Osteoarthritis(OA) and rheumatoid arthritis(RA) have long been framed as degenerative and autoimmune entities, respectively;mounting evidence instead supports a unified mechano-immune paradigm in which joint loading a...Osteoarthritis(OA) and rheumatoid arthritis(RA) have long been framed as degenerative and autoimmune entities, respectively;mounting evidence instead supports a unified mechano-immune paradigm in which joint loading and inflammatory signaling are reciprocally reinforcing. In this review, we synthesize advances across mechanotransduction(Piezo1;YAP/TAZ), focaladhesion/cytoskeletal regulation(vinculin, filamin-A;upstream talin-1/Kindlin-2/paxillin), and niche inflammatory mediators(HE4, IL-36/IL-38) to explain how mechanical stress and cytokines co-produce persistent catabolism, synovial invasion, and fibrotic remodeling. We articulate a dual-hit model in which OA is predominantly mechanical-overload-driven, with secondary inflammation, whereas RA is immune-driven but imposes abnormal mechanical stress that further distorts joint biomechanics;both converge on canonical hubs(NF-κB/MAPK/JAK-STAT) to accelerate matrix degradation and apoptosis. Building on this framework, we propose integrated multi-marker panels that combine mechanosensors and adhesion proteins with conventional assays(CRP, ESR, anti-CCP) to enhance differential diagnosis and prognostication, particularly in postmenopausal women, where estrogen decline heightens mechano-immune susceptibility, thereby offering a means to quantify the impact of mechano-immune dysregulation. Integrating mechanotransductive and cytoskeletal biomarkers with conventional serological indices has been reported to improve differential diagnosis between osteoarthritis and rheumatoid arthritis in exploratory studies. While the magnitude of diagnostic gain varies across cohorts, combined biomarker strategies generally show enhanced discriminatory performance compared with single-marker approaches. These findings highlight translational potential but require validation in large, standardized clinical populations before routine implementation. Finally, we map translational opportunities spanning Piezo1 inhibition(GsMTx4), YAP/TAZ blockade(verteporfin), IL-36 axis antagonism(IL-36Ra, IL-38), anti-HE4 strategies for RA-ILD, and adhesion-stabilizing approaches, alongside mechanoresponsive biomaterials for regenerative applications and precision medicine guided by biomarker profiles. Collectively, this review reframes OA and RA as mechano-immune syndromes and delineates a clinically actionable roadmap from biophysics to bedside.展开更多
石笋因具有高分辨率、可精确定年和陆地分布广泛等优势,已成为古气候研究的第四大支柱,中国科学家建立的69万年氧同位素集成序列更成为轨道与千年尺度研究的全球基准。然而,石笋氧同位素的指示意义仍存在争议,尤其在东亚季风区其气候解...石笋因具有高分辨率、可精确定年和陆地分布广泛等优势,已成为古气候研究的第四大支柱,中国科学家建立的69万年氧同位素集成序列更成为轨道与千年尺度研究的全球基准。然而,石笋氧同位素的指示意义仍存在争议,尤其在东亚季风区其气候解译面临挑战,准确测定石笋碳氧同位素是古气候重建的重要基础。当前,高精度双路进样同位素质谱测试仍面临微量样品分析稳定性不足、实验室间数据可比性缺乏统一标准等关键问题,且高精度双路进样同位素质谱测试尚缺乏系统性的质量控制体系研究,制约了高分辨率石笋序列的可靠性与国际可比性。为此,本文建立了超微量碳酸盐碳氧同位素双路进样测试的质控方法,从参考气稳定性、标准样品重复性等多维度系统评估了新一代双路进样质谱仪(Isoprime PrecisION)的可靠性与数据质量;并以陕西汉中地洞河溶洞编号为DDH-B15的石笋为对象,开展独立点对点重复性测试,同个石笋样品不同实验室的一致性测试结果确认了新建实验室分析方法的可靠性。本文进一步探讨了末次冰期20~17 ka BP(BP表示距1950之前)时段亚洲季风的显著增强现象,提出其驱动机制可能涉及冰期东部海岸线变迁与西太平洋海温异常所诱发的厄尔尼诺-南方涛动(ENSO)模态转变。本研究指出轨道尺度上中部地区氧同位素是东西部水汽氧同位素的混合状态,水汽来源对石笋氧同位素有重要影响,这对石笋氧同位素的解释提供了帮助。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
We read with great interest Deng et al.’s study 1 comparing sextant(6-core)and 12-core systematic biopsy in theMRI-targeted era,which valuably challenges the“more cores=higher accuracy”dogma by proposing a precisio...We read with great interest Deng et al.’s study 1 comparing sextant(6-core)and 12-core systematic biopsy in theMRI-targeted era,which valuably challenges the“more cores=higher accuracy”dogma by proposing a precision sampling strategy based on prostate cancer’s spatial distribution,aligning with personalized diagnosis trends.展开更多
