Medical image segmentation is a crucial task in clinical applications.However,obtaining labeled data for medical images is often challenging.This has led to the appeal of semi-supervised learning(SSL),a technique adep...Medical image segmentation is a crucial task in clinical applications.However,obtaining labeled data for medical images is often challenging.This has led to the appeal of semi-supervised learning(SSL),a technique adept at leveraging a modest amount of labeled data.Nonetheless,most prevailing SSL segmentation methods for medical images either rely on the single consistency training method or directly fine-tune SSL methods designed for natural images.In this paper,we propose an innovative semi-supervised method called multi-consistency training(MCT)for medical image segmentation.Our approach transcends the constraints of prior methodologies by considering consistency from a dual perspective:output consistency across different up-sampling methods and output consistency of the same data within the same network under various perturbations to the intermediate features.We design distinct semi-supervised loss regression methods for these two types of consistencies.To enhance the application of our MCT model,we also develop a dedicated decoder as the core of our neural network.Thorough experiments were conducted on the polyp dataset and the dental dataset,rigorously compared against other SSL methods.Experimental results demonstrate the superiority of our approach,achieving higher segmentation accuracy.Moreover,comprehensive ablation studies and insightful discussion substantiate the efficacy of our approach in navigating the intricacies of medical image segmentation.展开更多
软测量技术为工业过程中重要变量及难测变量的预测提供了一个有效的解决办法。然而,由于工业过程的复杂化和高昂的数据获取成本,使得标记数据与未标记数据分布不平衡。此时,构建高性能的软测量模型成为一个挑战。针对这一问题,提出了一...软测量技术为工业过程中重要变量及难测变量的预测提供了一个有效的解决办法。然而,由于工业过程的复杂化和高昂的数据获取成本,使得标记数据与未标记数据分布不平衡。此时,构建高性能的软测量模型成为一个挑战。针对这一问题,提出了一种基于时差的多输出tri-training异构软测量方法。通过构建一种新的tri-training框架,采用多输出的高斯过程回归(multi-output Gaussian process regression,MGPR)、相关向量机(multi-output relevance vector machine,MRVM)、最小二乘支持向量机(multi-output least squares support vector machine,MLSSVM)三种模型作为基线监督回归器,使用标记数据进行训练和迭代;同时,引入时间差分(time difference,TD)改进模型的动态特性,并通过卡尔曼滤波(Kalman filtering,KF)优化模型的参数,提高其预测性能;最后通过模拟污水处理平台(benchmark simulation model 1,BSM1)和实际污水处理厂对该模型进行了验证。结果表明,与传统的软测量建模方法相比,该模型能显著提高数据分布不平衡下软测量模型的自适应性和预测性能。展开更多
Accurate prediction of the remaining useful life(RUL)is crucial for the design and management of lithium-ion batteries.Although various machine learning models offer promising predictions,one critical but often overlo...Accurate prediction of the remaining useful life(RUL)is crucial for the design and management of lithium-ion batteries.Although various machine learning models offer promising predictions,one critical but often overlooked challenge is their demand for considerable run-to-failure data for training.Collection of such training data leads to prohibitive testing efforts as the run-to-failure tests can last for years.Here,we propose a semi-supervised representation learning method to enhance prediction accuracy by learning from data without RUL labels.Our approach builds on a sophisticated deep neural network that comprises an encoder and three decoder heads to extract time-dependent representation features from short-term battery operating data regardless of the existence of RUL labels.The approach is validated using three datasets collected from 34 batteries operating under various conditions,encompassing over 19,900 charge and discharge cycles.Our method achieves a root mean squared error(RMSE)within 25 cycles,even when only 1/50 of the training dataset is labelled,representing a reduction of 48%compared to the conventional approach.We also demonstrate the method's robustness with varying numbers of labelled data and different weights assigned to the three decoder heads.The projection of extracted features in low space reveals that our method effectively learns degradation features from unlabelled data.Our approach highlights the promise of utilising semi-supervised learning to reduce the data demand for reliability monitoring of energy devices.展开更多
Existing semi-supervisedmedical image segmentation algorithms use copy-paste data augmentation to correct the labeled-unlabeled data distribution mismatch.However,current copy-paste methods have three limitations:(1)t...Existing semi-supervisedmedical image segmentation algorithms use copy-paste data augmentation to correct the labeled-unlabeled data distribution mismatch.However,current copy-paste methods have three limitations:(1)training the model solely with copy-paste mixed pictures from labeled and unlabeled input loses a lot of labeled information;(2)low-quality pseudo-labels can cause confirmation bias in pseudo-supervised learning on unlabeled data;(3)the segmentation performance in low-contrast and local regions is less than optimal.We design a Stochastic Augmentation-Based Dual-Teaching Auxiliary Training Strategy(SADT),which enhances feature diversity and learns high-quality features to overcome these problems.To be more precise,SADT trains the Student Network by using pseudo-label-based training from Teacher Network 1 and supervised learning with labeled