Alzheimer’s disease(AD)is the most common form of dementia,affecting over 50 million people worldwide.This figure is projected to nearly double every 20 years,reaching 82 million by 2030 and 152 million by 2050(Alzhe...Alzheimer’s disease(AD)is the most common form of dementia,affecting over 50 million people worldwide.This figure is projected to nearly double every 20 years,reaching 82 million by 2030 and 152 million by 2050(Alzheimer’s Disease International).The apolipoproteinε4(APOE4)allele is the strongest genetic risk factor for late-onset AD(after age 65 years).Apolipoprotein E,a lipid transporter,exists in three variants:ε2,ε3,andε4.APOEε2(APOE2)is protective against AD,APOEε3(APOE3)is neutral,while APOE4 significantly increases the risk.Individuals with one copy of APOE4 have a 4-fold greater risk of developing AD,and those with two copies face an 8-fold risk compared to non-carriers.Even in cognitively normal individuals,APOE4 carriers exhibit brain metabolic and vascular deficits decades before amyloid-beta(Aβ)plaques and neurofibrillary tau tangles emerge-the hallmark pathologies of AD(Reiman et al.,2001,2005;Thambisetty et al.,2010).Notably,studies have demonstrated reduced glucose uptake,or hypometabolism,in brain regions vulnerable to AD in asymptomatic middle-aged APOE4 carriers,long before clinical symptoms arise(Reiman et al.,2001,2005).展开更多
To review the existing deep learning applications for diagnosing diabetic retinopathy and retinopathy of prematurity diseases,the available public retinal databases for the diseases and apply the International Journal...To review the existing deep learning applications for diagnosing diabetic retinopathy and retinopathy of prematurity diseases,the available public retinal databases for the diseases and apply the International Journal of Medical Informatics(IJMEDI)checklist were assessed the quality of included studies;an in-depth literature search in Scopus,Web of Science,IEEE and ACM databases targeting articles from inception up to 31st January 2023 was done by two independent reviewers.In the review,26 out of 1476 articles with a total of 36 models were included.Data size and model validation were found to be challenges for most studies.Deep learning models are gaining focus in the development of medical diagnosis tools and applications.However,there seems to be a critical issue with most of the studies being published,with some not including information about data sources and data sizes which is important for their performance verification.展开更多
It is important for modern hospital management to strengthen medical humanistic care and build a harmonious doctor-patient relationship.Innovative applications of the big data resources of patient experience in modern...It is important for modern hospital management to strengthen medical humanistic care and build a harmonious doctor-patient relationship.Innovative applications of the big data resources of patient experience in modern hospital management facilitate hospital management to realize real-time supervision,dynamic management and s&entitle decision-making based on patients experiences.It is helping the transformation of hospital management from an administrator^perspective to a patients perspective,and from experience-driven to data-driven.The technological innovations in hospital management based on patient experience data can assist the optimization and continuous improvement of healthcare quality,therefore help to increase patient satisfaction to the medical services.展开更多
Metal–organic frameworks(MOFs)have attracted significant research interest in biomimetic catalysis.However,the modulation of the activity of MOFs by precisely tuning the coordination of metal nodes is still a signifi...Metal–organic frameworks(MOFs)have attracted significant research interest in biomimetic catalysis.However,the modulation of the activity of MOFs by precisely tuning the coordination of metal nodes is still a significant challenge.Inspired by metalloenzymes with well-defined coordination structures,a series of MOFs containing halogen-coordinated copper nodes(Cu-X MOFs,X=Cl,Br,I)are employed to elucidate their structure–activity relationship.Intriguingly,experimental and theoretical results strongly support that precisely tuning the coordination of halogen atoms directly regulates the enzyme-like activities of Cu-X MOFs by influencing the spatial configuration and electronic structure of the Cu active center.The optimal Cu–Cl MOF exhibits excellent superoxide dismutase-like activity with a specific activity one order of magnitude higher than the reported Cu-based nanozymes.More importantly,by performing enzyme-mimicking catalysis,the Cu–Cl MOF nanozyme can significantly scavenge reactive oxygen species and alleviate oxidative stress,thus effectively relieving ocular chemical burns.Mechanistically,the antioxidant and antiapoptotic properties of Cu–Cl MOF are achieved by regulating the NRF2 and JNK or P38 MAPK pathways.Our work provides a novel way to refine MOF nanozymes by directly engineering the coordination microenvironment and,more significantly,demonstrating their potential therapeutic effect in ophthalmic disease.展开更多
Magnetic resonance imaging(MRI)plays an important role in medical diagnosis,generating petabytes of image data annually in large hospitals.This voluminous data stream requires a significant amount of network bandwidth...Magnetic resonance imaging(MRI)plays an important role in medical diagnosis,generating petabytes of image data annually in large hospitals.This voluminous data stream requires a significant amount of network bandwidth and extensive storage infrastructure.Additionally,local data processing demands substantial manpower and hardware investments.Data isolation across different healthcare institutions hinders crossinstitutional collaboration in clinics and research.In this work,we anticipate an innovative MRI system and its four generations that integrate emerging distributed cloud computing,6G bandwidth,edge computing,federated learning,and blockchain technology.This system is called Cloud-MRI,aiming at solving the problems of MRI data storage security,transmission speed,artificial intelligence(AI)algorithm maintenance,hardware upgrading,and collaborative work.The workflow commences with the transformation of k-space raw data into the standardized Imaging Society for Magnetic Resonance in Medicine Raw Data(ISMRMRD)format.Then,the data are uploaded to the cloud or edge nodes for fast image reconstruction,neural network training,and automatic analysis.Then,the outcomes are seamlessly transmitted to clinics or research institutes for diagnosis and other services.The Cloud-MRI system will save the raw imaging data,reduce the risk of data loss,facilitate inter-institutional medical collaboration,and finally improve diagnostic accuracy and work efficiency.展开更多