Chilo suppressalis(Walker)is one of the most important rice pests worldwide,posing a significant challenge to effective control.To develop a precision-timed,eco-friendly management strategy,overwintering population in...Chilo suppressalis(Walker)is one of the most important rice pests worldwide,posing a significant challenge to effective control.To develop a precision-timed,eco-friendly management strategy,overwintering population investigation and dynamic monitoring of C.suppressalis populations were conducted in the Meishan region of Sichuan,China,from 2023 to 2024.The optimal timing for insecticide application was estimated,followed by field trials evaluating the efficacy of different insecticides.Results demonstrated that the peak emergence of first-generation adults typically occurred in early July(under the environmental conditions of the Meishan region),with the ambient humidity below 75%and temperature around 29◦C.Pesticide efficacy trials show that insecticide combinations exhibited superior control.Notably,a combined treatment of emamectin benzoate⋅methoxyfenozide+chlorantraniliprole achieved the highest control efficacy(90.05%)and a corresponding yield of 12,491.55 kg/ha.All tested treatments were determined to be safe for rice growth.Furthermore,this optimized strategy resulted in notable economic benefits,including a 50%reduction in pesticide usage and cost savings of 4796.15 CNY compared to conventional practices.This study provides valuable insights into sustainable rice production and pest management and,for the first time,proposes a precision application time window based on intelligent monitoring.展开更多
This study aimed to develop a multimodal imaging histological model based on computed tomography(CT)images and carcinoembryonic antigen(CEA)values to predict the efficacy of preoperative neoadjuvant therapy in rectal ...This study aimed to develop a multimodal imaging histological model based on computed tomography(CT)images and carcinoembryonic antigen(CEA)values to predict the efficacy of preoperative neoadjuvant therapy in rectal cancer patients.Data were obtained from the Database of Colorectal Cancer of West China Hospital of Sichuan University.A total of 155 patients were enrolled and categorized into good and poor response groups based on pathological evaluation using the tumor regression grade system.Radiomics features were extracted from CT images using PyRadiomics software,and CEA data were collected and processed.Three types of models—a clinical model,a pure radiomics model,and an integrated model—were constructed using logistic regression,support vector machine,random forest(RF),and XGBoost algorithms.The results showed that the integrated model,particularly the RF and XGBoost models,demonstrated the best predictive performance.The RF model achieved an area under the curve(AUC)value of 0.96 in the test set,with accuracy,sensitivity,and specificity of 0.88,0.50,and 1.00,respectively.The XGBoost model had the highest AUC value of 0.97 in the test set,with accuracy,sensitivity,and specificity of 0.91,0.70,and 0.97,respectively.This model can be integrated into existing clinical practice to provide clinicians with additional insights for guiding treatment decisions.Future studies should recruit a larger and more diverse patient population to validate and refine the model,and prospective validation is needed to assess its real-world applicability.展开更多
Microbe-based soil inoculants offer a promising approach to sustainable agriculture by reducing reliance on agrochemicals and minimizing environmental damages.The heavy use of chemicals in conventional agriculture pos...Microbe-based soil inoculants offer a promising approach to sustainable agriculture by reducing reliance on agrochemicals and minimizing environmental damages.The heavy use of chemicals in conventional agriculture poses significant challenges to crop production and environmental health.This review explores the integration of microbe-based inoculants,strigolactones(SLs),and nanotechnology to enhance agricultural sustainability.Nanobiofertilizers containing nanoparticles such as Ag,Zn,Fe,ZnO,TiO_(2),SiO_(2),and MgO can provide essential crop protection,while algae species like Chlorella spp.,Arthrospira spp.,and Dunaliella spp.serve as promising biostimulants