Personalized Lower Limb Gait Reconstruction Modeling Based on RFA-ProMP

Personalized gait curves are generated to enhance patient adaptability to gait trajectories used for passive training in the early stage of rehabilitation for hemiplegic patients.The article utilizes the random forest algorithm to construct a gait parameter model,which maps the relationship between parameters such as height,weight,age,gender,and gait speed,achieving prediction of key points on the gait curve.To enhance prediction accuracy,an attention mechanism is introduced into the algorithm to focus more on the main features.Meanwhile,to ensure high similarity between the reconstructed gait curve and the normal one,probabilistic motion primitives(ProMP)are used to learn the probability distribution of normal gait data and construct a gait trajectorymodel.Finally,using the specified step speed as input,select a reference gait trajectory from the learned trajectory,and reconstruct the curve of the reference trajectoryusing the gait keypoints predictedby the parametermodel toobtain the final curve.Simulation results demonstrate that the method proposed in this paper achieves 98%and 96%curve correlations when generating personalized lower limb gait curves for different patients,respectively,indicating its suitability for such tasks.

Enhancing personalized exercise recommendation with student and exercise portraits

The exercise recommendation system is emerging as a promising application in online learning scenarios,providing personalized recommendations to assist students with explicit learning directions.Existing solutions generally follow a collaborative filtering paradigm,while the implicit connections between students(exercises)have been largely ignored.In this study,we aim to propose an exercise recommendation paradigm that can reveal the latent connections between student-student(exercise-exercise).Specifically,a new framework was proposed,namely personalized exercise recommendation with student and exercise portraits(PERP).It consists of three sequential and interdependent modules:Collaborative student exercise graph(CSEG)construction,joint random walk,and recommendation list optimization.Technically,CSEG is created as a unified heterogeneous graph with students’response behaviors and student(exercise)relationships.Then,a joint random walk to take full advantage of the spectral properties of nearly uncoupled Markov chains is performed on CSEG,which allows for full exploration of both similar exercises that students have finished and connections between students(exercises)with similar portraits.Finally,we propose to optimize the recommendation list to obtain different exercise suggestions.After analyses of two public datasets,the results demonstrated that PERP can satisfy novelty,accuracy,and diversity.

MAIPFE:An Efficient Multimodal Approach Integrating Pre-Emptive Analysis,Personalized Feature Selection,and Explainable AI

Medical Internet of Things(IoT)devices are becoming more and more common in healthcare.This has created a huge need for advanced predictive health modeling strategies that can make good use of the growing amount of multimodal data to find potential health risks early and help individuals in a personalized way.Existing methods,while useful,have limitations in predictive accuracy,delay,personalization,and user interpretability,requiring a more comprehensive and efficient approach to harness modern medical IoT devices.MAIPFE is a multimodal approach integrating pre-emptive analysis,personalized feature selection,and explainable AI for real-time health monitoring and disease detection.By using AI for early disease detection,personalized health recommendations,and transparency,healthcare will be transformed.The Multimodal Approach Integrating Pre-emptive Analysis,Personalized Feature Selection,and Explainable AI(MAIPFE)framework,which combines Firefly Optimizer,Recurrent Neural Network(RNN),Fuzzy C Means(FCM),and Explainable AI,improves disease detection precision over existing methods.Comprehensive metrics show the model’s superiority in real-time health analysis.The proposed framework outperformed existing models by 8.3%in disease detection classification precision,8.5%in accuracy,5.5%in recall,2.9%in specificity,4.5%in AUC(Area Under the Curve),and 4.9%in delay reduction.Disease prediction precision increased by 4.5%,accuracy by 3.9%,recall by 2.5%,specificity by 3.5%,AUC by 1.9%,and delay levels decreased by 9.4%.MAIPFE can revolutionize healthcare with preemptive analysis,personalized health insights,and actionable recommendations.The research shows that this innovative approach improves patient outcomes and healthcare efficiency in the real world.