Ursodeoxycholic acid(UDCA)is a naturally occurring,low-toxicity,and hydrophilic bile acid(BA)in the human body that is converted by intestinal flora using primary BA.Solute carrier family 7 member 11(SLC7A11)functions...Ursodeoxycholic acid(UDCA)is a naturally occurring,low-toxicity,and hydrophilic bile acid(BA)in the human body that is converted by intestinal flora using primary BA.Solute carrier family 7 member 11(SLC7A11)functions to uptake extracellular cystine in exchange for glutamate,and is highly expressed in a variety of human cancers.Retroperitoneal liposarcoma(RLPS)refers to liposarcoma originating from the retroperitoneal area.Lipidomics analysis revealed that UDCA was one of the most significantly downregulated metabolites in sera of RLPS patients compared with healthy subjects.The augmentation of UDCA concentration(≥25 mg/mL)demonstrated a suppressive effect on the proliferation of liposarcoma cells.[15N2]-cystine and[13C5]-glutamine isotope tracing revealed that UDCA impairs cystine uptake and glutathione(GSH)synthesis.Mechanistically,UDCA binds to the cystine transporter SLC7A11 to inhibit cystine uptake and impair GSH de novo synthesis,leading to reactive oxygen species(ROS)accumulation and mitochondrial oxidative damage.Furthermore,UDCA can promote the anti-cancer effects of ferroptosis inducers(Erastin,RSL3),the murine double minute 2(MDM2)inhibitors(Nutlin 3a,RG7112),cyclin dependent kinase 4(CDK4)inhibitor(Abemaciclib),and glutaminase inhibitor(CB839).Together,UDCA functions as a cystine exchange factor that binds to SLC7A11 for antitumor activity,and SLC7A11 is not only a new transporter for BA but also a clinically applicable target for UDCA.More importantly,in combination with other antitumor chemotherapy or physiotherapy treatments,UDCA may provide effective and promising treatment strategies for RLPS or other types of tumors in a ROS-dependent manner.展开更多
Cardiolipins(CLs),the mitochondria-specific class of phospholipids,are crucial to energy metabolism,cristae structure,and cell apoptosis.CLs present significant challenges in lipidomics analysis due to their structura...Cardiolipins(CLs),the mitochondria-specific class of phospholipids,are crucial to energy metabolism,cristae structure,and cell apoptosis.CLs present significant challenges in lipidomics analysis due to their structural diversity with up to four fatty acyl side chains.In this study,we developed CLAN(Cardio Lipin ANalysis),a comprehensive computational pipeline designed to improve the accuracy and coverage of cardiolipin identification.CLAN integrates three innovative modules:A cardiolipin identification module that utilizes specific fragmentation rules for precise characterization of CLs and their acyl side chains;a false positives detection module that employs retention time(RT)criteria to reduce false positives;and a prediction module that constructs regression models to identify CLs lacking authentic MS/MS spectra.CLAN achieved better identification accuracy and the highest recall rate for potential CL identification compared to the existing lipid identification tools.Furthermore,we applied CLAN program to an intermittent fasting mouse model,delineating tissue-specific CL alterations across 10 tissues.Every-other-day fasting(EODF)can partially counteract the disruption of the CL atlas across multiple tissues caused by high-fat-high-sugar diet feeding,providing novel insights into mitochondrial lipid metabolism under dietary interventions.Taken together,this work not only advances CL identification methodology but also underscores CLAN's potential in comprehensive analysis of CL atlas in the EODF animal model.CLAN is freely accessible on Git Hub.展开更多
Inositol 1,4,5-trisphosphate receptors(IP_(3)R)-mediated calcium ion(Ca^(2+))release plays a central role in the regulation of cell survival and death.Bcl-2 limits the Ca^(2+)release function of the IP3R through a dir...Inositol 1,4,5-trisphosphate receptors(IP_(3)R)-mediated calcium ion(Ca^(2+))release plays a central role in the regulation of cell survival and death.Bcl-2 limits the Ca^(2+)release function of the IP3R through a direct or indirect mechanism.However,the two mechanisms are overwhelmingly complex and not completely understood.Here,we convert the mechanisms into a set of ordinary differential equations.We firstly simulate the time evolution of Ca^(2+)concentration under two different levels of Bcl-2 for the direct and indirect mechanism models and compare them with experimental results available in the literature.Secondly,we employ one-and two-parameter bifurcation analysis to demonstrate that Bcl-2 can suppress Ca^(2+)signal from a global point of view both in the direct and indirect mechanism models.We then use mathematical analysis to clarify that the indirect mechanism is more efficient than the direct mechanism in repressing Ca^(2+)signal.Lastly,we predict that the two mechanisms restrict Ca^(2+)signal synergistically.Together,our study provides theoretical insights into Bcl-2 regulation in IP_(3)R-mediated Ca^(2+)release,which may be instrumental for the successful development of therapies to target Bcl-2 for cancer treatment.展开更多