and biofertilizers.Additionally,plant growth-promoting microorganisms such as Rhizobium,Azotobacter,Azospirillum,Pseudomonas,Bacillus,and Trichoderma,alongside synthetic SLs like GR24,contribute to improving crop yield and stress tolerance.Strigolactone signaling pathways have also been explored for their roles in plant growth and resilience.Recent innovations in biofertilizer research,particularly in genomics,transcriptomics,and metabolomics,have advanced our understanding of plant-microbe interactions.These omics-based technologies help develop tailored biofertilizer formulations suited to specific crops,soils,and environmental conditions.The combination of biofertilizers,nanoparticles,and SLs fosters nutrient uptake,enhances stress tolerance,and promotes overall plant growth.Case studies from various agroecosystems show that biofertilizers can improve soil health,boost crop yields,reduce chemical fertilizer dependency,and lower environmental impacts.With precision farming,biofertilizers offer sustainable solutions to various challenges,including climate change,soil degradation,and food security.This review discusses the mechanisms by which GR24,nanoparticle,and microbe-based biofertilizers benefit plants,emphasizing their potential for sustainable agriculture and future challenges.展开更多
The development of robust anode-electrolyte interfaces(AEI)with enhanced compatibility and mechanical strength is critical for regulating zinc-ion nucleation kinetics,suppressing dendrite formation,and advancing zinc-...The development of robust anode-electrolyte interfaces(AEI)with enhanced compatibility and mechanical strength is critical for regulating zinc-ion nucleation kinetics,suppressing dendrite formation,and advancing zinc-ion battery commercialization.To address persistent interface degradation during battery cycling,we propose a novel manufacturing strategy utilizing digital-light-processing(DLP)3D printing.This approach enables programmable regulation of gel-polymer electrolyte(GPE)structures through layer-by-layer photopolymerization,achieving precision regulation of macro-microstructures and interfacial stresses.The DLP-manufactured GPEs feature cross-scale structures combining dense porous networks with smooth surface topography,providing abundant electrochemical active sites and stable interfacial contact.Multiphase-field simulations integrated with in-situ/ex-situ characterizations reveal stress-enhanced zinc deposition mechanisms,where optimized interfacial stress eliminates AEI contact instability,ensuring rapid mass transfer between electrode and electrolyte.Under regulated interface stress,the symmetrical cell demonstrates stability exceeding 2000 hours,and the full cell retains 91.72%capacity after 8000 ultralong cycles,with reliable operation under extreme temperature conditions(-10℃/60℃).The precise regulation of interfacial stresses establishes stable AEI configurations,demonstrating a transformative approach to durable zinc-ion battery design.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
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
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
1.Introduction Crop breeding is transitioning to engineering by synthetic biology.Conventional breeding,constrained by limited genetic variation and lengthy development cycles,cannot meet the challenges of micronutrie...1.Introduction Crop breeding is transitioning to engineering by synthetic biology.Conventional breeding,constrained by limited genetic variation and lengthy development cycles,cannot meet the challenges of micronutrient malnutrition and yield reductions from climate change with sufficient speed or precision[1].Consequently,agriculture is transitioning from selection-based breeding to designbased engineering.Synthetic biology enables the precision modification of metabolic pathways and the construction of novel trait combinations[1,2].This special issue,Synthetic Biology for Crop Improvement,brings together 26 articles that showcase the field’s transition from laboratory curiosity to field-validated agricultural