data,which prevents the loss of rare labeled data.We introduce a bi-directional copy-pastemask with progressive high-entropy filtering to reduce data distribution disparities and mitigate confirmation bias in pseudo-supervision.For the mixed images,Deep-Shallow Spatial Contrastive Learning(DSSCL)is proposed in the feature spaces of Teacher Network 2 and the Student Network to improve the segmentation capabilities in low-contrast and local areas.In this procedure,the features retrieved by the Student Network are subjected to a random feature perturbation technique.On two openly available datasets,extensive trials show that our proposed SADT performs much better than the state-ofthe-art semi-supervised medical segmentation techniques.Using only 10%of the labeled data for training,SADT was able to acquire a Dice score of 90.10%on the ACDC(Automatic Cardiac Diagnosis Challenge)dataset.展开更多
BACKGROUND Cognitive frailty and depression are prevalent among the elderly,significantly impairing physical and cognitive functions,psychological well-being,and quality of life.Effective interventions are essential t...BACKGROUND Cognitive frailty and depression are prevalent among the elderly,significantly impairing physical and cognitive functions,psychological well-being,and quality of life.Effective interventions are essential to mitigate these adverse effects and enhance overall health outcomes in this population.AIM To evaluate the effects of exercise-cognitive dual-task training on frailty,cognitive function,psychological status,and quality of life in elderly patients with cognitive frailty and depression.METHODS A retrospective study was conducted on 130 patients with cognitive frailty and depression admitted between December 2021 and December 2023.Patients were divided into a control group receiving routine intervention and an observation group undergoing exercise-cognitive dual-task training in addition to routine care.Frailty,cognitive function,balance and gait,psychological status,and quality of life were assessed before and after the intervention.RESULTS After the intervention,the frailty score of the observation group was(5.32±0.69),lower than that of the control group(5.71±0.55).The Montreal cognitive assessment basic scale score in the observation group was(24.06±0.99),higher than the control group(23.43±1.40).The performance oriented mobility assessment score in the observation group was(21.81±1.24),higher than the control group(21.15±1.26).The self-efficacy in the observation group was(28.27±2.66),higher than the control group(30.05±2.66).The anxiety score in the hospital anxiety and depression scale(HADS)for the observation group was(5.86±0.68),lower than the control group(6.21±0.64).The depression score in the HADS for the observation group was(5.67±0.75),lower than the control group(6.27±0.92).Additionally,the scores for each dimension of the 36-item short form survey in the observation group were higher than those in the control group,with statistically significant differences(P<0.05).CONCLUSION Exercise-cognitive dual-task training is beneficial for improving frailty,enhancing cognitive function,and improving psychological status and quality of life in elderly patients with cognitive frailty and depression.展开更多
Accurate assessment of blast furnace conditions is a crucial component in the blast furnace control decision-making process.However,most adversarial models in the field currently update the parameters of the label pre...Accurate assessment of blast furnace conditions is a crucial component in the blast furnace control decision-making process.However,most adversarial models in the field currently update the parameters of the label predictor by minimising the objective function while maximising the objective function to update the domain discriminator's parameters.This strategy results in an excessive maximisation of the domain discriminator's loss.To address this,a friendly adversarial training-based tri-training furnace condition diagnosis model was proposed.This model employed a convolutional neural network-long short-term memory-attention mechanism network as a single-view feature extractor and used decision tree methods as three classifiers to compute the cosine similarity between features and representative vectors of each class.During the knowledge transfer process,the classifiers in this model have a specific goal;they not only seek to maximise the entropy of the target domain samples but also aim to minimise the entropy of the target domain samples when they are misclassified,thus resolving the trade-off in traditional models where robustness is improved at the expense of accuracy.Experimental results indicate that the diagnostic accuracy of this model reaches 96%,with an approximately 8%improvement over existing methods due to the inner optimisation approach.This model provides an effective and feasible solution for the efficient monitoring and diagnosis of blast furnace processes.展开更多
