In the rapidly evolving landscape of natural language processing(NLP)and sentiment analysis,improving the accuracy and efficiency of sentiment classification models is crucial.This paper investigates the performance o...In the rapidly evolving landscape of natural language processing(NLP)and sentiment analysis,improving the accuracy and efficiency of sentiment classification models is crucial.This paper investigates the performance of two advanced models,the Large Language Model(LLM)LLaMA model and NLP BERT model,in the context of airline review sentiment analysis.Through fine-tuning,domain adaptation,and the application of few-shot learning,the study addresses the subtleties of sentiment expressions in airline-related text data.Employing predictive modeling and comparative analysis,the research evaluates the effectiveness of Large Language Model Meta AI(LLaMA)and Bidirectional Encoder Representations from Transformers(BERT)in capturing sentiment intricacies.Fine-tuning,including domain adaptation,enhances the models'performance in sentiment classification tasks.Additionally,the study explores the potential of few-shot learning to improve model generalization using minimal annotated data for targeted sentiment analysis.By conducting experiments on a diverse airline review dataset,the research quantifies the impact of fine-tuning,domain adaptation,and few-shot learning on model performance,providing valuable insights for industries aiming to predict recommendations and enhance customer satisfaction through a deeper understanding of sentiment in user-generated content(UGC).This research contributes to refining sentiment analysis models,ultimately fostering improved customer satisfaction in the airline industry.展开更多
Treating ischemic stroke(IS)presents significant challenges;however,recent advancements suggest that glial cell-derived extracellular vesicles(GD-EVs)may offer a promising therapeutic strategy.This systematic review a...Treating ischemic stroke(IS)presents significant challenges;however,recent advancements suggest that glial cell-derived extracellular vesicles(GD-EVs)may offer a promising therapeutic strategy.This systematic review and meta-analysis evaluated the potential benefits of GD-EVs in IS by synthesizing data from preclinical studies.The review protocol was pre-registered with PROSPERO(CRD42024541149).Comprehensive literature searches were conducted across multiple databases,including PubMed,EMBASE,Web of Science,Cochrane Library,China National Knowledge Infrastructure,VIP Database for Chinese Technical Periodicals,Wanfang Database,and SinoMed,until April 10,2024,to identify relevant studies.Preclinical studies investigating the utilization of GD-EVs in animal models of IS were included.Study quality was assessed using the risk of bias tool from the Systematic Review Center for Laboratory Animal Experimentation.From an initial pool of 3028 studies,11 studies met the inclusion criteria.The analysis demonstrated that GD-EVs significantly improved neurological function,as evidenced by a reduction in the modified neurological severity score(standardized mean difference[SMD]:−1.69,95%confidence interval[CI]:−2.15 to−1.22,p<0.00001,and I2=0%).GD-EVs also significantly reduced infarct volume in rodent models(SMD:−4.78,95%CI:−6.91 to−2.66,p<0.0001,Tau2=0.99,and I2=42%)and decreased brain water content and the release of pro-inflammatory factors post-stroke.The methodological rigor of the included studies indicated sufficiently high overall quality.These findings suggest that GD-EVs hold significant promise as a novel therapeutic approach for IS and warrant further preclinical investigations before translation into clinical trials.展开更多
This article introduces and compares risk assessment models for venous thromboembolism in gynecological patients at home and abroad.The models assessed included the Caprini risk assessment model,the G-Caprini risk ass...This article introduces and compares risk assessment models for venous thromboembolism in gynecological patients at home and abroad.The models assessed included the Caprini risk assessment model,the G-Caprini risk assessment model,the Rogers risk assessment model,the Autar risk assessment model,the gynecological patient surgical venous thrombosis risk assessment scale,the Wells score,the COMPASS-CAT thrombus risk assessment model,the Khorana risk assessment model,the Padua risk assessment model,and the Chaoyang model.The purpose of this study is to provide a foundation for developing a risk assessment tool for gynecological venous thromboembolism tailored to Chinese patients and to assist clinical health care workers in selecting appropriate risk assessment tools and guiding individualized prevention measures.展开更多
Objectives:This systematic review aimed to assess the properties and feasibility of existing risk prediction models for post-intensive care syndrome outcomes in adult survivors of critical illness.Methods:As of Novemb...Objectives:This systematic review aimed to assess the properties and feasibility of existing risk prediction models for post-intensive care syndrome outcomes in adult survivors of critical illness.Methods:As of November 1,2023,Cochrane Library,PubMed,Embase,CINAHL,Web of Science,PsycInfo,China National Knowledge Infrastructure(CNKI),SinoMed,Wanfang database,and China Science and Technology Journal Database(VIP)were searched.Following the literature screening process,we extracted data encompassing participant sources,post-intensive care syndrome(PICS)outcomes,sample sizes,missing data,predictive factors,model development methodologies,and metrics for model performance and evaluation.We conducted a review and classification of the PICS domains and predictive factors identified in each study.The Prediction Model Risk of Bias Assessment Tool was employed to assess the quality and applicability of the studies.Results:This systematic review included a total of 16 studies,comprising two cognitive impairment studies,four psychological impairment studies,eight physiological impairment studies,and two studies on all three domains.The discriminative ability of prediction models measured by area under the receiver operating characteristic curve was 0.68e0.90.The predictive performance of most models was excellent,but most models were biased and overfitted.All predictive factors tend to encompass age,pre-ICU functional impairment,in-ICU experiences,and early-onset new symptoms.Conclusions:This review identified 16 prediction models and the predictive factors for PICS.Nonetheless,due to the numerous methodological and reporting shortcomings identified in the studies under review,clinicians should exercise caution when interpreting the predictions made by these models.To avert the development of PICS,it is imperative for clinicians to closely monitor prognostic factors,including the in-ICU experience and early-onset new symptoms.展开更多
Enterprise Resource Planning(ERP)systems play a pivotal role in modern organizations by integrating business processes,enhancing operational efficiency,and supporting decision-making.Evaluating the success of ERP impl...Enterprise Resource Planning(ERP)systems play a pivotal role in modern organizations by integrating business processes,enhancing operational efficiency,and supporting decision-making.Evaluating the success of ERP implementations remains a critical challenge for both researchers and practitioners.The DeLone&McLean(D&M)Information Systems(IS)Success Model has been widely adopted as a theoretical framework to assess ERP success,yet its application in dynamic and evolving technological landscapes requires further examination.This systematic review synthesizes empirical studies from 2017 to 2024 that apply the D&M Model to evaluate ERP system success.The study aims to:(1)identify key trends in the application of the D&M Model across different organizational and technological contexts,(2)analyze the most influential success factors-system quality,information quality,service quality,user satisfaction,use,and net benefits-and their interrelationships,and(3)highlight emerging challenges and opportunities for refining the model in ERP research.Findings reveal that while the D&M Model provides a robust foundation for assessing ERP success,contextual factors such as organizational climate,leadership support,and mandatory vs.voluntary usage significantly influence outcomes.Additionally,advancements in digital transformation,AI,and cloud-based ERP systems introduce new dimensions that the traditional model may not fully capture.The review also identifies gaps in longitudinal studies and cross-cultural validations of the D&M Model in ERP settings.Based on the analysis,this paper proposes an enhanced framework that integrates dynamic moderators and post-implementation metrics to better align the D&M Model with contemporary ERP environments.The study contributes to IS literature by offering a comprehensive evaluation of the D&M Model’s applicability and limitations in ERP research,while providing actionable insights for organizations seeking to optimize ERP success.展开更多