technology.The collection spans 13 plant species,from staple grains and major industrial crops to horticultural and medicinal plants,demonstrating the universal applicability of metabolic engineering.These studies reveal maturation toward field readiness:independent groups achieving reproducible results in identical pathways,greenhouse concepts advancing to multi-season field trials,and engineered traits delivering measurable agronomic value.This progression answers the central question in crop synthetic biology,shifting the paradigm from asking“can it work?”to demonstrating“how it works,and here are the yields”.This transformation is grounded in understanding and manipulating plant metabolism at molecular resolution[3].展开更多
Tibetan-Yi Corridor(TYC)is a crucial agro-pastoral region in the eastern Himalayas,linking Qinghai‒Xizang Plateau with the lowlands of East Asia and facilitating human migration for millennia.However,genomic research ...Tibetan-Yi Corridor(TYC)is a crucial agro-pastoral region in the eastern Himalayas,linking Qinghai‒Xizang Plateau with the lowlands of East Asia and facilitating human migration for millennia.However,genomic research on TYC populations remains limited,which limits the understanding of their origins and health.We provide genomic data from 1031 individuals belonging to Austroasiatic and Sino-Tibetan groups,including 147 whole-genome sequences from 13 underrepresented Tibeto-Burman and Austroasiatic communities.Our analysis reveals approximately 3.3 million new genetic variants and 4 distinct genetic backgrounds within TYC populations.Demographic reconstructions reveal strong genetic connections among TibetoBurman groups,Central Plain Sinitic populations,and Yangshao farmers,supporting a common origin for Sino-Tibetan speakers.We identify signatures of high-altitude adaptations typical of Tibetans and TYCspecific variants linked to pigmentation and hypoxia responses.Differentiation involves mechanisms such as HLA-DQB1,which are related to immune function.Several rare pathogenic variants,like CYP21A2 and PRX,are notably frequent.Variants influencing warfarin sensitivity show significant variation.Archaic human introgression further promotes genomic complexity,impacting cardiovascular and immune-related genes,which suggests adaptation through ancient human interactions.These findings refine the evolutionary history of TYC populations and underscore the need for broader genomic research to capture regional diversity and inform precision medicine.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
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
Tuberculosis(TB)remains one of the most persistent and formidable public health challenges globally.Despite the ambitious targets set by the World Health Organization End TB Strategy,the path to elimination is fraught...Tuberculosis(TB)remains one of the most persistent and formidable public health challenges globally.Despite the ambitious targets set by the World Health Organization End TB Strategy,the path to elimination is fraught with obstacles.According to the Global Tuberculosis Report 2025,while global incidence has been stabilization,the burden of multidrug-resistant tuberculosis(MDR-TB)and the long-term sequelae facing survivors continue to hinder progress[1].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
Male breast cancer(MBC)is rare,representing 0.5%–1%of all breast cancers,but its incidence is increasing due to improved diagnostics and awareness.MBC typically presents in older men,is human epidermal growth factor ...Male breast cancer(MBC)is rare,representing 0.5%–1%of all breast cancers,but its incidence is increasing due to improved diagnostics and awareness.MBC typically presents in older men,is human epidermal growth factor receptor 2(HER2)-negative and estrogen receptor(ER)-positive,and lacks routine screening,leading to delayed diagnosis and advanced disease.Major risk factors include hormonal imbalance,radiation exposure,obesity,alcohol use,and Breast Cancer Gene 1 and 2(BRCA1/2)mutations.Clinically,it may resemble gynecomastia but usually appears