Objective:To explore the clinical effect of personalized nutritional support in elderly women with gestational diabetes(GDM),and explore its impact on the incidence of maternal complications and pregnancy outcomes.Met...Objective:To explore the clinical effect of personalized nutritional support in elderly women with gestational diabetes(GDM),and explore its impact on the incidence of maternal complications and pregnancy outcomes.Methods:A total of 90 elderly pregnant women with gestational diabetes who were delivered in our hospital from January 2023 to January 2024 were selected as the research objects.They were randomly divided into an observation group and a control group,with 45 cases in each group.The control group only received routine pregnancy care and basic nutrition guidance,while the observation group received personalized nutrition support on this basis.Compare the blood glucose control,incidence of pregnancy complications,pregnancy outcomes,and neonatal outcomes between two groups of parturient.Result:After intervention,the fasting blood glucose(FPG),2-hour postprandial blood glucose(2hPG),and glycated hemoglobin(HbA1c)of the observation group were significantly lower than those of the control group,and the differences were statistically significant(p<0.05);The incidence of complications such as gestational hypertension syndrome,polyhydramnios,premature rupture of membranes,and postpartum hemorrhage in the observation group was significantly lower than that in the control group,and the difference was statistically significant(p<0.05);The cesarean section rate in the observation group was significantly lower than that in the control group,and the incidence of adverse neonatal outcomes such as fetal distress,macrosomia,neonatal asphyxia,and neonatal hypoglycemia in the observation group was significantly lower than that in the control group,with statistical significance(p<0.05).Conclusion:Individualized nutritional support for elderly women with gestational diabetes can effectively improve the level of maternal blood sugar control,reduce the incidence of complications during pregnancy,and improve the outcome of pregnancy and neonatal outcomes,which is of high clinical value.展开更多
Latest digital advancements have intensified the necessity for adaptive,data-driven and socially-centered learning ecosystems.This paper presents the formulation of a cross-platform,innovative,gamified and personalize...Latest digital advancements have intensified the necessity for adaptive,data-driven and socially-centered learning ecosystems.This paper presents the formulation of a cross-platform,innovative,gamified and personalized Learning Ecosystem,which integrates 3D/VR environments,as well as machine learning algorithms,and business intelligence frameworks to enhance learner-centered education and inferenced decision-making.This Learning System makes use of immersive,analytically assessed virtual learning spaces,therefore facilitating real-time monitoring of not just learning performance,but also overall engagement and behavioral patterns,via a comprehensive set of sustainability-oriented ESG-aligned Key Performance Indicators(KPIs).Machine learning models support predictive analysis,personalized feedback,and hybrid recommendation mechanisms,whilst dedicated dashboards translate complex educational data into actionable insights for all Use Cases of the System(Educational Institutions,Educators and Learners).Additionally,the presented Learning System introduces a structured Mentoring and Consulting Subsystem,thence reinforcing human-centered guidance alongside automated intelligence.The Platform’s modular architecture and simulation-centered evaluation approach actively support personalized,and continuously optimized learning pathways.Thence,it exemplifies a mature,adaptive Learning Ecosystem,supporting immersive technologies,analytics,and pedagogical support,hence,contributing to contemporary digital learning innovation and sociotechnical transformation in education.展开更多
Therapy discontinuation in inflammatory bowel disease,particularly involving immunomodulators,biologics,and small molecules,remains a controversial and evolving topic.This letter reflects on developments following the...Therapy discontinuation in inflammatory bowel disease,particularly involving immunomodulators,biologics,and small molecules,remains a controversial and evolving topic.This letter reflects on developments following the publication by Meštrovićet al,emphasizing the complex balance between risks of relapse,antidrug antibody formation,and potential complications of long-term immunosuppression.Recent evidence underscores high relapse rates following withdrawal-especially of anti-tumor necrosis factor agents-and highlights the lack of robust data for newer biologics.Updated guidelines from European Crohn’s and Colitis Organization,British Society of Gastroenterology,and American College of Gastroenterology all support cautious and individualized approaches,with strict criteria and close follow-up,particularly in Crohn’s disease.For ulcerative colitis,therapeutic cycling remains insufficiently addressed.We proposed a flowchart to support clinical decision-making and stress the importance of shared decisionmaking in the era of personalized medicine since,despite new drug classes and evolving strategies,the therapeutic ceiling in inflammatory bowel disease has yet to be fully overcome.展开更多
