Photodynamic therapy(PDT) has been widely investigated for cancer therapy. The intracellular accumulation of reactive oxygen species(ROS)-damaged protein facilitates tumor cell apoptosis. However, there is growing evi...Photodynamic therapy(PDT) has been widely investigated for cancer therapy. The intracellular accumulation of reactive oxygen species(ROS)-damaged protein facilitates tumor cell apoptosis. However, there is growing evidence that the ubiquitin-proteasome pathway(UPP) significantly impedes PDT by preventing the enrichment of ROS-damaged proteins in tumor cells. To tackle this challenge, we report a facile dual-drug nanoassembly based on the discovery of an interesting co-assembly of bortezomib(BTZ, a proteasome inhibitor) and pyropheophorbide a(PPa) for proteasome inhibition-mediated PDT sensitization.The precisely engineered nanoassembly with the optimal dose ratio of BTZ and PPa demonstrates multiple advantages, including simple fabrication, high drug co-loading efficiency, flexible dose adjustment,good colloidal stability, long systemic circulation, favorable tumor-specific accumulation, as well as significant enrichment of ROS-damaged proteins in tumor cells. As a result, the cooperative nanoassembly exhibits potent synergistic antitumor activity in vivo. This study provides a novel dual-drug engineering modality for multimodal cancer treatment.展开更多
New precisely cooperative attacks, such as the coordi- nated cross plane session termination （CXPST） attack, need thou- sands upon thousands machines to attack diverse selected links simultaneously with the given ra...New precisely cooperative attacks, such as the coordi- nated cross plane session termination （CXPST） attack, need thou- sands upon thousands machines to attack diverse selected links simultaneously with the given rate. However, almost all command and control（C＆C） mechanisms only provide publishing one com- mand to the whole once, so-called one-to-all C＆C model, and are not productive to support CXPST-alike attacks. In this paper, we present one-to-any C＆C model on coordination among the unco- operative controlled nodes. As an instance of one-to-any C＆C model, directional command publishing （DCP） mechanism lever- aging on Kademlia is provided with a range-mapping key creating algorithm for commands to compute the publishing range and a statistically stochastic node querying scheme to obtain the com- mands immediately. With theoretical analysis and simulation, it is indicated that one-to-any C＆C model fits for precisely coordi- nated operation on uncooperative controlled nodes with least complexity, better accuracy and efficiency. Furthermore, DCP mechanism can support one-to-all command publishing at the same time. As an example of future C＆C model, studying on one-to-any C＆C model may help to promote the development of more efficient countermeasures.展开更多
China launched the NigComSat-1R communications satellite with a Long March 3B/E from the Xichang Satellite Launch Center (XSLC) at 00:41 on December 20.Twenty six minutes after the lift-off,the satellite separated wit...China launched the NigComSat-1R communications satellite with a Long March 3B/E from the Xichang Satellite Launch Center (XSLC) at 00:41 on December 20.Twenty six minutes after the lift-off,the satellite separated with the rocket and entered precisely into a geostationary transfer orbit with a perigee of 203km,an apogee of 42007km and an inelination of 24.8 degrees.展开更多
Irregular craniofacial bone defects caused by craniofacial fractures always result in craniofacial bone and contour asymmetry and should therefore be reconstructed.Polyetheretherketone(PEEK)is an ideal substitute for ...Irregular craniofacial bone defects caused by craniofacial fractures always result in craniofacial bone and contour asymmetry and should therefore be reconstructed.Polyetheretherketone(PEEK)is an ideal substitute for autologous bone grafts and has been widely used in craniofacial bone defect reconstruction.The precise design of custom-made PEEK implants is particularly important to optimise reconstruction.Herein,the workflow and principles for the design and manufacture of PEEK implants are summarised,and a protocol for the precise design of an irregular craniofacial bone defect PEEK implant is presented.According to the method and principles,the design flow was efficient and could be standardised,and design errors could be avoided as much as possible.展开更多
Cancer of unknown primary(CUP)is a recalcitrant disease with poor prognosis because it lacks standard first-line therapy.CUP consists of diverse malignancy groups,making personalized precision therapy essential.The pr...Cancer of unknown primary(CUP)is a recalcitrant disease with poor prognosis because it lacks standard first-line therapy.CUP consists of diverse malignancy groups,making personalized precision therapy essential.The present study aimed to identify an effective therapy for a CUP patient using a patient-derived orthotopic xenograft(PDOX)model.This paper reports the usefulness of the PDOX model to precisely identify effective and ineffective chemotherapy and to compare the efficacy of S.typhimurium A1-R with first-line chemotherapy using the CUP PDOX model.The present study is the first to use a CUP PDOX model,which was able to precisely distinguish the chemotherapeutic course.We found that a carboplatinum(CAR)-based regimen was effective for this CUP patient.We also demonstrated that S.typhimurium A1-R was more effective against the CUP tumor than first-line chemotherapy.Our results indicate that S.typhimurium A1-R has clinical potential for CUP,a resistant disease that requires effective therapy.展开更多
CONSPECTUS:Nanozymes are nanomaterials with intrinsic enzyme-like properties that can overcome the current limitations of natural enzymes,such as high preparation cost,instability,restricted application scenarios,etc....CONSPECTUS:Nanozymes are nanomaterials with intrinsic enzyme-like properties that can overcome the current limitations of natural enzymes,such as high preparation cost,instability,restricted application scenarios,etc.Since the Fe3O4 nanoparticles(NPs)were shown to possess the peroxidase(POD)-like activity in 2007,thousands of nanomaterials were reported to mimic the catalytic properties of various types of enzymes including catalase(CAT),haloperoxidase,superoxide dismutase(SOD),glucose oxidase,glutathione peroxidase,hydrolase,nuclease,nitroreductase,and others.展开更多