The classification of respiratory sounds is crucial in diagnosing and monitoring respiratory diseases.However,auscultation is highly subjective,making it challenging to analyze respiratory sounds accurately.Although d...The classification of respiratory sounds is crucial in diagnosing and monitoring respiratory diseases.However,auscultation is highly subjective,making it challenging to analyze respiratory sounds accurately.Although deep learning has been increasingly applied to this task,most existing approaches have primarily relied on supervised learning.Since supervised learning requires large amounts of labeled data,recent studies have explored self-supervised and semi-supervised methods to overcome this limitation.However,these approaches have largely assumed a closedset setting,where the classes present in the unlabeled data are considered identical to those in the labeled data.In contrast,this study explores an open-set semi-supervised learning setting,where the unlabeled data may contain additional,unknown classes.To address this challenge,a distance-based prototype network is employed to classify respiratory sounds in an open-set setting.In the first stage,the prototype network is trained using labeled and unlabeled data to derive prototype representations of known classes.In the second stage,distances between unlabeled data and known class prototypes are computed,and samples exceeding an adaptive threshold are identified as unknown.A new prototype is then calculated for this unknown class.In the final stage,semi-supervised learning is employed to classify labeled and unlabeled data into known and unknown classes.Compared to conventional closed-set semisupervised learning approaches,the proposed method achieved an average classification accuracy improvement of 2%–5%.Additionally,in cases of data scarcity,utilizing unlabeled data further improved classification performance by 6%–8%.The findings of this study are expected to significantly enhance respiratory sound classification performance in practical clinical settings.展开更多
Exercise is a therapeutic approach in cancer treatment,providing several benefits.Moreover,exercise is associated with a reduced risk for developing a range of cancers and for their recurrence,as well as with improvin...Exercise is a therapeutic approach in cancer treatment,providing several benefits.Moreover,exercise is associated with a reduced risk for developing a range of cancers and for their recurrence,as well as with improving survival,even though the underlying mechanisms remain unclear.Preclinical and clinical evidence shows that the acute effects of a single exercise session can suppress the growth of various cancer cell lines in vitro.This suppression is potentially due to altered concentrations of hormones(e.g.,insulin)and cytokines(e.g.,tumor necrosis factor alpha and interleukin 6)after exercise.These factors,known to be involved in tumorigenesis,may explain why exercise is associated with reduced cancer incidence,recurrence,and mortality.However,the effects of short-(<8 weeks)and long-term(≥8 weeks)exercise programs on cancer cells have been reported with mixed results.Although more research is needed,it appears that interventions incorporating both exercise and diet seem to have greater inhibitory effects on cancer cell growth in both apparently healthy subjects as well as in cancer patients.Although speculative,these suppressive effects on cancer cells may be driven by changes in body weight and composition as well as by a reduction in low-grade inflammation often associated with sedentary behavior,low muscle mass,and excess fat mass in cancer patients.Taken together,such interventions could alter the systemic levels of suppressive circulating factors,leading to a less favorable environment for tumorigenesis.While regular exercise and a healthy diet may establish a more cancer-suppressive environment,each acute bout of exercise provides a further“dose”of anticancer medicine.Therefore,integrating regular exercise could potentially play a significant role in cancer management,highlighting the need for future investigations in this promising area of research.展开更多
Standardized residency training programs primarily focus on developing clinical diagnostic and treatment skills,often allocating limited time to research activities.However,enhancing research skills is of paramount im...Standardized residency training programs primarily focus on developing clinical diagnostic and treatment skills,often allocating limited time to research activities.However,enhancing research skills is of paramount importance for residents,as it fosters critical thinking,problem-solving abilities,and a deeper understanding of applying scientific principles to clinical practice.This paper explores the necessity and significance of integrating research training into residency programs,emphasizing its role in cultivating well-rounded physicians capable of advancing medical knowledge.This study proposes a competency-based research training model that encompasses research literacy,study design,biostatistics,and scientific writing.Additionally,online asynchronous training modules,robust mentorship,and balanced time management strategies are recommended to enhance residents’research engagement without compromising clinical training.By implementing these measures,residency programs can improve residents’research capabilities,contributing to both individual professional growth and the broader advancement of medical science.展开更多
