Cardiovascular diseases are the leading cause of death,requiring innovative approaches for prevention,diagnosis,and treatment.Personalized medicine customizes interventions according to individual characteristics,with...Cardiovascular diseases are the leading cause of death,requiring innovative approaches for prevention,diagnosis,and treatment.Personalized medicine customizes interventions according to individual characteristics,with artificial intelligence(AI)playing a key role in analyzing complex data to improve diagnostic accuracy,predict outcomes,and optimize therapies.AI can identify patterns in imaging and biomarkers,facilitating the earlier detection of medical conditions.Wearable devices and health applications facilitate continuous monitoring and personalized care.Emerging fields such as digital Chinese medicine offer additional perspectives by integrating traditional diagnostic principles with modern digital tools,contributing to holistic and individualized cardiovascular care.This study examines the advancements and challenges in personalized cardiovascular medicine,highlighting the need to address issues such as data privacy,algorithmic bias,and accessibility to promote the equitable application of personalized medicine.展开更多
Diabetes mellitus(DM)comprises distinct subtypes-including type 1 DM,type 2 DM,and gestational DM-all characterized by chronic hyperglycemia and sub-stantial morbidity.Conventional diagnostic and therapeutic strategie...Diabetes mellitus(DM)comprises distinct subtypes-including type 1 DM,type 2 DM,and gestational DM-all characterized by chronic hyperglycemia and sub-stantial morbidity.Conventional diagnostic and therapeutic strategies often fall short in addressing the complex,multifactorial nature of DM.This review ex-plores how multi-omics integration enhances our mechanistic understanding of DM and informs emerging personalized therapeutic approaches.We consolidated genomic,transcriptomic,proteomic,metabolomic,and microbiomic data from major databases and peer-reviewed publications(2015-2025),with an emphasis on clinical relevance.Multi-omics investigations have identified convergent mole-cular networks underlyingβ-cell dysfunction,insulin resistance,and diabetic complications.The combination of metabolomics and microbiomics highlights critical interactions between metabolic intermediates and gut dysbiosis.Novel biomarkers facilitate early detection of DM and its complications,while single-cell multi-omics and machine learning further refine risk stratification.By dissecting DM heterogeneity more precisely,multi-omics integration enables targeted in-terventions and preventive strategies.Future efforts should focus on data har-monization,ethical considerations,and real-world validation to fully leverage multi-omics in addressing the global DM burden.展开更多
Personalized nursing is a necessary means to improve the satisfaction of emergency pediatric nursing.It can enhance the responsiveness of nursing services,strengthen the emotional connection between nurses and patient...Personalized nursing is a necessary means to improve the satisfaction of emergency pediatric nursing.It can enhance the responsiveness of nursing services,strengthen the emotional connection between nurses and patients,and provide a theoretical basis for clinical practice.Therefore,in the context of the new era,it is necessary to deeply analyze the essence and connotation of personalized nursing,and analyze the existing deficiencies in current emergency pediatric personalized nursing,so as to develop effective improvement plans.Research shows that personalized nursing can significantly improve the satisfaction of emergency pediatric nursing,largely avoid nursing risks,and has strong clinical application value.This article summarizes and explores the research on the influence of personalized nursing on improving the satisfaction of emergency pediatric nursing,and puts forward corresponding views.展开更多
With the continuous advancement of artificial intelligence(AI)technology,personalized learning systems are increasingly applied in higher education.Particularly within STEM(Science,Technology,Engineering,and Mathemati...With the continuous advancement of artificial intelligence(AI)technology,personalized learning systems are increasingly applied in higher education.Particularly within STEM(Science,Technology,Engineering,and Mathematics)education,AI demonstrates significant advantages through adaptive learning pathways,instant feedback,and individualized resource allocation.However,current research predominantly focuses on the technical architecture and application effectiveness of such systems,with insufficient exploration of how AI-enabled personalized learning systems influence university students’learning motivation and academic achievement through educational psychological mechanisms.This paper adopts an educational psychology perspective to construct a causal mechanism model linking“learning motivation-learning behavior-academic achievement.”Findings indicate that AI-powered personalized learning systems enhance learning autonomy,boost self-efficacy,and optimize feedback mechanisms.These effects collectively stimulate university students’learning motivation in STEM disciplines,thereby promoting academic achievement.Building upon empirical research,this paper proposes implications for educational practice and policy formulation,emphasizing the necessity of advancing higher education reform through the dual influence of technology and psychological mechanisms.展开更多
Deep phenotyping and genetic characterization of individuals are fundamental to assessing the metabolic status and determining nutrition-specific requirements.This study aimed to ascertain the utmost effectiveness of ...Deep phenotyping and genetic characterization of individuals are fundamental to assessing the metabolic status and determining nutrition-specific requirements.This study aimed to ascertain the utmost effectiveness of personalized interventions by aligning dietary adjustments with both the genotype and metabolotype of individuals.Therefore,we assessed here the usefulness of a polygenic score(PGS)characterizing a potential pro-inflammatory profile(PGSi)as a nutrigenetic tool to discern individuals from the Danish PREVENTOMICS cohort that could better respond to precision nutrition(PN)plans,specifically targeted at counteracting the low-grade inflammatory profile typically found in obesity.The cohort followed a PN plan to counteract the pro-inflammatory profile(PNi group)or generic dietary recommendations(Control)for 10 weeks.PGSi was applied for genetic stratification(Low/High).The effects of the intervention on anthropometrics and biomarkers related to inflammatory profile and carbohydrate metabolism were assessed.Around 30%of subjects had a high genetic predisposition to pro-inflammatory status(high-PGSi).These individuals demonstrated the most effective response to the dietary plan,experiencing improved body composition,with significant decreases in body weight(∆:-4.84%;P=0.039)and body fat(∆:-4.86%;P=0.007),and beneficial changes in pro-and anti-inflammatory biomarkers,with significant increases in IL-10(∆:71.3%;P=0.025)and decreases in TNF-α(∆:-3.0%;P=0.048),CRP(∆:-31.1%),ICAM1(∆:-5.8%),and MCP1(∆:-4.2%)circulating levels,compared to low-PGSi individuals.Both phenotypic and genetic stratification contributed to a better understanding of metabolic heterogeneity in response to diet.This approach allows for refinement of the prediction of individual requirements and potentially for better management of obesity.展开更多