Machado-Joseph disease,or spinocerebellar ataxia type 3(SCA3),represents the most common autosomal dominant cerebellar ataxia worldwide.Despite its progressive and debilitating nature,disease-modifying therapies remai...Machado-Joseph disease,or spinocerebellar ataxia type 3(SCA3),represents the most common autosomal dominant cerebellar ataxia worldwide.Despite its progressive and debilitating nature,disease-modifying therapies remain elusive.Repetitive transcranial magnetic stimulation(rTMS)has emerged as a promising non-invasive intervention;however,its clinical application has been hindered by inconsistent protocols and a lack of mechanistic understanding.A recent landmark study published in Brain Stimulation by Chen et al.addressed these challenges by combining a high-dose intermittent theta-burst stimulation(iTBS)protocol with concurrent transcranial magnetic stimulation-electroencephalography(TMS-EEG).This commentary provides an in-depth analysis of their findings,highlighting the restoration of cerebello-cortical inhibition(CBI)as a key therapeutic mechanism.Furthermore,we discuss the broader implications of this work,proposing that future translational research should integrate accelerated iTBS(aiTBS)paradigms,cortical response measurements(CRM),and individualized neuro-navigation to establish a new era of precision neuromodulation for ataxia.展开更多
Nature|大型汉族人群队列助力中国台湾精准医疗精准医学的发展依赖于大规模、具有深度表型和基因变异图谱数据的人群队列,然而这在非欧洲人群中数据仍严重不足。中国台湾精准医学计划(Taiwan Precision Medicine Initiative,TPMI)旨在...Nature|大型汉族人群队列助力中国台湾精准医疗精准医学的发展依赖于大规模、具有深度表型和基因变异图谱数据的人群队列,然而这在非欧洲人群中数据仍严重不足。中国台湾精准医学计划(Taiwan Precision Medicine Initiative,TPMI)旨在建立一个具有广泛代表性的中国台湾汉族人群队列,以支持大规模基因组与健康医学研究(2025年10月15日在线发表,doi:10.1038/s41586-025-09680-x)。展开更多
The deuterium labeling has garnered significant interest in drug discovery due to its critical role on improving pharmacokinetic and metabolic properties.However,despite its pharmaceutical value,the general and rapid ...The deuterium labeling has garnered significant interest in drug discovery due to its critical role on improving pharmacokinetic and metabolic properties.However,despite its pharmaceutical value,the general and rapid syntheses of aromatic scaffolds that contains deuterium remain an important yet elusive task.State-of-the-art approaches mainly relied on the transition metal-catalyzed C-H deuteration via the assistance of directing groups(DGs),which often suffered from over-deuteration and lengthy step counts required for installation and/or removal of DG.Herein,we report a generalizable synthetic linchpin strategy for the facile preparation of the ortho-deuterated aromatic core.Through capture of aryne-derived 1,3-zwitterion with heavy water,we synthesized an array of ortho-deuterated aryl sulfonium salts.These novel linchpins not only participated the transition metal catalyzed cross-coupling reaction as nucleophiles,but also served as aryl radical reservoirs under photochemical or electrochemical conditions,enabling facile and precise access to structurally diverse deuterated aromatics.Moreover,we have disclosed a novel EDA complex enabled direct arylation of phosphines under visible-light irradiation,further expanding the utility of our platform approach.展开更多
Gastrointestinal(GI)cancers remain a leading cause of cancer-related morbidity and mortality worldwide.Artificial intelligence(AI),particularly machine learning and deep learning(DL),has shown promise in enhancing can...Gastrointestinal(GI)cancers remain a leading cause of cancer-related morbidity and mortality worldwide.Artificial intelligence(AI),particularly machine learning and deep learning(DL),has shown promise in enhancing cancer detection,diagnosis,and prognostication.A narrative review of literature published from January 2015 to march 2025 was conducted using PubMed,Web of Science,and Scopus.Search terms included"gastrointestinal cancer","artificial intelligence","machine learning","deep learning","radiomics","multimodal detection"and"predictive modeling".Studies were included if they focused on clinically relevant AI applications in GI oncology.AI algorithms for GI cancer detection have achieved high performance across imaging modalities,with endoscopic DL systems reporting accuracies of 85%-97%for polyp detection and segmentation.Radiomics-based models have predicted molecular biomarkers such as programmed cell death ligand 2 expression with area under the curves up to 0.92.Large language models applied to radiology reports demonstrated diagnostic accuracy comparable to junior radiologists(78.9%vs 80.0%),though without incremental value when combined with human interpretation.Multimodal AI approaches integrating imaging,pathology,and clinical data show emerging potential for precision oncology.AI in GI oncology has reached clinically relevant accuracy levels in multiple diagnostic tasks,with multimodal approaches and predictive biomarker modeling offering new opportunities for personalized care.However,broader validation,integration into clinical workflows,and attention to ethical,legal,and social implications remain critical for widespread adoption.展开更多
Objective:The increasing global prevalence of mental health disorders highlights the urgent need for the development of innovative diagnostic methods.Conditions such as anxiety,depression,stress,bipolar disorder(BD),a...Objective:The increasing global prevalence of mental health disorders highlights the urgent need for the development of innovative diagnostic methods.Conditions such as anxiety,depression,stress,bipolar disorder(BD),and autism spectrum disorder(ASD)frequently arise from the complex interplay of demographic,biological,and socioeconomic factors,resulting in aggravated symptoms.This review investigates machine intelligence approaches for the early detection and prediction of mental health conditions.Methods:The preferred reporting items for systematic reviews and meta-analyses(PRISMA)framework was employed to conduct a systematic review and analysis covering the period 2018 to 2025.The potential impact of machine intelligence methods was assessed by considering various strategies,hybridization of algorithms,tools,techniques,and datasets,and their applicability.Results:Through a systematic review of studies concentrating on the prediction and evaluation of mental disorders using machine intelligence algorithms,advancements,limitations,and gaps in current methodologies were highlighted.The datasets and tools utilized in these investigations were examined,offering a detailed overview of the status of computational models in understanding and diagnosing mental health disorders.Recent research indicated considerable improvements in diagnostic accuracy and treatment effectiveness,particularly for depression and anxiety,which have shown the greatest methodological diversity and notable advancements in machine intelligence.Conclusions:Despite these improvements,challenges persist,including the need for more diverse datasets,ethical issues surrounding data privacy and algorithmic bias,and obstacles to integrating these technologies into clinical settings.This synthesis emphasizes the transformative potential of machine intelligence in enhancing mental healthcare.展开更多
