug manufacturing,and drug delivery systems.Finally,the paper examines the main limitations of microfluidics and AI in drug R&D and offers an outlook on the future convergence of these technologies.展开更多
Drug discovery and development affects various aspects of human health and dramatically impacts the pharmaceutical market.However,investments in a new drug often go unrewarded due to the long and complex process of dr...Drug discovery and development affects various aspects of human health and dramatically impacts the pharmaceutical market.However,investments in a new drug often go unrewarded due to the long and complex process of drug research and development(R&D).With the advancement of experimental technology and computer hardware,artificial intelligence(AI)has recently emerged as a leading tool in analyzing abundant and high-dimensional data.Explosive growth in the size of biomedical data provides advantages in applying AI in all stages of drug R&D.Driven by big data in biomedicine,AI has led to a revolution in drug R&D,due to its ability to discover new drugs more efficiently and at lower cost.This review begins with a brief overview of common AI models in the field of drug discovery;then,it summarizes and discusses in depth their specific applications in various stages of drug R&D,such as target discovery,drug discovery and design,preclinical research,automated drug synthesis,and influences in the pharmaceutical market.Finally,the major limitations of AI in drug R&D are fully discussed and possible solutions are proposed.展开更多
We recently reported that inhibitors against human dihydroorotate dehydrogenase(DHODH)have broad-spectrum antiviral activities including their inhibitory efficacies on SARS-CoV-2 replication in infected cells.However,...We recently reported that inhibitors against human dihydroorotate dehydrogenase(DHODH)have broad-spectrum antiviral activities including their inhibitory efficacies on SARS-CoV-2 replication in infected cells.However,there are limited data from clinical studies to prove the application of DHODH inhibitors in Coronavirus disease 2019(COVID-19)patients.In the present study,we evaluated Leflunomide,an approved DHODH inhibitor widely used as a modest immune regulator to treat autoimmune diseases,in treating COVID-19 disease with a small-scale of patients.Cases of 10 laboratory-confirmed COVID-19 patients of moderate type with obvious opacity in the lung were included.Five of the patients were treated with Leflunomide,and another five were treated as blank controls without a placebo.All the patients accepted standard supportive treatment for COVID-19.The patients given Leflunomide had a shorter viral shedding time(median of5 days)than the controls(median of 11 days,P=0.046).The patients given Leflunomide also showed a significant reduction in C-reactive protein levels,indicating that immunopathological inflammation was well controlled.No obvious adverse effects were observed in Leflunomide-treated patients,and they all discharged from the hospital faster than controls.This preliminary study on a small-scale compassionate use of Leflunomide provides clues for further understanding of Leflunomide as a potential antiviral drug against COVID-19.展开更多
In this paper, the flow characteristics of the double wall structure are presented and the effect of the broken pin size on the cooling performance and flow field of the double wall configuration is investigated. A pe...In this paper, the flow characteristics of the double wall structure are presented and the effect of the broken pin size on the cooling performance and flow field of the double wall configuration is investigated. A periodic plate model with seven units is adopted, and there are an impingement hole and a film hole in each unit. Under five blowing ratios, six different sizes of the broken pin are compared, and the double wall configuration without broken pins is taken as the baseline.The results show that if the broken pins height is too small, the cooling effectiveness usually cannot be improved. With the presence of broken pins with a height of more than 0.4, the effectiveness is improved due to the enhancement of reattachment and recirculation of coolant. With the increase of the broken pin height, the cooling effectiveness increases. However, the increase of the diameter does not always improve the cooling performance, since the limiting effect of the wall jet. In this study, Case 6 with the largest broken pin always has the best cooling performance, but also the largest flow resistance. In Case 6 temperature is reduced by almost 15 K compared to the baseline, and more areas have relatively higher cooling effectiveness.展开更多
Designing single-atom catalysts for oxygen reduction reaction(ORR)are fashionable but challenging to boost the zinc-air battery performance.Significantly enhanced ORR activity by manganese(Mn)singleatom catalysts can ...Designing single-atom catalysts for oxygen reduction reaction(ORR)are fashionable but challenging to boost the zinc-air battery performance.Significantly enhanced ORR activity by manganese(Mn)singleatom catalysts can be achieved by accurately regulating the coordination number of isolated Mn atoms.Theoretical calculations indicate that the single Mn-N5sites possess lower free energy barrier and higher oxygen adsorption performance than single Mn-N4sites to accelerate the ORR kinetics.Target to it,here we synthesize an atomically dispersed Mn-N5catalyst by precisely axial coordination of pyridinic-N doped into two-dimensional(2D)porous nanocarbon sheets(~3.56 nm thickness),which reveals outstanding catalytic activity and ultrahigh stability for the ORR in zinc-air battery owing to the inhomogeneous charge distribution of Mn-N5sites compared to the conventional single-site Mn-N4catalyst and Pt/C.This work gives a new strategy for in situ regulating the electronic structure of metal single-atoms and further promoting the overall ORR performance in energy systems.展开更多
