Personalized nutritional care for immune function recovery in postoperative gastrointestinal surgery patients:An observational study

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.

FedCLCC:A personalized federated learning algorithm for edge cloud collaboration based on contrastive learning and conditional computing

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.

Genetic insights in infectious diseases:Insights from a case report and implications for personalized medicine

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.

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。

Justification of and Response to the Specificity of Workers’Personality Rights

The pervasive existence of subordination in the work-place endows workers’personality rights with a distinct specificity that differs from other civil subjects.The specificity of workers’personality rights is primarily manifested in three aspects:the exercise of rights is restricted by the employer;personality rights infringements often accompany violations of workers’economic property rights;and the scope of rights is not limited to the duration of employment.To respond to the specificity arising in the labor domain,certain disputes concerning workers’personality rights should be handled through labor dispute resolution procedures.In individual cases,judicial authorities should differentiate among protection levels based on the specific type of personality rights involved,with a focus on examining the reasons,methods,and extent of the employer’s restrictive actions,thereby establishing a practical and reasonable review system.。

Beyond Algorithms: A Comprehensive Analysis of AI-Driven Personalization in Strategic Communications

This comprehensive study investigates the multifaceted impact of AI-powered personalization on strategic communications, delving deeply into its opportunities, challenges, and future directions. Employing a rigorous mixed-methods approach, we conduct an in-depth analysis of the effects of AI-driven personalization on audience engagement, brand perception, and conversion rates across various industries and communication channels. Our findings reveal that while AI-powered personalization significantly enhances communication effectiveness and offers unprecedented opportunities for audience connection, it also raises critical ethical considerations and implementation challenges. The study contributes substantially to the growing body of literature on AI in communications, offering both theoretical insights and practical guidelines for professionals navigating this rapidly evolving landscape. Furthermore, we propose a novel framework for ethical AI implementation in strategic communications and outline a robust agenda for future research in this dynamic field.

Personal Thermal Management by Radiative Cooling and Heating

Maintaining thermal comfort within the human body is crucial for optimal health and overall well-being.By merely broadening the setpoint of indoor temperatures,we could significantly slash energy usage in building heating,ventilation,and air-conditioning systems.In recent years,there has been a surge in advancements in personal thermal management(PTM),aiming to regulate heat and moisture transfer within our immediate surroundings,clothing,and skin.The advent of PTM is driven by the rapid development in nano/micro-materials and energy science and engineering.An emerging research area in PTM is personal radiative thermal management(PRTM),which demonstrates immense potential with its high radiative heat transfer efficiency and ease of regulation.However,it is less taken into account in traditional textiles,and there currently lies a gap in our knowledge and understanding of PRTM.In this review,we aim to present a thorough analysis of advanced textile materials and technologies for PRTM.Specifically,we will introduce and discuss the underlying radiation heat transfer mechanisms,fabrication methods of textiles,and various indoor/outdoor applications in light of their different regulation functionalities,including radiative cooling,radiative heating,and dual-mode thermoregulation.Furthermore,we will shine a light on the current hurdles,propose potential strategies,and delve into future technology trends for PRTM with an emphasis on functionalities and applications.

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.

Framework for Personalized Chronic Pain Management:Harnessing AI and Personality Insights for Effective Care

This paper introduces a cutting-edge framework for personalized chronic pain management,leveraging the power of artificial intelligence(AI)and personality insights.It explores the intricate relationship between personality traits and pain perception,expression,and management,identifying key correlations that influence an individual’s experience of pain.By integrating personality psychology with AI-driven personality assessment,this framework offers a novel approach to tailoring chronic pain management strategies for each patient’s unique personality profile.It highlights the relevance of well-established personality theories such as the Big Five and the Myers-Briggs Type Indicator(MBTI)in shaping personalized pain management plans.Additionally,the paper introduces multimodal AI-driven personality assessment,emphasizing the ethical considerations and data collection processes necessary for its implementation.Through illustrative case studies,the paper exemplifies how this framework can lead to more effective and patient-centered pain relief,ultimately enhancing overall well-being.In conclusion,the paper positions the need of an“AI-Powered Holistic Pain Management Initiative”which has the potential to transform chronic pain management by providing personalized,data-driven solutions and create a multifaceted research impact influencing clinical practice,patient outcomes,healthcare policy,and the broader scientific community’s understanding of personalized medicine and AI-driven interventions.