Background:Giant cell arteritis(GCA),the most common systemic vasculitis affecting elderly individuals,currently lacks specific therapies.This study aimed to systematically identify therapeutic targets for GCA through...Background:Giant cell arteritis(GCA),the most common systemic vasculitis affecting elderly individuals,currently lacks specific therapies.This study aimed to systematically identify therapeutic targets for GCA through integration of large-scale multi-omics datasets.Methods:We constructed a multi-stage analytical framework encompassing 32 proteomic datasets(covering 2914 unique plasma proteins)and 6 transcriptomic datasets.Multi-omics integration strategies,including two-sample Mendelian randomization,colocalization analysis,and functional enrichment analysis,were employed to identify and validate causal relationships between candidate targets and GCA risk across 4 independent European-ancestry GCA cohorts.Single-cell RNA sequencing analysis of peripheral blood mononuclear cells from untreated GCA patients was performed to characterize hub gene-immune cell relationships.Results:We identified 43 plasma proteins causally associated with GCA[false discovery rate(FDR)<0.05],with 17 representing novel therapeutic targets.Through dual validation using proteome-wide association studies and transcriptome-wide association studies,we identified 13 high-confidence candidate targets with distinct tissue-specific expression patterns.Unc-51 like kinase 3(ULK3)emerged as the strongest protective factor(odds ratio=0.47,95%confidence interval:0.37–0.71)through autophagy regulation,while SLAMF7 represents an immediate drug repositioning opportunity as the target of food and drug administration-approved elotuzumab.Five targets have existing approved drugs(SLAMF7,ICAM1,IL18,IL6ST,CTSS).Single-cell analysis revealed profound disruption of hub gene-immune cell relationships in untreated GCA patients,with cell-type-specific alterations in inflammatory gene expression,and TYMP as the most critical hub gene.Conclusions:This study provides a clinically-actionable atlas of 43 potential therapeutic targets in GCA,identifying novel mechanisms including autophagy modulation and metabolic reprogramming,with immediate drug repositioning opportunities and precision medicine strategies based on tissue-specific and cell-type-specific expression patterns.These findings require experimental validation before clinical translation.展开更多
Wu et al recently applied multi-region 16S rRNA sequencing to characterize the gastric cancer microbiome,demonstrating improved taxonomic resolution and detection sensitivity over conventional single-region approaches...Wu et al recently applied multi-region 16S rRNA sequencing to characterize the gastric cancer microbiome,demonstrating improved taxonomic resolution and detection sensitivity over conventional single-region approaches.While the study represents a valuable methodological step forward,it remains limited by singlecenter design,lack of quantitative calibration,and insufficient control for contamination and inter-laboratory variability.This editorial critically appraises these methodological gaps and emphasizes that future efforts must focus on harmonized,consensus-driven workflows to ensure reproducibility and clinical reliability.The translational potential of multi-region 16S lies in moving from descriptive microbial profiling to actionable clinical integration,particularly for recurrence prediction,treatment-response monitoring,and perioperative complication risk assessment.By addressing these methodological,economic,and ethical challenges,the field can advance toward evidence-based and clinically deployable microbiome-guided precision oncology.展开更多
Agricultural greenhouses(AGHs)are increasingly used globally to control the crop growth environment,which are vital for food production,resource conservation,and rural economies.Advances in high-quality data acquisiti...Agricultural greenhouses(AGHs)are increasingly used globally to control the crop growth environment,which are vital for food production,resource conservation,and rural economies.Advances in high-quality data acquisition methods and information retrieval algorithms have improved the ability to extract AGHs from remote sensing images(e.g.,satellite and uncrewed aerial vehicle(UAV)).Research on this topic began in 1989,and the number of related studies has increased annually.This paper provides a review of the development of remote sensing of AGHs and research hotspots.It summarizes the current status and trends of data sources,identification features,methods,and accuracy of AGHs extraction.Due to the unique spectral,textural,and geometric characteristics of AGHs,research studies have primarily utilized optical remote sensing data from sensors with spatial resolutions of 30 m or more,such as Landsat,Sentinel,Gaofen(GF),and Worldview,to extract AGHs.Machine learning and deep learning methods have provided more precise results for extracting AGHs than threshold segmentation methods.In contrast,deep learning algorithms have been primarily used with high-spatial resolution data and small-scale study areas,with accuracy rates generally exceeding 90.00%.However,future research may use higher spatial resolution images to improve the accuracy and detail of AGH extraction.Recent studies have integrated multiple data sources and performed time-series analysis to improve monitoring of dynamic changes in AGHs.Moreover,emphasis should be placed on optimizing data fusion techniques,implementing sample transfer methods,expanding the number of sensors,and increasing the application of artificial intelligence(AI)in monitoring AGHs.These efforts will provide more reliable methods and tools to improve agricultural production and resource utilization efficiency.This review provides resources for researchers and decision-makers involved in modern agricultural development,as well as scientific evidence for the sustainable development of rural areas.展开更多
Complex genetic architecture is the major cause of heterogeneity in epilepsy,which poses challenges for accurate diagnosis and precise treatment.A large number of epilepsy candidate genes have been identified from cli...Complex genetic architecture is the major cause of heterogeneity in epilepsy,which poses challenges for accurate diagnosis and precise treatment.A large number of epilepsy candidate genes have been identified from clinical studies,particularly with the widespread use of next-generation sequencing.Validating these candidate genes is emerging as a valuable yet challenging task.Drosophila serves as an ideal animal model for validating candidate genes associated with neurogenetic disorders such as epilepsy,due to its rapid reproduction rate,powerful genetic tools,and efficient use of ethological and electrophysiological assays.Here,we systematically summarize the advantageous techniques of the Drosophila model used to investigate epilepsy genes,including genetic tools for manipulating target gene expression,ethological assays for seizure-like behaviors,electrophysiological techniques,and functional imaging for recording neural activity.We then introduce several typical strategies for identifying epilepsy genes and provide new insights into gene-gene interactions in epilepsy with polygenic causes.We summarize well-established precision medicine strategies for epilepsy and discuss prospective treatment options,including drug therapy and gene therapy for genetic epilepsy based on the Drosophila model.Finally,we also address genetic counseling and assisted reproductive technology as potential approaches for the prevention of genetic epilepsy.展开更多