Personalized and continuous care intervention affects rehabilitation,living quality,and negative emotions of patients with breast cancer

BACKGROUND Breast cancer is among the most common malignancies worldwide.With progress in treatment methods and levels,the overall survival period has been prolonged,and the demand for quality care has increased.AIM To investigate the effect of individualized and continuous care intervention in patients with breast cancer.METHODS Two hundred patients with breast cancer who received systemic therapy at The First Affiliated Hospital of Hebei North University(January 2021 to July 2023)were retrospectively selected as research participants.Among them,134 received routine care intervention(routing group)and 66 received personalized and continuous care(intervention group).Self-rating anxiety scale(SAS),self-rating depression scale(SDS),and Functional Assessment of Cancer Therapy-Breast(FACT-B)scores,including limb shoulder joint activity,complication rate,and care satisfaction,were compared between both groups after care.RESULTS SAS and SDS scores were lower in the intervention group than in the routing group at one and three months after care.The total FACT-B scores and five dimensions in the intervention group were higher than those in the routing group at three months of care.The range of motion of shoulder anteflexion,posterior extension,abduction,internal rotation,and external rotation in the intervention group was higher than that in the routing group one month after care.The incidence of postoperative complications was 18.18%lower in the intervention group than in the routing group(34.33%;P<0.05).Satisfaction with care was 90.91% higher in the intervention group than in the routing group(78.36%;P<0.05).CONCLUSION Personalized and continuous care can alleviate negative emotions in patients with breast cancer,quicken rehabilitation of limb function,decrease the incidence of complications,and improve living quality and care satisfaction.

Personalized Health Monitoring Systems: Integrating Wearable and AI

The integration of wearable technologies and artificial intelligence (AI) has revolutionized healthcare, enabling advanced personal health monitoring systems. This article explores the transformative impact of wearable technologies and AI on healthcare, highlighting the development and theoretical application of the Integrated Personal Health Monitoring System (IPHMS). By integrating data from various wearable devices, such as smartphones, Apple Watches, and Oura Rings, the IPHMS framework aims to revolutionize personal health monitoring through real-time alerts, comprehensive tracking, and personalized insights. Despite its potential, the practical implementation faces challenges, including data privacy, system interoperability, and scalability. The evolution of healthcare technology from traditional methods to AI-enhanced wearables underscores a significant advancement towards personalized care, necessitating further research and innovation to address existing limitations and fully realize the benefits of such integrated health monitoring systems.

Effects of personalized oral hygiene management on oral health status of pregnant women

BACKGROUND The Cariostat caries activity test(CAT)was used to evaluate the effectiveness of personalized oral hygiene management combining oral health education and professional mechanical tooth cleaning on the oral health status of pregnant women.AIM To investigate whether personalized oral hygiene management enhances the oral health status of pregnant women.METHODS A total of 114 pregnant women who were examined at Dalian Women’s and Children’s Medical Center were divided into four groups:High-risk experimental group(n=29;CAT score≥2;received personalized oral hygiene management training),low-risk experimental group(n=29;CAT score≤1;received oral health education),high-risk control group(n=28;CAT score≥2),and low-risk control group(n=28;CAT score≤1).No hygiene intervention was provided to control groups.CAT scores at different times were compared using independent samples t-test and least significant difference t-test.RESULTS No significant difference in baseline CAT scores was observed between the experimental and control groups,either in the high-risk or low-risk groups.CAT scores were reduced significantly after 3(1.74±0.47 vs 2.50±0.38,P<0.0001)and 6 months(0.53±0.50 vs 2.45±0.42,P<0.0001)of personalized oral hygiene management intervention but not after oral health education alone(0.43±0.39 vs 0.46±0.33,P>0.05 and 0.45±0.36 vs 0.57±0.32,P>0.05,respectively).Within groups,the decrease in CAT scores was significant(2.43±0.44 vs 1.74±0.47 vs 0.53±0.50,P<0.0001)for only the high-risk experimental group.CONCLUSION Personalized oral hygiene management is effective in improving the oral health of pregnant women and can improve pregnancy outcomes and the oral health of the general population.