as a unilateral,painless mass or nipple discharge.Advances in imaging and liquid biopsy have enhanced early detection.Molecular mechanisms involve hormonal signaling,HER2/epidermal growth factor receptor(EGFR)pathways,tumor suppressor gene alterations,and epigenetic changes.While standard treatments mirror those for female breast cancer,emerging options such as cyclin-dependent kinase 4 and 6(CDK4/6),and poly(ADP-ribose)polymerase(PARP)inhibitors,immunotherapy,and precision medicine are reshaping management.Incorporating artificial intelligence,molecular profiling,and male-specific clinical trials is essential to improve outcomes and bridge current diagnostic and therapeutic gaps.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
文摘Osteoarthritis(OA) and rheumatoid arthritis(RA) have long been framed as degenerative and autoimmune entities, respectively;mounting evidence instead supports a unified mechano-immune paradigm in which joint loading and inflammatory signaling are reciprocally reinforcing. In this review, we synthesize advances across mechanotransduction(Piezo1;YAP/TAZ), focaladhesion/cytoskeletal regulation(vinculin, filamin-A;upstream talin-1/Kindlin-2/paxillin), and niche inflammatory mediators(HE4, IL-36/IL-38) to explain how mechanical stress and cytokines co-produce persistent catabolism, synovial invasion, and fibrotic remodeling. We articulate a dual-hit model in which OA is predominantly mechanical-overload-driven, with secondary inflammation, whereas RA is immune-driven but imposes abnormal mechanical stress that further distorts joint biomechanics;both converge on canonical hubs(NF-κB/MAPK/JAK-STAT) to accelerate matrix degradation and apoptosis. Building on this framework, we propose integrated multi-marker panels that combine mechanosensors and adhesion proteins with conventional assays(CRP, ESR, anti-CCP) to enhance differential diagnosis and prognostication, particularly in postmenopausal women, where estrogen decline heightens mechano-immune susceptibility, thereby offering a means to quantify the impact of mechano-immune dysregulation. Integrating mechanotransductive and cytoskeletal biomarkers with conventional serological indices has been reported to improve differential diagnosis between osteoarthritis and rheumatoid arthritis in exploratory studies. While the magnitude of diagnostic gain varies across cohorts, combined biomarker strategies generally show enhanced discriminatory performance compared with single-marker approaches. These findings highlight translational potential but require validation in large, standardized clinical populations before routine implementation. Finally, we map translational opportunities spanning Piezo1 inhibition(GsMTx4), YAP/TAZ blockade(verteporfin), IL-36 axis antagonism(IL-36Ra, IL-38), anti-HE4 strategies for RA-ILD, and adhesion-stabilizing approaches, alongside mechanoresponsive biomaterials for regenerative applications and precision medicine guided by biomarker profiles. Collectively, this review reframes OA and RA as mechano-immune syndromes and delineates a clinically actionable roadmap from biophysics to bedside.
文摘石笋因具有高分辨率、可精确定年和陆地分布广泛等优势,已成为古气候研究的第四大支柱,中国科学家建立的69万年氧同位素集成序列更成为轨道与千年尺度研究的全球基准。然而,石笋氧同位素的指示意义仍存在争议,尤其在东亚季风区其气候解译面临挑战,准确测定石笋碳氧同位素是古气候重建的重要基础。当前,高精度双路进样同位素质谱测试仍面临微量样品分析稳定性不足、实验室间数据可比性缺乏统一标准等关键问题,且高精度双路进样同位素质谱测试尚缺乏系统性的质量控制体系研究,制约了高分辨率石笋序列的可靠性与国际可比性。为此,本文建立了超微量碳酸盐碳氧同位素双路进样测试的质控方法,从参考气稳定性、标准样品重复性等多维度系统评估了新一代双路进样质谱仪(Isoprime PrecisION)的可靠性与数据质量;并以陕西汉中地洞河溶洞编号为DDH-B15的石笋为对象,开展独立点对点重复性测试,同个石笋样品不同实验室的一致性测试结果确认了新建实验室分析方法的可靠性。本文进一步探讨了末次冰期20~17 ka BP(BP表示距1950之前)时段亚洲季风的显著增强现象,提出其驱动机制可能涉及冰期东部海岸线变迁与西太平洋海温异常所诱发的厄尔尼诺-南方涛动(ENSO)模态转变。本研究指出轨道尺度上中部地区氧同位素是东西部水汽氧同位素的混合状态,水汽来源对石笋氧同位素有重要影响,这对石笋氧同位素的解释提供了帮助。
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)。
文摘We read with great interest Deng et al.’s study 1 comparing sextant(6-core)and 12-core systematic biopsy in theMRI-targeted era,which valuably challenges the“more cores=higher accuracy”dogma by proposing a precision sampling strategy based on prostate cancer’s spatial distribution,aligning with personalized diagnosis trends.