Post-kidney transplant rejection is a critical factor influencing transplant success rates and the survival of transplanted organs.With the rapid advancement of artificial intelligence technologies,machine learning(ML...Post-kidney transplant rejection is a critical factor influencing transplant success rates and the survival of transplanted organs.With the rapid advancement of artificial intelligence technologies,machine learning(ML)has emerged as a powerful data analysis tool,widely applied in the prediction,diagnosis,and mechanistic study of kidney transplant rejection.This mini-review systematically summarizes the recent applications of ML techniques in post-kidney transplant rejection,covering areas such as the construction of predictive models,identification of biomarkers,analysis of pathological images,assessment of immune cell infiltration,and formulation of personalized treatment strategies.By integrating multi-omics data and clinical information,ML has significantly enhanced the accuracy of early rejection diagnosis and the capability for prognostic evaluation,driving the development of precision medicine in the field of kidney transplantation.Furthermore,this article discusses the challenges faced in existing research and potential future directions,providing a theoretical basis and technical references for related studies.展开更多
In this study,an automated multimodal system for detecting,classifying,and dating fruit was developed using a two-stage YOLOv11 pipeline.In the first stage,the YOLOv11 detection model locates individual date fruits in...In this study,an automated multimodal system for detecting,classifying,and dating fruit was developed using a two-stage YOLOv11 pipeline.In the first stage,the YOLOv11 detection model locates individual date fruits in real time by drawing bounding boxes around them.These bounding boxes are subsequently passed to a YOLOv11 classification model,which analyzes cropped images and assigns class labels.An additional counting module automatically tallies the detected fruits,offering a near-instantaneous estimation of quantity.The experimental results suggest high precision and recall for detection,high classification accuracy(across 15 classes),and near-perfect counting in real time.This paper presents a multi-stage pipeline for date fruit detection,classification,and automated counting,employing YOLOv11-based models to achieve high accuracy while maintaining real-time throughput.The results demonstrated that the detection precision exceeded 90%,the classification accuracy approached 92%,and the counting module correlated closely with the manual tallies.These findings confirm the potential of reducing manual labour and enhancing operational efficiency in post-harvesting processes.Future studies will include dataset expansion,user-centric interfaces,and integration with harvesting robotics.展开更多
The latest generation of aero engines has set higher standards for thrust-to-weight ratio and energy conversion efficiency,making it imperative to address the challenge of efficiently and accurately machining film coo...The latest generation of aero engines has set higher standards for thrust-to-weight ratio and energy conversion efficiency,making it imperative to address the challenge of efficiently and accurately machining film cooling holes.It has been demonstrated that conventional long-pulse lasers are incapable of meeting the elevated quality surface finish requirements for these holes,a consequence of the severe thermal defects.The employment of backside water-assisted laser drilling technology confers a number of distinct advantages in terms of mitigating laser thermal damage,thus representing a highly promising solution to this challenge.However,significant accumulation of bubbles and machining products during the backside water-assisted laser drilling process has been demonstrated to have a detrimental effect on laser transmission and machining stability,thereby reducing machining quality.In order to surmount these challenges,a novel method has been proposed,namely an ultrasonic shock water flow-assisted picosecond laser drilling technique.Numerical models for ultrasonic acoustic streaming and particle tracking for machining product transport have been established to investigate the mechanism.The simulation results demonstrated that the majority of the machining products could rapidly move away from the machining area because of the action of acoustic streaming,thereby avoiding the accumulation of bubbles and products.Subsequent analysis,comparing the process performance in micro-hole machining,confirmed that the ultrasonic field could effectively eliminate bubble and chip accumulation,thus significantly improving micro-hole quality.Furthermore,the impact of ultrasonic and laser parameters on micro-hole quality under varying machining methods was thoroughly investigated.The findings demonstrated that the novel methodology outlined in this study yielded superior-quality micro-holes at elevated ultrasonic and laser power levels,in conjunction with reduced laser frequency and scanning velocity.The taper of the micro-holes produced by the new method was reduced by more than 25%compared with the other conventional methods.展开更多
The rapid growth of biomedical data,particularly multi-omics data including genomes,transcriptomics,proteomics,metabolomics,and epigenomics,medical research and clinical decision-making confront both new opportunities...The rapid growth of biomedical data,particularly multi-omics data including genomes,transcriptomics,proteomics,metabolomics,and epigenomics,medical research and clinical decision-making confront both new opportunities and obstacles.The huge and diversified nature of these datasets cannot always be managed using traditional data analysis methods.As a consequence,deep learning has emerged as a strong tool for analysing numerous omics data due to its ability to handle complex and non-linear relationships.This paper explores the fundamental concepts of deep learning and how they are used in multi-omics medical data mining.We demonstrate how autoencoders,variational autoencoders,multimodal models,attention mechanisms,transformers,and graph neural networks enable pattern analysis and recognition across all omics data.Deep learning has been found to be effective in illness classification,biomarker identification,gene network learning,and therapeutic efficacy prediction.We also consider critical problems like as data quality,model explainability,whether findings can be repeated,and computational power requirements.We now consider future elements of combining omics with clinical and imaging data,explainable AI,federated learning,and real-time diagnostics.Overall,this study emphasises the need of collaborating across disciplines to advance deep learning-based multi-omics research for precision medicine and comprehending complicated disorders.展开更多