With the rapid development of artificial intelligence(AI)technology,the teaching mode in the field of education is undergoing profound changes.Especially the design and implementation of personalized learning paths ha...With the rapid development of artificial intelligence(AI)technology,the teaching mode in the field of education is undergoing profound changes.Especially the design and implementation of personalized learning paths have become an important direction of intelligent teaching reform.The traditional“one-size-fits-all”teaching model has gradually failed to meet the individualized learning needs of students.However,through the advantages of data analysis and real-time feedback,AI technology can provide tailor-made teaching content and learning paths based on students’learning progress,interests,and abilities.This study explores the innovation of the personalized learning path model based on AI technology,and analyzes the potential and challenges of this model in improving teaching effectiveness,promoting the all-round development of students,and optimizing the interaction between teachers and students.Through case analysis and empirical research,this paper summarizes the implementation methods of the AI-driven personalized learning path,the innovation of teaching models,and their application prospects in educational reform.Meanwhile,the research also discussed the ethical issues of AI technology in education,data privacy protection,and its impact on the teacher-student relationship,and proposed corresponding solutions.展开更多
The relationship between genetics and infectious diseases is important in shaping our understanding of disease susceptibility,progression,and treatment.Recent research shows the impact of genetic variations,such as he...The relationship between genetics and infectious diseases is important in shaping our understanding of disease susceptibility,progression,and treatment.Recent research shows the impact of genetic variations,such as heme-oxygenase promoter length,on diseases like malaria and sepsis,revealing both protective and inconclusive effects.Studies on vaccine responses highlight genetic markers like human leukocyte antigens,emphasizing the potential for personalized immunization strategies.The ongoing battle against drug-resistant tuberculosis(TB)illustrates the complexity of genomic variants in predicting resistance,highlighting the need for integrated diagnostic tools.Additionally,genome-wide association studies reveal antibiotic resistance mechanisms in bacterial genomes,while host genetic polymorphisms,such as those in solute carrier family 11 member 1 and vitamin D receptor,demonstrate their role in TB susceptibility.Advanced techniques like metagenomic next-generation sequencing promise detailed pathogen detection but face challenges in cost and accessibility.A case report involving a highly virulent Mycobacterium TB strain with the pks1 gene further highlights the need for genetic insights in understanding disease severity and developing targeted interventions.This evolving landscape emphasizes the role of genetics in infectious diseases,while also addressing the need for standardized studies and accessible technologies.展开更多
Managing type 2 diabetes mellitus remains a significant challenge,particularly for individuals with persistently poor glycemic control.Although inadequate glycemic regulation is a well-established public health concer...Managing type 2 diabetes mellitus remains a significant challenge,particularly for individuals with persistently poor glycemic control.Although inadequate glycemic regulation is a well-established public health concern and a major contributor to diabetes-related complications,evidence on the effectiveness of intensive and supportive interventions across diverse patient subgroups is scarce.This editorial examines findings from a prospective study evaluating the influence of glycemic history on treatment outcomes in poorly controlled diabetes.The study highlights that personalized care models outperform generalized approaches by addressing the unique trajectories of glycemic deterioration.Newly diagnosed patients demonstrated the most favorable response to intervention,while those with consistently elevated glycated hemoglobin(≥10%)faced the greatest challenges in achieving glycemic control.These findings underscore the limitations of a onesize-fits-all strategy,reinforcing the need for patient-centered care that integrates individualized monitoring and timely intervention.Diabetes management requires prioritizing personalized treatment strategies that mitigate therapeutic inertia and ensure equitable,effective care for all patients.展开更多
This study proposes a learner profile framework based on multi-feature fusion,aiming to enhance the precision of personalized learning recommendations by integrating learners’static attributes(e.g.,demographic data a...This study proposes a learner profile framework based on multi-feature fusion,aiming to enhance the precision of personalized learning recommendations by integrating learners’static attributes(e.g.,demographic data and historical academic performance)with dynamic behavioral patterns(e.g.,real-time interactions and evolving interests over time).The research employs Term Frequency-Inverse Document Frequency(TF-IDF)for semantic feature extraction,integrates the Analytic Hierarchy Process(AHP)for feature weighting,and introduces a time decay function inspired by Newton’s law of cooling to dynamically model changes in learners’interests.Empirical results demonstrate that this framework effectively captures the dynamic evolution of learners’behaviors and provides context-aware learning resource recommendations.The study introduces a novel paradigm for learner modeling in educational technology,combining methodological innovation with a scalable technical architecture,thereby laying a foundation for the development of adaptive learning systems.展开更多
Hepatocellular carcinoma(HCC)recurrence after liver transplantation(LT)presents a significant challenge,with recurrence rates ranging from 8%to 20%globally.Current biomarkers,such as alpha-fetoprotein(AFP)and des-gamm...Hepatocellular carcinoma(HCC)recurrence after liver transplantation(LT)presents a significant challenge,with recurrence rates ranging from 8%to 20%globally.Current biomarkers,such as alpha-fetoprotein(AFP)and des-gamma-carboxy prothrombin(DCP),lack specificity,limiting their utility in risk strati-fication.YKL-40,a glycoprotein involved in extracellular matrix remodeling,hepatic stellate cell activation,and immune modulation,has emerged as a promising biomarker for post-LT surveillance.Elevated serum levels of YKL-40 are associated with advanced liver disease,tumor progression,and poorer post-LT outcomes,highlighting its potential to address gaps in early detection and personalized management of HCC recurrence.This manuscript synthesizes clinical and mechanistic evidence to evaluate YKL-40’s predictive utility in post-LT care.While preliminary findings demonstrate its specificity for liver-related pathologies,challenges remain,including assay standardization,lack of pro-spective validation,and the need to distinguish between malignant and non-malignant causes of elevated levels.Integrating YKL-40 into multi-biomarker panels with AFP and DCP could enhance predictive accuracy and enable tailored therapeutic strategies.Future research should focus on multicenter studies to validate YKL-40’s clinical utility,address confounding factors like graft rejection and systemic inflammation,and explore its role in predictive models driven by emerging technologies such as artificial intelligence.YKL-40 holds transformative potential in reshaping post-LT care through precision medicine,providing a pathway for better outcomes and improved management of high-risk LT recipients.展开更多