The Effect of Personalized Comprehensive Care on the Nursing Care of Severe Pneumonia Patients

Objective:To explore the value of receiving personalized comprehensive care for patients with severe pneumonia.Methods:73 patients with severe pneumonia who visited the clinic from February 2020 to February 2023 were included in this study.The patients were randomly grouped into Group A and Group B.Group A received personalized comprehensive care whereas Group B received conventional care.The value of care was compared.Results:The duration of mechanical ventilation time,the time taken for fever and dyspnea relief,and the hospitalization time of Group A were shorter than those in Group B(P<0.05).The blood gas indexes such as PaO_(2),PaCO_(2),and blood pH of Group A were better than those of Group B(P<0.05).The pulmonary function indexes such as peak expiratory flow(PEF),forced vital capacity(FVC),and forced expiratory volume in 1 second(FEV_(1))of Group A were better than those of Group B,P<0.05.Moreover,the patients in Group A were generally more satisfied with the care given compared to the patients in Group B(P<0.05).Conclusion:Personalized comprehensive care improves blood gas indexes,enhances lung function,accelerates the relief of symptoms,and also enhances patient satisfaction in severe pneumonia patients.

多主体协同开发社区教育课程的PERSONAL模式探索——基于“我爱我家”的个案研究

通过对“我爱我家”这一社区自发课程的跟踪调查,发现社区自发课程开发实质上是一个组织学习过程,经过个体直觉、知识解释、知识整合等环节实现组织的制度化学习,在推动学习型无边界组织生成的同时,生成社区教育课程。在这一过程中,政策(policies)支持、专家(expert)指导、区域需求和区域特征(regional)、社区系统(system)、组织化(organization)等多要素叠加发挥作用,通过不断拓展新鲜(new)领域的积极行动(action)整合所有资源,并推动跨越边界的学习(learning)贯穿始终。因此,多主体协同的社区自发课程可以不完全地概括为PERSONAL模式。要以PERSONAL模式发展社区自发课程,需要以学习者为中心,注重发展人际互动,营造有利于跨组织交互学习的教育生态系统。

Therapeutic potential of exosome-based personalized delivery platform in chronic inflammatory diseases

In the inflammatory microenvironment,there are numerous exosomes secreted by immune cells(Macrophages,neutrophils,dendritic cells),mesenchymal stem cells(MSCs)and platelets as intercellular communicators,which participate in the regulation of inflammation by modulating gene expression and releasing anti-inflammatory factors.Due to their good biocompatibility,accurate targeting,low toxicity and immunogenicity,these exosomes are able to selectively deliver therapeutic drugs to the site of inflammation through interactions between their surface-antibody or modified ligand with cell surface receptors.Therefore,the role of exosome-based biomimetic delivery strategies in inflammatory diseases has attracted increasing attention.Here we review current knowledge and techniques for exosome identification,isolation,modification and drug loading.More importantly,we highlight progress in using exosomes to treat chronic inflammatory diseases such as rheumatoid arthritis(RA),osteoarthritis(OA),atherosclerosis(AS),and inflammatory bowel disease(IBD).Finally,we also discuss their potential and challenges as anti-inflammatory drug carriers.