Hydrogel is a kind of three-dimensional crosslinked polymer material with high moisture content.However,due to the network defects of polymer gels,traditional hydrogels are usually brittle and fragile,which limits the...Hydrogel is a kind of three-dimensional crosslinked polymer material with high moisture content.However,due to the network defects of polymer gels,traditional hydrogels are usually brittle and fragile,which limits their practical applications.Herein,we present a Hofmeister effect-aided facile strategy to prepare high-performance poly(vinyl alcohol)/montmorillonite nanocomposite hydrogels.Layered montmorillonite nanosheets can not only serve as crosslinking agents to enhance the mechanical properties of the hydrogel but also promote the ion conduction.More importantly,based on the Hofmeister effect,the presence of(NH_(4))_(2)SO_(4)can endow nanocomposite hydrogels with excellent mechanical properties by affecting PVA chains'aggregation state and crystallinity.As a result,the as-prepared nanocomposite hydrogels possess unique physical properties,including robust mechanical and electrical properties.The as-prepared hydrogels can be further assembled into a high-performance flexible sensor,which can sensitively detect large-scale and small-scale human activities.The simple design concept of this work is believed to provide a new prospect for developing robust nanocomposite hydrogels and flexible devices in the future.展开更多
Bioactive compounds in plants,which can be synthesized using N-arylationmethods such as the Buchwald-Hartwig reaction,are essential in drug discovery for their pharmacological effects.Important descriptors are necessa...Bioactive compounds in plants,which can be synthesized using N-arylationmethods such as the Buchwald-Hartwig reaction,are essential in drug discovery for their pharmacological effects.Important descriptors are necessary for the estimation of yields in these reactions.This study explores ten metaheuristic algorithms for descriptor selection and model a voting ensemble for evaluation.The algorithms were evaluated based on computational time and the number of selected descriptors.Analyses show that robust performance is obtained with more descriptors,compared to cases where fewer descriptors are selected.The essential descriptor was deduced based on the frequency of occurrence within the 50 extracted data subsets,and better performance was achieved with the voting ensemble than other algorithms with RMSE of 6.4270 and R^(2) of 0.9423.The results and deductions from this study can be readily applied in the decision-making process of chemical synthesis by saving the computational cost associated with initial descriptor selection for yield estimation.The ensemble model has also shown robust performance in its yield estimation ability and efficiency.展开更多
Purpose-Obesity is a global epidemic requiring innovative solutions.This study aims to examine the effectiveness of an online weight loss coaching platform(OWLCP)that provides personalized advice through coach-partici...Purpose-Obesity is a global epidemic requiring innovative solutions.This study aims to examine the effectiveness of an online weight loss coaching platform(OWLCP)that provides personalized advice through coach-participant interactions.Additionally,this research explores how artificial intelligence(AI)-driven optimization methods can enhance coaching productivity,scalability and cost-efficiency.By integrating AI into digital weight loss coaching to optimize personalized meal planning and feedback on food logs,we demonstrate how user engagement and effectiveness can be enhanced.Design/methodology/approach-A sample of participants engaged with the OWLCP,receiving tailored weight loss coaching.The study assesses the impact of human-coach interactions on weight loss outcomes.Furthermore,three AI-based optimization methods were evaluated for their ability to generate personalized meal plans,supporting human coaches in providing more effective feedback.Additionally,the study discusses the future integration of machine learning-generated meal plans with large language models(LLMs)to improve AI-driven coaching.Findings-Results indicate that AI-supported automation can enhance the efficiency of digital weight loss coaching while maintaining the effectiveness of personalized guidance.The study demonstrates that AI-driven feedback on dietary habits improves user adherence and machine learning-based meal plans offer tailored solutions for sustainable weight management.The integration of AI technologies into OWLCP has the potential to scale personalized coaching while reducing human workload.Practical implications-This study provides insights into the application of AI in digital health education,demonstrating how automated coaching can complement human expertise to offer cost-effective and scalable weight management solutions.The findings support the use of AI-driven personalization in health coaching as an educational tool for fostering sustainable lifestyle changes.Originality/value-This research highlights the transformative role of AI in optimizing digital weight loss coaching.By investigating AI-based meal plan generation and its impact on coaching effectiveness,the research contributes to the evolving field of intelligent digital health interventions.Furthermore,it outlines the potential for integrating LLMs to refine and enhance online coaching platforms.展开更多
In pathological examinations,tissue must first be stained to meet specific diagnostic requirements,a meticulous process demanding significant time and expertise from specialists.With advancements in deep learning,this...In pathological examinations,tissue must first be stained to meet specific diagnostic requirements,a meticulous process demanding significant time and expertise from specialists.With advancements in deep learning,this staining process can now be achieved through computational methods known as virtual staining.This technique replicates the visual effects of traditional histological staining in pathological imaging,enhancing efficiency and reducing costs.Extensive research in virtual staining for pathology has already demonstrated its effectiveness in generating clinically relevant stained images across a variety of diagnostic scenarios.Unlike previous reviews that broadly cover the clinical applications of virtual staining,this paper focuses on the technical methodologies,encompassing current models,datasets,and evaluation methods.It highlights the unique challenges of virtual staining compared to traditional image translation,discusses limitations in existing work,and explores future perspectives.Adopting a macro perspective,we avoid overly intricate technical details to make the content accessible to clinical experts.Additionally,we provide a brief introduction to the purpose of virtual staining from a medical standpoint,which may inspire algorithm-focused researchers.This paper aims to promote a deeper understanding of interdisciplinary knowledge between algorithm developers and clinicians,fostering the integration of technical solutions and medical expertise in the development of virtual staining models.This collaboration seeks to create more efficient,generalized,and versatile virtual staining models for a wide range of clinical applications.展开更多