Federated learning(FL)is a distributed machine learning paradigm for edge cloud computing.FL can facilitate data-driven decision-making in tactical scenarios,effectively addressing both data volume and infrastructure ...Federated learning(FL)is a distributed machine learning paradigm for edge cloud computing.FL can facilitate data-driven decision-making in tactical scenarios,effectively addressing both data volume and infrastructure challenges in edge environments.However,the diversity of clients in edge cloud computing presents significant challenges for FL.Personalized federated learning(pFL)received considerable attention in recent years.One example of pFL involves exploiting the global and local information in the local model.Current pFL algorithms experience limitations such as slow convergence speed,catastrophic forgetting,and poor performance in complex tasks,which still have significant shortcomings compared to the centralized learning.To achieve high pFL performance,we propose FedCLCC:Federated Contrastive Learning and Conditional Computing.The core of FedCLCC is the use of contrastive learning and conditional computing.Contrastive learning determines the feature representation similarity to adjust the local model.Conditional computing separates the global and local information and feeds it to their corresponding heads for global and local handling.Our comprehensive experiments demonstrate that FedCLCC outperforms other state-of-the-art FL algorithms.展开更多
BACKGROUND Gastrointestinal(GI)surgery can significantly affect the nutritional status and immune function of patients.This study aimed to investigate the effects of personalized nutritional care on the recovery of im...BACKGROUND Gastrointestinal(GI)surgery can significantly affect the nutritional status and immune function of patients.This study aimed to investigate the effects of personalized nutritional care on the recovery of immune function in patients who underwent postoperative GI surgery.AIM To study examines personalized nutritional care’s impact on immune function recovery,nutritional status,and clinical outcomes after GI surgery.METHODS This observational study included 80 patients who underwent GI surgery between 2021 and 2023.Patients received personalized nutritional care based on their individual needs and surgical outcomes.Immune function markers including lymphocyte subsets,immunoglobulins,and cytokines were measured preoperatively and at regular intervals postoperatively.Nutritional status,clinical outcomes,and quality of life were assessed.RESULTS Patients receiving personalized nutritional care showed significant improvements in immune function markers compared to baseline.At 4 weeks postoperatively,CD4+T-cell counts increased by 25%(P<0.001),while interleukin-6 levels decreased by 40%(P<0.001).Nutritional status,as measured by prealbumin and transferrin levels,improved by 30%(P<0.01).Postoperative complications reduced by 35%compared to historical controls.The quality-of-life scores improved by 40%at 3 months postoperatively.CONCLUSION Personalized nutritional care enhances immune function recovery,improves nutritional status,and reduces complications in patients undergoing postoperative GI surgery,highlighting its crucial role in optimizing patient outcomes following such procedures.展开更多
BACKGROUND Liver metastases are very common in pancreatic neuroendocrine tumors(pNETs).When surgical resection is possible,it is typically associated with survival benefits in patients with pNET and liver metastases.P...BACKGROUND Liver metastases are very common in pancreatic neuroendocrine tumors(pNETs).When surgical resection is possible,it is typically associated with survival benefits in patients with pNET and liver metastases.Patient-derived organoids are a powerful preclinical platform that show great potential for predicting treatment response,and they have been increasingly applied in precision medicine and cancer research.CASE SUMMARY A 51-year-old man was admitted to the hospital with the chief complaint of in-termittent dull pain in the upper abdomen for over 3 years.Computerized to-mography showed multiple space-occupying lesions in the liver and a neoplasm in the pancreatic body.Pathological results suggested a grade 3 pancreas-derived hepatic neuroendocrine tumor.In combination with relevant examinations,the patient was diagnosed with pNET with liver metastases(grade 3).Transarterial chemoembolization was initially performed with oxaliplatin and 5-fluorouracil,after which the chemotherapy regimen was switched to liposomal irinotecan and cisplatin for a subsequent perfusion,guided by organoid-based drug sensitivity testing.Following interventional treatment,the tumor had decreased in size.However,due to poor treatment compliance and the patient’s preference for sur-INTRODUCTION Pancreatic neuroendocrine tumors(pNETs)are a rare and heterogeneous group of neoplasms arising from pancreatic islet cells,with variations in histology,clinical characteristics,and prognosis[1].They may present as non-infiltrative,slow-growing tumors,locally invasive tumors,or even rapidly metastasizing tumors[2].Most metastases localize to the liver,and approximately 28%-77%of patients with pNETs will experience liver metastases in their lifetime[3].Patients with liver metastases may be subjected to local complications such as biliary obstruction,liver insufficiency,and carcinoid syndrome.Additionally,liver metastases are a major risk factor for the prognosis of patients with pNETs[4].When feasible,surgical resection is significantly associated with the best long-term survival outcomes[5].Therefore,for patients with pNET liver metastases,comprehensive assessment and multidisciplinary approaches are required to determine the feasibility of surgical resection and the optimal treatment to improve the prognosis.Over the past decade,the advent of in vitro three-dimensional technologies including organoids has revolutionized the development of human cancer models.Patient-derived organoids(PDOs),an in vitro three-dimensional microstructure,can faithfully recapitulate the intricate spatial architecture and cell heterogeneity of the tissue,and simulate the biological behaviors and functions of parental tumors while preserving biological,genetic and molecular features[6,7].As a po-werful preclinical platform,PDOs have been increasingly applied in precision medicine and cancer research.Importantly,there is a significant association between the use of PDO-based drug sensitivity testing and clinical responses to chemotherapy,radiotherapy and targeted therapy in multiple cancer types[8-10].Although gastroenteropancreatic neuroendocrine neoplasm organoids have been confirmed to retain the pathohistological and functional phenotypes of parental tumors[7],their roles in the prediction of clinical outcomes have not been presented.Here,we report a case of pNET with liver metastases who successfully received surgical resection after personalized treatment guided by PDO-based drug sensitivity testing,resulting in a favorable prognosis.展开更多
Immune adjuvants are extremely important in tumor vaccines,which can amplify antigen-specific immune responses and enhance anti-tumor efficacy.Nevertheless,well-designed adjuvants and rational combination of adjuvants...Immune adjuvants are extremely important in tumor vaccines,which can amplify antigen-specific immune responses and enhance anti-tumor efficacy.Nevertheless,well-designed adjuvants and rational combination of adjuvants and antigens still remain a challenge in tumor vaccines.In this study,we designed and formulated carrier-free double-adjuvant nanoparticles(FPC-NPs)by self-assembling of fluoroalkane-grafted polyethylenimide(PEI)(Toll-like receptor 4(TLR4)agonist)and cytosine-phosphateguanine(CpG)(TLR9 agonist),and then obtained personalized tumor vaccines(FPC-NPs@TAAs)by electrostatic adsorption of tumor-associated antigens(TAAs)on the surface of FPC-NPs.The results showed that FPC-NPs@TAAs could promote cellular internalization of adjuvants,deliver antigens and adjuvants to the same antigen-presenting cell,which can effectively activate dendritic cells,encourage cross-presentation of antigens,and reduce the proportion of M2-type macrophages.Our work presents a simple method to realize the dual adjuvant combination of TLR4 and TLR9 via well-designed carrier-free nanoparticles,showing great promise for developing personalized tumor vaccines to enhance the efficacy of immunotherapy.展开更多