Gastrointestinal tumors require personalized treatment strategies due to their heterogeneity and complexity.Multimodal artificial intelligence(AI)addresses this challenge by integrating diverse data sources-including ...Gastrointestinal tumors require personalized treatment strategies due to their heterogeneity and complexity.Multimodal artificial intelligence(AI)addresses this challenge by integrating diverse data sources-including computed tomography(CT),magnetic resonance imaging(MRI),endoscopic imaging,and genomic profiles-to enable intelligent decision-making for individualized therapy.This approach leverages AI algorithms to fuse imaging,endoscopic,and omics data,facilitating comprehensive characterization of tumor biology,prediction of treatment response,and optimization of therapeutic strategies.By combining CT and MRI for structural assessment,endoscopic data for real-time visual inspection,and genomic information for molecular profiling,multimodal AI enhances the accuracy of patient stratification and treatment personalization.The clinical implementation of this technology demonstrates potential for improving patient outcomes,advancing precision oncology,and supporting individualized care in gastrointestinal cancers.Ultimately,multimodal AI serves as a transformative tool in oncology,bridging data integration with clinical application to effectively tailor therapies.展开更多
基金financially supported by the Liaoning Revitalization Talents Program (No. XLYC1907129)the Excellent Youth Science Foundation of Liaoning Province (No. 2020-YQ-06)the China Postdoctoral Science Foundation (No. 2020M670794)。
文摘Photodynamic therapy(PDT) has been widely investigated for cancer therapy. The intracellular accumulation of reactive oxygen species(ROS)-damaged protein facilitates tumor cell apoptosis. However, there is growing evidence that the ubiquitin-proteasome pathway(UPP) significantly impedes PDT by preventing the enrichment of ROS-damaged proteins in tumor cells. To tackle this challenge, we report a facile dual-drug nanoassembly based on the discovery of an interesting co-assembly of bortezomib(BTZ, a proteasome inhibitor) and pyropheophorbide a(PPa) for proteasome inhibition-mediated PDT sensitization.The precisely engineered nanoassembly with the optimal dose ratio of BTZ and PPa demonstrates multiple advantages, including simple fabrication, high drug co-loading efficiency, flexible dose adjustment,good colloidal stability, long systemic circulation, favorable tumor-specific accumulation, as well as significant enrichment of ROS-damaged proteins in tumor cells. As a result, the cooperative nanoassembly exhibits potent synergistic antitumor activity in vivo. This study provides a novel dual-drug engineering modality for multimodal cancer treatment.
基金Supported by the National Natural Science Foundation of China(61402526,61502528)
文摘New precisely cooperative attacks, such as the coordi- nated cross plane session termination （CXPST） attack, need thou- sands upon thousands machines to attack diverse selected links simultaneously with the given rate. However, almost all command and control（C＆C） mechanisms only provide publishing one com- mand to the whole once, so-called one-to-all C＆C model, and are not productive to support CXPST-alike attacks. In this paper, we present one-to-any C＆C model on coordination among the unco- operative controlled nodes. As an instance of one-to-any C＆C model, directional command publishing （DCP） mechanism lever- aging on Kademlia is provided with a range-mapping key creating algorithm for commands to compute the publishing range and a statistically stochastic node querying scheme to obtain the com- mands immediately. With theoretical analysis and simulation, it is indicated that one-to-any C＆C model fits for precisely coordi- nated operation on uncooperative controlled nodes with least complexity, better accuracy and efficiency. Furthermore, DCP mechanism can support one-to-all command publishing at the same time. As an example of future C＆C model, studying on one-to-any C＆C model may help to promote the development of more efficient countermeasures.
文摘China launched the NigComSat-1R communications satellite with a Long March 3B/E from the Xichang Satellite Launch Center (XSLC) at 00:41 on December 20.Twenty six minutes after the lift-off,the satellite separated with the rocket and entered precisely into a geostationary transfer orbit with a perigee of 203km,an apogee of 42007km and an inelination of 24.8 degrees.
基金the Program of Shanghai Municipal Commission of Health and Family Planning(No.20194Y0033)。
文摘Irregular craniofacial bone defects caused by craniofacial fractures always result in craniofacial bone and contour asymmetry and should therefore be reconstructed.Polyetheretherketone(PEEK)is an ideal substitute for autologous bone grafts and has been widely used in craniofacial bone defect reconstruction.The precise design of custom-made PEEK implants is particularly important to optimise reconstruction.Herein,the workflow and principles for the design and manufacture of PEEK implants are summarised,and a protocol for the precise design of an irregular craniofacial bone defect PEEK implant is presented.According to the method and principles,the design flow was efficient and could be standardised,and design errors could be avoided as much as possible.
文摘Cancer of unknown primary(CUP)is a recalcitrant disease with poor prognosis because it lacks standard first-line therapy.CUP consists of diverse malignancy groups,making personalized precision therapy essential.The present study aimed to identify an effective therapy for a CUP patient using a patient-derived orthotopic xenograft(PDOX)model.This paper reports the usefulness of the PDOX model to precisely identify effective and ineffective chemotherapy and to compare the efficacy of S.typhimurium A1-R with first-line chemotherapy using the CUP PDOX model.The present study is the first to use a CUP PDOX model,which was able to precisely distinguish the chemotherapeutic course.We found that a carboplatinum(CAR)-based regimen was effective for this CUP patient.We also demonstrated that S.typhimurium A1-R was more effective against the CUP tumor than first-line chemotherapy.Our results indicate that S.typhimurium A1-R has clinical potential for CUP,a resistant disease that requires effective therapy.
基金supported by the National Key R&D Program of China(No.2022YFA1205801)the National Natural Science Foundation of China(Nos.T2225026,82172087,52202344,82071308)the Beijing Institute of Technology Research Fund Program for Young Scholars.
文摘CONSPECTUS:Nanozymes are nanomaterials with intrinsic enzyme-like properties that can overcome the current limitations of natural enzymes,such as high preparation cost,instability,restricted application scenarios,etc.Since the Fe3O4 nanoparticles(NPs)were shown to possess the peroxidase(POD)-like activity in 2007,thousands of nanomaterials were reported to mimic the catalytic properties of various types of enzymes including catalase(CAT),haloperoxidase,superoxide dismutase(SOD),glucose oxidase,glutathione peroxidase,hydrolase,nuclease,nitroreductase,and others.