Purpose We aimed to determine:(a)the chronic effects of interval training(IT)combined with blood flow restriction(BFR)on physiological adaptations(aerobic/anaerobic capacity and muscle responses)and performance enhanc...Purpose We aimed to determine:(a)the chronic effects of interval training(IT)combined with blood flow restriction(BFR)on physiological adaptations(aerobic/anaerobic capacity and muscle responses)and performance enhancement(endurance and sprints),and(b)the influence of participant characteristics and intervention protocols on these effects.Methods Searches were conducted in PubMed,Web of Science(Core Collection),Cochrane Library(Embase,ClinicalTrials.gov,and International Clinical Trials Registry Platform),and Chinese National Knowledge Infrastructure on April 2,with updates on October 17,2024.Pooled effects for each outcome were summarized using Hedge's g(g)through meta-analysis-based random effects models,and subgroup and regression analyses were used to explore moderators.Results A total of 24 studies with 621 participants were included.IT combined with BFR(IT+BFR)significantly improved maximal oxygen uptake(VO2_(max))(g=0.63,I^(2)=63%),mean power during the Wingate 30-s test(g=0.70,I^(2)=47%),muscle strength(g=0.88,I^(2)=64%),muscle endurance(g=0.43,I^(2)=0%),time to fatigue(g=1.26,I^(2)=86%),and maximal aerobic speed(g=0.74,I^(2)=0%)compared to IT alone.Subgroup analysis indicated that participant characteristics including training status,IT intensity,and IT modes significantly moderated VO2_(max)(subgroup differences:p<0.05).Specifically,IT+BFR showed significantly superior improvements in VO2_(max)compared to IT alone in trained individuals(g=0.76)at supra-maximal intensity(g=1.29)and moderate intensity(g=1.08)as well as in walking(g=1.64)and running(g=0.63)modes.Meta-regression analysis showed cuff width(β=0.14)was significantly associated with VO2_(max)change,identifying 8.23 cm as the minimum threshold required for significant improvement.Subgroup analyses regarding muscle strength did not reveal any significant moderators.Conclusion IT+BFR enhances physiological adaptations and optimizes aspects of endurance performance,with moderators including training status,IT protocol(intensity,mode,and type),and cuff width.This intervention addresses various IT-related challenges and provides tailored protocols and benefits for diverse populations.展开更多
This paper reports a case of cerebral stem infarction with quadriplegia and complete dependence on daily life.The course of the disease lasted more than 7 months.Frenchay's improved articulation Disorder Assessmen...This paper reports a case of cerebral stem infarction with quadriplegia and complete dependence on daily life.The course of the disease lasted more than 7 months.Frenchay's improved articulation Disorder Assessment Form has been assessed as severe articulation disorder.The patient has significantly improved his speech function and quality of life after systematic head control training,respiratory function training,articulation motor training,and articulation training.In the course of treatment,emphasis was placed on head postural control training and respiratory function training,and emphasis was placed on the strength and coordination training of articulatory organs,and the results were remarkable.After the patient was discharged from the hospital,the follow-up of basic daily life communication was not limited.展开更多
Background Schizophrenia is characterised by pervasive cognitive deficits that significantly impair daily functioning and quality of life.Pharmacological treatments have limited efficacy in addressing these deficits,h...Background Schizophrenia is characterised by pervasive cognitive deficits that significantly impair daily functioning and quality of life.Pharmacological treatments have limited efficacy in addressing these deficits,highlighting the need for adjunctive interventions like computerised cognitive training(CCT).Aims This study aimed to evaluate the effects of a 30-session CCT programme on mental well-being and cognitive performance in individuals with schizophrenia.Additionally,it assessed the usability and acceptability of CCT in this population.Methods A double-blind,randomised clinical trial was conducted with 54 participants assigned to intervention and control groups.Cognitive and mental health outcomes were assessed using validated tools such as the Depression Anxiety Stress Scale 21,the Warwick-Edinburgh Mental Wellbeing Scale and the Cambridge Neuropsychological Test Automated Battery.Usability was measured with the System Usability Scale(SUS).Assessments were conducted at baseline,post-intervention and 3 months post-follow-up.Results The CCT intervention significantly improved mental well-being,reduced stress and enhanced working memory(paired associate learning,spatial working memory and spatial span)compared with controls.However,no significant effects were observed for anxiety,depression or executive function.Usability scores were high(SUS=83.51),and compliance rates were strong(92.7%),indicating favourable participant engagement.Conclusion CCT demonstrated potential as an adjunctive treatment for schizophrenia,with significant improvements in targeted cognitive and mental health domains.The high usability and compliance rates support its feasibility for broader implementation.Further research is needed to optimise protocols and explore long-term benefits.CCT offers a promising approach to addressing mental health and cognitive challenges in schizophrenia,particularly for stress and working memory.Its usability and acceptability suggest it could be seamlessly integrated into clinical practice.展开更多