BACKGROUND The trend of risk prediction models for diabetic peripheral neuropathy(DPN)is increasing,but few studies focus on the quality of the model and its practical application.AIM To conduct a comprehensive system...BACKGROUND The trend of risk prediction models for diabetic peripheral neuropathy(DPN)is increasing,but few studies focus on the quality of the model and its practical application.AIM To conduct a comprehensive systematic review and rigorous evaluation of prediction models for DPN.METHODS A meticulous search was conducted in PubMed,EMBASE,Cochrane,CNKI,Wang Fang DATA,and VIP Database to identify studies published until October 2023.The included and excluded criteria were applied by the researchers to screen the literature.Two investigators independently extracted data and assessed the quality using a data extraction form and a bias risk assessment tool.Disagreements were resolved through consultation with a third investigator.Data from the included studies were extracted utilizing the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies.Additionally,the bias risk and applicability of the models were evaluated by the Prediction Model Risk of Bias Assessment Tool.RESULTS The systematic review included 14 studies with a total of 26 models.The area under the receiver operating characteristic curve of the 26 models was 0.629-0.938.All studies had high risks of bias,mainly due to participants,outcomes,and analysis.The most common predictors included glycated hemoglobin,age,duration of diabetes,lipid abnormalities,and fasting blood glucose.CONCLUSION The predictor model presented good differentiation,calibration,but there were significant methodological flaws and high risk of bias.Future studies should focus on improving the study design and study report,updating the model and verifying its adaptability and feasibility in clinical practice.展开更多
The integration of Learning Management Systems(LMSs)into educational settings is becoming increasingly common,especially in the digital field.Understanding the factors influencing the acceptance and effective use of L...The integration of Learning Management Systems(LMSs)into educational settings is becoming increasingly common,especially in the digital field.Understanding the factors influencing the acceptance and effective use of LMS is essential to ensure successful implementation.The Technology Acceptance Model(TAM)has been widely used to check user acceptance of various technologies,including LMS.This study conducted a systematic literature review(SLR)to analyze existing research on the application of TAM in the context of LMS.A comprehensive search of the academic database was conducted to identify relevant studies published in 2010-2025.The review synthesizes findings related to the core constructs of TAM—Perceived Usability,Perceived Ease of Use,Behavioral Intent,and Actual Use—as well as extended factors such as system quality,self-efficacy,and social influence.The results reveal circumstantial evidence supporting the predictive power of TAM in LMS adoption,while also highlighting emerging trends and gaps in the literature.This review contributes to a deeper understanding of user acceptance in a digital learning environment and provides recommendations for future research and practical LMS implementation strategies.展开更多
BACKGROUND Partial hepatectomy continues to be the primary treatment approach for liver tumors,and post-hepatectomy liver failure(PHLF)remains the most critical lifethreatening complication following surgery.AIM To co...BACKGROUND Partial hepatectomy continues to be the primary treatment approach for liver tumors,and post-hepatectomy liver failure(PHLF)remains the most critical lifethreatening complication following surgery.AIM To comprehensively review the PHLF prognostic models developed in recent years and objectively assess the risk of bias in these models.METHODS This review followed the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guideline.Three databases were searched from November 2019 to December 2022,and references as well as cited literature in all included studies were manually screened in March 2023.Based on the defined inclusion criteria,articles on PHLF prognostic models were selected,and data from all included articles were extracted by two independent reviewers.The PROBAST was used to evaluate the quality of each included article.RESULTS A total of thirty-four studies met the eligibility criteria and were included in the analysis.Nearly all of the models(32/34,94.1%)were developed and validated exclusively using private data sources.Predictive variables were categorized into five distinct types,with the majority of studies(32/34,94.1%)utilizing multiple types of data.The area under the curve for the training models included ranged from 0.697 to 0.956.Analytical issues resulted in a high risk of bias across all studies included.CONCLUSION The validation performance of the existing models was substantially lower compared to the development models.All included studies were evaluated as having a high risk of bias,primarily due to issues within the analytical domain.The progression of modeling technology,particularly in artificial intelligence modeling,necessitates the use of suitable quality assessment tools.展开更多
Objective:Generative artificial intelligence(AI)technology,represented by large language models(LLMs),has gradually been developed for traditional Chinese medicine(TCM);however,challenges remain in effectively enhanci...Objective:Generative artificial intelligence(AI)technology,represented by large language models(LLMs),has gradually been developed for traditional Chinese medicine(TCM);however,challenges remain in effectively enhancing AI applications for TCM.Therefore,this study is the first systematic review to analyze LLMs in TCM retrospectively,focusing on and summarizing the evidence of their performance in generative tasks.Methods:We extensively searched electronic databases for articles published until June 2024 to identify publicly available studies on LLMs in TCM.Two investigators independently selected and extracted the related information and evaluation metrics.Based on the available data,this study used descriptive analysis for a comprehensive systematic review of LLM technology related to TCM.Results:Ten studies published between 2023 and 2024 met our eligibility criteria and were included in this review,including 40%LLMs in the TCM vertical domain,40%containing TCM data,and 20%honoring the TCM contribution,with a foundational model parameter range from 1.8 to 33 billion.All included studies used manual or automatic evaluation metrics to evaluate model performance and fully discussed the challenges and contributions through an overview of LLMs in TCM.Conclusions:LLMs have achieved significant advantages in TCM applications and can effectively address intelligent TCM tasks.Further in-depth development of LLMs is needed in various vertical TCM fields,including clinical and fundamental research.Focusing on the functional segmentation development direction of generative AI technologies in TCM application scenarios to meet the practical needs-oriented demands of TCM digitalization is essential.展开更多
Background:Knee osteoarthritis(KOA)characterized by degeneration of knee cartilage and subsequent bone hyperplasia is a prevalent joint condition primarily affecting aging adults.The pathophysiology of KOA remains poo...Background:Knee osteoarthritis(KOA)characterized by degeneration of knee cartilage and subsequent bone hyperplasia is a prevalent joint condition primarily affecting aging adults.The pathophysiology of KOA remains poorly understood,as it involves complex mechanisms that result in the same outcome.Consequently,researchers are interested in studying KOA and require appropriate animal models for basic research.Chinese herbal compounds,which consist of multiple herbs with diverse pharmacological properties,possess characteristics such as multicomponent,multipathway,and multitarget effects.The potential benefits in the treatment of KOA continue to attract attention.Purpose:This study aims to provide a comprehensive overview of the advantages,limitations,and specific considerations in selecting different species and methods for KOA animal models.This will help researchers make informed decisions when choosing an animal model.Methods:Online academic databases(e.g.,PubMed,Google Scholar,Web of Science,and CNKI)were searched using the search terms“knee osteoarthritis,”“animal models,”“traditional Chinese medicine,”and their combinations,primarily including KOA studies published from 2010 to 2023.Results:Based on literature retrieval,this review provides a comprehensive overview of the methods of establishing KOA animal models;introduces the current status of advantages and disadvantages of various animal models,including mice,rats,rabbits,dogs,and sheep/goats;and presents the current status of methods used to establish KOA animal models.Conclusion:This study provides a review of the animal models used in recent KOA research,discusses the common modeling methods,and emphasizes the role of traditional Chinese medicine compounds in the treatment of KOA.展开更多