Hypermutable cancers create opportunities for the development of various immunotherapies,such as immune checkpoint blockade(ICB)therapy.However,emergent studies have revealed that many hypermutated tumors have poor pr...Hypermutable cancers create opportunities for the development of various immunotherapies,such as immune checkpoint blockade(ICB)therapy.However,emergent studies have revealed that many hypermutated tumors have poor prognosis due to heterogeneous tumor antigen landscapes,yet the underlying mechanisms remain poorly understood.To understand the mechanisms that govern the responses to therapies,we develop mathematical models to explore the impact of combining chemotherapy and ICB therapy on heterogeneous tumors.Our results uncover how chemotherapy reduces antigenic heterogeneity,creating improved immunological conditions within tumors,which,in turn,enhances the therapeutic effect when combined with ICB.Furthermore,our results show that the recovery of the immune system after chemotherapy is crucial for enhancing the response to chemo-ICB combination therapy.展开更多
Selective catalysis,particularly when differentiating substrates with similar reactivities in a mixture,is a significant challenge.In this study,anomaly detection algorithms—tools traditionally used for identifying o...Selective catalysis,particularly when differentiating substrates with similar reactivities in a mixture,is a significant challenge.In this study,anomaly detection algorithms—tools traditionally used for identifying outliers in data cleaning—are applied to catalyst screening.We focus on developing catalytic methods to selectively oxidize cyclic alkanes over linear alkanes in mixtures such as naphtha.By inserting cyclohexane oxidation data one by one into a database of n-hexane oxidization,we used several anomaly detection algorithms to evaluate whether the inserted cyclohexane oxidation data could be considered anomalous.Conditions identified as anomalies imply that they are likely not suitable for n-hexane oxidization.As these anomalies come from conditions for cyclohexane oxidation,they are promising conditions for selective oxidation of cyclohexane while leaving n-hexane unaltered.These anomalies were thus further investigated,leading to the discovery of a specific catalytic approach that selectively oxidizes cyclohexane.This application of anomaly detection offers a novel method to search for selective catalyst for chemical reactions involving mixed substrates.展开更多
Neural networks excel at capturing local spatial patterns through convolutional modules,but they may struggle to identify and effectively utilize the morphological and amplitude periodic nature of physiological signal...Neural networks excel at capturing local spatial patterns through convolutional modules,but they may struggle to identify and effectively utilize the morphological and amplitude periodic nature of physiological signals.In this work,we propose a novel network named filtering module fully convolutional network(FM-FCN),which fuses traditional filtering techniques with neural networks to amplify physiological signals and suppress noise.First,instead of using a fully connected layer,we use an FCN to preserve the time-dimensional correlation information of physiological signals,enabling multiple cycles of signals in the network and providing a basis for signal processing.Second,we introduce the FM as a network module that adapts to eliminate unwanted interference,leveraging the structure of the filter.This approach builds a bridge between deep learning and signal processing methodologies.Finally,we evaluate the performance of FM-FCN using remote photoplethysmography.Experimental results demonstrate that FM-FCN outperforms the second-ranked method in terms of both blood volume pulse(BVP)signal and heart rate(HR)accuracy.It substantially improves the quality of BVP waveform reconstruction,with a decrease of 20.23%in mean absolute error(MAE)and an increase of 79.95%in signal-to-noise ratio(SNR).Regarding HR estimation accuracy,FM-FCN achieves a decrease of 35.85%in MAE,29.65%in error standard deviation,and 32.88%decrease in 95%limits of agreement width,meeting clinical standards for HR accuracy requirements.The results highlight its potential in improving the accuracy and reliability of vital sign measurement through high-quality BVP signal extraction.The codes and datasets are available online at https://github.com/zhaoqi106/FM-FCN.展开更多
基金supported by National Institute on Aging(NIH-NIA)R01AG054459(to ALL).
文摘Alzheimer’s disease(AD)is the most common form of dementia,affecting over 50 million people worldwide.This figure is projected to nearly double every 20 years,reaching 82 million by 2030 and 152 million by 2050(Alzheimer’s Disease International).The apolipoproteinε4(APOE4)allele is the strongest genetic risk factor for late-onset AD(after age 65 years).Apolipoprotein E,a lipid transporter,exists in three variants:ε2,ε3,andε4.APOEε2(APOE2)is protective against AD,APOEε3(APOE3)is neutral,while APOE4 significantly increases the risk.Individuals with one copy of APOE4 have a 4-fold greater risk of developing AD,and those with two copies face an 8-fold risk compared to non-carriers.Even in cognitively normal individuals,APOE4 carriers exhibit brain metabolic and vascular deficits decades before amyloid-beta(Aβ)plaques and neurofibrillary tau tangles emerge-the hallmark pathologies of AD(Reiman et al.,2001,2005;Thambisetty et al.,2010).Notably,studies have demonstrated reduced glucose uptake,or hypometabolism,in brain regions vulnerable to AD in asymptomatic middle-aged APOE4 carriers,long before clinical symptoms arise(Reiman et al.,2001,2005).