In recent years,railway construction in China has developed vigorously.With continuous improvements in the highspeed railway network,the focus is gradually shifting from large-scale construction to large-scale operati...In recent years,railway construction in China has developed vigorously.With continuous improvements in the highspeed railway network,the focus is gradually shifting from large-scale construction to large-scale operations.However,several challenges have emerged within the high-speed railway dispatching and command system,including the heavy workload faced by dispatchers,the difficulty of quantifying subjective expertise,and the need for effective training of professionals.Amid the growing application of artificial intelligence technologies in railway systems,this study leverages Large Language Model(LLM)technology.LLMs bring enhanced intelligence,predictive capabilities,robust memory,and adaptability to diverse real-world scenarios.This study proposes a human-computer interactive intelligent scheduling auxiliary training system built on LLM technology.The system offers capabilities including natural dialogue,knowledge reasoning,and human feedback learning.With broad applicability,the system is suitable for vocational education,guided inquiry,knowledge-based Q&A,and other training scenarios.Validation results demonstrate its effectiveness in auxiliary training,providing substantial support for educators,students,and dispatching personnel in colleges and professional settings.展开更多
Large amounts of labeled data are usually needed for training deep neural networks in medical image studies,particularly in medical image classification.However,in the field of semi-supervised medical image analysis,l...Large amounts of labeled data are usually needed for training deep neural networks in medical image studies,particularly in medical image classification.However,in the field of semi-supervised medical image analysis,labeled data is very scarce due to patient privacy concerns.For researchers,obtaining high-quality labeled images is exceedingly challenging because it involves manual annotation and clinical understanding.In addition,skin datasets are highly suitable for medical image classification studies due to the inter-class relationships and the inter-class similarities of skin lesions.In this paper,we propose a model called Coalition Sample Relation Consistency(CSRC),a consistency-based method that leverages Canonical Correlation Analysis(CCA)to capture the intrinsic relationships between samples.Considering that traditional consistency-based models only focus on the consistency of prediction,we additionally explore the similarity between features by using CCA.We enforce feature relation consistency based on traditional models,encouraging the model to learn more meaningful information from unlabeled data.Finally,considering that cross-entropy loss is not as suitable as the supervised loss when studying with imbalanced datasets(i.e.,ISIC 2017 and ISIC 2018),we improve the supervised loss to achieve better classification accuracy.Our study shows that this model performs better than many semi-supervised methods.展开更多
In the realm of medical image segmentation,particularly in cardiac magnetic resonance imaging(MRI),achieving robust performance with limited annotated data is a significant challenge.Performance often degrades when fa...In the realm of medical image segmentation,particularly in cardiac magnetic resonance imaging(MRI),achieving robust performance with limited annotated data is a significant challenge.Performance often degrades when faced with testing scenarios from unknown domains.To address this problem,this paper proposes a novel semi-supervised approach for cardiac magnetic resonance image segmentation,aiming to enhance predictive capabilities and domain generalization(DG).This paper establishes an MT-like model utilizing pseudo-labeling and consistency regularization from semi-supervised learning,and integrates uncertainty estimation to improve the accuracy of pseudo-labels.Additionally,to tackle the challenge of domain generalization,a data manipulation strategy is introduced,extracting spatial and content-related information from images across different domains,enriching the dataset with a multi-domain perspective.This papers method is meticulously evaluated on the publicly available cardiac magnetic resonance imaging dataset M&Ms,validating its effectiveness.Comparative analyses against various methods highlight the out-standing performance of this papers approach,demonstrating its capability to segment cardiac magnetic resonance images in previously unseen domains even with limited annotated data.展开更多