基金supported by grants from the Open Research Fund of the Zhejiang Key Laboratory of Precision Psychiatry(2025A2)the Natural Science Foundation of Zhejiang Province(LY23C090002)。
文摘Machado-Joseph disease,or spinocerebellar ataxia type 3(SCA3),represents the most common autosomal dominant cerebellar ataxia worldwide.Despite its progressive and debilitating nature,disease-modifying therapies remain elusive.Repetitive transcranial magnetic stimulation(rTMS)has emerged as a promising non-invasive intervention;however,its clinical application has been hindered by inconsistent protocols and a lack of mechanistic understanding.A recent landmark study published in Brain Stimulation by Chen et al.addressed these challenges by combining a high-dose intermittent theta-burst stimulation(iTBS)protocol with concurrent transcranial magnetic stimulation-electroencephalography(TMS-EEG).This commentary provides an in-depth analysis of their findings,highlighting the restoration of cerebello-cortical inhibition(CBI)as a key therapeutic mechanism.Furthermore,we discuss the broader implications of this work,proposing that future translational research should integrate accelerated iTBS(aiTBS)paradigms,cortical response measurements(CRM),and individualized neuro-navigation to establish a new era of precision neuromodulation for ataxia.
文摘Nature|大型汉族人群队列助力中国台湾精准医疗精准医学的发展依赖于大规模、具有深度表型和基因变异图谱数据的人群队列,然而这在非欧洲人群中数据仍严重不足。中国台湾精准医学计划(Taiwan Precision Medicine Initiative,TPMI)旨在建立一个具有广泛代表性的中国台湾汉族人群队列,以支持大规模基因组与健康医学研究(2025年10月15日在线发表,doi:10.1038/s41586-025-09680-x)。
基金supported by the National Natural Science Foundation of China (Nos.22271010 and 21702013)。
文摘The deuterium labeling has garnered significant interest in drug discovery due to its critical role on improving pharmacokinetic and metabolic properties.However,despite its pharmaceutical value,the general and rapid syntheses of aromatic scaffolds that contains deuterium remain an important yet elusive task.State-of-the-art approaches mainly relied on the transition metal-catalyzed C-H deuteration via the assistance of directing groups(DGs),which often suffered from over-deuteration and lengthy step counts required for installation and/or removal of DG.Herein,we report a generalizable synthetic linchpin strategy for the facile preparation of the ortho-deuterated aromatic core.Through capture of aryne-derived 1,3-zwitterion with heavy water,we synthesized an array of ortho-deuterated aryl sulfonium salts.These novel linchpins not only participated the transition metal catalyzed cross-coupling reaction as nucleophiles,but also served as aryl radical reservoirs under photochemical or electrochemical conditions,enabling facile and precise access to structurally diverse deuterated aromatics.Moreover,we have disclosed a novel EDA complex enabled direct arylation of phosphines under visible-light irradiation,further expanding the utility of our platform approach.
文摘Gastrointestinal(GI)cancers remain a leading cause of cancer-related morbidity and mortality worldwide.Artificial intelligence(AI),particularly machine learning and deep learning(DL),has shown promise in enhancing cancer detection,diagnosis,and prognostication.A narrative review of literature published from January 2015 to march 2025 was conducted using PubMed,Web of Science,and Scopus.Search terms included"gastrointestinal cancer","artificial intelligence","machine learning","deep learning","radiomics","multimodal detection"and"predictive modeling".Studies were included if they focused on clinically relevant AI applications in GI oncology.AI algorithms for GI cancer detection have achieved high performance across imaging modalities,with endoscopic DL systems reporting accuracies of 85%-97%for polyp detection and segmentation.Radiomics-based models have predicted molecular biomarkers such as programmed cell death ligand 2 expression with area under the curves up to 0.92.Large language models applied to radiology reports demonstrated diagnostic accuracy comparable to junior radiologists(78.9%vs 80.0%),though without incremental value when combined with human interpretation.Multimodal AI approaches integrating imaging,pathology,and clinical data show emerging potential for precision oncology.AI in GI oncology has reached clinically relevant accuracy levels in multiple diagnostic tasks,with multimodal approaches and predictive biomarker modeling offering new opportunities for personalized care.However,broader validation,integration into clinical workflows,and attention to ethical,legal,and social implications remain critical for widespread adoption.
文摘Objective:The increasing global prevalence of mental health disorders highlights the urgent need for the development of innovative diagnostic methods.Conditions such as anxiety,depression,stress,bipolar disorder(BD),and autism spectrum disorder(ASD)frequently arise from the complex interplay of demographic,biological,and socioeconomic factors,resulting in aggravated symptoms.This review investigates machine intelligence approaches for the early detection and prediction of mental health conditions.Methods:The preferred reporting items for systematic reviews and meta-analyses(PRISMA)framework was employed to conduct a systematic review and analysis covering the period 2018 to 2025.The potential impact of machine intelligence methods was assessed by considering various strategies,hybridization of algorithms,tools,techniques,and datasets,and their applicability.Results:Through a systematic review of studies concentrating on the prediction and evaluation of mental disorders using machine intelligence algorithms,advancements,limitations,and gaps in current methodologies were highlighted.The datasets and tools utilized in these investigations were examined,offering a detailed overview of the status of computational models in understanding and diagnosing mental health disorders.Recent research indicated considerable improvements in diagnostic accuracy and treatment effectiveness,particularly for depression and anxiety,which have shown the greatest methodological diversity and notable advancements in machine intelligence.Conclusions:Despite these improvements,challenges persist,including the need for more diverse datasets,ethical issues surrounding data privacy and algorithmic bias,and obstacles to integrating these technologies into clinical settings.This synthesis emphasizes the transformative potential of machine intelligence in enhancing mental healthcare.
基金supported by grants from the Fundamental Research Funds for the Central Universities(No.2025ZFJH03)the Central Guidance Fund for Local Science and Technology Development(No.2024ZY01054)the CAMS Innovation Fund for Medical Sciences(No.2019-I2M-5-045).