基金funded by the National Key R&D Project‘Innovation and Integration of Key Technologies for Integration of Agricultural Machinery and Agronomy in Weak Links of Hybrid Mid-season Rice in Hilly Areas of Southwest China’(2023YFD2301901).
文摘Chilo suppressalis(Walker)is one of the most important rice pests worldwide,posing a significant challenge to effective control.To develop a precision-timed,eco-friendly management strategy,overwintering population investigation and dynamic monitoring of C.suppressalis populations were conducted in the Meishan region of Sichuan,China,from 2023 to 2024.The optimal timing for insecticide application was estimated,followed by field trials evaluating the efficacy of different insecticides.Results demonstrated that the peak emergence of first-generation adults typically occurred in early July(under the environmental conditions of the Meishan region),with the ambient humidity below 75%and temperature around 29◦C.Pesticide efficacy trials show that insecticide combinations exhibited superior control.Notably,a combined treatment of emamectin benzoate⋅methoxyfenozide+chlorantraniliprole achieved the highest control efficacy(90.05%)and a corresponding yield of 12,491.55 kg/ha.All tested treatments were determined to be safe for rice growth.Furthermore,this optimized strategy resulted in notable economic benefits,including a 50%reduction in pesticide usage and cost savings of 4796.15 CNY compared to conventional practices.This study provides valuable insights into sustainable rice production and pest management and,for the first time,proposes a precision application time window based on intelligent monitoring.
基金supported by the 1-3-5 projects for artificial intelligence(Grant No.:ZYAI24067)West China Hospital,Sichuan University and the medical research project(Grant No.:S2024045),Sichuan Medical Association.
文摘This study aimed to develop a multimodal imaging histological model based on computed tomography(CT)images and carcinoembryonic antigen(CEA)values to predict the efficacy of preoperative neoadjuvant therapy in rectal cancer patients.Data were obtained from the Database of Colorectal Cancer of West China Hospital of Sichuan University.A total of 155 patients were enrolled and categorized into good and poor response groups based on pathological evaluation using the tumor regression grade system.Radiomics features were extracted from CT images using PyRadiomics software,and CEA data were collected and processed.Three types of models—a clinical model,a pure radiomics model,and an integrated model—were constructed using logistic regression,support vector machine,random forest(RF),and XGBoost algorithms.The results showed that the integrated model,particularly the RF and XGBoost models,demonstrated the best predictive performance.The RF model achieved an area under the curve(AUC)value of 0.96 in the test set,with accuracy,sensitivity,and specificity of 0.88,0.50,and 1.00,respectively.The XGBoost model had the highest AUC value of 0.97 in the test set,with accuracy,sensitivity,and specificity of 0.91,0.70,and 0.97,respectively.This model can be integrated into existing clinical practice to provide clinicians with additional insights for guiding treatment decisions.Future studies should recruit a larger and more diverse patient population to validate and refine the model,and prospective validation is needed to assess its real-world applicability.