In Internet of Vehicles,VehicleInfrastructure-Cloud cooperation supports diverse intelligent driving and intelligent transportation applications.Federated Learning(FL)is the emerging computation paradigm to provide ef...In Internet of Vehicles,VehicleInfrastructure-Cloud cooperation supports diverse intelligent driving and intelligent transportation applications.Federated Learning(FL)is the emerging computation paradigm to provide efficient and privacypreserving collaborative learning.However,in Io V environment,federated learning faces the challenges introduced by high mobility of vehicles and nonIndependently Identically Distribution(non-IID)of data.High mobility causes FL clients quit and the communication offline.The non-IID data leads to slow and unstable convergence of global model and single global model's weak adaptability to clients with different localization characteristics.Accordingly,this paper proposes a personalized aggregation strategy for hierarchical Federated Learning in Io V environment,including Fed SA(Special Asynchronous Federated Learning with Self-adaptive Aggregation)for low-level FL between a Road Side Unit(RSU)and the vehicles within its coverage,and Fed Att(Federated Learning with Attention Mechanism)for high-level FL between a cloud server and multiple RSUs.Agents self-adaptively obtain model aggregation weight based on Advantage Actor-Critic(A2C)algorithm.Experiments show the proposed strategy encourages vehicles to participate in global aggregation,and outperforms existing methods in training performance.展开更多
With the rapid development of advanced networking and computing technologies such as the Internet of Things, network function virtualization, and 5G infrastructure, new development opportunities are emerging for Marit...With the rapid development of advanced networking and computing technologies such as the Internet of Things, network function virtualization, and 5G infrastructure, new development opportunities are emerging for Maritime Meteorological Sensor Networks(MMSNs). However, the increasing number of intelligent devices joining the MMSN poses a growing threat to network security. Current Artificial Intelligence(AI) intrusion detection techniques turn intrusion detection into a classification problem, where AI excels. These techniques assume sufficient high-quality instances for model construction, which is often unsatisfactory for real-world operation with limited attack instances and constantly evolving characteristics. This paper proposes an Adaptive Personalized Federated learning(APFed) framework that allows multiple MMSN owners to engage in collaborative training. By employing an adaptive personalized update and a shared global classifier, the adverse effects of imbalanced, Non-Independent and Identically Distributed(Non-IID) data are mitigated, enabling the intrusion detection model to possess personalized capabilities and good global generalization. In addition, a lightweight intrusion detection model is proposed to detect various attacks with an effective adaptation to the MMSN environment. Finally, extensive experiments on a classical network dataset show that the attack classification accuracy is improved by about 5% compared to most baselines in the global scenarios.展开更多
A brain-computer interface(BCI)based on motor imagery(MI)provides additional control pathways by decoding the intentions of the brain.MI ability has great intra-individual variability,and the majority of MI-BCI system...A brain-computer interface(BCI)based on motor imagery(MI)provides additional control pathways by decoding the intentions of the brain.MI ability has great intra-individual variability,and the majority of MI-BCI systems are unable to adapt to this variability,leading to poor training effects.Therefore,prediction of MI ability is needed.In this study,we propose an MI ability predictor based on multi-frequency EEG features.To validate the performance of the predictor,a video-guided paradigm and a traditional MI paradigm are designed,and the predictor is applied to both paradigms.The results demonstrate that all subjects achieved>85%prediction precision in both applications,with a maximum of 96%.This study indicates that the predictor can accurately predict the individuals’MI ability in different states,provide the scientific basis for personalized training,and enhance the effect of MI-BCI training.展开更多
This letter addressed the impactful study by Zhong et al,which introduced a risk prediction and stratification model for surgical adverse events following minimally invasive esophagectomy.By identifying key risk facto...This letter addressed the impactful study by Zhong et al,which introduced a risk prediction and stratification model for surgical adverse events following minimally invasive esophagectomy.By identifying key risk factors such as chronic obstructive pulmonary disease and hypoalbuminemia,the model demonstrated strong predictive accuracy and offered a pathway to personalized perioperative care.This correspondence highlighted the clinical significance,emphasizing its potential to optimize patient outcomes through tailored inter-ventions.Further prospective validation and application across diverse settings are essential to realize its full potential in advancing esophageal surgery practices.展开更多
The rapidly evolving environment of assisted reproductive technology(ART)requires consideration of how new innovations are reshaping clinical practice as much as research.In particular,there are three key areas that,w...The rapidly evolving environment of assisted reproductive technology(ART)requires consideration of how new innovations are reshaping clinical practice as much as research.In particular,there are three key areas that,while full of promise,also present significant challenges:the use of artificial intelligence(AI)in embryo selection,the impact of personalized medicine on ART success rates,and the ethical considerations of genetic screening of embryos[1].This letter is meant to provoke further discussion and highlight the need for harmonized global guidelines as these advances continue to reshape the reproductive medicine environment.展开更多
文摘Cardiovascular diseases are the leading cause of death,requiring innovative approaches for prevention,diagnosis,and treatment.Personalized medicine customizes interventions according to individual characteristics,with artificial intelligence(AI)playing a key role in analyzing complex data to improve diagnostic accuracy,predict outcomes,and optimize therapies.AI can identify patterns in imaging and biomarkers,facilitating the earlier detection of medical conditions.Wearable devices and health applications facilitate continuous monitoring and personalized care.Emerging fields such as digital Chinese medicine offer additional perspectives by integrating traditional diagnostic principles with modern digital tools,contributing to holistic and individualized cardiovascular care.This study examines the advancements and challenges in personalized cardiovascular medicine,highlighting the need to address issues such as data privacy,algorithmic bias,and accessibility to promote the equitable application of personalized medicine.