Recommendation systems have become indispensable for providing tailored suggestions and capturing evolving user preferences based on interaction histories.The collaborative filtering(CF)model,which depends exclusively...Recommendation systems have become indispensable for providing tailored suggestions and capturing evolving user preferences based on interaction histories.The collaborative filtering(CF)model,which depends exclusively on user-item interactions,commonly encounters challenges,including the cold-start problem and an inability to effectively capture the sequential and temporal characteristics of user behavior.This paper introduces a personalized recommendation system that combines deep learning techniques with Bayesian Personalized Ranking(BPR)optimization to address these limitations.With the strong support of Long Short-Term Memory(LSTM)networks,we apply it to identify sequential dependencies of user behavior and then incorporate an attention mechanism to improve the prioritization of relevant items,thereby enhancing recommendations based on the hybrid feedback of the user and its interaction patterns.The proposed system is empirically evaluated using publicly available datasets from movie and music,and we evaluate the performance against standard recommendation models,including Popularity,BPR,ItemKNN,FPMC,LightGCN,GRU4Rec,NARM,SASRec,and BERT4Rec.The results demonstrate that our proposed framework consistently achieves high outcomes in terms of HitRate,NDCG,MRR,and Precision at K=100,with scores of(0.6763,0.1892,0.0796,0.0068)on MovieLens-100K,(0.6826,0.1920,0.0813,0.0068)on MovieLens-1M,and(0.7937,0.3701,0.2756,0.0078)on Last.fm.The results show an average improvement of around 15%across all metrics compared to existing sequence models,proving that our framework ranks and recommends items more accurately.展开更多
Vaginal delivery is a fascinating physiological process,but also a high-risk process.Up to 85%–90%of vaginal deliveries lead to perineal trauma,with nearly 11%of severe perineal tearing.It is a common occurrence,espe...Vaginal delivery is a fascinating physiological process,but also a high-risk process.Up to 85%–90%of vaginal deliveries lead to perineal trauma,with nearly 11%of severe perineal tearing.It is a common occurrence,especially for first-time mothers.Computational childbirth plays an essential role in the prediction and prevention of these traumas,but fast personalization of the pelvis and floor muscles is challenging due to their anatomical complexity.This study introduces a novel shape-prediction-based personalization of the pelvis and floor muscles for perineal tearing management and childbirth simulation.300 subjects were selected from public Computed Tomography(CT)databases.The pelvic bone nmjmeshes were generated using a coarse-to-fine non-rigid mesh alignment procedure.The floor muscle meshes were personalized using the bone mesh deformation information.A feature-to-pelvic structure reconstruction pipeline was proposed,incorporating various strategies.Ten-fold cross-validation helped determine the optimal reconstruction strategy,regression method,and feature sizes.The mesh-to-mesh distance metric was employed for evaluating.The statistical shape relation-based strategy,coupled with multi-output ridge regression,was the optimal approach for pelvic structure reconstruction.With a feature set ranging from 3 to 38,the mean errors were 2.672 to 1.613 mm,and 3.237 to 1.415 mm in muscle attachment regions.The best-and worst-case predictions had errors of 1.227±0.959 mm and 2.900±2.309 mm,respectively.This study provides a novel approach to achieving fast personalized childbirth modeling and simulation for perineal tearing management.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
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
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
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