Using gas-liquid segmented micromixers to prepare nanoparticles that have a homogeneous particle size, controllable shape, and monodispersity advantages. Although nanoparticle aggregation within a microfluid has been ...Using gas-liquid segmented micromixers to prepare nanoparticles that have a homogeneous particle size, controllable shape, and monodispersity advantages. Although nanoparticle aggregation within a microfluid has been shown to be affected by the shear effect, the shear effect triggering conditions in gasliquid two-phase flow is unclear and the aggregation behavior of nanoparticles under the shear effect is difficult to predict, resulting in uncontrollable physical and chemical properties of nanoparticle aggregates. In this study, a numerical simulation of nanoparticle aggregation in gas-liquid two-phase flow under the shear effect is performed using the CFD-DEM method. Then, the effects of total flow rate,gas-liquid two-phase flow ratio, and particle volume fraction on particle aggregation were analyzed to achieve control of particle aggregation shape and size. Meanwhile, the triggering mechanism of the shear effect and the mechanism of the shear effect on the aggregation of nanoparticles were clarified. The results show that increasing the total flow rate or decreasing the gas-liquid two-phase flow rate ratio can induce the shear effect, which reduces the particle aggregation size and makes the morphology tend to be spherical. Moreover, increasing the particle volume fraction, and total flow rate or decreasing the gas-liquid two-phase flow rate ratio also increases the number of particle collisions and induce interparticle adhesion. Hence, particle adhesion and the shear effect compete with each other and together affect particle aggregation.展开更多
Thrombosis remains a major global health concern mainly characterized by high rates of morbidity and mortality.Animal models serve as an indispensable tool to understand the underlying pathogenesis of thrombosis and a...Thrombosis remains a major global health concern mainly characterized by high rates of morbidity and mortality.Animal models serve as an indispensable tool to understand the underlying pathogenesis of thrombosis and assess the efficacy of novel antithrombotic drugs.Currently,zebrafish has emerged as a valuable model organism for thrombosis research.However,the traditional method of studying zebrafish thrombosis requires a laborious and time-consuming procedure,including anesthesia and manual immobilization of zebrafish.In this study,based on hydrodynamic force,a lateral-immobilization zebrafish microfluidic chip(LIZMC)was designed to evaluate the cardiovascular system of multiple larvae within a single microscope field of view.Specifically,coupling with microscope imaging,real-time monitoring of the peripheral blood circulation in the tail of phenylhydrazine(PHZ)-induced zebrafish thrombosis was enabled.Furthermore,the reliability of LIZMC for in vivo evaluation of antithrombotic agents in zebrafish was verified using aspirin.Collectively,this novel LIZMC-based system can be used for in vivo zebrafish thrombosis studies and rapid screening of antithrombotic agents.展开更多
Ursolic acid(UA)is a naturally occurring ursane triterpenoid,which exhibits a wide range of unique biological activities.To clarify its mechanism of action(MOA),a series of fluorescent derivatives of UA(5a-c)were desi...Ursolic acid(UA)is a naturally occurring ursane triterpenoid,which exhibits a wide range of unique biological activities.To clarify its mechanism of action(MOA),a series of fluorescent derivatives of UA(5a-c)were designed and synthesized by conjugation with 7-nitrobenzo-2-oxa-1,3-diazole(NBD)fluorophore.Among them,5c exhibited similar anti-proliferative activity with UA against HCT116 cells(half maximal inhibitory concentration(IC_(50))=9.21±0.50μmol/L).Cell imaging experiment indicated that 5c was rapidly taken up in HCT116 cells in a dose and time-dependent manner.Then,5c was found to localize in endoplasmic reticulum(ER),lysosomes,and mitochondria,but not in nucleus of HCT116 cells by confocal microscopy studies.Preliminary MOA proved that UA induced autophagy with a unique intracellular distribution mechanism involving ER and lysosome.In all,our work provides new clues for revealing the molecular mechanism of UA as an antitumor agent.展开更多
This review explores the concept of life-on-a-chip,which involves the creation of miniaturized biological systems,such as organs,tissues,and model organisms,on microscale platforms called microfluidic chips.These chip...This review explores the concept of life-on-a-chip,which involves the creation of miniaturized biological systems,such as organs,tissues,and model organisms,on microscale platforms called microfluidic chips.These chips consist of intricately etched channels,wells,and chambers that enable precise control and observation of fluids,cells,and biochemical reactions,facilitating the simulation of various aspects of human or animal physiology and the study of responses to different stimuli,drugs,or disease conditions.The review highlights the application of a novel technology,“Beyond Limit Manufacturing”(BLM),in the development of sophisticated three-dimensional cell models and model organism microchips.Modelorganism-on-a-chip and organ-on-a-chip(OoC)are among the thriving developments in the field of microfluidics,allowing for the reconstruction of living microenvironments and implementation of multiple stimuli.The review discusses the latest advancements in life-on-a-chip technology using BLM and outlines potential future research directions,emphasizing the significant role of these chips in studying complex biological processes in a controlled and scalable manner.展开更多