文摘Diabetes mellitus(DM)comprises distinct subtypes-including type 1 DM,type 2 DM,and gestational DM-all characterized by chronic hyperglycemia and sub-stantial morbidity.Conventional diagnostic and therapeutic strategies often fall short in addressing the complex,multifactorial nature of DM.This review ex-plores how multi-omics integration enhances our mechanistic understanding of DM and informs emerging personalized therapeutic approaches.We consolidated genomic,transcriptomic,proteomic,metabolomic,and microbiomic data from major databases and peer-reviewed publications(2015-2025),with an emphasis on clinical relevance.Multi-omics investigations have identified convergent mole-cular networks underlyingβ-cell dysfunction,insulin resistance,and diabetic complications.The combination of metabolomics and microbiomics highlights critical interactions between metabolic intermediates and gut dysbiosis.Novel biomarkers facilitate early detection of DM and its complications,while single-cell multi-omics and machine learning further refine risk stratification.By dissecting DM heterogeneity more precisely,multi-omics integration enables targeted in-terventions and preventive strategies.Future efforts should focus on data har-monization,ethical considerations,and real-world validation to fully leverage multi-omics in addressing the global DM burden.
文摘Personalized nursing is a necessary means to improve the satisfaction of emergency pediatric nursing.It can enhance the responsiveness of nursing services,strengthen the emotional connection between nurses and patients,and provide a theoretical basis for clinical practice.Therefore,in the context of the new era,it is necessary to deeply analyze the essence and connotation of personalized nursing,and analyze the existing deficiencies in current emergency pediatric personalized nursing,so as to develop effective improvement plans.Research shows that personalized nursing can significantly improve the satisfaction of emergency pediatric nursing,largely avoid nursing risks,and has strong clinical application value.This article summarizes and explores the research on the influence of personalized nursing on improving the satisfaction of emergency pediatric nursing,and puts forward corresponding views.
文摘With the continuous advancement of artificial intelligence(AI)technology,personalized learning systems are increasingly applied in higher education.Particularly within STEM(Science,Technology,Engineering,and Mathematics)education,AI demonstrates significant advantages through adaptive learning pathways,instant feedback,and individualized resource allocation.However,current research predominantly focuses on the technical architecture and application effectiveness of such systems,with insufficient exploration of how AI-enabled personalized learning systems influence university students’learning motivation and academic achievement through educational psychological mechanisms.This paper adopts an educational psychology perspective to construct a causal mechanism model linking“learning motivation-learning behavior-academic achievement.”Findings indicate that AI-powered personalized learning systems enhance learning autonomy,boost self-efficacy,and optimize feedback mechanisms.These effects collectively stimulate university students’learning motivation in STEM disciplines,thereby promoting academic achievement.Building upon empirical research,this paper proposes implications for educational practice and policy formulation,emphasizing the necessity of advancing higher education reform through the dual influence of technology and psychological mechanisms.
基金supported through the European Union’s Horizon 2020 Research and Innovation Program(818318)。
文摘Deep phenotyping and genetic characterization of individuals are fundamental to assessing the metabolic status and determining nutrition-specific requirements.This study aimed to ascertain the utmost effectiveness of personalized interventions by aligning dietary adjustments with both the genotype and metabolotype of individuals.Therefore,we assessed here the usefulness of a polygenic score(PGS)characterizing a potential pro-inflammatory profile(PGSi)as a nutrigenetic tool to discern individuals from the Danish PREVENTOMICS cohort that could better respond to precision nutrition(PN)plans,specifically targeted at counteracting the low-grade inflammatory profile typically found in obesity.The cohort followed a PN plan to counteract the pro-inflammatory profile(PNi group)or generic dietary recommendations(Control)for 10 weeks.PGSi was applied for genetic stratification(Low/High).The effects of the intervention on anthropometrics and biomarkers related to inflammatory profile and carbohydrate metabolism were assessed.Around 30%of subjects had a high genetic predisposition to pro-inflammatory status(high-PGSi).These individuals demonstrated the most effective response to the dietary plan,experiencing improved body composition,with significant decreases in body weight(∆:-4.84%;P=0.039)and body fat(∆:-4.86%;P=0.007),and beneficial changes in pro-and anti-inflammatory biomarkers,with significant increases in IL-10(∆:71.3%;P=0.025)and decreases in TNF-α(∆:-3.0%;P=0.048),CRP(∆:-31.1%),ICAM1(∆:-5.8%),and MCP1(∆:-4.2%)circulating levels,compared to low-PGSi individuals.Both phenotypic and genetic stratification contributed to a better understanding of metabolic heterogeneity in response to diet.This approach allows for refinement of the prediction of individual requirements and potentially for better management of obesity.