Owing to the persisting hype in pushing toward global carbon neutrality,the study scope of atmospheric science is rapidly expanding.Among numerous trending topics,energy meteorology has been attracting the most attent...Owing to the persisting hype in pushing toward global carbon neutrality,the study scope of atmospheric science is rapidly expanding.Among numerous trending topics,energy meteorology has been attracting the most attention hitherto.One essential skill of solar energy meteorologists is solar power curve modeling,which seeks to map irradiance and auxiliary weather variables to solar power,by statistical and/or physical means.In this regard,this tutorial review aims to deliver a complete overview of those fundamental scientific and engineering principles pertaining to the solar power curve.Solar power curves can be modeled in two primary ways,one of regression and the other of model chain.Both classes of modeling approaches,alongside their hybridization and probabilistic extensions,which allow accuracy improvement and uncertainty quantification,are scrutinized and contrasted thoroughly in this review.展开更多
With the rapid growth of manuscript submissions,finding eligible reviewers for every submission has become a heavy task.Recommender systems are powerful tools developed in computer science and information science to d...With the rapid growth of manuscript submissions,finding eligible reviewers for every submission has become a heavy task.Recommender systems are powerful tools developed in computer science and information science to deal with this problem.However,most existing approaches resort to text mining techniques to match manuscripts with potential reviewers,which require high-quality textual information to perform well.In this paper,we propose a reviewer recommendation algorithm based on a network diffusion process on a scholar-paper multilayer network,with no requirement for textual information.The network incorporates the relationship of scholar-paper pairs,the collaboration among scholars,and the bibliographic coupling among papers.Experimental results show that our proposed algorithm outperforms other state-of-the-art recommendation methods that use graph random walk and matrix factorization and methods that use machine learning and natural language processing,with improvements of over 7.62%in recall,5.66%in hit rate,and 47.53%in ranking score.Our work sheds light on the effectiveness of multilayer network diffusion-based methods in the reviewer recommendation problem,which will help to facilitate the peer-review process and promote information retrieval research in other practical scenes.展开更多
Distraction spinal cord injury is caused by some degree of distraction or longitudinal tension on the spinal cord and commonly occurs in patients who undergo corrective operation for severe spinal deformity.With the i...Distraction spinal cord injury is caused by some degree of distraction or longitudinal tension on the spinal cord and commonly occurs in patients who undergo corrective operation for severe spinal deformity.With the increased degree and duration of distraction,spinal cord injuries become more serious in terms of their neurophysiology,histology,and behavior.Very few studies have been published on the specific characteristics of distraction spinal cord injury.In this study,we systematically review 22 related studies involving animal models of distraction spinal cord injury,focusing particularly on the neurophysiological,histological,and behavioral characteristics of this disease.In addition,we summarize the mechanisms underlying primary and secondary injuries caused by distraction spinal cord injury and clarify the effects of different degrees and durations of distraction on the primary injuries associated with spinal cord injury.We provide new concepts for the establishment of a model of distraction spinal cord injury and related basic research,and provide reference guidelines for the clinical diagnosis and treatment of this disease.展开更多
The fundamental scientific and engineering knowledge concerning the solar power curve,which maps solar irradiance and other auxiliary meteorological variables to photovoltaic output power,has been gathered and put for...The fundamental scientific and engineering knowledge concerning the solar power curve,which maps solar irradiance and other auxiliary meteorological variables to photovoltaic output power,has been gathered and put forward in the preceding tutorial review.Despite the many pages of that review,it was incomplete in the sense that it did not elaborate on the applications of this very important tool of solar energy meteorology.Indeed,solar power curves are ubiquitously needed in a broad spectrum of solar forecasting and solar resource assessment tasks.Hence,this tutorial review should continue from where it left off and present examples concerning the usage of solar power curves.In a nutshell,this tutorial review,together with the preceding one,should elucidate how surface shortwave radiation data,be they ground-based,satelliteretrieved,or model-output,are bridged to various power system operations via solar power curves.展开更多
[Objectives]To systematically evaluate the impact of interventions based on the Common Sense Model of Self-Regulation(CSM)on the psychosocial adaptation of young and middle-aged patients with acute coronary syndrome(A...[Objectives]To systematically evaluate the impact of interventions based on the Common Sense Model of Self-Regulation(CSM)on the psychosocial adaptation of young and middle-aged patients with acute coronary syndrome(ACS),providing evidence-based support for clinical practice.[Methods]A systematic review was conducted using a literature search method,systematically searching through Chinese and English databases such as PubMed,Embase,Cochrane Library,CINAHL,China National Knowledge Infrastructure(CNKI),Wanfang Database,and VIP Database,from the database inception to December 31,2024.The search focused on studies related to the impact of the CSM on the psychosocial adaptation of young and middle-aged ACS patients.Two researchers independently performed literature screening,quality assessment,and data extraction.[Results]A total of 18 studies were included,comprising 12 randomized controlled trials,4 quasi-experimental studies,and 2 cohort studies,involving 2847 young and middle-aged ACS patients.Interventions based on the CSM significantly improved patients disease perception,emotional regulation,self-efficacy,and quality of life.Patients in the intervention group showed significant reductions in anxiety and depression levels,cardiac-related fear,and improvements in disease perception accuracy,treatment adherence,and social function recovery.[Conclusions]Interventions based on the CSM can effectively promote the psychosocial adaptation of young and middle-aged ACS patients,improve their disease perception and emotional state,and enhance their quality of life.It is recommended that this model be widely applied in the clinical care of young and middle-aged ACS patients.展开更多
Accurate estimation of evapotranspiration(ET)is crucial for efficient water resource management,particularly in the face of climate change and increasing water scarcity.This study performs a bibliometric analysis of 3...Accurate estimation of evapotranspiration(ET)is crucial for efficient water resource management,particularly in the face of climate change and increasing water scarcity.This study performs a bibliometric analysis of 352 articles and a systematic review of 35 peer-reviewed papers,selected according to PRISMA guidelines,to evaluate the performance of Hybrid Artificial Neural Networks(HANNs)in ET estimation.The findings demonstrate that HANNs,particularly those combining Multilayer Perceptrons(MLPs),Recurrent Neural Networks(RNNs),and Convolutional Neural Networks(CNNs),are highly effective in capturing the complex nonlinear relationships and tem-poral dependencies characteristic of hydrological processes.These hybrid models,often integrated with optimization algorithms and fuzzy logic frameworks,significantly improve the predictive accuracy and generalization capabilities of ET estimation.The growing adoption of advanced evaluation metrics,such as Kling-Gupta Efficiency(KGE)and Taylor Diagrams,highlights the increasing demand for more robust performance assessments beyond traditional methods.Despite the promising results,challenges remain,particularly regarding model interpretability,computational efficiency,and data scarcity.Future research should prioritize the integration of interpretability techniques,such as attention mechanisms,Local Interpretable Model-Agnostic Explanations(LIME),and feature importance analysis,to enhance model transparency and foster stakeholder trust.Additionally,improving HANN models’scalability and computational efficiency is crucial,especially for large-scale,real-world applications.Approaches such as transfer learning,parallel processing,and hyperparameter optimization will be essential in overcoming these challenges.This study underscores the transformative potential of HANN models for precise ET estimation,particularly in water-scarce and climate-vulnerable regions.By integrating CNNs for automatic feature extraction and leveraging hybrid architectures,HANNs offer considerable advantages for optimizing water management,particularly agriculture.Addressing challenges related to interpretability and scalability will be vital to ensuring the widespread deployment and operational success of HANNs in global water resource management.展开更多