Question-Answering Pair Matching Based on Question Classification and Ensemble Sentence Embedding

Question-answering(QA)models find answers to a given question.The necessity of automatically finding answers is increasing because it is very important and challenging from the large-scale QA data sets.In this paper,we deal with the QA pair matching approach in QA models,which finds the most relevant question and its recommended answer for a given question.Existing studies for the approach performed on the entire dataset or datasets within a category that the question writer manually specifies.In contrast,we aim to automatically find the category to which the question belongs by employing the text classification model and to find the answer corresponding to the question within the category.Due to the text classification model,we can effectively reduce the search space for finding the answers to a given question.Therefore,the proposed model improves the accuracy of the QA matching model and significantly reduces the model inference time.Furthermore,to improve the performance of finding similar sentences in each category,we present an ensemble embedding model for sentences,improving the performance compared to the individual embedding models.Using real-world QA data sets,we evaluate the performance of the proposed QA matching model.As a result,the accuracy of our final ensemble embedding model based on the text classification model is 81.18%,which outperforms the existing models by 9.81%∼14.16%point.Moreover,in terms of the model inference speed,our model is faster than the existing models by 2.61∼5.07 times due to the effective reduction of search spaces by the text classification model.

Personalized HRTF Prediction Based on Light GBM Using Anthropometric Data

This paper proposes a personalized headrelated transfer function(HRTF)prediction method based on Light GBM using anthropometric data.Considering the overfitting problems of the current training-based prediction methods,we use Light GBM and a specific network structure to prevent over-fitting and enhance the prediction performance.By decomposing and combining the data to be predicted,we set up 90 Light GBM models to separately predict the 90instants of HRTF in log domain.At the same time,the method of 10-fold cross-validation is used to score the accuracy of the model.For models with scores below 80 points,Bayesian optimization is used to adjust model hyperparameters to obtain a better model structure.The results obtained by Light GBM are evaluated with spectral distortion(SD)which can show the fitting error between the prediction and the original data.The mean SD values of both ears on the whole test set are 2.32 d B and 2.28 d B respectively.Compared with the non-linear regression method and the latest method,SD value of Light GBM-based method relatively decreases by 83.8%and 48.5%.

Advances and future directions in keloid research:Pathogenesis,diagnosis and personalized treatment strategies

Keloids,which are abnormal manifestations of wound healing,can result in significant functional impairment and aesthetic deformities.The pathogenesis of keloids is multifaceted and complex and influenced by various factors,such as genetics,the environment,and immune responses.The evolution of keloid treatment has progressed from traditional surgical excision to a contemporary combination of therapies including injection and radiation treatments,among others.This article provides a comprehensive review of keloid pathogenesis and treatment,emphasizing the latest advances in the field.Ultimately,this review underscores the necessity for continued research to enhance our understanding of keloid pathogenesis and to devise more effective treatments for this challenging condition.

FedNRM:A Federal Personalized News Recommendation Model Achieving User Privacy Protection

In recent years,the type and quantity of news are growing rapidly,and it is not easy for users to find the news they are interested in the massive amount of news.A news recommendation system can score and predict the candidate news,and finally recommend the news with high scores to users.However,existing user models usually only consider users’long-term interests and ignore users’recent interests,which affects users’usage experience.Therefore,this paper introduces gated recurrent unit(GRU)sequence network to capture users’short-term interests and combines users’short-term interests and long-terminterests to characterize users.While existing models often only use the user’s browsing history and ignore the variability of different users’interest in the same news,we introduce additional user’s ID information and apply the personalized attention mechanism for user representation.Thus,we achieve a more accurate user representation.We also consider the risk of compromising user privacy if the user model training is placed on the server side.To solve this problem,we design the training of the user model locally on the client side by introducing a federated learning framework to keep the user’s browsing history on the client side.We further employ secure multiparty computation to request news representations from the server side,which protects privacy to some extent.Extensive experiments on a real-world news dataset show that our proposed news recommendation model has a better improvement in several performance evaluation metrics.Compared with the current state-of-the-art federated news recommendation models,our model has increased by 0.54%in AUC,1.97%in MRR,2.59%in nDCG@5%,and 1.89%in nDCG@10.At the same time,because we use a federated learning framework,compared with other centralized news recommendation methods,we achieve privacy protection for users.