基金Supported by DAAD,Google Research,and the Organization for Women in Science for the Developing World(OWSD).
文摘To review the existing deep learning applications for diagnosing diabetic retinopathy and retinopathy of prematurity diseases,the available public retinal databases for the diseases and apply the International Journal of Medical Informatics(IJMEDI)checklist were assessed the quality of included studies;an in-depth literature search in Scopus,Web of Science,IEEE and ACM databases targeting articles from inception up to 31st January 2023 was done by two independent reviewers.In the review,26 out of 1476 articles with a total of 36 models were included.Data size and model validation were found to be challenges for most studies.Deep learning models are gaining focus in the development of medical diagnosis tools and applications.However,there seems to be a critical issue with most of the studies being published,with some not including information about data sources and data sizes which is important for their performance verification.
文摘It is important for modern hospital management to strengthen medical humanistic care and build a harmonious doctor-patient relationship.Innovative applications of the big data resources of patient experience in modern hospital management facilitate hospital management to realize real-time supervision,dynamic management and s&entitle decision-making based on patients experiences.It is helping the transformation of hospital management from an administrator^perspective to a patients perspective,and from experience-driven to data-driven.The technological innovations in hospital management based on patient experience data can assist the optimization and continuous improvement of healthcare quality,therefore help to increase patient satisfaction to the medical services.
基金the National Key R&D Program of China(Grant No.2020YFA0908100)the National Nature Science Foundation(Grant Nos.12274356,82070931,and 82271045)+1 种基金Fundamental Research Funds for the Central Universities(20720220022)the 111 Project(B16029)。
文摘Metal–organic frameworks(MOFs)have attracted significant research interest in biomimetic catalysis.However,the modulation of the activity of MOFs by precisely tuning the coordination of metal nodes is still a significant challenge.Inspired by metalloenzymes with well-defined coordination structures,a series of MOFs containing halogen-coordinated copper nodes(Cu-X MOFs,X=Cl,Br,I)are employed to elucidate their structure–activity relationship.Intriguingly,experimental and theoretical results strongly support that precisely tuning the coordination of halogen atoms directly regulates the enzyme-like activities of Cu-X MOFs by influencing the spatial configuration and electronic structure of the Cu active center.The optimal Cu–Cl MOF exhibits excellent superoxide dismutase-like activity with a specific activity one order of magnitude higher than the reported Cu-based nanozymes.More importantly,by performing enzyme-mimicking catalysis,the Cu–Cl MOF nanozyme can significantly scavenge reactive oxygen species and alleviate oxidative stress,thus effectively relieving ocular chemical burns.Mechanistically,the antioxidant and antiapoptotic properties of Cu–Cl MOF are achieved by regulating the NRF2 and JNK or P38 MAPK pathways.Our work provides a novel way to refine MOF nanozymes by directly engineering the coordination microenvironment and,more significantly,demonstrating their potential therapeutic effect in ophthalmic disease.
基金supported by the National Natural Science Foundation of China(62122064,62331021,62371410)the Natural Science Foundation of Fujian Province of China(2023J02005 and 2021J011184)+1 种基金the President Fund of Xiamen University(20720220063)the Nanqiang Outstanding Talents Program of Xiamen University.
文摘Magnetic resonance imaging(MRI)plays an important role in medical diagnosis,generating petabytes of image data annually in large hospitals.This voluminous data stream requires a significant amount of network bandwidth and extensive storage infrastructure.Additionally,local data processing demands substantial manpower and hardware investments.Data isolation across different healthcare institutions hinders crossinstitutional collaboration in clinics and research.In this work,we anticipate an innovative MRI system and its four generations that integrate emerging distributed cloud computing,6G bandwidth,edge computing,federated learning,and blockchain technology.This system is called Cloud-MRI,aiming at solving the problems of MRI data storage security,transmission speed,artificial intelligence(AI)algorithm maintenance,hardware upgrading,and collaborative work.The workflow commences with the transformation of k-space raw data into the standardized Imaging Society for Magnetic Resonance in Medicine Raw Data(ISMRMRD)format.Then,the data are uploaded to the cloud or edge nodes for fast image reconstruction,neural network training,and automatic analysis.Then,the outcomes are seamlessly transmitted to clinics or research institutes for diagnosis and other services.The Cloud-MRI system will save the raw imaging data,reduce the risk of data loss,facilitate inter-institutional medical collaboration,and finally improve diagnostic accuracy and work efficiency.