基金The 2024 Guangdong University of Science and Technology Teaching,Science and Innovation Project(GKJXXZ2024028)。
文摘With the rapid development of artificial intelligence(AI)technology,the teaching mode in the field of education is undergoing profound changes.Especially the design and implementation of personalized learning paths have become an important direction of intelligent teaching reform.The traditional“one-size-fits-all”teaching model has gradually failed to meet the individualized learning needs of students.However,through the advantages of data analysis and real-time feedback,AI technology can provide tailor-made teaching content and learning paths based on students’learning progress,interests,and abilities.This study explores the innovation of the personalized learning path model based on AI technology,and analyzes the potential and challenges of this model in improving teaching effectiveness,promoting the all-round development of students,and optimizing the interaction between teachers and students.Through case analysis and empirical research,this paper summarizes the implementation methods of the AI-driven personalized learning path,the innovation of teaching models,and their application prospects in educational reform.Meanwhile,the research also discussed the ethical issues of AI technology in education,data privacy protection,and its impact on the teacher-student relationship,and proposed corresponding solutions.
文摘The relationship between genetics and infectious diseases is important in shaping our understanding of disease susceptibility,progression,and treatment.Recent research shows the impact of genetic variations,such as heme-oxygenase promoter length,on diseases like malaria and sepsis,revealing both protective and inconclusive effects.Studies on vaccine responses highlight genetic markers like human leukocyte antigens,emphasizing the potential for personalized immunization strategies.The ongoing battle against drug-resistant tuberculosis(TB)illustrates the complexity of genomic variants in predicting resistance,highlighting the need for integrated diagnostic tools.Additionally,genome-wide association studies reveal antibiotic resistance mechanisms in bacterial genomes,while host genetic polymorphisms,such as those in solute carrier family 11 member 1 and vitamin D receptor,demonstrate their role in TB susceptibility.Advanced techniques like metagenomic next-generation sequencing promise detailed pathogen detection but face challenges in cost and accessibility.A case report involving a highly virulent Mycobacterium TB strain with the pks1 gene further highlights the need for genetic insights in understanding disease severity and developing targeted interventions.This evolving landscape emphasizes the role of genetics in infectious diseases,while also addressing the need for standardized studies and accessible technologies.
基金Supported by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education,No.NRF-RS-2023-00237287。
文摘Managing type 2 diabetes mellitus remains a significant challenge,particularly for individuals with persistently poor glycemic control.Although inadequate glycemic regulation is a well-established public health concern and a major contributor to diabetes-related complications,evidence on the effectiveness of intensive and supportive interventions across diverse patient subgroups is scarce.This editorial examines findings from a prospective study evaluating the influence of glycemic history on treatment outcomes in poorly controlled diabetes.The study highlights that personalized care models outperform generalized approaches by addressing the unique trajectories of glycemic deterioration.Newly diagnosed patients demonstrated the most favorable response to intervention,while those with consistently elevated glycated hemoglobin(≥10%)faced the greatest challenges in achieving glycemic control.These findings underscore the limitations of a onesize-fits-all strategy,reinforcing the need for patient-centered care that integrates individualized monitoring and timely intervention.Diabetes management requires prioritizing personalized treatment strategies that mitigate therapeutic inertia and ensure equitable,effective care for all patients.
基金This work is supported by the Ministry of Education of Humanities and Social Science projects in China(No.20YJCZH124)Guangdong Province Education and Teaching Reform Project No.640:Research on the Teaching Practice and Application of Online Peer Assessment Methods in the Context of Artificial Intelligence.
文摘This study proposes a learner profile framework based on multi-feature fusion,aiming to enhance the precision of personalized learning recommendations by integrating learners’static attributes(e.g.,demographic data and historical academic performance)with dynamic behavioral patterns(e.g.,real-time interactions and evolving interests over time).The research employs Term Frequency-Inverse Document Frequency(TF-IDF)for semantic feature extraction,integrates the Analytic Hierarchy Process(AHP)for feature weighting,and introduces a time decay function inspired by Newton’s law of cooling to dynamically model changes in learners’interests.Empirical results demonstrate that this framework effectively captures the dynamic evolution of learners’behaviors and provides context-aware learning resource recommendations.The study introduces a novel paradigm for learner modeling in educational technology,combining methodological innovation with a scalable technical architecture,thereby laying a foundation for the development of adaptive learning systems.
文摘Hepatocellular carcinoma(HCC)recurrence after liver transplantation(LT)presents a significant challenge,with recurrence rates ranging from 8%to 20%globally.Current biomarkers,such as alpha-fetoprotein(AFP)and des-gamma-carboxy prothrombin(DCP),lack specificity,limiting their utility in risk strati-fication.YKL-40,a glycoprotein involved in extracellular matrix remodeling,hepatic stellate cell activation,and immune modulation,has emerged as a promising biomarker for post-LT surveillance.Elevated serum levels of YKL-40 are associated with advanced liver disease,tumor progression,and poorer post-LT outcomes,highlighting its potential to address gaps in early detection and personalized management of HCC recurrence.This manuscript synthesizes clinical and mechanistic evidence to evaluate YKL-40’s predictive utility in post-LT care.While preliminary findings demonstrate its specificity for liver-related pathologies,challenges remain,including assay standardization,lack of pro-spective validation,and the need to distinguish between malignant and non-malignant causes of elevated levels.Integrating YKL-40 into multi-biomarker panels with AFP and DCP could enhance predictive accuracy and enable tailored therapeutic strategies.Future research should focus on multicenter studies to validate YKL-40’s clinical utility,address confounding factors like graft rejection and systemic inflammation,and explore its role in predictive models driven by emerging technologies such as artificial intelligence.YKL-40 holds transformative potential in reshaping post-LT care through precision medicine,providing a pathway for better outcomes and improved management of high-risk LT recipients.