Objective:The objective of this study is to develop and evaluate a structured clinical rotation-based emergency response training program to enhance nurses’emergency competence,theoretical knowledge,and technical ski...Objective:The objective of this study is to develop and evaluate a structured clinical rotation-based emergency response training program to enhance nurses’emergency competence,theoretical knowledge,and technical skills.Methods:A comprehensive emergency training program was developed,and a randomized controlled trial was implemented from June 2022 to May 2023 at a tertiary general hospital in Chongqing,China.The study involved 214 nurses,with 106 participants in the intervention group receiving a 3-month innovative emergency response competence training and 108 in the control group undergoing conventional training.Postintervention assessments evaluated emergency response capabilities using the Emergency Response Ability Assessment Scale for Nurses in Public Health Emergencies,theoretical knowledge through a self-designed comprehensive theoretical assessment instrument,technical skills using a standardized skill assessment form,and training satisfaction through two distinct feedback instruments.Results:The emergency response ability scores were significantly higher in the intervention group compared to controls(3.99±0.18 vs.2.53±0.25,P<0.05).Theoretical assessment scores showed marked improvement in the intervention group versus the control group(85.31±4.40 vs.52.45±6.19,P<0.05).Technical skill performance was significantly better in the intervention group than that in controls(94.47±1.64 vs.86.39±2.36,P<0.05).Training satisfaction was higher among intervention group nurses compared to controls(4.53±0.23 vs.4.00±0.38,P<0.05),with nursing managers also reporting greater satisfaction with the intervention program versus conventional training(4.57±0.49 vs.3.92±0.79,P<0.05).Conclusion:The clinical rotation-based structured emergency response training program effectively enhances nurses’emergency competencies,theoretical knowledge,and technical skills.These findings provide both theoretical foundations and practical guidelines for developing emergency response and specialized nursing competence training programs.展开更多
Endoscopic ultrasound(EUS)is an indispensable tool for the diagnosis and management of various diseases,particularly biliopancreatic disorders,as it provides detailed visualization of the gastrointestinal tract and su...Endoscopic ultrasound(EUS)is an indispensable tool for the diagnosis and management of various diseases,particularly biliopancreatic disorders,as it provides detailed visualization of the gastrointestinal tract and surrounding structures.As the demand for diagnostic and interventional EUS procedures increases,ensuring high-quality training for endoscopists is essential to improve patient outcomes.This mini-review provides an overview of the current state of EUS training and emphasizes the importance of a structured approach that integrates theoretical knowledge and hands-on experience.We discuss different training methods,focusing on the main courses available worldwide,and highlight their advantages and limitations.In addition,we examine the challenges of training for diagnostic and interventional EUS,such as limited access to training centers and the need for personalized feedback.Overall,improving EUS training programs is essential to enhance physician skills and ensure this advanced technique is used safely and efficiently in clinical practice.展开更多
Here we compare the efficacy of anti-obesity drugs alone or combined with exercise training on body weight and exercise capacity of obese patients.Randomized clinical trials that assessed the impact of any anti-obesit...Here we compare the efficacy of anti-obesity drugs alone or combined with exercise training on body weight and exercise capacity of obese patients.Randomized clinical trials that assessed the impact of any anti-obesity drug alone or combined with exercise training on body weight,body fat,fat-free mass and cardiorespiratory fitness in obese patients were retrieved from Pubmed and EMBASE up to May 2024.Risk of bias assessment was performed with RoB 2.0,and the GRADE approach assessed the certainty of evidence(CoE)of each main outcome.We included four publications summing up 202 patients.Two publications used orlistat as an anti-obesity drug treatment,while the other two adopted GLP-1 receptor agonist(liraglutide or tirzepatide)as a pharmacotherapy for weight management.Orlistat combined with exercise was superior to change body weight(mean difference(MD):−2.27 kg;95%CI:−2.86 to−1.69;CoE:very low),fat mass(MD:−2.89;95%CI:−3.87 to−1.91;CoE:very low),fat-free mass(MD:0.56;95%CI:0.40–0.72;CoE:very low),and VO_(2)Peak(MD:2.64;95%CI:2.52–2.76;CoE:very low).GLP-1 receptor agonist drugs combined with exercise had a great effect on body weight(MD:−3.96 kg;95%CI:−5.07 to−2.85;CoE:low),fat mass(MD:−1.76;95%CI:−2.24 to−1.27;CoE:low),fat-free mass(MD:0.50;95%CI:−0.98 to 1.98;CoE:very low)and VO_(2)Peak(MD:2.47;95%CI:1.31–3.63;CoE:very low).The results reported here suggest that exercise training remains an important approach in weight management when combined with pharmacological treatment.展开更多