Network architectures assisted by Generative Artificial Intelligence(GAI)are envisioned as foundational elements of sixth-generation(6G)communication system.To deliver ubiquitous intelligent services and meet diverse ...Network architectures assisted by Generative Artificial Intelligence(GAI)are envisioned as foundational elements of sixth-generation(6G)communication system.To deliver ubiquitous intelligent services and meet diverse service requirements,6G network architecture should offer personalized services to various mobile devices.Federated learning(FL)with personalized local training,as a privacypreserving machine learning(ML)approach,can be applied to address these challenges.In this paper,we propose a meta-learning-based personalized FL(PFL)method that improves both communication and computation efficiency by utilizing over-the-air computations.Its“pretraining-and-fine-tuning”principle makes it particularly suitable for enabling edge nodes to access personalized GAI services while preserving local privacy.Experiment results demonstrate the outperformance and efficacy of the proposed algorithm,and notably indicate enhanced communication efficiency without compromising accuracy.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
This perspective review explores the transformative potential of personalized herbal medicine,examines the integration of ancient herbal knowledge with modern personalized medicine,delves into the principles of person...This perspective review explores the transformative potential of personalized herbal medicine,examines the integration of ancient herbal knowledge with modern personalized medicine,delves into the principles of personalized medicine particularly in the context of herbal treatments,and investigates the principles of personalized medicine and elucidates how they are being applied to herbal medicine.It emphasizes the individualized nature of this approach and how it is facilitated through genetic analysis and health profiling.This review also highlights key advancements in herbal medicine,such as deoxyribonucleic acid(DNA)analysis and bioinformatics,and their role in the development of precise and personalized herbal remedies.The outcomes of personalized herbal medicine reveal how genetic variations are being considered to tailor treatments,create target-specific therapies,and customize dosage regimens.Furthermore,this review illustrates the evolution of herbal medicine with technological advancements,particularly DNA analysis and bioinformatics,to enhance precision and personalization.The challenge associated with implementing personalized herbal medicine more broadly includes issues of accessibility,regulation,education and ethics.It underscores the transformative potential of personalized herbal medicine.It calls for continued exploration,research and collaboration in this burgeoning field.This emerging field encourages researchers,practitioners,and stakeholders to engage in advancing healthcare practices that are increasingly personalized,evidence-based,and centered on patient's needs.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
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
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
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
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
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
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
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
文摘Objective:To explore the clinical effect of personalized nutritional support in elderly women with gestational diabetes(GDM),and explore its impact on the incidence of maternal complications and pregnancy outcomes.Methods:A total of 90 elderly pregnant women with gestational diabetes who were delivered in our hospital from January 2023 to January 2024 were selected as the research objects.They were randomly divided into an observation group and a control group,with 45 cases in each group.The control group only received routine pregnancy care and basic nutrition guidance,while the observation group received personalized nutrition support on this basis.Compare the blood glucose control,incidence of pregnancy complications,pregnancy outcomes,and neonatal outcomes between two groups of parturient.Result:After intervention,the fasting blood glucose(FPG),2-hour postprandial blood glucose(2hPG),and glycated hemoglobin(HbA1c)of the observation group were significantly lower than those of the control group,and the differences were statistically significant(p<0.05);The incidence of complications such as gestational hypertension syndrome,polyhydramnios,premature rupture of membranes,and postpartum hemorrhage in the observation group was significantly lower than that in the control group,and the difference was statistically significant(p<0.05);The cesarean section rate in the observation group was significantly lower than that in the control group,and the incidence of adverse neonatal outcomes such as fetal distress,macrosomia,neonatal asphyxia,and neonatal hypoglycemia in the observation group was significantly lower than that in the control group,with statistical significance(p<0.05).Conclusion:Individualized nutritional support for elderly women with gestational diabetes can effectively improve the level of maternal blood sugar control,reduce the incidence of complications during pregnancy,and improve the outcome of pregnancy and neonatal outcomes,which is of high clinical value.