基金supported by the Natural Science Foundation of Xinjiang Uygur Autonomous Region(Grant No.2022D01B 187)。
文摘Federated learning(FL)is a distributed machine learning paradigm for edge cloud computing.FL can facilitate data-driven decision-making in tactical scenarios,effectively addressing both data volume and infrastructure challenges in edge environments.However,the diversity of clients in edge cloud computing presents significant challenges for FL.Personalized federated learning(pFL)received considerable attention in recent years.One example of pFL involves exploiting the global and local information in the local model.Current pFL algorithms experience limitations such as slow convergence speed,catastrophic forgetting,and poor performance in complex tasks,which still have significant shortcomings compared to the centralized learning.To achieve high pFL performance,we propose FedCLCC:Federated Contrastive Learning and Conditional Computing.The core of FedCLCC is the use of contrastive learning and conditional computing.Contrastive learning determines the feature representation similarity to adjust the local model.Conditional computing separates the global and local information and feeds it to their corresponding heads for global and local handling.Our comprehensive experiments demonstrate that FedCLCC outperforms other state-of-the-art FL algorithms.
文摘BACKGROUND Gastrointestinal(GI)surgery can significantly affect the nutritional status and immune function of patients.This study aimed to investigate the effects of personalized nutritional care on the recovery of immune function in patients who underwent postoperative GI surgery.AIM To study examines personalized nutritional care’s impact on immune function recovery,nutritional status,and clinical outcomes after GI surgery.METHODS This observational study included 80 patients who underwent GI surgery between 2021 and 2023.Patients received personalized nutritional care based on their individual needs and surgical outcomes.Immune function markers including lymphocyte subsets,immunoglobulins,and cytokines were measured preoperatively and at regular intervals postoperatively.Nutritional status,clinical outcomes,and quality of life were assessed.RESULTS Patients receiving personalized nutritional care showed significant improvements in immune function markers compared to baseline.At 4 weeks postoperatively,CD4+T-cell counts increased by 25%(P<0.001),while interleukin-6 levels decreased by 40%(P<0.001).Nutritional status,as measured by prealbumin and transferrin levels,improved by 30%(P<0.01).Postoperative complications reduced by 35%compared to historical controls.The quality-of-life scores improved by 40%at 3 months postoperatively.CONCLUSION Personalized nutritional care enhances immune function recovery,improves nutritional status,and reduces complications in patients undergoing postoperative GI surgery,highlighting its crucial role in optimizing patient outcomes following such procedures.
基金Supported by Chongqing Natural Science Foundation General Project,No.CSTB2023NSCQ-MSX0182 and No.CSTB2023NSCQMSX0252Clinical Research Special Project of The Second Affiliated Hospital of Army Medical University,No.2024 F022.
文摘BACKGROUND Liver metastases are very common in pancreatic neuroendocrine tumors(pNETs).When surgical resection is possible,it is typically associated with survival benefits in patients with pNET and liver metastases.Patient-derived organoids are a powerful preclinical platform that show great potential for predicting treatment response,and they have been increasingly applied in precision medicine and cancer research.CASE SUMMARY A 51-year-old man was admitted to the hospital with the chief complaint of in-termittent dull pain in the upper abdomen for over 3 years.Computerized to-mography showed multiple space-occupying lesions in the liver and a neoplasm in the pancreatic body.Pathological results suggested a grade 3 pancreas-derived hepatic neuroendocrine tumor.In combination with relevant examinations,the patient was diagnosed with pNET with liver metastases(grade 3).Transarterial chemoembolization was initially performed with oxaliplatin and 5-fluorouracil,after which the chemotherapy regimen was switched to liposomal irinotecan and cisplatin for a subsequent perfusion,guided by organoid-based drug sensitivity testing.Following interventional treatment,the tumor had decreased in size.However,due to poor treatment compliance and the patient’s preference for sur-INTRODUCTION Pancreatic neuroendocrine tumors(pNETs)are a rare and heterogeneous group of neoplasms arising from pancreatic islet cells,with variations in histology,clinical characteristics,and prognosis[1].They may present as non-infiltrative,slow-growing tumors,locally invasive tumors,or even rapidly metastasizing tumors[2].Most metastases localize to the liver,and approximately 28%-77%of patients with pNETs will experience liver metastases in their lifetime[3].Patients with liver metastases may be subjected to local complications such as biliary obstruction,liver insufficiency,and carcinoid syndrome.Additionally,liver metastases are a major risk factor for the prognosis of patients with pNETs[4].When feasible,surgical resection is significantly associated with the best long-term survival outcomes[5].Therefore,for patients with pNET liver metastases,comprehensive assessment and multidisciplinary approaches are required to determine the feasibility of surgical resection and the optimal treatment to improve the prognosis.Over the past decade,the advent of in vitro three-dimensional technologies including organoids has revolutionized the development of human cancer models.Patient-derived organoids(PDOs),an in vitro three-dimensional microstructure,can faithfully recapitulate the intricate spatial architecture and cell heterogeneity of the tissue,and simulate the biological behaviors and functions of parental tumors while preserving biological,genetic and molecular features[6,7].As a po-werful preclinical platform,PDOs have been increasingly applied in precision medicine and cancer research.Importantly,there is a significant association between the use of PDO-based drug sensitivity testing and clinical responses to chemotherapy,radiotherapy and targeted therapy in multiple cancer types[8-10].Although gastroenteropancreatic neuroendocrine neoplasm organoids have been confirmed to retain the pathohistological and functional phenotypes of parental tumors[7],their roles in the prediction of clinical outcomes have not been presented.Here,we report a case of pNET with liver metastases who successfully received surgical resection after personalized treatment guided by PDO-based drug sensitivity testing,resulting in a favorable prognosis.
基金supported by Noncommunicable Chronic Diseases-National Science and Technology Major Project(No.2023ZD0500800)National Natural Science Foundation of China(Nos.82302390,82172090 and 82072059)+4 种基金CAMS Innovation Fund for Medical Sciences(Nos.2021-I2M-1-058,2022-I2M-2-003 and 2023-I2M-2-008)China Postdoctoral Science Foundation(No.2022M720502)Tianjin Municipal Natural Science Foundation(Nos.22JCQNJC00070 and 24ZXZSSS00200)CAMS Union Young Scholars Support Program(No.2022051)Fundamental Research Funds for the Central Universities(No.2019PT320028).