Versatile and Comfortable Janus Fabrics for Switchable Personal Thermal Management and Electromagnetic Interference Shielding

Existing personal thermal regulating fabrics fall short of meeting the demands for sustainable and protective outdoor temperature management.Here,a versatile and comfortable Janus fabric has been developed by embedding boron nitride nanosheets within a porous polyurethane matrix(BNNS@TPU)and introducing Ti3C2Tx MXene into another layer of TPU pores(MXene/TPU).The well-distributed BNNS in porous TPU matrix enhances refractive index difference,increases porosity and optimizes pore size distribution,resulting in an excellent solar reflectivity(R=94.22%),while the distinct distribution of MXene in porous TPU effectively improves solar absorptivity(α=93.57%)and enhances the conduction loss of electromagnetic waves due to multiple scattering and reflection effects.With a simple flip,Janus fabric can switch between sub-ambient cooling of~7.2℃ and super-ambient heating of~46.0℃ to adapt to changing weather and seasonal conditions.The fabric achieves an electromagnetic interference shielding efficiency of 36 dB,protecting the human body from electromagnetic radiation,attributed to the hierarchical distribution of highly conductive MXene.Furthermore,Janus fabric offers excellent comfort,abrasion resistance,washability,and flame retardancy for practical wear.This study presents an effective strategy for developing personal thermal regulating fabrics with adaptability to environmental changes and resistance to electromagnetic radiation.

Correlation of personality with individual reproductive success in shrub-nesting birds depends on their life history style

Two questions in the research of animal personality—whether there is a correlation between a personality trait and individual reproductive success,and what is the genetic basis underlying a personality trait—remain unresolved.We addressed these two questions in three shrub-nesting birds,the Azure-winged Magpie(Cyanopica cyanus,AM),White-collared Blackbird(Turdus albocinctus,WB),and Brown-cheeked Laughingthrush(Trochalopteron henrici,BL).The personality type of an individual was first identified according to its response to a territorial intruder.Then,we compared the fleeing distance,breeding parameters,and differential expressed genes(DEGs) in the brain transcriptome between bold and shy breeders.In the three species,bold breeders exhibited more aggressiveness towards an intruder of their territory than did shy breeders.The reproductive success of bold breeders was significantly higher than that of shy breeders in AM but not in WB and BL.The three species shared one DEG,crabp1,which was up-regulated in bold relative to in shy individuals.By regulating the expression of corticotropin-releasing hormone,higher crabp1 gene expression can decrease cellular response to retinoic acid.Therefore,bold individuals are insensitive to external stresses and able to exhibit more aggressiveness to intruders than their shier counterparts.Aggressiveness is beneficial to bold individuals in AM but not in WB and BL because the former could evoke neighbors to make the same response of defending against intruders but the latter could not.Although a personality trait may have the same genetic basis across species,its correlation with reproductive success depends largely on the life history style of a species.

Mutations in Ras homolog family member A in patients with peripheral T-cell lymphoma and implications for personalized medicine

Genome sequencing has revealed frequent mutations in Ras homolog family member A(RHOA)among various cancers with unique aberrant profiles and pathogenic effects,especially in peripheral T-cell lymphoma(PTCL).The discrete positional distribution and types of RHOA amino acid substitutions vary according to the tumor type,thereby leading to different functional and biological properties,which provide new insight into the molecular pathogenesis and potential targeted therapies for various tumors.However,the similarities and discrepancies in characteristics of RHOA mutations among various histologic subtypes of PTCL have not been fully elucidated.Herein we highlight the inconsistencies and complexities of the type and location of RHOA mutations and demonstrate the contribution of RHOA variants to the pathogenesis of PTCL by combining epigenetic abnormalities and activating multiple downstream pathways.The promising potential of targeting RHOA as a therapeutic modality is also outlined.This review provides new insight in the field of personalized medicine to improve the clinical outcomes for patients.

Research on Multi-modal In-Vehicle Intelligent Personal Assistant Design

Intelligent personal assistants play a pivotal role in in-vehicle systems,significantly enhancing life efficiency,driving safety,and decision-making support.In this study,the multi-modal design elements of intelligent personal assistants within the context of visual,auditory,and somatosensory interactions with drivers were discussed.Their impact on the driver’s psychological state through various modes such as visual imagery,voice interaction,and gesture interaction were explored.The study also introduced innovative designs for in-vehicle intelligent personal assistants,incorporating design principles such as driver-centricity,prioritizing passenger safety,and utilizing timely feedback as a criterion.Additionally,the study employed design methods like driver behavior research and driving situation analysis to enhance the emotional connection between drivers and their vehicles,ultimately improving driver satisfaction and trust.