Objective:Peripheral nerve repair is required after traumatic injury.This common condition represents a major public health problem worldwide.Recovery after nerve repair depends on several factors,including the severi...Objective:Peripheral nerve repair is required after traumatic injury.This common condition represents a major public health problem worldwide.Recovery after nerve repair depends on several factors,including the severity of the injury,the nerve involved,and the surgeon’s technical skills.Despite the precise microsurgical repair of nerve lesions,adequate functional recovery is not always achieved and,therefore,the regeneration process and surgical techniques are still being studied.Pre-clinical animal models are essential for this research and,for this reason,the focus of the present systematic review(according to the PRISMA statement)was to analyze the different animal models used in pre-clinical peripheral nerve repair studies.Data sources:Original articles,published in English from 2000 to 2018,were collected using the Web of Science,Scopus,and PubMed databases.Data selection:Only preclinical trials on direct nerve repair were included in this review.The articles were evaluated by the first two authors,in accordance with predefined data fields.Outcome measures:The primary outcomes included functional motor abilities,daily activity and regeneration rate.Secondary outcomes included coaptation technique and animal model.Results:This review yielded 267 articles,of which,after completion of the screening,49 studies were analyzed.There were 1425 animals in those 49 studies,being rats,mice,guinea pigs,rabbits,cats and dogs the different pre-clinical models.The nerves used were classified into three groups:head and neck(11),forelimb(8)and hindlimb(30).The techniques used to perform the coaptation were:microsuture(46),glue(12),laser(8)and mechanical(2).The follow-up examinations were histology(43),electrophysiological analysis(24)and behavioral observation(22).Conclusion:The most widely used animal model in the study of peripheral nerve repair is the rat.Other animal models are also used but the cost-benefit of the rat model provides several strengths over the others.Suture techniques are currently the first option for nerve repair,but the use of glues,lasers and bioengineering materials is increasing.Hence,further research in this field is required to improve clinical practice.展开更多
There is scientific progress in the evaluation methods of recent Earth system models(ESMs).Methods range from single variable to multi-variables,multi-processes,multi-phenomena quantitative evaluations in five layers(...There is scientific progress in the evaluation methods of recent Earth system models(ESMs).Methods range from single variable to multi-variables,multi-processes,multi-phenomena quantitative evaluations in five layers(spheres)of the Earth system,from climatic mean assessment to climate change(such as trends,periodicity,interdecadal variability),extreme values,abnormal characters and quantitative evaluations of phenomena,from qualitative assessment to quantitative calculation of reliability and uncertainty for model simulations.Researchers started considering independence and similarity between models in multi-model use,as well as the quantitative evaluation of climate prediction and projection efect and the quantitative uncertainty contribution analysis.In this manuscript,the simulations and projections by both CMIP5 and CMIP3 that have been published after 2007 are reviewed and summarized.展开更多
Transcranial direct current stimulation(tDCS)is a promising method for altering cortical excitability with clinical implications.It has been increasingly used in neurodevelopmental disorders,especially attention-defic...Transcranial direct current stimulation(tDCS)is a promising method for altering cortical excitability with clinical implications.It has been increasingly used in neurodevelopmental disorders,especially attention-deficit hyperactivity disorder(ADHD),but its efficacy(based on effect size calculations),safety,and stimulation parameters have not been systematically examined.In this systematic review,we aimed to(1)explore the effectiveness of tDCS on the clinical symptoms and neuropsychological deficits of ADHD patients,(2)evaluate the safety of tDCS application,especially in children with ADHD,(3)model the electrical field intensity in the target regions based on the commonly-applied and effective versus less-effective protocols,and(4)discuss and propose advanced tDCS parameters.Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses approach,a literature search identified 14 empirical experiments investigating tDCS effects in ADHD.Partial improving effects of tDCS on cognitive deficits(response inhibition,working memory,attention,and cognitive flexibility)or clinical symptoms(e.g.,impulsivity and inattention)are reported in10 studies.No serious adverse effects are reported in 747 sessions of tDCS.The left and right dorsolateral prefrontal cortex are the regions most often targeted,and anodal tDCS the protocol most often applied.An intensity of 2 mA induced stronger electrical fields than 1 mA in adults with ADHD and was associated with significant behavioral changes.In ADHD children,however,the electrical field induced by 1 mA,which is likely larger than the electrical field induced by 1 mA in adults due to the smaller head size of children,was sufficient to result in significant behavioral change.Overall,tDCS seems to be a promising method for improving ADHD deficits.However,the clinical utility of tDCS in ADHD cannot yet be concluded and requires further systematic investigation in larger sample sizes.Cortical regions involved in ADHD pathophysiology,stimulation parameters(e.g.intensity,duration,polarity,and electrode size),and types of symptom/deficit are potential determinants of tDCS efficacy in ADHD.Developmental aspects of tDCS in childhood ADHD should be considered as well.展开更多
This review investigates the recent developments of heterogeneous objects modeling in additive manufacturing(AM),as well as general problems and widespread solutions to the modeling methods of heterogeneous objects.Pr...This review investigates the recent developments of heterogeneous objects modeling in additive manufacturing(AM),as well as general problems and widespread solutions to the modeling methods of heterogeneous objects.Prevalent heterogeneous object representations are generally categorized based on the different expression or data structure employed therein,and the state-of-the-art of process planning procedures for AM is reviewed via different vigorous solutions for part orientation,slicing methods,and path planning strategies.Finally,some evident problems and possible future directions of investigation are discussed.展开更多
文摘In the rapidly evolving landscape of natural language processing(NLP)and sentiment analysis,improving the accuracy and efficiency of sentiment classification models is crucial.This paper investigates the performance of two advanced models,the Large Language Model(LLM)LLaMA model and NLP BERT model,in the context of airline review sentiment analysis.Through fine-tuning,domain adaptation,and the application of few-shot learning,the study addresses the subtleties of sentiment expressions in airline-related text data.Employing predictive modeling and comparative analysis,the research evaluates the effectiveness of Large Language Model Meta AI(LLaMA)and Bidirectional Encoder Representations from Transformers(BERT)in capturing sentiment intricacies.Fine-tuning,including domain adaptation,enhances the models'performance in sentiment classification tasks.Additionally,the study explores the potential of few-shot learning to improve model generalization using minimal annotated data for targeted sentiment analysis.By conducting experiments on a diverse airline review dataset,the research quantifies the impact of fine-tuning,domain adaptation,and few-shot learning on model performance,providing valuable insights for industries aiming to predict recommendations and enhance customer satisfaction through a deeper understanding of sentiment in user-generated content(UGC).This research contributes to refining sentiment analysis models,ultimately fostering improved customer satisfaction in the airline industry.