文摘Immune adjuvants are extremely important in tumor vaccines,which can amplify antigen-specific immune responses and enhance anti-tumor efficacy.Nevertheless,well-designed adjuvants and rational combination of adjuvants and antigens still remain a challenge in tumor vaccines.In this study,we designed and formulated carrier-free double-adjuvant nanoparticles(FPC-NPs)by self-assembling of fluoroalkane-grafted polyethylenimide(PEI)(Toll-like receptor 4(TLR4)agonist)and cytosine-phosphateguanine(CpG)(TLR9 agonist),and then obtained personalized tumor vaccines(FPC-NPs@TAAs)by electrostatic adsorption of tumor-associated antigens(TAAs)on the surface of FPC-NPs.The results showed that FPC-NPs@TAAs could promote cellular internalization of adjuvants,deliver antigens and adjuvants to the same antigen-presenting cell,which can effectively activate dendritic cells,encourage cross-presentation of antigens,and reduce the proportion of M2-type macrophages.Our work presents a simple method to realize the dual adjuvant combination of TLR4 and TLR9 via well-designed carrier-free nanoparticles,showing great promise for developing personalized tumor vaccines to enhance the efficacy of immunotherapy.
基金supported by the National Natural Science Foundation of China under Grant 61931005Beijing Natural Science Foundation under Grant L202018the Key Laboratory of Internet of Vehicle Technical Innovation and Testing(CAICT),Ministry of Industry and Information Technology under Grant No.KL-2023-001。
文摘In Internet of Vehicles,VehicleInfrastructure-Cloud cooperation supports diverse intelligent driving and intelligent transportation applications.Federated Learning(FL)is the emerging computation paradigm to provide efficient and privacypreserving collaborative learning.However,in Io V environment,federated learning faces the challenges introduced by high mobility of vehicles and nonIndependently Identically Distribution(non-IID)of data.High mobility causes FL clients quit and the communication offline.The non-IID data leads to slow and unstable convergence of global model and single global model's weak adaptability to clients with different localization characteristics.Accordingly,this paper proposes a personalized aggregation strategy for hierarchical Federated Learning in Io V environment,including Fed SA(Special Asynchronous Federated Learning with Self-adaptive Aggregation)for low-level FL between a Road Side Unit(RSU)and the vehicles within its coverage,and Fed Att(Federated Learning with Attention Mechanism)for high-level FL between a cloud server and multiple RSUs.Agents self-adaptively obtain model aggregation weight based on Advantage Actor-Critic(A2C)algorithm.Experiments show the proposed strategy encourages vehicles to participate in global aggregation,and outperforms existing methods in training performance.
基金supported by the National Natural Science Foundation of China under Grant 62371181the Project on Excellent Postgraduate Dissertation of Hohai University (422003482)the Changzhou Science and Technology International Cooperation Program under Grant CZ20230029。
文摘With the rapid development of advanced networking and computing technologies such as the Internet of Things, network function virtualization, and 5G infrastructure, new development opportunities are emerging for Maritime Meteorological Sensor Networks(MMSNs). However, the increasing number of intelligent devices joining the MMSN poses a growing threat to network security. Current Artificial Intelligence(AI) intrusion detection techniques turn intrusion detection into a classification problem, where AI excels. These techniques assume sufficient high-quality instances for model construction, which is often unsatisfactory for real-world operation with limited attack instances and constantly evolving characteristics. This paper proposes an Adaptive Personalized Federated learning(APFed) framework that allows multiple MMSN owners to engage in collaborative training. By employing an adaptive personalized update and a shared global classifier, the adverse effects of imbalanced, Non-Independent and Identically Distributed(Non-IID) data are mitigated, enabling the intrusion detection model to possess personalized capabilities and good global generalization. In addition, a lightweight intrusion detection model is proposed to detect various attacks with an effective adaptation to the MMSN environment. Finally, extensive experiments on a classical network dataset show that the attack classification accuracy is improved by about 5% compared to most baselines in the global scenarios.
基金supported by the Natural Science Foundation of Hebei Province(F2024202019)the National Natural Science Foundation of China(32201072).
文摘A brain-computer interface(BCI)based on motor imagery(MI)provides additional control pathways by decoding the intentions of the brain.MI ability has great intra-individual variability,and the majority of MI-BCI systems are unable to adapt to this variability,leading to poor training effects.Therefore,prediction of MI ability is needed.In this study,we propose an MI ability predictor based on multi-frequency EEG features.To validate the performance of the predictor,a video-guided paradigm and a traditional MI paradigm are designed,and the predictor is applied to both paradigms.The results demonstrate that all subjects achieved>85%prediction precision in both applications,with a maximum of 96%.This study indicates that the predictor can accurately predict the individuals’MI ability in different states,provide the scientific basis for personalized training,and enhance the effect of MI-BCI training.
文摘This letter addressed the impactful study by Zhong et al,which introduced a risk prediction and stratification model for surgical adverse events following minimally invasive esophagectomy.By identifying key risk factors such as chronic obstructive pulmonary disease and hypoalbuminemia,the model demonstrated strong predictive accuracy and offered a pathway to personalized perioperative care.This correspondence highlighted the clinical significance,emphasizing its potential to optimize patient outcomes through tailored inter-ventions.Further prospective validation and application across diverse settings are essential to realize its full potential in advancing esophageal surgery practices.
文摘The rapidly evolving environment of assisted reproductive technology(ART)requires consideration of how new innovations are reshaping clinical practice as much as research.In particular,there are three key areas that,while full of promise,also present significant challenges:the use of artificial intelligence(AI)in embryo selection,the impact of personalized medicine on ART success rates,and the ethical considerations of genetic screening of embryos[1].This letter is meant to provoke further discussion and highlight the need for harmonized global guidelines as these advances continue to reshape the reproductive medicine environment.