基金Siksha‘O’Anusandhan(Deemed to be University),IndiaGraphic Era(Deemed to be University),India+1 种基金Bankura Sammilani College,IndiaRaiganj University,India for their support。
文摘Microbe-based soil inoculants offer a promising approach to sustainable agriculture by reducing reliance on agrochemicals and minimizing environmental damages.The heavy use of chemicals in conventional agriculture poses significant challenges to crop production and environmental health.This review explores the integration of microbe-based inoculants,strigolactones(SLs),and nanotechnology to enhance agricultural sustainability.Nanobiofertilizers containing nanoparticles such as Ag,Zn,Fe,ZnO,TiO_(2),SiO_(2),and MgO can provide essential crop protection,while algae species like Chlorella spp.,Arthrospira spp.,and Dunaliella spp.serve as promising biostimulants and biofertilizers.Additionally,plant growth-promoting microorganisms such as Rhizobium,Azotobacter,Azospirillum,Pseudomonas,Bacillus,and Trichoderma,alongside synthetic SLs like GR24,contribute to improving crop yield and stress tolerance.Strigolactone signaling pathways have also been explored for their roles in plant growth and resilience.Recent innovations in biofertilizer research,particularly in genomics,transcriptomics,and metabolomics,have advanced our understanding of plant-microbe interactions.These omics-based technologies help develop tailored biofertilizer formulations suited to specific crops,soils,and environmental conditions.The combination of biofertilizers,nanoparticles,and SLs fosters nutrient uptake,enhances stress tolerance,and promotes overall plant growth.Case studies from various agroecosystems show that biofertilizers can improve soil health,boost crop yields,reduce chemical fertilizer dependency,and lower environmental impacts.With precision farming,biofertilizers offer sustainable solutions to various challenges,including climate change,soil degradation,and food security.This review discusses the mechanisms by which GR24,nanoparticle,and microbe-based biofertilizers benefit plants,emphasizing their potential for sustainable agriculture and future challenges.
基金supported by National Natural Science Foundation of China(U22A20193)National Key Research and Development Program of China(2024YFB3409000)+1 种基金TCL science and technology innovation fund(20231751)Fundamental Research Funds for the Central Universities(No.2024ZYGXZR066)。
文摘The development of robust anode-electrolyte interfaces(AEI)with enhanced compatibility and mechanical strength is critical for regulating zinc-ion nucleation kinetics,suppressing dendrite formation,and advancing zinc-ion battery commercialization.To address persistent interface degradation during battery cycling,we propose a novel manufacturing strategy utilizing digital-light-processing(DLP)3D printing.This approach enables programmable regulation of gel-polymer electrolyte(GPE)structures through layer-by-layer photopolymerization,achieving precision regulation of macro-microstructures and interfacial stresses.The DLP-manufactured GPEs feature cross-scale structures combining dense porous networks with smooth surface topography,providing abundant electrochemical active sites and stable interfacial contact.Multiphase-field simulations integrated with in-situ/ex-situ characterizations reveal stress-enhanced zinc deposition mechanisms,where optimized interfacial stress eliminates AEI contact instability,ensuring rapid mass transfer between electrode and electrolyte.Under regulated interface stress,the symmetrical cell demonstrates stability exceeding 2000 hours,and the full cell retains 91.72%capacity after 8000 ultralong cycles,with reliable operation under extreme temperature conditions(-10℃/60℃).The precise regulation of interfacial stresses establishes stable AEI configurations,demonstrating a transformative approach to durable zinc-ion battery design.
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.
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.
文摘1.Introduction Crop breeding is transitioning to engineering by synthetic biology.Conventional breeding,constrained by limited genetic variation and lengthy development cycles,cannot meet the challenges of micronutrient malnutrition and yield reductions from climate change with sufficient speed or precision[1].Consequently,agriculture is transitioning from selection-based breeding to designbased engineering.Synthetic biology enables the precision modification of metabolic pathways and the construction of novel trait combinations[1,2].This special issue,Synthetic Biology for Crop Improvement,brings together 26 articles that showcase the field’s transition from laboratory curiosity to field-validated agricultural technology.The collection spans 13 plant species,from staple grains and major industrial crops to horticultural and medicinal plants,demonstrating the universal applicability of metabolic engineering.These studies reveal maturation toward field readiness:independent groups achieving reproducible results in identical pathways,greenhouse concepts advancing to multi-season field trials,and engineered traits delivering measurable agronomic value.This progression answers the central question in crop synthetic biology,shifting the paradigm from asking“can it work?”to demonstrating“how it works,and here are the yields”.This transformation is grounded in understanding and manipulating plant metabolism at molecular resolution[3].