“A child receiving a single shot containing mRNA vaccines that protect against multiple diseases,all delivered with one lipid nanoparticle”—this is how Professor Drew Weissman,the 2023 Nobel laureate in Physiology ...“A child receiving a single shot containing mRNA vaccines that protect against multiple diseases,all delivered with one lipid nanoparticle”—this is how Professor Drew Weissman,the 2023 Nobel laureate in Physiology or Medicine[1],described the potential of messenger RNA(mRNA)therapy in an interview with Forbes[2].In 2024,the Nobel Prize was again awarded to RNA researchers,this time to Victor Ambros and Gary Ruvkun for the discovery of microRNA and its role in post-transcriptional gene regulation[3],further underscoring the transformative capacity of RNA therapeutics in the 21st century healthcare.展开更多
The identification and optimization of mutations in nanobodies are crucial for enhancing their thera-peutic potential in disease prevention and control.However,this process is often complex and time-consuming,which li...The identification and optimization of mutations in nanobodies are crucial for enhancing their thera-peutic potential in disease prevention and control.However,this process is often complex and time-consuming,which limit its widespread application in practice.In this study,we developed a work-flow,named Evolutionary-Nanobody(EvoNB),to predict key mutation sites of nanobodies by combining protein language models(PLMs)and molecular dynamic(MD)simulations.By fine-tuning the ESM2 model on a large-scale nanobody dataset,the ability of EvoNB to capture specific sequence features of nanobodies was significantly enhanced.The fine-tuned EvoNB model demonstrated higher predictive accuracy in the conserved framework and highly variable complementarity-determining regions of nanobodies.Additionally,we selected four widely representative nanobodyeantigen complexes to verify the predicted effects of mutations.MD simulations analyzed the energy changes caused by these mu-tations to predict their impact on binding affinity to the targets.The results showed that multiple mu-tations screened by EvoNB significantly enhanced the binding affinity between nanobody and its target,further validating the potential of this workflow for designing and optimizing nanobody mutations.Additionally,sequence-based predictions are generally less dependent on structural absence,allowing them to be more easily integrated with tools for structural predictions,such as AlphaFold 3.Through mutation prediction and systematic analysis of key sites,we can quickly predict the most promising variants for experimental validation without relying on traditional evolutionary or selection processes.The EvoNB workflow provides an effective tool for the rapid optimization of nanobodies and facilitates the application of PLMs in the biomedical field.展开更多
During the production of natural gas hydrates,micron-sized sand particles coexist with hydrate within the transportation pipeline,posing a significant threat to the safety of pipeline flow.However,the influence of san...During the production of natural gas hydrates,micron-sized sand particles coexist with hydrate within the transportation pipeline,posing a significant threat to the safety of pipeline flow.However,the influence of sand particles on hydrate formation mechanisms and rheological properties remains poorly understood.Consequently,using a high-pressure reactor system,the phase equilibrium conditions,hydrate formation characteristics,hydrate concentration,and the slurry viscosity in micron-sized sand system are investigated in this work.Furthermore,the effects of sand particle size,sand concentration,and initial pressure on these properties are analyzed.The results indicate that a high concentration of micron-sized sand particles enhances the formation of methane hydrates.When the volume fraction of sand particles exceeds or equals 3%,the phase equilibrium conditions of the methane hydrate shift to the left relative to that of the pure water system(lower temperature,higher pressure).This shift becomes more pronounced with smaller particle sizes.Besides,under these sand concentration conditions,methane hydrates exhibit secondary or even multiple formation events,though the formation rate decreases.Additionally,the torque increases significantly and fluctuates considerably.The Roscoe-Brinkman model yields the most accurate slurry viscosity calculations,and as sand concentration increases,both hydrate concentration and slurry viscosity also increase.展开更多
The emergence of severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)variants capable of evading both convalescent and vaccine-triggered antibody responses has underscored the pivotal role of T-cell immunity in...The emergence of severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)variants capable of evading both convalescent and vaccine-triggered antibody responses has underscored the pivotal role of T-cell immunity in antiviral defense.Here,we develop the ConFormer network for epitope prediction,which couples convolutional neural network(CNN)local features with Transformer global representations to enhance binding prediction performance,and employ the deep learning algorithm and bioinformatics workflows to identify conserved T-cell epitopes within the SARS-CoV-2 proteome.Five epitopes are identified as potential inducers of T-cell immune responses.Notably,the multi-valent vaccine composed of these five peptides significantly activates cluster of differentiation(CD)8^(+)and CD4^(+)T cells both in vitro and in vivo.The serum of mice immunized with this vaccine is able to neutralize the five major SARS-CoV-2 variants of concern.This study provides a candidate peptide vaccine with the potential to trigger antiviral T-cell responses,thereby offering the prospect of immune protection against SARS-CoV-2 variants.展开更多