文摘Background:Giant cell arteritis(GCA),the most common systemic vasculitis affecting elderly individuals,currently lacks specific therapies.This study aimed to systematically identify therapeutic targets for GCA through integration of large-scale multi-omics datasets.Methods:We constructed a multi-stage analytical framework encompassing 32 proteomic datasets(covering 2914 unique plasma proteins)and 6 transcriptomic datasets.Multi-omics integration strategies,including two-sample Mendelian randomization,colocalization analysis,and functional enrichment analysis,were employed to identify and validate causal relationships between candidate targets and GCA risk across 4 independent European-ancestry GCA cohorts.Single-cell RNA sequencing analysis of peripheral blood mononuclear cells from untreated GCA patients was performed to characterize hub gene-immune cell relationships.Results:We identified 43 plasma proteins causally associated with GCA[false discovery rate(FDR)<0.05],with 17 representing novel therapeutic targets.Through dual validation using proteome-wide association studies and transcriptome-wide association studies,we identified 13 high-confidence candidate targets with distinct tissue-specific expression patterns.Unc-51 like kinase 3(ULK3)emerged as the strongest protective factor(odds ratio=0.47,95%confidence interval:0.37–0.71)through autophagy regulation,while SLAMF7 represents an immediate drug repositioning opportunity as the target of food and drug administration-approved elotuzumab.Five targets have existing approved drugs(SLAMF7,ICAM1,IL18,IL6ST,CTSS).Single-cell analysis revealed profound disruption of hub gene-immune cell relationships in untreated GCA patients,with cell-type-specific alterations in inflammatory gene expression,and TYMP as the most critical hub gene.Conclusions:This study provides a clinically-actionable atlas of 43 potential therapeutic targets in GCA,identifying novel mechanisms including autophagy modulation and metabolic reprogramming,with immediate drug repositioning opportunities and precision medicine strategies based on tissue-specific and cell-type-specific expression patterns.These findings require experimental validation before clinical translation.
文摘Wu et al recently applied multi-region 16S rRNA sequencing to characterize the gastric cancer microbiome,demonstrating improved taxonomic resolution and detection sensitivity over conventional single-region approaches.While the study represents a valuable methodological step forward,it remains limited by singlecenter design,lack of quantitative calibration,and insufficient control for contamination and inter-laboratory variability.This editorial critically appraises these methodological gaps and emphasizes that future efforts must focus on harmonized,consensus-driven workflows to ensure reproducibility and clinical reliability.The translational potential of multi-region 16S lies in moving from descriptive microbial profiling to actionable clinical integration,particularly for recurrence prediction,treatment-response monitoring,and perioperative complication risk assessment.By addressing these methodological,economic,and ethical challenges,the field can advance toward evidence-based and clinically deployable microbiome-guided precision oncology.
基金Under the auspices of the Strategic Priority Research Program of the Chinese Academy of Sciences(No.XDA28050400)Jilin Province Key Research and Development Project(No.20230202040NC)Common Application Support Platform for National Civil Space Infrastructure Land Observation Satellites(No.2017-000052-73-01-001735)。
文摘Agricultural greenhouses(AGHs)are increasingly used globally to control the crop growth environment,which are vital for food production,resource conservation,and rural economies.Advances in high-quality data acquisition methods and information retrieval algorithms have improved the ability to extract AGHs from remote sensing images(e.g.,satellite and uncrewed aerial vehicle(UAV)).Research on this topic began in 1989,and the number of related studies has increased annually.This paper provides a review of the development of remote sensing of AGHs and research hotspots.It summarizes the current status and trends of data sources,identification features,methods,and accuracy of AGHs extraction.Due to the unique spectral,textural,and geometric characteristics of AGHs,research studies have primarily utilized optical remote sensing data from sensors with spatial resolutions of 30 m or more,such as Landsat,Sentinel,Gaofen(GF),and Worldview,to extract AGHs.Machine learning and deep learning methods have provided more precise results for extracting AGHs than threshold segmentation methods.In contrast,deep learning algorithms have been primarily used with high-spatial resolution data and small-scale study areas,with accuracy rates generally exceeding 90.00%.However,future research may use higher spatial resolution images to improve the accuracy and detail of AGH extraction.Recent studies have integrated multiple data sources and performed time-series analysis to improve monitoring of dynamic changes in AGHs.Moreover,emphasis should be placed on optimizing data fusion techniques,implementing sample transfer methods,expanding the number of sensors,and increasing the application of artificial intelligence(AI)in monitoring AGHs.These efforts will provide more reliable methods and tools to improve agricultural production and resource utilization efficiency.This review provides resources for researchers and decision-makers involved in modern agricultural development,as well as scientific evidence for the sustainable development of rural areas.
基金supported by the Guangdong Basic and Applied Basic Research Foundation,No.2022A1515111123(to JQ)。
文摘Complex genetic architecture is the major cause of heterogeneity in epilepsy,which poses challenges for accurate diagnosis and precise treatment.A large number of epilepsy candidate genes have been identified from clinical studies,particularly with the widespread use of next-generation sequencing.Validating these candidate genes is emerging as a valuable yet challenging task.Drosophila serves as an ideal animal model for validating candidate genes associated with neurogenetic disorders such as epilepsy,due to its rapid reproduction rate,powerful genetic tools,and efficient use of ethological and electrophysiological assays.Here,we systematically summarize the advantageous techniques of the Drosophila model used to investigate epilepsy genes,including genetic tools for manipulating target gene expression,ethological assays for seizure-like behaviors,electrophysiological techniques,and functional imaging for recording neural activity.We then introduce several typical strategies for identifying epilepsy genes and provide new insights into gene-gene interactions in epilepsy with polygenic causes.We summarize well-established precision medicine strategies for epilepsy and discuss prospective treatment options,including drug therapy and gene therapy for genetic epilepsy based on the Drosophila model.Finally,we also address genetic counseling and assisted reproductive technology as potential approaches for the prevention of genetic epilepsy.