Ranking of Web Pages in a Personalized Search

The basic idea behind a personalized web search is to deliver search results that are tailored to meet user needs, which is one of the growing concepts in web technologies. The personalized web search presented in this paper is based on exploiting the implicit feedbacks of user satisfaction during her web browsing history to construct a user profile storing the web pages the user is highly interested in. A weight is assigned to each page stored in the user’s profile;this weight reflects the user’s interest in this page. We name this weight the relative rank of the page, since it depends on the user issuing the query. Therefore, the ranking algorithm provided in this paper is based on the principle that;the rank assigned to a page is the addition of two rank values R_rank and A_rank. A_rank is an absolute rank, since it is fixed for all users issuing the same query, it only depends on the link structures of the web and on the keywords of the query. Thus, it could be calculated by the PageRank algorithm suggested by Brin and Page in 1998 and used by the google search engine. While, R_rank is the relative rank, it is calculated by the methods given in this paper which depends mainly on recording implicit measures of user satisfaction during her previous browsing history.

Personalized Learning Path Recommendations for Software Testing Courses Based on Knowledge Graphs

Software testing courses are characterized by strong practicality,comprehensiveness,and diversity.Due to the differences among students and the needs to design personalized solutions for their specific requirements,the design of the existing software testing courses fails to meet the demands for personalized learning.Knowledge graphs,with their rich semantics and good visualization effects,have a wide range of applications in the field of education.In response to the current problem of software testing courses which fails to meet the needs for personalized learning,this paper offers a learning path recommendation based on knowledge graphs to provide personalized learning paths for students.

Integration of molecular testing for the personalized management of patients with diffuse large B-cell lymphoma and follicular lymphoma

Diffuse large B-cell lymphoma(DLBCL)and follicular lymphoma(FL)are the most common forms of aggressive and indolent lymphoma,respectively.The majority of patients are cured by standard R-CHOP immunochemotherapy,but 30%–40%of DLBCL and 20%of FL patients relapse or are refractory(R/R).DLBCL and FL are phenotypically and genetically hereterogenous B-cell neoplasms.To date,the diagnosis of DLBCL and FL has been based on morphology,immunophenotyping and cytogenetics.However,next-generation sequencing(NGS)is widening our understanding of the genetic basis of the B-cell lymphomas.In this review we will discuss how integrating the NGS-based characterization of somatic gene mutations with diagnostic or prognostic value in DLBCL and FL could help refine B-cell lymphoma classification as part of a multidisciplinary pathology work-up.We will also discuss how molecular testing can identify candidates for clinical trials with targeted therapies and help predict therapeutic outcome to currently available treatments,including chimeric antigen receptor T-cell,as well as explore the application of circulating cell-free DNA,a non-invasive method for patient monitoring.We conclude that molecular analyses can drive improvements in patient outcomes due to an increased understanding of the different pathogenic pathways affected by each DLBCL subtype and indolent FL vs R/R FL.

A Personalized Adverse Drug Reaction Early Warning Method Based on Contextual Ontology and Rules Learning

Background: The fatality of adverse drug reactions (ADR) has become one of the major causes of the non-natural disease deaths globally, with the issue of drug safety emerging as a common topic of concern. Objective: The personalized ADR early warning method, based on contextual ontology and rule learning, proposed in this study aims to provide a reference method for personalized health and medical information services. Methods: First, the patient data is formalized, and the user contextual ontology is constructed, reflecting the characteristics of the patient population. The concept of ontology rule learning is then proposed, which is to mine the rules contained in the data set through machine learning to improve the efficiency and scientificity of ontology rule generation. Based on the contextual ontology of ADR, the high-level context information is identified and predicted by means of reasoning, so the occurrence of the specific adverse reaction in patients from different populations is extracted. Results: Finally, using diabetes drugs as an example, contextual information is identified and predicted through reasoning, to mine the occurrence of specific adverse reactions in different patient populations, and realize personalized medication decision-making and early warning of ADR.