β-Ga_(2)O_(3)is a promising candidate for solarblind ultraviolet photodetection owing to its suitable bandgap of approximately 4.9 eV,excellent photoresponse characteristics,and high stability.However,the lack of a s...β-Ga_(2)O_(3)is a promising candidate for solarblind ultraviolet photodetection owing to its suitable bandgap of approximately 4.9 eV,excellent photoresponse characteristics,and high stability.However,the lack of a sufficient driving force within the material leads to extensive bulk charge recombination,limiting its photocurrent and thus posing significant challenges in designing high-performance Ga_(2)O_(3)-based photodetection.In this study,we propose a gradient doping strategy to achieve a Sn-doping concentration gradient along theβ-Ga_(2)O_(3)film thickness.By combining sol-gel synthesis with rapid thermal annealing,a spatially graded band structure with a full-space built-in electric field is constructed,which increases the width of band bending over a large region and is crucial for significantly enhancing carrier separation and transport in the bulk.The resulting gradient Sn-dopedβ-Ga_(2)O_(3)enables exceptional photoelectric performance without an external bias under 254 nm irradiation,including a superior responsivity of 66.88 mA W^(-1),a high detectivity of 8.12×10^(11)Jones,and a fast rise/decay time of 79/65 ms,outstanding most existing similar reported photoelectrochemical(PEC)type optoelectronic devices.Additionally,the device exhibits excellent long-term stability and enables high-resolution underwater ultraviolet imaging.This study demonstrates that the gradient doping strategy provides a feasible approach for enhancing the PEC performance ofβ-Ga_(2)O_(3)photoelectrodes.展开更多
基金supported by the National Science and Technology Major Project,China(No.2017-II-0006-0019)the National Natural Science Foundation of China(No.52375266)the Shaanxi Science Foundation for Distinguished Young Scholars,China(No.2022JC-36)。
文摘A Hybrid Free-Form Deformation(HFFD)method is developed to improve shape preservation in mesh deformation for perforated surfaces,which traditional Free-Form Deformation(FFD)techniques struggle to handle effectively.The proposed method enables high-fidelity parameterized deformation for both flat and curved perforated surfaces while maintaining mesh quality with minimal geometric distortion.To evaluate its effectiveness,comparative studies between HFFD and conventional FFD methods are conducted,demonstrating superior performance in mesh quality and geometric fidelity.The HFFD-based framework is further applied to the Multidisciplinary Design Optimization(MDO)of a double-wall turbine blade leading edge.Results indicate an 11.6%increase in cooling efficiency and a 16.21%reduction in maximum stress.Additionally,compared to traditional geometry-based parameterization in MDO,the HFFD approach improves model processing efficiency by 84.15%and overall optimization efficiency by20.05%.These findings demonstrate HFFD's potential to significantly improve complex engineering design optimization by achieving precise shape preservation and improving computational efficiency.
基金supported in part by the National Natural Science Foundation of China(Grant Nos.:82425104 and 82173690)the National Key R&D Program of China(Grant Nos:2022YFC3400501 and 2022YFC3400504)the Shanghai Rising-Star Program,China(Grant No:23QA1402800).
文摘Understanding the metabolism of endogenous and exogenous substances in the human body is essential for elucidating disease mechanisms and evaluating the safety and efficacy of drug candidates during the drug development process.Recent advancements in artificial intelligence(AI),particularly in machine learning(ML)and deep learning(DL)techniques,have introduced innovative approaches to metabolism research,enabling more accurate predictions and insights.This paper emphasizes computational and AI-driven methodologies,highlighting how ML enhances predictive modeling for human metabolism at the molecular level and facilitates integration into genome-scale metabolic models(GEMs)at the omics level.Challenges still remain,including data heterogeneity and model interpretability.This work aims to provide valuable insights and references for researchers in drug discovery and development,ultimately contributing to the advancement of precision medicine.
基金s supported by the National Natural Science Foundation of China(82425104)the National Key Research and Development Program of China(2022YFC3400501).
文摘Drug research and development(R&D)plays a crucial role in supporting public health.However,the traditional drug-discovery paradigm is hindered by significant drawbacks,including high costs,lengthy development timelines,high failure rates,and limited output of new drugs.Recent advances in micro/nanotechnology,along with progress in computer science,have positioned microfluidics and artificial intelligence(AI)as promising transformative tools for drug development.Microfluidics offers miniaturized,multiplexed,and versatile platforms for high-dimensional data acquisition,while AI enables the rapid processing of complex,large-scale microfluidic data;together,they are accelerating a paradigm shift in the drug-discovery process.This paper first outlines the mainstream microfluidic strategies and AI models used in drug R&D.It then summarizes and discusses real-world applications of the integrated use of these technologies across various stages of drug discovery,including early drug discovery,drug screening,drug evaluation,drug manufacturing,and drug delivery systems.Finally,the paper examines the main limitations of microfluidics and AI in drug R&D and offers an outlook on the future convergence of these technologies.