基金supported by grants from the National Natural Science Foundation of China(Grant NOs.:82272935,91957120 and 21974114)the Postdoctoral Fellowship Program of CPSF(Program No.:GZC20240901)+5 种基金the Xiamen Medical Industry Combined Guidance Project,China(Project No.:3502Z20244ZD2022)the Scientific Research Foundation for Advanced Talents,Xiang'an Hospital of Xiamen University,China(Grant No.:PM20180917008)the Fundamental Research Funds for the Central Universities,China(Grant No.:20720210001)Major Science and Technology Special Project of Fujian Province,China(Project No.:2022YZ036012)Joint Laboratory of School of Medicine,Xiamen University-Shanghai Jiangxia Blood Technology Co.,Ltd.,China(Grant No.:XDHT2020010C)Joint Research Center of School of Medicine,Xiamen University-Jiangsu Charity Biotech Co.,Ltd.,China(Grant No.:20233160C0002).
文摘Ursodeoxycholic acid(UDCA)is a naturally occurring,low-toxicity,and hydrophilic bile acid(BA)in the human body that is converted by intestinal flora using primary BA.Solute carrier family 7 member 11(SLC7A11)functions to uptake extracellular cystine in exchange for glutamate,and is highly expressed in a variety of human cancers.Retroperitoneal liposarcoma(RLPS)refers to liposarcoma originating from the retroperitoneal area.Lipidomics analysis revealed that UDCA was one of the most significantly downregulated metabolites in sera of RLPS patients compared with healthy subjects.The augmentation of UDCA concentration(≥25 mg/mL)demonstrated a suppressive effect on the proliferation of liposarcoma cells.[15N2]-cystine and[13C5]-glutamine isotope tracing revealed that UDCA impairs cystine uptake and glutathione(GSH)synthesis.Mechanistically,UDCA binds to the cystine transporter SLC7A11 to inhibit cystine uptake and impair GSH de novo synthesis,leading to reactive oxygen species(ROS)accumulation and mitochondrial oxidative damage.Furthermore,UDCA can promote the anti-cancer effects of ferroptosis inducers(Erastin,RSL3),the murine double minute 2(MDM2)inhibitors(Nutlin 3a,RG7112),cyclin dependent kinase 4(CDK4)inhibitor(Abemaciclib),and glutaminase inhibitor(CB839).Together,UDCA functions as a cystine exchange factor that binds to SLC7A11 for antitumor activity,and SLC7A11 is not only a new transporter for BA but also a clinically applicable target for UDCA.More importantly,in combination with other antitumor chemotherapy or physiotherapy treatments,UDCA may provide effective and promising treatment strategies for RLPS or other types of tumors in a ROS-dependent manner.
基金supported by grants from the National Key Research and Development Program of China(No.2022YFE0205800)the National Natural Science Foundation of China(No.21974114)+5 种基金Major Science and Technology Special Project of Fujian Province(No.2022YZ036012)the Fundamental Research Funds for the Central Universities(No.20720220003)Project“111”sponsored by the State Bureau of Foreign Experts and Ministry of Education of China(No.BP0618017)to S.-H.LinNatural Science Foundation of Fujian Province of China(No.2022J01330)Natural Science Foundation of Xiamen City of China(No.3502Z20227208)the China Scholarship Council(No.202308350047)to J.Zeng。
文摘Cardiolipins(CLs),the mitochondria-specific class of phospholipids,are crucial to energy metabolism,cristae structure,and cell apoptosis.CLs present significant challenges in lipidomics analysis due to their structural diversity with up to four fatty acyl side chains.In this study,we developed CLAN(Cardio Lipin ANalysis),a comprehensive computational pipeline designed to improve the accuracy and coverage of cardiolipin identification.CLAN integrates three innovative modules:A cardiolipin identification module that utilizes specific fragmentation rules for precise characterization of CLs and their acyl side chains;a false positives detection module that employs retention time(RT)criteria to reduce false positives;and a prediction module that constructs regression models to identify CLs lacking authentic MS/MS spectra.CLAN achieved better identification accuracy and the highest recall rate for potential CL identification compared to the existing lipid identification tools.Furthermore,we applied CLAN program to an intermittent fasting mouse model,delineating tissue-specific CL alterations across 10 tissues.Every-other-day fasting(EODF)can partially counteract the disruption of the CL atlas across multiple tissues caused by high-fat-high-sugar diet feeding,providing novel insights into mitochondrial lipid metabolism under dietary interventions.Taken together,this work not only advances CL identification methodology but also underscores CLAN's potential in comprehensive analysis of CL atlas in the EODF animal model.CLAN is freely accessible on Git Hub.
基金supported by Shanxi Province Science Foundation for Youths(Grant No.201901D211159)the National Natural Science Foundation of China(Grant Nos.11504214,11874310,and 12090052).
文摘Inositol 1,4,5-trisphosphate receptors(IP_(3)R)-mediated calcium ion(Ca^(2+))release plays a central role in the regulation of cell survival and death.Bcl-2 limits the Ca^(2+)release function of the IP3R through a direct or indirect mechanism.However,the two mechanisms are overwhelmingly complex and not completely understood.Here,we convert the mechanisms into a set of ordinary differential equations.We firstly simulate the time evolution of Ca^(2+)concentration under two different levels of Bcl-2 for the direct and indirect mechanism models and compare them with experimental results available in the literature.Secondly,we employ one-and two-parameter bifurcation analysis to demonstrate that Bcl-2 can suppress Ca^(2+)signal from a global point of view both in the direct and indirect mechanism models.We then use mathematical analysis to clarify that the indirect mechanism is more efficient than the direct mechanism in repressing Ca^(2+)signal.Lastly,we predict that the two mechanisms restrict Ca^(2+)signal synergistically.Together,our study provides theoretical insights into Bcl-2 regulation in IP_(3)R-mediated Ca^(2+)release,which may be instrumental for the successful development of therapies to target Bcl-2 for cancer treatment.