文摘Latest digital advancements have intensified the necessity for adaptive,data-driven and socially-centered learning ecosystems.This paper presents the formulation of a cross-platform,innovative,gamified and personalized Learning Ecosystem,which integrates 3D/VR environments,as well as machine learning algorithms,and business intelligence frameworks to enhance learner-centered education and inferenced decision-making.This Learning System makes use of immersive,analytically assessed virtual learning spaces,therefore facilitating real-time monitoring of not just learning performance,but also overall engagement and behavioral patterns,via a comprehensive set of sustainability-oriented ESG-aligned Key Performance Indicators(KPIs).Machine learning models support predictive analysis,personalized feedback,and hybrid recommendation mechanisms,whilst dedicated dashboards translate complex educational data into actionable insights for all Use Cases of the System(Educational Institutions,Educators and Learners).Additionally,the presented Learning System introduces a structured Mentoring and Consulting Subsystem,thence reinforcing human-centered guidance alongside automated intelligence.The Platform’s modular architecture and simulation-centered evaluation approach actively support personalized,and continuously optimized learning pathways.Thence,it exemplifies a mature,adaptive Learning Ecosystem,supporting immersive technologies,analytics,and pedagogical support,hence,contributing to contemporary digital learning innovation and sociotechnical transformation in education.
文摘Therapy discontinuation in inflammatory bowel disease,particularly involving immunomodulators,biologics,and small molecules,remains a controversial and evolving topic.This letter reflects on developments following the publication by Meštrovićet al,emphasizing the complex balance between risks of relapse,antidrug antibody formation,and potential complications of long-term immunosuppression.Recent evidence underscores high relapse rates following withdrawal-especially of anti-tumor necrosis factor agents-and highlights the lack of robust data for newer biologics.Updated guidelines from European Crohn’s and Colitis Organization,British Society of Gastroenterology,and American College of Gastroenterology all support cautious and individualized approaches,with strict criteria and close follow-up,particularly in Crohn’s disease.For ulcerative colitis,therapeutic cycling remains insufficiently addressed.We proposed a flowchart to support clinical decision-making and stress the importance of shared decisionmaking in the era of personalized medicine since,despite new drug classes and evolving strategies,the therapeutic ceiling in inflammatory bowel disease has yet to be fully overcome.
基金funded by Soonchunhyang University,Grant Number 20250029。
文摘Recommendation systems have become indispensable for providing tailored suggestions and capturing evolving user preferences based on interaction histories.The collaborative filtering(CF)model,which depends exclusively on user-item interactions,commonly encounters challenges,including the cold-start problem and an inability to effectively capture the sequential and temporal characteristics of user behavior.This paper introduces a personalized recommendation system that combines deep learning techniques with Bayesian Personalized Ranking(BPR)optimization to address these limitations.With the strong support of Long Short-Term Memory(LSTM)networks,we apply it to identify sequential dependencies of user behavior and then incorporate an attention mechanism to improve the prioritization of relevant items,thereby enhancing recommendations based on the hybrid feedback of the user and its interaction patterns.The proposed system is empirically evaluated using publicly available datasets from movie and music,and we evaluate the performance against standard recommendation models,including Popularity,BPR,ItemKNN,FPMC,LightGCN,GRU4Rec,NARM,SASRec,and BERT4Rec.The results demonstrate that our proposed framework consistently achieves high outcomes in terms of HitRate,NDCG,MRR,and Precision at K=100,with scores of(0.6763,0.1892,0.0796,0.0068)on MovieLens-100K,(0.6826,0.1920,0.0813,0.0068)on MovieLens-1M,and(0.7937,0.3701,0.2756,0.0078)on Last.fm.The results show an average improvement of around 15%across all metrics compared to existing sequence models,proving that our framework ranks and recommends items more accurately.