基金funded by the Natural Science Foundation of Zhejiang Province(LR21H300001)National Key R&D Program of China(2022YFC3400501)+4 种基金National Natural Science Foundation of China(22220102001,U1909208,81872798,and 81825020)Leading Talent of the“Ten Thousand Plan”-National High-Level Talents Special Support Plan of ChinaFundamental Research Fund of Central University(2018QNA7023)Key R&D Program of Zhejiang Province(2020C03010)“Double Top-Class”University(181201*194232101)。
文摘Drug discovery and development affects various aspects of human health and dramatically impacts the pharmaceutical market.However,investments in a new drug often go unrewarded due to the long and complex process of drug research and development(R&D).With the advancement of experimental technology and computer hardware,artificial intelligence(AI)has recently emerged as a leading tool in analyzing abundant and high-dimensional data.Explosive growth in the size of biomedical data provides advantages in applying AI in all stages of drug R&D.Driven by big data in biomedicine,AI has led to a revolution in drug R&D,due to its ability to discover new drugs more efficiently and at lower cost.This review begins with a brief overview of common AI models in the field of drug discovery;then,it summarizes and discusses in depth their specific applications in various stages of drug R&D,such as target discovery,drug discovery and design,preclinical research,automated drug synthesis,and influences in the pharmaceutical market.Finally,the major limitations of AI in drug R&D are fully discussed and possible solutions are proposed.
基金supported by the Science and Technology Key Project on Novel Coronavirus Pneumonia,Hubei Province(project number:2020FCA002 to K.H.)the Application&Frontier Research Program of Wuhan Government(2019020701011463 to K.X.)+2 种基金Taikang Insurance Group Co.,LtdBeijing Taikang Yicai FoundationSpecial Fund for COVID-19 Research of Wuhan University for their great supports to this work。
文摘We recently reported that inhibitors against human dihydroorotate dehydrogenase(DHODH)have broad-spectrum antiviral activities including their inhibitory efficacies on SARS-CoV-2 replication in infected cells.However,there are limited data from clinical studies to prove the application of DHODH inhibitors in Coronavirus disease 2019(COVID-19)patients.In the present study,we evaluated Leflunomide,an approved DHODH inhibitor widely used as a modest immune regulator to treat autoimmune diseases,in treating COVID-19 disease with a small-scale of patients.Cases of 10 laboratory-confirmed COVID-19 patients of moderate type with obvious opacity in the lung were included.Five of the patients were treated with Leflunomide,and another five were treated as blank controls without a placebo.All the patients accepted standard supportive treatment for COVID-19.The patients given Leflunomide had a shorter viral shedding time(median of5 days)than the controls(median of 11 days,P=0.046).The patients given Leflunomide also showed a significant reduction in C-reactive protein levels,indicating that immunopathological inflammation was well controlled.No obvious adverse effects were observed in Leflunomide-treated patients,and they all discharged from the hospital faster than controls.This preliminary study on a small-scale compassionate use of Leflunomide provides clues for further understanding of Leflunomide as a potential antiviral drug against COVID-19.
基金supported by the National Natural Science Foundation of China (No. 51975471)Aviation Power Foundation of China (No. 6141B090319)+3 种基金Natural Science Basic Research Plan in Shaanxi Province of China (No.2018JM5173)National Natural Science Foundation of China(No. 51975471)National Science and Technology Major Project (2017-VIII-0003-0114)Natural Science Foundation of Shaanxi Province (No. 2018JQ5041)。
文摘In this paper, the flow characteristics of the double wall structure are presented and the effect of the broken pin size on the cooling performance and flow field of the double wall configuration is investigated. A periodic plate model with seven units is adopted, and there are an impingement hole and a film hole in each unit. Under five blowing ratios, six different sizes of the broken pin are compared, and the double wall configuration without broken pins is taken as the baseline.The results show that if the broken pins height is too small, the cooling effectiveness usually cannot be improved. With the presence of broken pins with a height of more than 0.4, the effectiveness is improved due to the enhancement of reattachment and recirculation of coolant. With the increase of the broken pin height, the cooling effectiveness increases. However, the increase of the diameter does not always improve the cooling performance, since the limiting effect of the wall jet. In this study, Case 6 with the largest broken pin always has the best cooling performance, but also the largest flow resistance. In Case 6 temperature is reduced by almost 15 K compared to the baseline, and more areas have relatively higher cooling effectiveness.