基金Project supported by the National Natural Science Foundation of China(Grant No.12274356)the Fundamental Research Funds for the Central Universities(Grant No.20720220022)the 111 Project(Grant No.B16029)。
文摘Hydrogel is a kind of three-dimensional crosslinked polymer material with high moisture content.However,due to the network defects of polymer gels,traditional hydrogels are usually brittle and fragile,which limits their practical applications.Herein,we present a Hofmeister effect-aided facile strategy to prepare high-performance poly(vinyl alcohol)/montmorillonite nanocomposite hydrogels.Layered montmorillonite nanosheets can not only serve as crosslinking agents to enhance the mechanical properties of the hydrogel but also promote the ion conduction.More importantly,based on the Hofmeister effect,the presence of(NH_(4))_(2)SO_(4)can endow nanocomposite hydrogels with excellent mechanical properties by affecting PVA chains'aggregation state and crystallinity.As a result,the as-prepared nanocomposite hydrogels possess unique physical properties,including robust mechanical and electrical properties.The as-prepared hydrogels can be further assembled into a high-performance flexible sensor,which can sensitively detect large-scale and small-scale human activities.The simple design concept of this work is believed to provide a new prospect for developing robust nanocomposite hydrogels and flexible devices in the future.
基金The work described in this paper was substantially supported by the grant from the Research Grants Council of the Hong Kong Special Administrative Region[CityU 11200218]one grant from the Health and Medical Research Fund,the Food and Health Bureau,The Government of the Hong Kong Special Administrative Region[07181426]+1 种基金and the funding from Hong Kong Institute for Data Science(HKIDS)at City University of Hong Kong.The work described in this paper was partially supported by two grants from City University of Hong Kong(CityU 11202219,CityU 11203520)This research was substantially sponsored by the research project(Grant No.32000464)supported by the National Natural Science Foundation of China and was substantially supported by the Shenzhen Research Institute,City University of Hong Kong.The authors extend their appreciation to the Deputyship for Research&Innovation,Ministry of Education in Saudi Arabia for funding this research with the project number(442/77).
文摘Bioactive compounds in plants,which can be synthesized using N-arylationmethods such as the Buchwald-Hartwig reaction,are essential in drug discovery for their pharmacological effects.Important descriptors are necessary for the estimation of yields in these reactions.This study explores ten metaheuristic algorithms for descriptor selection and model a voting ensemble for evaluation.The algorithms were evaluated based on computational time and the number of selected descriptors.Analyses show that robust performance is obtained with more descriptors,compared to cases where fewer descriptors are selected.The essential descriptor was deduced based on the frequency of occurrence within the 50 extracted data subsets,and better performance was achieved with the voting ensemble than other algorithms with RMSE of 6.4270 and R^(2) of 0.9423.The results and deductions from this study can be readily applied in the decision-making process of chemical synthesis by saving the computational cost associated with initial descriptor selection for yield estimation.The ensemble model has also shown robust performance in its yield estimation ability and efficiency.
文摘Purpose-Obesity is a global epidemic requiring innovative solutions.This study aims to examine the effectiveness of an online weight loss coaching platform(OWLCP)that provides personalized advice through coach-participant interactions.Additionally,this research explores how artificial intelligence(AI)-driven optimization methods can enhance coaching productivity,scalability and cost-efficiency.By integrating AI into digital weight loss coaching to optimize personalized meal planning and feedback on food logs,we demonstrate how user engagement and effectiveness can be enhanced.Design/methodology/approach-A sample of participants engaged with the OWLCP,receiving tailored weight loss coaching.The study assesses the impact of human-coach interactions on weight loss outcomes.Furthermore,three AI-based optimization methods were evaluated for their ability to generate personalized meal plans,supporting human coaches in providing more effective feedback.Additionally,the study discusses the future integration of machine learning-generated meal plans with large language models(LLMs)to improve AI-driven coaching.Findings-Results indicate that AI-supported automation can enhance the efficiency of digital weight loss coaching while maintaining the effectiveness of personalized guidance.The study demonstrates that AI-driven feedback on dietary habits improves user adherence and machine learning-based meal plans offer tailored solutions for sustainable weight management.The integration of AI technologies into OWLCP has the potential to scale personalized coaching while reducing human workload.Practical implications-This study provides insights into the application of AI in digital health education,demonstrating how automated coaching can complement human expertise to offer cost-effective and scalable weight management solutions.The findings support the use of AI-driven personalization in health coaching as an educational tool for fostering sustainable lifestyle changes.Originality/value-This research highlights the transformative role of AI in optimizing digital weight loss coaching.By investigating AI-based meal plan generation and its impact on coaching effectiveness,the research contributes to the evolving field of intelligent digital health interventions.Furthermore,it outlines the potential for integrating LLMs to refine and enhance online coaching platforms.
基金supported by the National Natural Science Foundation of China under Grant 62371409Fujian Provincial Natural Science Foundation of China under Grant 2023J01005.