Digital economy has become a new driving force for China’s economic growth,continuously boosting economic development and rapidly integrating into various fields of China’s economy and society.The advent of the digi...Digital economy has become a new driving force for China’s economic growth,continuously boosting economic development and rapidly integrating into various fields of China’s economy and society.The advent of the digital economy era has reshaped the development pattern of the financial industry.The rapid development of financial technology has promoted the transformation of financial formats and put forward higher requirements for financial talent training in the new era.Digital finance is not only a key part of the transformation and upgrading of China’s financial industry but also an integral component of China’s modern financial ecosystem.Against the backdrop of the digital economy,cultivating digital finance talents in vocational colleges is of great significance to China’s economic development.This paper analyzes the predicaments faced in digital finance talent training,explores in depth the reform of digital finance talent training modes,and proposes policy suggestions for the digital finance talent training system based on the development characteristics of digital finance.展开更多
基金the Innovation Program of Shanghai Industrial Synergy(No.XTCX-KJ-2023-2-12)。
文摘Medical image segmentation is a crucial task in clinical applications.However,obtaining labeled data for medical images is often challenging.This has led to the appeal of semi-supervised learning(SSL),a technique adept at leveraging a modest amount of labeled data.Nonetheless,most prevailing SSL segmentation methods for medical images either rely on the single consistency training method or directly fine-tune SSL methods designed for natural images.In this paper,we propose an innovative semi-supervised method called multi-consistency training(MCT)for medical image segmentation.Our approach transcends the constraints of prior methodologies by considering consistency from a dual perspective:output consistency across different up-sampling methods and output consistency of the same data within the same network under various perturbations to the intermediate features.We design distinct semi-supervised loss regression methods for these two types of consistencies.To enhance the application of our MCT model,we also develop a dedicated decoder as the core of our neural network.Thorough experiments were conducted on the polyp dataset and the dental dataset,rigorously compared against other SSL methods.Experimental results demonstrate the superiority of our approach,achieving higher segmentation accuracy.Moreover,comprehensive ablation studies and insightful discussion substantiate the efficacy of our approach in navigating the intricacies of medical image segmentation.
文摘软测量技术为工业过程中重要变量及难测变量的预测提供了一个有效的解决办法。然而,由于工业过程的复杂化和高昂的数据获取成本,使得标记数据与未标记数据分布不平衡。此时,构建高性能的软测量模型成为一个挑战。针对这一问题,提出了一种基于时差的多输出tri-training异构软测量方法。通过构建一种新的tri-training框架,采用多输出的高斯过程回归(multi-output Gaussian process regression,MGPR)、相关向量机(multi-output relevance vector machine,MRVM)、最小二乘支持向量机(multi-output least squares support vector machine,MLSSVM)三种模型作为基线监督回归器,使用标记数据进行训练和迭代;同时,引入时间差分(time difference,TD)改进模型的动态特性,并通过卡尔曼滤波(Kalman filtering,KF)优化模型的参数,提高其预测性能;最后通过模拟污水处理平台(benchmark simulation model 1,BSM1)和实际污水处理厂对该模型进行了验证。结果表明,与传统的软测量建模方法相比,该模型能显著提高数据分布不平衡下软测量模型的自适应性和预测性能。
基金supported by the National Natural Science Foundation of China(No.52207229)the Key Research and Development Program of Ningxia Hui Autonomous Region of China(No.2024BEE02003)+1 种基金the financial support from the AEGiS Research Grant 2024,University of Wollongong(No.R6254)the financial support from the China Scholarship Council(No.202207550010).
文摘Accurate prediction of the remaining useful life(RUL)is crucial for the design and management of lithium-ion batteries.Although various machine learning models offer promising predictions,one critical but often overlooked challenge is their demand for considerable run-to-failure data for training.Collection of such training data leads to prohibitive testing efforts as the run-to-failure tests can last for years.Here,we propose a semi-supervised representation learning method to enhance prediction accuracy by learning from data without RUL labels.Our approach builds on a sophisticated deep neural network that comprises an encoder and three decoder heads to extract time-dependent representation features from short-term battery operating data regardless of the existence of RUL labels.The approach is validated using three datasets collected from 34 batteries operating under various conditions,encompassing over 19,900 charge and discharge cycles.Our method achieves a root mean squared error(RMSE)within 25 cycles,even when only 1/50 of the training dataset is labelled,representing a reduction of 48%compared to the conventional approach.We also demonstrate the method's robustness with varying numbers of labelled data and different weights assigned to the three decoder heads.The projection of extracted features in low space reveals that our method effectively learns degradation features from unlabelled data.Our approach highlights the promise of utilising semi-supervised learning to reduce the data demand for reliability monitoring of energy devices.