基金supported by the National Natural Science Foundation of China(22275026 and 21805024)the Natural Science Foundation of Chongqing,China(cstc2021jcyj-msxm X0783,cstc2019jscx-msxm X0393 and cstc2018jcyj AX0461)+1 种基金the Scientific and Technological Research Program of Chongqing Municipal Education Commission(KJZD-K202101303,KJQN201901335 and KJQN202001322)the Scientific Research Program of Chongqing Urban Administration(CGKZ2020-26)。
文摘Designing single-atom catalysts for oxygen reduction reaction(ORR)are fashionable but challenging to boost the zinc-air battery performance.Significantly enhanced ORR activity by manganese(Mn)singleatom catalysts can be achieved by accurately regulating the coordination number of isolated Mn atoms.Theoretical calculations indicate that the single Mn-N5sites possess lower free energy barrier and higher oxygen adsorption performance than single Mn-N4sites to accelerate the ORR kinetics.Target to it,here we synthesize an atomically dispersed Mn-N5catalyst by precisely axial coordination of pyridinic-N doped into two-dimensional(2D)porous nanocarbon sheets(~3.56 nm thickness),which reveals outstanding catalytic activity and ultrahigh stability for the ORR in zinc-air battery owing to the inhomogeneous charge distribution of Mn-N5sites compared to the conventional single-site Mn-N4catalyst and Pt/C.This work gives a new strategy for in situ regulating the electronic structure of metal single-atoms and further promoting the overall ORR performance in energy systems.
基金supported by the Shanghai Beyond Limits Manufacturing Projectthe National Natural Science Foundation of China(Nos.11502044, U1906233)+2 种基金the Fundamental Research Funds for the Central Universities (No.DUT22JC08)Dalian city supports innovation and entrepreneurship projects for high-level talents (No.2021RD16)Liaoning Province’s Xing Liao Talents Program (No.XLYC2002108)。
文摘Using gas-liquid segmented micromixers to prepare nanoparticles that have a homogeneous particle size, controllable shape, and monodispersity advantages. Although nanoparticle aggregation within a microfluid has been shown to be affected by the shear effect, the shear effect triggering conditions in gasliquid two-phase flow is unclear and the aggregation behavior of nanoparticles under the shear effect is difficult to predict, resulting in uncontrollable physical and chemical properties of nanoparticle aggregates. In this study, a numerical simulation of nanoparticle aggregation in gas-liquid two-phase flow under the shear effect is performed using the CFD-DEM method. Then, the effects of total flow rate,gas-liquid two-phase flow ratio, and particle volume fraction on particle aggregation were analyzed to achieve control of particle aggregation shape and size. Meanwhile, the triggering mechanism of the shear effect and the mechanism of the shear effect on the aggregation of nanoparticles were clarified. The results show that increasing the total flow rate or decreasing the gas-liquid two-phase flow rate ratio can induce the shear effect, which reduces the particle aggregation size and makes the morphology tend to be spherical. Moreover, increasing the particle volume fraction, and total flow rate or decreasing the gas-liquid two-phase flow rate ratio also increases the number of particle collisions and induce interparticle adhesion. Hence, particle adhesion and the shear effect compete with each other and together affect particle aggregation.
基金supported in part by the National Natural Science Foundation of China(Nos.81825020 and 82150208)the Shanghai Science and Technology Commission Biomedical Science and Technology Support Special Project(Nos.21S11907900 and 20S11901000)+3 种基金the Fundamental Research Funds for the Central Universitiesthe Shanghai“Beyond Limits Manufacturing”Projectsponsored by National Program for Special Supports of Eminent ProfessionalsNational Program for Support of Top-notch Young Professionals。
文摘Thrombosis remains a major global health concern mainly characterized by high rates of morbidity and mortality.Animal models serve as an indispensable tool to understand the underlying pathogenesis of thrombosis and assess the efficacy of novel antithrombotic drugs.Currently,zebrafish has emerged as a valuable model organism for thrombosis research.However,the traditional method of studying zebrafish thrombosis requires a laborious and time-consuming procedure,including anesthesia and manual immobilization of zebrafish.In this study,based on hydrodynamic force,a lateral-immobilization zebrafish microfluidic chip(LIZMC)was designed to evaluate the cardiovascular system of multiple larvae within a single microscope field of view.Specifically,coupling with microscope imaging,real-time monitoring of the peripheral blood circulation in the tail of phenylhydrazine(PHZ)-induced zebrafish thrombosis was enabled.Furthermore,the reliability of LIZMC for in vivo evaluation of antithrombotic agents in zebrafish was verified using aspirin.Collectively,this novel LIZMC-based system can be used for in vivo zebrafish thrombosis studies and rapid screening of antithrombotic agents.