基金supported by National Natural Science Foundation of China(No.82274457 and No.82104822)Young Elite Scientists Sponsorship Program by CACM(No.CACM-(2022-QNRC2-B06))+1 种基金Funding for Clinical Research at High-Level Traditional Chinese Medicine Hospitals in China Central(DZMG-QNGG0005)the Fundamental Research Funds for the Central Universities(2025-BUCMXJKY045).
文摘Treating ischemic stroke(IS)presents significant challenges;however,recent advancements suggest that glial cell-derived extracellular vesicles(GD-EVs)may offer a promising therapeutic strategy.This systematic review and meta-analysis evaluated the potential benefits of GD-EVs in IS by synthesizing data from preclinical studies.The review protocol was pre-registered with PROSPERO(CRD42024541149).Comprehensive literature searches were conducted across multiple databases,including PubMed,EMBASE,Web of Science,Cochrane Library,China National Knowledge Infrastructure,VIP Database for Chinese Technical Periodicals,Wanfang Database,and SinoMed,until April 10,2024,to identify relevant studies.Preclinical studies investigating the utilization of GD-EVs in animal models of IS were included.Study quality was assessed using the risk of bias tool from the Systematic Review Center for Laboratory Animal Experimentation.From an initial pool of 3028 studies,11 studies met the inclusion criteria.The analysis demonstrated that GD-EVs significantly improved neurological function,as evidenced by a reduction in the modified neurological severity score(standardized mean difference[SMD]:−1.69,95%confidence interval[CI]:−2.15 to−1.22,p<0.00001,and I2=0%).GD-EVs also significantly reduced infarct volume in rodent models(SMD:−4.78,95%CI:−6.91 to−2.66,p<0.0001,Tau2=0.99,and I2=42%)and decreased brain water content and the release of pro-inflammatory factors post-stroke.The methodological rigor of the included studies indicated sufficiently high overall quality.These findings suggest that GD-EVs hold significant promise as a novel therapeutic approach for IS and warrant further preclinical investigations before translation into clinical trials.
基金funded by the National College Students Innovation and Entrepreneurship Training Program(S202310760049).
文摘This article introduces and compares risk assessment models for venous thromboembolism in gynecological patients at home and abroad.The models assessed included the Caprini risk assessment model,the G-Caprini risk assessment model,the Rogers risk assessment model,the Autar risk assessment model,the gynecological patient surgical venous thrombosis risk assessment scale,the Wells score,the COMPASS-CAT thrombus risk assessment model,the Khorana risk assessment model,the Padua risk assessment model,and the Chaoyang model.The purpose of this study is to provide a foundation for developing a risk assessment tool for gynecological venous thromboembolism tailored to Chinese patients and to assist clinical health care workers in selecting appropriate risk assessment tools and guiding individualized prevention measures.
基金supported by the Scientific Research Project of Shanghai Municipal Health Commission(202140047)the Characteristic Research Project of Shanghai General Hospital(CCTR-2022N03)the Technology Standardization Management and Promotion Project of Shanghai Shenkang Hospital Development Center(SHDC22022219)and the funding organization has played no roles in the survey's design,implementation,and analysis.
文摘Objectives:This systematic review aimed to assess the properties and feasibility of existing risk prediction models for post-intensive care syndrome outcomes in adult survivors of critical illness.Methods:As of November 1,2023,Cochrane Library,PubMed,Embase,CINAHL,Web of Science,PsycInfo,China National Knowledge Infrastructure(CNKI),SinoMed,Wanfang database,and China Science and Technology Journal Database(VIP)were searched.Following the literature screening process,we extracted data encompassing participant sources,post-intensive care syndrome(PICS)outcomes,sample sizes,missing data,predictive factors,model development methodologies,and metrics for model performance and evaluation.We conducted a review and classification of the PICS domains and predictive factors identified in each study.The Prediction Model Risk of Bias Assessment Tool was employed to assess the quality and applicability of the studies.Results:This systematic review included a total of 16 studies,comprising two cognitive impairment studies,four psychological impairment studies,eight physiological impairment studies,and two studies on all three domains.The discriminative ability of prediction models measured by area under the receiver operating characteristic curve was 0.68e0.90.The predictive performance of most models was excellent,but most models were biased and overfitted.All predictive factors tend to encompass age,pre-ICU functional impairment,in-ICU experiences,and early-onset new symptoms.Conclusions:This review identified 16 prediction models and the predictive factors for PICS.Nonetheless,due to the numerous methodological and reporting shortcomings identified in the studies under review,clinicians should exercise caution when interpreting the predictions made by these models.To avert the development of PICS,it is imperative for clinicians to closely monitor prognostic factors,including the in-ICU experience and early-onset new symptoms.
文摘Enterprise Resource Planning(ERP)systems play a pivotal role in modern organizations by integrating business processes,enhancing operational efficiency,and supporting decision-making.Evaluating the success of ERP implementations remains a critical challenge for both researchers and practitioners.The DeLone&McLean(D&M)Information Systems(IS)Success Model has been widely adopted as a theoretical framework to assess ERP success,yet its application in dynamic and evolving technological landscapes requires further examination.This systematic review synthesizes empirical studies from 2017 to 2024 that apply the D&M Model to evaluate ERP system success.The study aims to:(1)identify key trends in the application of the D&M Model across different organizational and technological contexts,(2)analyze the most influential success factors-system quality,information quality,service quality,user satisfaction,use,and net benefits-and their interrelationships,and(3)highlight emerging challenges and opportunities for refining the model in ERP research.Findings reveal that while the D&M Model provides a robust foundation for assessing ERP success,contextual factors such as organizational climate,leadership support,and mandatory vs.voluntary usage significantly influence outcomes.Additionally,advancements in digital transformation,AI,and cloud-based ERP systems introduce new dimensions that the traditional model may not fully capture.The review also identifies gaps in longitudinal studies and cross-cultural validations of the D&M Model in ERP settings.Based on the analysis,this paper proposes an enhanced framework that integrates dynamic moderators and post-implementation metrics to better align the D&M Model with contemporary ERP environments.The study contributes to IS literature by offering a comprehensive evaluation of the D&M Model’s applicability and limitations in ERP research,while providing actionable insights for organizations seeking to optimize ERP success.