基金supported by the Major Project of the National Social Science Foundation of China(82402203)the Major Project of the National SocialScience Foundation of China(23&ZD203)+3 种基金the Open Project of the Key Laboratory of Forensic Genetics of the Ministry of Public Security(2022FGKFKT05)the Center for Archaeological Science of Sichuan University(23SASA01)the 1.3.5 Project for Disciplines of Excellence,West China Hospital,Sichuan University(ZYJC20002)the Sichuan Science and Technology Program(2024NSFSC1518).
文摘Tibetan-Yi Corridor(TYC)is a crucial agro-pastoral region in the eastern Himalayas,linking Qinghai‒Xizang Plateau with the lowlands of East Asia and facilitating human migration for millennia.However,genomic research on TYC populations remains limited,which limits the understanding of their origins and health.We provide genomic data from 1031 individuals belonging to Austroasiatic and Sino-Tibetan groups,including 147 whole-genome sequences from 13 underrepresented Tibeto-Burman and Austroasiatic communities.Our analysis reveals approximately 3.3 million new genetic variants and 4 distinct genetic backgrounds within TYC populations.Demographic reconstructions reveal strong genetic connections among TibetoBurman groups,Central Plain Sinitic populations,and Yangshao farmers,supporting a common origin for Sino-Tibetan speakers.We identify signatures of high-altitude adaptations typical of Tibetans and TYCspecific variants linked to pigmentation and hypoxia responses.Differentiation involves mechanisms such as HLA-DQB1,which are related to immune function.Several rare pathogenic variants,like CYP21A2 and PRX,are notably frequent.Variants influencing warfarin sensitivity show significant variation.Archaic human introgression further promotes genomic complexity,impacting cardiovascular and immune-related genes,which suggests adaptation through ancient human interactions.These findings refine the evolutionary history of TYC populations and underscore the need for broader genomic research to capture regional diversity and inform precision medicine.
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.
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.
文摘Tuberculosis(TB)remains one of the most persistent and formidable public health challenges globally.Despite the ambitious targets set by the World Health Organization End TB Strategy,the path to elimination is fraught with obstacles.According to the Global Tuberculosis Report 2025,while global incidence has been stabilization,the burden of multidrug-resistant tuberculosis(MDR-TB)and the long-term sequelae facing survivors continue to hinder progress[1].
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Male breast cancer(MBC)is rare,representing 0.5%–1%of all breast cancers,but its incidence is increasing due to improved diagnostics and awareness.MBC typically presents in older men,is human epidermal growth factor receptor 2(HER2)-negative and estrogen receptor(ER)-positive,and lacks routine screening,leading to delayed diagnosis and advanced disease.Major risk factors include hormonal imbalance,radiation exposure,obesity,alcohol use,and Breast Cancer Gene 1 and 2(BRCA1/2)mutations.Clinically,it may resemble gynecomastia but usually appears as a unilateral,painless mass or nipple discharge.Advances in imaging and liquid biopsy have enhanced early detection.Molecular mechanisms involve hormonal signaling,HER2/epidermal growth factor receptor(EGFR)pathways,tumor suppressor gene alterations,and epigenetic changes.While standard treatments mirror those for female breast cancer,emerging options such as cyclin-dependent kinase 4 and 6(CDK4/6),and poly(ADP-ribose)polymerase(PARP)inhibitors,immunotherapy,and precision medicine are reshaping management.Incorporating artificial intelligence,molecular profiling,and male-specific clinical trials is essential to improve outcomes and bridge current diagnostic and therapeutic gaps.
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.