基金supported in part by the National Key Research and Development Program of China(No.2022YFC3400501)the National Natural Science Foundation of China(Nos.81825020 and 82150208 to H.L.)+2 种基金the Shanghai Science and Technology Commission Biomedical Science and Technology Support Special Project(Nos.21S11907900 and 20S11901000 to Z.Z.)sponsored by the National Program for Special Supports of Eminent Professionalsthe National Program for Support of Top-Notch Young Professionals.
文摘Ursolic acid(UA)is a naturally occurring ursane triterpenoid,which exhibits a wide range of unique biological activities.To clarify its mechanism of action(MOA),a series of fluorescent derivatives of UA(5a-c)were designed and synthesized by conjugation with 7-nitrobenzo-2-oxa-1,3-diazole(NBD)fluorophore.Among them,5c exhibited similar anti-proliferative activity with UA against HCT116 cells(half maximal inhibitory concentration(IC_(50))=9.21±0.50μmol/L).Cell imaging experiment indicated that 5c was rapidly taken up in HCT116 cells in a dose and time-dependent manner.Then,5c was found to localize in endoplasmic reticulum(ER),lysosomes,and mitochondria,but not in nucleus of HCT116 cells by confocal microscopy studies.Preliminary MOA proved that UA induced autophagy with a unique intracellular distribution mechanism involving ER and lysosome.In all,our work provides new clues for revealing the molecular mechanism of UA as an antitumor agent.
基金the “Shanghai Beyond Limits Manufacturing Project” for supporting the realization of the BLM concept for microchip products.
文摘This review explores the concept of life-on-a-chip,which involves the creation of miniaturized biological systems,such as organs,tissues,and model organisms,on microscale platforms called microfluidic chips.These chips consist of intricately etched channels,wells,and chambers that enable precise control and observation of fluids,cells,and biochemical reactions,facilitating the simulation of various aspects of human or animal physiology and the study of responses to different stimuli,drugs,or disease conditions.The review highlights the application of a novel technology,“Beyond Limit Manufacturing”(BLM),in the development of sophisticated three-dimensional cell models and model organism microchips.Modelorganism-on-a-chip and organ-on-a-chip(OoC)are among the thriving developments in the field of microfluidics,allowing for the reconstruction of living microenvironments and implementation of multiple stimuli.The review discusses the latest advancements in life-on-a-chip technology using BLM and outlines potential future research directions,emphasizing the significant role of these chips in studying complex biological processes in a controlled and scalable manner.
基金supported by the National Key Research and Development Program of China(2022YFB3305900)the Science and Technology Innovation Action Plan Computational Biology Program(24JS2830400)+1 种基金the Fundamental Research Funds for the Central Universities(222202517006)the Programme of Introducing Talents of Discipline to Universities(the 111 Project)(B17017).
文摘“A child receiving a single shot containing mRNA vaccines that protect against multiple diseases,all delivered with one lipid nanoparticle”—this is how Professor Drew Weissman,the 2023 Nobel laureate in Physiology or Medicine[1],described the potential of messenger RNA(mRNA)therapy in an interview with Forbes[2].In 2024,the Nobel Prize was again awarded to RNA researchers,this time to Victor Ambros and Gary Ruvkun for the discovery of microRNA and its role in post-transcriptional gene regulation[3],further underscoring the transformative capacity of RNA therapeutics in the 21st century healthcare.
基金supported by the National Natural Science Foundation of China(Grant Nos.:92477103,22273023,12474285 and 22373116)the National Key R&D Program of China(Grant No.:2019YFA0905200)+5 种基金Shanghai Municipal Natural Science Foundation(Grant No.:23ZR1418200)Natural Science Foundation of Chongqing,China(Grant No.:CSTB2023NSCQ-MSX0616)Shanghai Frontiers Science Center of Molecule Intelligent SynthesesShanghai Future Discipline Program(Quantum Science and Tech-nology)Shanghai Municipal Education Commission’s“Artificial Intelligence-Driven Research Paradigm Reform and Discipline Advancement Program”the Fundamental Research Funds for the Central Universities.
文摘The identification and optimization of mutations in nanobodies are crucial for enhancing their thera-peutic potential in disease prevention and control.However,this process is often complex and time-consuming,which limit its widespread application in practice.In this study,we developed a work-flow,named Evolutionary-Nanobody(EvoNB),to predict key mutation sites of nanobodies by combining protein language models(PLMs)and molecular dynamic(MD)simulations.By fine-tuning the ESM2 model on a large-scale nanobody dataset,the ability of EvoNB to capture specific sequence features of nanobodies was significantly enhanced.The fine-tuned EvoNB model demonstrated higher predictive accuracy in the conserved framework and highly variable complementarity-determining regions of nanobodies.Additionally,we selected four widely representative nanobodyeantigen complexes to verify the predicted effects of mutations.MD simulations analyzed the energy changes caused by these mu-tations to predict their impact on binding affinity to the targets.The results showed that multiple mu-tations screened by EvoNB significantly enhanced the binding affinity between nanobody and its target,further validating the potential of this workflow for designing and optimizing nanobody mutations.Additionally,sequence-based predictions are generally less dependent on structural absence,allowing them to be more easily integrated with tools for structural predictions,such as AlphaFold 3.Through mutation prediction and systematic analysis of key sites,we can quickly predict the most promising variants for experimental validation without relying on traditional evolutionary or selection processes.The EvoNB workflow provides an effective tool for the rapid optimization of nanobodies and facilitates the application of PLMs in the biomedical field.