基金Supported by Capital’s Funds for Health Improvement and Research,No.2024-4-4135.
文摘BACKGROUND The trend of risk prediction models for diabetic peripheral neuropathy(DPN)is increasing,but few studies focus on the quality of the model and its practical application.AIM To conduct a comprehensive systematic review and rigorous evaluation of prediction models for DPN.METHODS A meticulous search was conducted in PubMed,EMBASE,Cochrane,CNKI,Wang Fang DATA,and VIP Database to identify studies published until October 2023.The included and excluded criteria were applied by the researchers to screen the literature.Two investigators independently extracted data and assessed the quality using a data extraction form and a bias risk assessment tool.Disagreements were resolved through consultation with a third investigator.Data from the included studies were extracted utilizing the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies.Additionally,the bias risk and applicability of the models were evaluated by the Prediction Model Risk of Bias Assessment Tool.RESULTS The systematic review included 14 studies with a total of 26 models.The area under the receiver operating characteristic curve of the 26 models was 0.629-0.938.All studies had high risks of bias,mainly due to participants,outcomes,and analysis.The most common predictors included glycated hemoglobin,age,duration of diabetes,lipid abnormalities,and fasting blood glucose.CONCLUSION The predictor model presented good differentiation,calibration,but there were significant methodological flaws and high risk of bias.Future studies should focus on improving the study design and study report,updating the model and verifying its adaptability and feasibility in clinical practice.
文摘The integration of Learning Management Systems(LMSs)into educational settings is becoming increasingly common,especially in the digital field.Understanding the factors influencing the acceptance and effective use of LMS is essential to ensure successful implementation.The Technology Acceptance Model(TAM)has been widely used to check user acceptance of various technologies,including LMS.This study conducted a systematic literature review(SLR)to analyze existing research on the application of TAM in the context of LMS.A comprehensive search of the academic database was conducted to identify relevant studies published in 2010-2025.The review synthesizes findings related to the core constructs of TAM—Perceived Usability,Perceived Ease of Use,Behavioral Intent,and Actual Use—as well as extended factors such as system quality,self-efficacy,and social influence.The results reveal circumstantial evidence supporting the predictive power of TAM in LMS adoption,while also highlighting emerging trends and gaps in the literature.This review contributes to a deeper understanding of user acceptance in a digital learning environment and provides recommendations for future research and practical LMS implementation strategies.
基金Supported by The Science and Technology Innovation 2030-Major Project,No.2021ZD0140406.
文摘BACKGROUND Partial hepatectomy continues to be the primary treatment approach for liver tumors,and post-hepatectomy liver failure(PHLF)remains the most critical lifethreatening complication following surgery.AIM To comprehensively review the PHLF prognostic models developed in recent years and objectively assess the risk of bias in these models.METHODS This review followed the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guideline.Three databases were searched from November 2019 to December 2022,and references as well as cited literature in all included studies were manually screened in March 2023.Based on the defined inclusion criteria,articles on PHLF prognostic models were selected,and data from all included articles were extracted by two independent reviewers.The PROBAST was used to evaluate the quality of each included article.RESULTS A total of thirty-four studies met the eligibility criteria and were included in the analysis.Nearly all of the models(32/34,94.1%)were developed and validated exclusively using private data sources.Predictive variables were categorized into five distinct types,with the majority of studies(32/34,94.1%)utilizing multiple types of data.The area under the curve for the training models included ranged from 0.697 to 0.956.Analytical issues resulted in a high risk of bias across all studies included.CONCLUSION The validation performance of the existing models was substantially lower compared to the development models.All included studies were evaluated as having a high risk of bias,primarily due to issues within the analytical domain.The progression of modeling technology,particularly in artificial intelligence modeling,necessitates the use of suitable quality assessment tools.
基金supported by the National Multidisciplinary Innovation Team of Traditional Chinese Medicine(ZYYCXTD-D-202204)China Postdoctoral Science Foundation(2023M742627)+1 种基金Postdoctoral Fellowship Program of CPSF(GZC20231928)Foundation of State Key Laboratory of Component-based Chinese Medicine(CBCM2023201).
文摘Objective:Generative artificial intelligence(AI)technology,represented by large language models(LLMs),has gradually been developed for traditional Chinese medicine(TCM);however,challenges remain in effectively enhancing AI applications for TCM.Therefore,this study is the first systematic review to analyze LLMs in TCM retrospectively,focusing on and summarizing the evidence of their performance in generative tasks.Methods:We extensively searched electronic databases for articles published until June 2024 to identify publicly available studies on LLMs in TCM.Two investigators independently selected and extracted the related information and evaluation metrics.Based on the available data,this study used descriptive analysis for a comprehensive systematic review of LLM technology related to TCM.Results:Ten studies published between 2023 and 2024 met our eligibility criteria and were included in this review,including 40%LLMs in the TCM vertical domain,40%containing TCM data,and 20%honoring the TCM contribution,with a foundational model parameter range from 1.8 to 33 billion.All included studies used manual or automatic evaluation metrics to evaluate model performance and fully discussed the challenges and contributions through an overview of LLMs in TCM.Conclusions:LLMs have achieved significant advantages in TCM applications and can effectively address intelligent TCM tasks.Further in-depth development of LLMs is needed in various vertical TCM fields,including clinical and fundamental research.Focusing on the functional segmentation development direction of generative AI technologies in TCM application scenarios to meet the practical needs-oriented demands of TCM digitalization is essential.