Compensatory Damages in Personal Information Public Interest Litigation in China: Challenges and Prospects

In contrast to private interest litigation,public interest litigation provides a more potent solution to personal information infringements marked by extensive scope,unspecified victims,and limited individual loss.However,com⁃pensatory damages remain a contentious issue,both in theory and in practice,within the legal framework of personal in⁃formation public interest litigation.Through an empirical study conducted within China's judicial practice,this paper reveals that the pending issue concerning the nature and function of compensatory damages has caused highly contra⁃dictory verdicts regarding their calculation and allocation,as well as their relationship with other forms of pecuniary li⁃abilities.Only by acknowledging the role of compensatory damages imposed in personal information public interest liti⁃gation as"Skimming off Excess Profits",and affirming their function as deterrence rather than compensation can they truly achieve the broader objective of safeguarding personal information security and promoting public welfare,as well as avoid disrupting the harmony of the existing legal landscape.

Transforming Healthcare:AI-NLP Fusion Framework for Precision Decision-Making and Personalized Care Optimization in the Era of IoMT

In the rapidly evolving landscape of healthcare,the integration of Artificial Intelligence(AI)and Natural Language Processing(NLP)holds immense promise for revolutionizing data analytics and decision-making processes.Current techniques for personalized medicine,disease diagnosis,treatment recommendations,and resource optimization in the Internet of Medical Things(IoMT)vary widely,including methods such as rule-based systems,machine learning algorithms,and data-driven approaches.However,many of these techniques face limitations in accuracy,scalability,and adaptability to complex clinical scenarios.This study investigates the synergistic potential of AI-driven optimization techniques and NLP applications in the context of the IoMT.Through the integration of advanced data analytics methodologies with NLP capabilities,we propose a comprehensive framework designed to enhance personalized medicine,streamline disease diagnosis,provide treatment recommendations,and optimize resource allocation.Using a systematic methodology data was collected from open data repositories,then preprocessed using data cleaning,missing value imputation,feature engineering,and data normalization and scaling.Optimization algorithms,such as Gradient Descent,Adam Optimization,and Stochastic Gradient Descent,were employed in the framework to enhance model performance.These were integrated with NLP processes,including Text Preprocessing,Tokenization,and Sentiment Analysis to facilitate comprehensive analysis of the data to provide actionable insights from the vast streams of data generated by IoMT devices.Lastly,through a synthesis of existing research and real-world case studies,we demonstrated the impact of AI-NLP fusion on healthcare outcomes and operational efficiency.The simulation produced compelling results,achieving an average diagnostic accuracy of 93.5%for the given scenarios,and excelled even further in instances involving rare diseases,achieving an accuracy rate of 98%.With regard to patient-specific treatment plans it generated them with an average precision of 96.7%.Improvements in early risk stratification and enhanced documentation were also noted.Furthermore,the study addresses ethical considerations and challenges associated with deploying AI and NLP in healthcare decision-making processes,offering insights into risk-mitigating strategies.This research contributes to advancing the understanding of AI-driven optimization algorithms in healthcare data analytics,with implications for healthcare practitioners,researchers,and policymakers.By leveraging AI and NLP technologies in IoMT environments,this study paves the way for innovative strategies to enhance patient care and operational effectiveness.Ultimately,this work underscores the transformative potential of AI-NLP fusion in shaping the future of healthcare.

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.

Personality Trait Detection via Transfer Learning

Personality recognition plays a pivotal role when developing user-centric solutions such as recommender systems or decision support systems across various domains,including education,e-commerce,or human resources.Tra-ditional machine learning techniques have been broadly employed for personality trait identification;nevertheless,the development of new technologies based on deep learning has led to new opportunities to improve their performance.This study focuses on the capabilities of pre-trained language models such as BERT,RoBERTa,ALBERT,ELECTRA,ERNIE,or XLNet,to deal with the task of personality recognition.These models are able to capture structural features from textual content and comprehend a multitude of language facets and complex features such as hierarchical relationships or long-term dependencies.This makes them suitable to classify multi-label personality traits from reviews while mitigating computational costs.The focus of this approach centers on developing an architecture based on different layers able to capture the semantic context and structural features from texts.Moreover,it is able to fine-tune the previous models using the MyPersonality dataset,which comprises 9,917 status updates contributed by 250 Facebook users.These status updates are categorized according to the well-known Big Five personality model,setting the stage for a comprehensive exploration of personality traits.To test the proposal,a set of experiments have been performed using different metrics such as the exact match ratio,hamming loss,zero-one-loss,precision,recall,F1-score,and weighted averages.The results reveal ERNIE is the top-performing model,achieving an exact match ratio of 72.32%,an accuracy rate of 87.17%,and 84.41%of F1-score.The findings demonstrate that the tested models substantially outperform other state-of-the-art studies,enhancing the accuracy by at least 3%and confirming them as powerful tools for personality recognition.These findings represent substantial advancements in personality recognition,making them appropriate for the development of user-centric applications.

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 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.