基金funded by Vietnam National University Ho Chi Minh City(VNU-HCM)under grant number DS.C2025-28-06.
文摘Vaginal delivery is a fascinating physiological process,but also a high-risk process.Up to 85%–90%of vaginal deliveries lead to perineal trauma,with nearly 11%of severe perineal tearing.It is a common occurrence,especially for first-time mothers.Computational childbirth plays an essential role in the prediction and prevention of these traumas,but fast personalization of the pelvis and floor muscles is challenging due to their anatomical complexity.This study introduces a novel shape-prediction-based personalization of the pelvis and floor muscles for perineal tearing management and childbirth simulation.300 subjects were selected from public Computed Tomography(CT)databases.The pelvic bone nmjmeshes were generated using a coarse-to-fine non-rigid mesh alignment procedure.The floor muscle meshes were personalized using the bone mesh deformation information.A feature-to-pelvic structure reconstruction pipeline was proposed,incorporating various strategies.Ten-fold cross-validation helped determine the optimal reconstruction strategy,regression method,and feature sizes.The mesh-to-mesh distance metric was employed for evaluating.The statistical shape relation-based strategy,coupled with multi-output ridge regression,was the optimal approach for pelvic structure reconstruction.With a feature set ranging from 3 to 38,the mean errors were 2.672 to 1.613 mm,and 3.237 to 1.415 mm in muscle attachment regions.The best-and worst-case predictions had errors of 1.227±0.959 mm and 2.900±2.309 mm,respectively.This study provides a novel approach to achieving fast personalized childbirth modeling and simulation for perineal tearing management.
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.
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.
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.
基金supported in part by the National Key R&D Program of China under Grant 2024YFE0200700in part by the National Natural Science Foundation of China under Grant 62201504.
文摘Network architectures assisted by Generative Artificial Intelligence(GAI)are envisioned as foundational elements of sixth-generation(6G)communication system.To deliver ubiquitous intelligent services and meet diverse service requirements,6G network architecture should offer personalized services to various mobile devices.Federated learning(FL)with personalized local training,as a privacypreserving machine learning(ML)approach,can be applied to address these challenges.In this paper,we propose a meta-learning-based personalized FL(PFL)method that improves both communication and computation efficiency by utilizing over-the-air computations.Its“pretraining-and-fine-tuning”principle makes it particularly suitable for enabling edge nodes to access personalized GAI services while preserving local privacy.Experiment results demonstrate the outperformance and efficacy of the proposed algorithm,and notably indicate enhanced communication efficiency without compromising accuracy.
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.
基金Research Management Centre,Xiamen University Malaysia(No.XMUMRF/2020-C6/ITCM/0004)。
文摘This perspective review explores the transformative potential of personalized herbal medicine,examines the integration of ancient herbal knowledge with modern personalized medicine,delves into the principles of personalized medicine particularly in the context of herbal treatments,and investigates the principles of personalized medicine and elucidates how they are being applied to herbal medicine.It emphasizes the individualized nature of this approach and how it is facilitated through genetic analysis and health profiling.This review also highlights key advancements in herbal medicine,such as deoxyribonucleic acid(DNA)analysis and bioinformatics,and their role in the development of precise and personalized herbal remedies.The outcomes of personalized herbal medicine reveal how genetic variations are being considered to tailor treatments,create target-specific therapies,and customize dosage regimens.Furthermore,this review illustrates the evolution of herbal medicine with technological advancements,particularly DNA analysis and bioinformatics,to enhance precision and personalization.The challenge associated with implementing personalized herbal medicine more broadly includes issues of accessibility,regulation,education and ethics.It underscores the transformative potential of personalized herbal medicine.It calls for continued exploration,research and collaboration in this burgeoning field.This emerging field encourages researchers,practitioners,and stakeholders to engage in advancing healthcare practices that are increasingly personalized,evidence-based,and centered on patient's needs.
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.
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.
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 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.
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.
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.