文摘Post-kidney transplant rejection is a critical factor influencing transplant success rates and the survival of transplanted organs.With the rapid advancement of artificial intelligence technologies,machine learning(ML)has emerged as a powerful data analysis tool,widely applied in the prediction,diagnosis,and mechanistic study of kidney transplant rejection.This mini-review systematically summarizes the recent applications of ML techniques in post-kidney transplant rejection,covering areas such as the construction of predictive models,identification of biomarkers,analysis of pathological images,assessment of immune cell infiltration,and formulation of personalized treatment strategies.By integrating multi-omics data and clinical information,ML has significantly enhanced the accuracy of early rejection diagnosis and the capability for prognostic evaluation,driving the development of precision medicine in the field of kidney transplantation.Furthermore,this article discusses the challenges faced in existing research and potential future directions,providing a theoretical basis and technical references for related studies.
基金supported by the Deanship of Scientific Research,Vice Presidency for Graduate Studies and Scientific Research,King Faisal University,Saudi Arabia,Grant No.KFU250098.
文摘In this study,an automated multimodal system for detecting,classifying,and dating fruit was developed using a two-stage YOLOv11 pipeline.In the first stage,the YOLOv11 detection model locates individual date fruits in real time by drawing bounding boxes around them.These bounding boxes are subsequently passed to a YOLOv11 classification model,which analyzes cropped images and assigns class labels.An additional counting module automatically tallies the detected fruits,offering a near-instantaneous estimation of quantity.The experimental results suggest high precision and recall for detection,high classification accuracy(across 15 classes),and near-perfect counting in real time.This paper presents a multi-stage pipeline for date fruit detection,classification,and automated counting,employing YOLOv11-based models to achieve high accuracy while maintaining real-time throughput.The results demonstrated that the detection precision exceeded 90%,the classification accuracy approached 92%,and the counting module correlated closely with the manual tallies.These findings confirm the potential of reducing manual labour and enhancing operational efficiency in post-harvesting processes.Future studies will include dataset expansion,user-centric interfaces,and integration with harvesting robotics.
基金supported by the National Natural Science Foundation of China(No.52205468,No.52275431,No.52375186)China Postdoctoral Science Foundation(No.2025M771349)Zhejiang Province Natural Science Foundation(No.LD22E050001)。
文摘The latest generation of aero engines has set higher standards for thrust-to-weight ratio and energy conversion efficiency,making it imperative to address the challenge of efficiently and accurately machining film cooling holes.It has been demonstrated that conventional long-pulse lasers are incapable of meeting the elevated quality surface finish requirements for these holes,a consequence of the severe thermal defects.The employment of backside water-assisted laser drilling technology confers a number of distinct advantages in terms of mitigating laser thermal damage,thus representing a highly promising solution to this challenge.However,significant accumulation of bubbles and machining products during the backside water-assisted laser drilling process has been demonstrated to have a detrimental effect on laser transmission and machining stability,thereby reducing machining quality.In order to surmount these challenges,a novel method has been proposed,namely an ultrasonic shock water flow-assisted picosecond laser drilling technique.Numerical models for ultrasonic acoustic streaming and particle tracking for machining product transport have been established to investigate the mechanism.The simulation results demonstrated that the majority of the machining products could rapidly move away from the machining area because of the action of acoustic streaming,thereby avoiding the accumulation of bubbles and products.Subsequent analysis,comparing the process performance in micro-hole machining,confirmed that the ultrasonic field could effectively eliminate bubble and chip accumulation,thus significantly improving micro-hole quality.Furthermore,the impact of ultrasonic and laser parameters on micro-hole quality under varying machining methods was thoroughly investigated.The findings demonstrated that the novel methodology outlined in this study yielded superior-quality micro-holes at elevated ultrasonic and laser power levels,in conjunction with reduced laser frequency and scanning velocity.The taper of the micro-holes produced by the new method was reduced by more than 25%compared with the other conventional methods.
文摘The rapid growth of biomedical data,particularly multi-omics data including genomes,transcriptomics,proteomics,metabolomics,and epigenomics,medical research and clinical decision-making confront both new opportunities and obstacles.The huge and diversified nature of these datasets cannot always be managed using traditional data analysis methods.As a consequence,deep learning has emerged as a strong tool for analysing numerous omics data due to its ability to handle complex and non-linear relationships.This paper explores the fundamental concepts of deep learning and how they are used in multi-omics medical data mining.We demonstrate how autoencoders,variational autoencoders,multimodal models,attention mechanisms,transformers,and graph neural networks enable pattern analysis and recognition across all omics data.Deep learning has been found to be effective in illness classification,biomarker identification,gene network learning,and therapeutic efficacy prediction.We also consider critical problems like as data quality,model explainability,whether findings can be repeated,and computational power requirements.We now consider future elements of combining omics with clinical and imaging data,explainable AI,federated learning,and real-time diagnostics.Overall,this study emphasises the need of collaborating across disciplines to advance deep learning-based multi-omics research for precision medicine and comprehending complicated disorders.
基金Supported by Xuhui District Health Commission,No.SHXH202214.
文摘Gastrointestinal tumors require personalized treatment strategies due to their heterogeneity and complexity.Multimodal artificial intelligence(AI)addresses this challenge by integrating diverse data sources-including computed tomography(CT),magnetic resonance imaging(MRI),endoscopic imaging,and genomic profiles-to enable intelligent decision-making for individualized therapy.This approach leverages AI algorithms to fuse imaging,endoscopic,and omics data,facilitating comprehensive characterization of tumor biology,prediction of treatment response,and optimization of therapeutic strategies.By combining CT and MRI for structural assessment,endoscopic data for real-time visual inspection,and genomic information for molecular profiling,multimodal AI enhances the accuracy of patient stratification and treatment personalization.The clinical implementation of this technology demonstrates potential for improving patient outcomes,advancing precision oncology,and supporting individualized care in gastrointestinal cancers.Ultimately,multimodal AI serves as a transformative tool in oncology,bridging data integration with clinical application to effectively tailor therapies.