Banking of perinatal mesenchymal stem/stromal cells for stem cellbased personalized medicine over lifetime:Matters arising

Mesenchymal stromal/stem cells(MSCs)are currently applied in regenerative medicine and tissue engineering.Numerous clinical studies have indicated that MSCs from different tissue sources can provide therapeutic benefits for patients.MSCs derived from either human adult or perinatal tissues have their own unique advantages in their medical practices.Usually,clinical studies are conducted by using of cultured MSCs after thawing or short-term cryopreserved-then-thawed MSCs prior to administration for the treatment of a wide range of diseases and medical disorders.Currently,cryogenically banking perinatal MSCs for potential personalized medicine for later use in lifetime has raised growing interest in China as well as in many other countries.Meanwhile,this has led to questions regarding the availability,stability,consistency,multipotency,and therapeutic efficiency of the potential perinatal MSC-derived therapeutic products after longterm cryostorage.This opinion review does not minimize any therapeutic benefit of perinatal MSCs in many diseases after short-term cryopreservation.This article mainly describes what is known about banking perinatal MSCs in China and,importantly,it is to recognize the limitation and uncertainty of the perinatal MSCs stored in cryobanks for stem cell medical treatments in whole life.This article also provides several recommendations for banking of perinatal MSCs for potentially future personalized medicine,albeit it is impossible to anticipate whether the donor will benefit from banked MSCs during her/his lifetime.

Personalized medicine approach to osteoporosis management in women: integrating genetics, pharmacogenomics, and precision treatments

Osteoporosis has emerged as a significant health issue among postmenopausal women.Addressing this concern necessitates a multifaceted approach encompassing genetics,pharmacogenomics,bone turnover markers,lifestyle factors,concurrent medical conditions,biomarkers,and advanced imaging techniques.Nonetheless,challenges in terms of cost-effectiveness and ethical considerations do exist.Fortunately,the convergence of technological progress and research endeavors offers a promising trajectory.The integration of genetic testing and pharmacogenomics into clinical practice holds substantial potential.This integration empowers healthcare professionals to forecast treatment responses and pinpoint individuals with elevated susceptibility,thereby enabling the implementation of tailored and efficacious interventions that optimize outcomes–personalized medicine.Given the intricate nature of osteoporosis,personalized strategies stand to greatly benefit women grappling with this condition.Further research and collaborative efforts are imperative to propel advancements within this domain,paving the way for further breakthroughs.

Effect of Personalized Education on the Awareness Rate of Protective Knowledge Among Inpatients with Newly Diagnosed Pulmonary Tuberculosis

Objective:This study aims to explore the impact of personalized education on the awareness rate of protective knowledge among inpatients newly treated for pulmonary tuberculosis.Methods:325 initial pulmonary tuberculosis inpatients admitted to our hospital between January 2018 and December 2022 were selected as the research subjects.Using the randomized controlled trial method,they were divided into an experimental group of 163 cases and a control group of 162 cases.The experimental group received personalized education,including personalized guidance on patients'disease awareness,treatment compliance,and preventive measures.The control group received routine health education.After the experiment,the awareness rate of protective knowledge of the two groups of patients was evaluated.Results:The total awareness rate of the experimental group was 76.07%,which was significantly higher than that of the control group,which was 55.63%,and the difference was statistically significant at P<0.05.The transmission route,suspicious symptoms,medical institutions,preferential policies,whether it can be cured,and the full awareness rate of the experimental group were all higher than those of the control group,and the difference was statistically significant at P<0.05.Conclusion:Personalized education positively impacts the awareness rate of protective knowledge among inpatients newly treated for pulmonary tuberculosis.Therefore,when hospitals provide medical services for newly diagnosed pulmonary tuberculosis patients,they should strengthen personalized education,improve patients'awareness of the disease and self-protection ability,and reduce the risk of infection.