基金supported by the Natural Science Starting Project of Sichuan Provincial Youth Foundation Project(2025ZNSFSC1356)Southwest Petroleum University,China(2023QHZ019)+1 种基金General Project of the Sichuan Provincial Natural Science Foundation,China(24NSFSC1295)Open fund of Dazhou Industrial Technology Institute of Intelligent Manufacturing,China(ZNZZ2215).
文摘During the production of natural gas hydrates,micron-sized sand particles coexist with hydrate within the transportation pipeline,posing a significant threat to the safety of pipeline flow.However,the influence of sand particles on hydrate formation mechanisms and rheological properties remains poorly understood.Consequently,using a high-pressure reactor system,the phase equilibrium conditions,hydrate formation characteristics,hydrate concentration,and the slurry viscosity in micron-sized sand system are investigated in this work.Furthermore,the effects of sand particle size,sand concentration,and initial pressure on these properties are analyzed.The results indicate that a high concentration of micron-sized sand particles enhances the formation of methane hydrates.When the volume fraction of sand particles exceeds or equals 3%,the phase equilibrium conditions of the methane hydrate shift to the left relative to that of the pure water system(lower temperature,higher pressure).This shift becomes more pronounced with smaller particle sizes.Besides,under these sand concentration conditions,methane hydrates exhibit secondary or even multiple formation events,though the formation rate decreases.Additionally,the torque increases significantly and fluctuates considerably.The Roscoe-Brinkman model yields the most accurate slurry viscosity calculations,and as sand concentration increases,both hydrate concentration and slurry viscosity also increase.
基金supported in part by the National Natural Science Foundation of China(82150208 and 82425104)the National Key Research and Development Program of China(2022YFC3400501)the Shanghai Rising-Star Program(23QA1402800).
文摘The emergence of severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)variants capable of evading both convalescent and vaccine-triggered antibody responses has underscored the pivotal role of T-cell immunity in antiviral defense.Here,we develop the ConFormer network for epitope prediction,which couples convolutional neural network(CNN)local features with Transformer global representations to enhance binding prediction performance,and employ the deep learning algorithm and bioinformatics workflows to identify conserved T-cell epitopes within the SARS-CoV-2 proteome.Five epitopes are identified as potential inducers of T-cell immune responses.Notably,the multi-valent vaccine composed of these five peptides significantly activates cluster of differentiation(CD)8^(+)and CD4^(+)T cells both in vitro and in vivo.The serum of mice immunized with this vaccine is able to neutralize the five major SARS-CoV-2 variants of concern.This study provides a candidate peptide vaccine with the potential to trigger antiviral T-cell responses,thereby offering the prospect of immune protection against SARS-CoV-2 variants.
基金supported by the National Natural Science Foundation of China(12304102,62574029)Natural Science Foundation of Chongqing(CSTB2023NSCQ-MSX0479)+1 种基金Science and Technology Research Project of Chongqing Education Committee(KJQN202400558)Doctoral Scientific Research Fund of Chongqing Normal University(23XLB029)。
文摘β-Ga_(2)O_(3)is a promising candidate for solarblind ultraviolet photodetection owing to its suitable bandgap of approximately 4.9 eV,excellent photoresponse characteristics,and high stability.However,the lack of a sufficient driving force within the material leads to extensive bulk charge recombination,limiting its photocurrent and thus posing significant challenges in designing high-performance Ga_(2)O_(3)-based photodetection.In this study,we propose a gradient doping strategy to achieve a Sn-doping concentration gradient along theβ-Ga_(2)O_(3)film thickness.By combining sol-gel synthesis with rapid thermal annealing,a spatially graded band structure with a full-space built-in electric field is constructed,which increases the width of band bending over a large region and is crucial for significantly enhancing carrier separation and transport in the bulk.The resulting gradient Sn-dopedβ-Ga_(2)O_(3)enables exceptional photoelectric performance without an external bias under 254 nm irradiation,including a superior responsivity of 66.88 mA W^(-1),a high detectivity of 8.12×10^(11)Jones,and a fast rise/decay time of 79/65 ms,outstanding most existing similar reported photoelectrochemical(PEC)type optoelectronic devices.Additionally,the device exhibits excellent long-term stability and enables high-resolution underwater ultraviolet imaging.This study demonstrates that the gradient doping strategy provides a feasible approach for enhancing the PEC performance ofβ-Ga_(2)O_(3)photoelectrodes.