基金supported by the Cutting Edge Development Fund of Advanced Medical Research Institute(GYY2023QY01)the China Postdoctoral Science Foundation(certificate number:2023M732093)。
文摘Background:Knee osteoarthritis(KOA)characterized by degeneration of knee cartilage and subsequent bone hyperplasia is a prevalent joint condition primarily affecting aging adults.The pathophysiology of KOA remains poorly understood,as it involves complex mechanisms that result in the same outcome.Consequently,researchers are interested in studying KOA and require appropriate animal models for basic research.Chinese herbal compounds,which consist of multiple herbs with diverse pharmacological properties,possess characteristics such as multicomponent,multipathway,and multitarget effects.The potential benefits in the treatment of KOA continue to attract attention.Purpose:This study aims to provide a comprehensive overview of the advantages,limitations,and specific considerations in selecting different species and methods for KOA animal models.This will help researchers make informed decisions when choosing an animal model.Methods:Online academic databases(e.g.,PubMed,Google Scholar,Web of Science,and CNKI)were searched using the search terms“knee osteoarthritis,”“animal models,”“traditional Chinese medicine,”and their combinations,primarily including KOA studies published from 2010 to 2023.Results:Based on literature retrieval,this review provides a comprehensive overview of the methods of establishing KOA animal models;introduces the current status of advantages and disadvantages of various animal models,including mice,rats,rabbits,dogs,and sheep/goats;and presents the current status of methods used to establish KOA animal models.Conclusion:This study provides a review of the animal models used in recent KOA research,discusses the common modeling methods,and emphasizes the role of traditional Chinese medicine compounds in the treatment of KOA.
基金supported by the National Natural Science Foundation of China(project no.42375192),and the China Meteorological Administration Climate Change Special Program(CMA-CCSPproject no.QBZ202315)+2 种基金supported by the National Natural Science Foundation of China(project no.42030608)supported by the National Research,Development and Innovation Fund,project no.OTKA-FK 142702by the Hungarian Academy of Sciences through the Sustainable Development and Technologies National Programme(FFT NP FTA)and the János Bolyai Research Scholarship.
文摘Owing to the persisting hype in pushing toward global carbon neutrality,the study scope of atmospheric science is rapidly expanding.Among numerous trending topics,energy meteorology has been attracting the most attention hitherto.One essential skill of solar energy meteorologists is solar power curve modeling,which seeks to map irradiance and auxiliary weather variables to solar power,by statistical and/or physical means.In this regard,this tutorial review aims to deliver a complete overview of those fundamental scientific and engineering principles pertaining to the solar power curve.Solar power curves can be modeled in two primary ways,one of regression and the other of model chain.Both classes of modeling approaches,alongside their hybridization and probabilistic extensions,which allow accuracy improvement and uncertainty quantification,are scrutinized and contrasted thoroughly in this review.
基金Project supported by the National Natural Science Foundation of China(Grant No.T2293771)the New Cornerstone Science Foundation through the XPLORER PRIZE.
文摘With the rapid growth of manuscript submissions,finding eligible reviewers for every submission has become a heavy task.Recommender systems are powerful tools developed in computer science and information science to deal with this problem.However,most existing approaches resort to text mining techniques to match manuscripts with potential reviewers,which require high-quality textual information to perform well.In this paper,we propose a reviewer recommendation algorithm based on a network diffusion process on a scholar-paper multilayer network,with no requirement for textual information.The network incorporates the relationship of scholar-paper pairs,the collaboration among scholars,and the bibliographic coupling among papers.Experimental results show that our proposed algorithm outperforms other state-of-the-art recommendation methods that use graph random walk and matrix factorization and methods that use machine learning and natural language processing,with improvements of over 7.62%in recall,5.66%in hit rate,and 47.53%in ranking score.Our work sheds light on the effectiveness of multilayer network diffusion-based methods in the reviewer recommendation problem,which will help to facilitate the peer-review process and promote information retrieval research in other practical scenes.
基金supported by the National Natural Science Foundation of China,No.81772421(to YH).
文摘Distraction spinal cord injury is caused by some degree of distraction or longitudinal tension on the spinal cord and commonly occurs in patients who undergo corrective operation for severe spinal deformity.With the increased degree and duration of distraction,spinal cord injuries become more serious in terms of their neurophysiology,histology,and behavior.Very few studies have been published on the specific characteristics of distraction spinal cord injury.In this study,we systematically review 22 related studies involving animal models of distraction spinal cord injury,focusing particularly on the neurophysiological,histological,and behavioral characteristics of this disease.In addition,we summarize the mechanisms underlying primary and secondary injuries caused by distraction spinal cord injury and clarify the effects of different degrees and durations of distraction on the primary injuries associated with spinal cord injury.We provide new concepts for the establishment of a model of distraction spinal cord injury and related basic research,and provide reference guidelines for the clinical diagnosis and treatment of this disease.
基金supported by the National Natural Science Foundation of China(project no.42375192)supported by the National Natural Science Foundation of China(project no.42030608)+3 种基金China Meteorological Administration Climate Change Special Program(CMA-CCSPproject no.QBZ202315)supported by the National Research,Development and Innovation Fund,project no.OTKA-FK 142702the János Bolyai Research Scholarship。
文摘The fundamental scientific and engineering knowledge concerning the solar power curve,which maps solar irradiance and other auxiliary meteorological variables to photovoltaic output power,has been gathered and put forward in the preceding tutorial review.Despite the many pages of that review,it was incomplete in the sense that it did not elaborate on the applications of this very important tool of solar energy meteorology.Indeed,solar power curves are ubiquitously needed in a broad spectrum of solar forecasting and solar resource assessment tasks.Hence,this tutorial review should continue from where it left off and present examples concerning the usage of solar power curves.In a nutshell,this tutorial review,together with the preceding one,should elucidate how surface shortwave radiation data,be they ground-based,satelliteretrieved,or model-output,are bridged to various power system operations via solar power curves.
基金Supported by Philosophy and Social Sciences Research Project of Hubei Provincial Department of Education(22Q149,19Y090).
文摘[Objectives]To systematically evaluate the impact of interventions based on the Common Sense Model of Self-Regulation(CSM)on the psychosocial adaptation of young and middle-aged patients with acute coronary syndrome(ACS),providing evidence-based support for clinical practice.[Methods]A systematic review was conducted using a literature search method,systematically searching through Chinese and English databases such as PubMed,Embase,Cochrane Library,CINAHL,China National Knowledge Infrastructure(CNKI),Wanfang Database,and VIP Database,from the database inception to December 31,2024.The search focused on studies related to the impact of the CSM on the psychosocial adaptation of young and middle-aged ACS patients.Two researchers independently performed literature screening,quality assessment,and data extraction.[Results]A total of 18 studies were included,comprising 12 randomized controlled trials,4 quasi-experimental studies,and 2 cohort studies,involving 2847 young and middle-aged ACS patients.Interventions based on the CSM significantly improved patients disease perception,emotional regulation,self-efficacy,and quality of life.Patients in the intervention group showed significant reductions in anxiety and depression levels,cardiac-related fear,and improvements in disease perception accuracy,treatment adherence,and social function recovery.[Conclusions]Interventions based on the CSM can effectively promote the psychosocial adaptation of young and middle-aged ACS patients,improve their disease perception and emotional state,and enhance their quality of life.It is recommended that this model be widely applied in the clinical care of young and middle-aged ACS patients.