文摘In pathological examinations,tissue must first be stained to meet specific diagnostic requirements,a meticulous process demanding significant time and expertise from specialists.With advancements in deep learning,this staining process can now be achieved through computational methods known as virtual staining.This technique replicates the visual effects of traditional histological staining in pathological imaging,enhancing efficiency and reducing costs.Extensive research in virtual staining for pathology has already demonstrated its effectiveness in generating clinically relevant stained images across a variety of diagnostic scenarios.Unlike previous reviews that broadly cover the clinical applications of virtual staining,this paper focuses on the technical methodologies,encompassing current models,datasets,and evaluation methods.It highlights the unique challenges of virtual staining compared to traditional image translation,discusses limitations in existing work,and explores future perspectives.Adopting a macro perspective,we avoid overly intricate technical details to make the content accessible to clinical experts.Additionally,we provide a brief introduction to the purpose of virtual staining from a medical standpoint,which may inspire algorithm-focused researchers.This paper aims to promote a deeper understanding of interdisciplinary knowledge between algorithm developers and clinicians,fostering the integration of technical solutions and medical expertise in the development of virtual staining models.This collaboration seeks to create more efficient,generalized,and versatile virtual staining models for a wide range of clinical applications.
基金Xiamen University overseas study program for graduate studentsNational Natural Science Foundation of China,Grant/Award Numbers:11971405,12471475,32270693,82241236+1 种基金Basic and Applied Basic Research Foundation of Guangdong Province,Grant/Award Number:2021B1515020042Central University Basic Research Fund of China,Grant/Award Numbers:20720240151,20720230023。
文摘Hypermutable cancers create opportunities for the development of various immunotherapies,such as immune checkpoint blockade(ICB)therapy.However,emergent studies have revealed that many hypermutated tumors have poor prognosis due to heterogeneous tumor antigen landscapes,yet the underlying mechanisms remain poorly understood.To understand the mechanisms that govern the responses to therapies,we develop mathematical models to explore the impact of combining chemotherapy and ICB therapy on heterogeneous tumors.Our results uncover how chemotherapy reduces antigenic heterogeneity,creating improved immunological conditions within tumors,which,in turn,enhances the therapeutic effect when combined with ICB.Furthermore,our results show that the recovery of the immune system after chemotherapy is crucial for enhancing the response to chemo-ICB combination therapy.
基金funding support from the National Key R&D Program of China(2021YFA1502500)the National Natural Science Foundation of China(22125502,22071207,22121001)the Fundamental Research Funds for the Central Universities(20720220011,20720240151)。
文摘Selective catalysis,particularly when differentiating substrates with similar reactivities in a mixture,is a significant challenge.In this study,anomaly detection algorithms—tools traditionally used for identifying outliers in data cleaning—are applied to catalyst screening.We focus on developing catalytic methods to selectively oxidize cyclic alkanes over linear alkanes in mixtures such as naphtha.By inserting cyclohexane oxidation data one by one into a database of n-hexane oxidization,we used several anomaly detection algorithms to evaluate whether the inserted cyclohexane oxidation data could be considered anomalous.Conditions identified as anomalies imply that they are likely not suitable for n-hexane oxidization.As these anomalies come from conditions for cyclohexane oxidation,they are promising conditions for selective oxidation of cyclohexane while leaving n-hexane unaltered.These anomalies were thus further investigated,leading to the discovery of a specific catalytic approach that selectively oxidizes cyclohexane.This application of anomaly detection offers a novel method to search for selective catalyst for chemical reactions involving mixed substrates.
基金supported by Ministry of Science and Technology of the People’s Republic of China(STI2030-Major Projects 2021ZD0201900)National Natural Science Foundation of China(grant mo.12090052)+2 种基金Natural Science Foundation of Liaoning Province(grant no.2023-MS-288)Fundamental Research Funds for the Central Universities(grant no.20720230017)Basic Public Welfare Research Program of Zhejiang Province(grant no.LGF20F030005).
文摘Neural networks excel at capturing local spatial patterns through convolutional modules,but they may struggle to identify and effectively utilize the morphological and amplitude periodic nature of physiological signals.In this work,we propose a novel network named filtering module fully convolutional network(FM-FCN),which fuses traditional filtering techniques with neural networks to amplify physiological signals and suppress noise.First,instead of using a fully connected layer,we use an FCN to preserve the time-dimensional correlation information of physiological signals,enabling multiple cycles of signals in the network and providing a basis for signal processing.Second,we introduce the FM as a network module that adapts to eliminate unwanted interference,leveraging the structure of the filter.This approach builds a bridge between deep learning and signal processing methodologies.Finally,we evaluate the performance of FM-FCN using remote photoplethysmography.Experimental results demonstrate that FM-FCN outperforms the second-ranked method in terms of both blood volume pulse(BVP)signal and heart rate(HR)accuracy.It substantially improves the quality of BVP waveform reconstruction,with a decrease of 20.23%in mean absolute error(MAE)and an increase of 79.95%in signal-to-noise ratio(SNR).Regarding HR estimation accuracy,FM-FCN achieves a decrease of 35.85%in MAE,29.65%in error standard deviation,and 32.88%decrease in 95%limits of agreement width,meeting clinical standards for HR accuracy requirements.The results highlight its potential in improving the accuracy and reliability of vital sign measurement through high-quality BVP signal extraction.The codes and datasets are available online at https://github.com/zhaoqi106/FM-FCN.