文摘Accurate estimation of evapotranspiration(ET)is crucial for efficient water resource management,particularly in the face of climate change and increasing water scarcity.This study performs a bibliometric analysis of 352 articles and a systematic review of 35 peer-reviewed papers,selected according to PRISMA guidelines,to evaluate the performance of Hybrid Artificial Neural Networks(HANNs)in ET estimation.The findings demonstrate that HANNs,particularly those combining Multilayer Perceptrons(MLPs),Recurrent Neural Networks(RNNs),and Convolutional Neural Networks(CNNs),are highly effective in capturing the complex nonlinear relationships and tem-poral dependencies characteristic of hydrological processes.These hybrid models,often integrated with optimization algorithms and fuzzy logic frameworks,significantly improve the predictive accuracy and generalization capabilities of ET estimation.The growing adoption of advanced evaluation metrics,such as Kling-Gupta Efficiency(KGE)and Taylor Diagrams,highlights the increasing demand for more robust performance assessments beyond traditional methods.Despite the promising results,challenges remain,particularly regarding model interpretability,computational efficiency,and data scarcity.Future research should prioritize the integration of interpretability techniques,such as attention mechanisms,Local Interpretable Model-Agnostic Explanations(LIME),and feature importance analysis,to enhance model transparency and foster stakeholder trust.Additionally,improving HANN models’scalability and computational efficiency is crucial,especially for large-scale,real-world applications.Approaches such as transfer learning,parallel processing,and hyperparameter optimization will be essential in overcoming these challenges.This study underscores the transformative potential of HANN models for precise ET estimation,particularly in water-scarce and climate-vulnerable regions.By integrating CNNs for automatic feature extraction and leveraging hybrid architectures,HANNs offer considerable advantages for optimizing water management,particularly agriculture.Addressing challenges related to interpretability and scalability will be vital to ensuring the widespread deployment and operational success of HANNs in global water resource management.
文摘Objective:Peripheral nerve repair is required after traumatic injury.This common condition represents a major public health problem worldwide.Recovery after nerve repair depends on several factors,including the severity of the injury,the nerve involved,and the surgeon’s technical skills.Despite the precise microsurgical repair of nerve lesions,adequate functional recovery is not always achieved and,therefore,the regeneration process and surgical techniques are still being studied.Pre-clinical animal models are essential for this research and,for this reason,the focus of the present systematic review(according to the PRISMA statement)was to analyze the different animal models used in pre-clinical peripheral nerve repair studies.Data sources:Original articles,published in English from 2000 to 2018,were collected using the Web of Science,Scopus,and PubMed databases.Data selection:Only preclinical trials on direct nerve repair were included in this review.The articles were evaluated by the first two authors,in accordance with predefined data fields.Outcome measures:The primary outcomes included functional motor abilities,daily activity and regeneration rate.Secondary outcomes included coaptation technique and animal model.Results:This review yielded 267 articles,of which,after completion of the screening,49 studies were analyzed.There were 1425 animals in those 49 studies,being rats,mice,guinea pigs,rabbits,cats and dogs the different pre-clinical models.The nerves used were classified into three groups:head and neck(11),forelimb(8)and hindlimb(30).The techniques used to perform the coaptation were:microsuture(46),glue(12),laser(8)and mechanical(2).The follow-up examinations were histology(43),electrophysiological analysis(24)and behavioral observation(22).Conclusion:The most widely used animal model in the study of peripheral nerve repair is the rat.Other animal models are also used but the cost-benefit of the rat model provides several strengths over the others.Suture techniques are currently the first option for nerve repair,but the use of glues,lasers and bioengineering materials is increasing.Hence,further research in this field is required to improve clinical practice.
基金supported by the Ministry of Science and Technology 973 Project(No.2010CB950501-03)the National Natural Science Foundation(No.41175066)
文摘There is scientific progress in the evaluation methods of recent Earth system models(ESMs).Methods range from single variable to multi-variables,multi-processes,multi-phenomena quantitative evaluations in five layers(spheres)of the Earth system,from climatic mean assessment to climate change(such as trends,periodicity,interdecadal variability),extreme values,abnormal characters and quantitative evaluations of phenomena,from qualitative assessment to quantitative calculation of reliability and uncertainty for model simulations.Researchers started considering independence and similarity between models in multi-model use,as well as the quantitative evaluation of climate prediction and projection efect and the quantitative uncertainty contribution analysis.In this manuscript,the simulations and projections by both CMIP5 and CMIP3 that have been published after 2007 are reviewed and summarized.
基金This review was supported by the Department of Psychology and Neurosciences,Leibniz-Institut fiir Arbeitsforschung Ministry of Science,Research and Technology,Deputy of Scholarship and Students Affairs,Iran(95000171)the German Ministry of Research and Education(German Center for Brain Stimulation grant number 01EE1403C).
文摘Transcranial direct current stimulation(tDCS)is a promising method for altering cortical excitability with clinical implications.It has been increasingly used in neurodevelopmental disorders,especially attention-deficit hyperactivity disorder(ADHD),but its efficacy(based on effect size calculations),safety,and stimulation parameters have not been systematically examined.In this systematic review,we aimed to(1)explore the effectiveness of tDCS on the clinical symptoms and neuropsychological deficits of ADHD patients,(2)evaluate the safety of tDCS application,especially in children with ADHD,(3)model the electrical field intensity in the target regions based on the commonly-applied and effective versus less-effective protocols,and(4)discuss and propose advanced tDCS parameters.Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses approach,a literature search identified 14 empirical experiments investigating tDCS effects in ADHD.Partial improving effects of tDCS on cognitive deficits(response inhibition,working memory,attention,and cognitive flexibility)or clinical symptoms(e.g.,impulsivity and inattention)are reported in10 studies.No serious adverse effects are reported in 747 sessions of tDCS.The left and right dorsolateral prefrontal cortex are the regions most often targeted,and anodal tDCS the protocol most often applied.An intensity of 2 mA induced stronger electrical fields than 1 mA in adults with ADHD and was associated with significant behavioral changes.In ADHD children,however,the electrical field induced by 1 mA,which is likely larger than the electrical field induced by 1 mA in adults due to the smaller head size of children,was sufficient to result in significant behavioral change.Overall,tDCS seems to be a promising method for improving ADHD deficits.However,the clinical utility of tDCS in ADHD cannot yet be concluded and requires further systematic investigation in larger sample sizes.Cortical regions involved in ADHD pathophysiology,stimulation parameters(e.g.intensity,duration,polarity,and electrode size),and types of symptom/deficit are potential determinants of tDCS efficacy in ADHD.Developmental aspects of tDCS in childhood ADHD should be considered as well.
基金supported by the National Nature Science Foundation of China,Nos.51575483 and U1609207.
文摘This review investigates the recent developments of heterogeneous objects modeling in additive manufacturing(AM),as well as general problems and widespread solutions to the modeling methods of heterogeneous objects.Prevalent heterogeneous object representations are generally categorized based on the different expression or data structure employed therein,and the state-of-the-art of process planning procedures for AM is reviewed via different vigorous solutions for part orientation,slicing methods,and path planning strategies.Finally,some evident problems and possible future directions of investigation are discussed.