基金supported by the Natural Science Foundation of China(No.41804112,author:Chengyun Song).
文摘Existing semi-supervisedmedical image segmentation algorithms use copy-paste data augmentation to correct the labeled-unlabeled data distribution mismatch.However,current copy-paste methods have three limitations:(1)training the model solely with copy-paste mixed pictures from labeled and unlabeled input loses a lot of labeled information;(2)low-quality pseudo-labels can cause confirmation bias in pseudo-supervised learning on unlabeled data;(3)the segmentation performance in low-contrast and local regions is less than optimal.We design a Stochastic Augmentation-Based Dual-Teaching Auxiliary Training Strategy(SADT),which enhances feature diversity and learns high-quality features to overcome these problems.To be more precise,SADT trains the Student Network by using pseudo-label-based training from Teacher Network 1 and supervised learning with labeled data,which prevents the loss of rare labeled data.We introduce a bi-directional copy-pastemask with progressive high-entropy filtering to reduce data distribution disparities and mitigate confirmation bias in pseudo-supervision.For the mixed images,Deep-Shallow Spatial Contrastive Learning(DSSCL)is proposed in the feature spaces of Teacher Network 2 and the Student Network to improve the segmentation capabilities in low-contrast and local areas.In this procedure,the features retrieved by the Student Network are subjected to a random feature perturbation technique.On two openly available datasets,extensive trials show that our proposed SADT performs much better than the state-ofthe-art semi-supervised medical segmentation techniques.Using only 10%of the labeled data for training,SADT was able to acquire a Dice score of 90.10%on the ACDC(Automatic Cardiac Diagnosis Challenge)dataset.
文摘BACKGROUND Cognitive frailty and depression are prevalent among the elderly,significantly impairing physical and cognitive functions,psychological well-being,and quality of life.Effective interventions are essential to mitigate these adverse effects and enhance overall health outcomes in this population.AIM To evaluate the effects of exercise-cognitive dual-task training on frailty,cognitive function,psychological status,and quality of life in elderly patients with cognitive frailty and depression.METHODS A retrospective study was conducted on 130 patients with cognitive frailty and depression admitted between December 2021 and December 2023.Patients were divided into a control group receiving routine intervention and an observation group undergoing exercise-cognitive dual-task training in addition to routine care.Frailty,cognitive function,balance and gait,psychological status,and quality of life were assessed before and after the intervention.RESULTS After the intervention,the frailty score of the observation group was(5.32±0.69),lower than that of the control group(5.71±0.55).The Montreal cognitive assessment basic scale score in the observation group was(24.06±0.99),higher than the control group(23.43±1.40).The performance oriented mobility assessment score in the observation group was(21.81±1.24),higher than the control group(21.15±1.26).The self-efficacy in the observation group was(28.27±2.66),higher than the control group(30.05±2.66).The anxiety score in the hospital anxiety and depression scale(HADS)for the observation group was(5.86±0.68),lower than the control group(6.21±0.64).The depression score in the HADS for the observation group was(5.67±0.75),lower than the control group(6.27±0.92).Additionally,the scores for each dimension of the 36-item short form survey in the observation group were higher than those in the control group,with statistically significant differences(P<0.05).CONCLUSION Exercise-cognitive dual-task training is beneficial for improving frailty,enhancing cognitive function,and improving psychological status and quality of life in elderly patients with cognitive frailty and depression.
基金Thanks are given to Hebei Province Innovation Capacity Enhancement Programme Project(23560301D)the Natural Science Foundation of Hebei Province(E2024105036)the Tangshan Talent Funding Project(B202302007).
文摘Accurate assessment of blast furnace conditions is a crucial component in the blast furnace control decision-making process.However,most adversarial models in the field currently update the parameters of the label predictor by minimising the objective function while maximising the objective function to update the domain discriminator's parameters.This strategy results in an excessive maximisation of the domain discriminator's loss.To address this,a friendly adversarial training-based tri-training furnace condition diagnosis model was proposed.This model employed a convolutional neural network-long short-term memory-attention mechanism network as a single-view feature extractor and used decision tree methods as three classifiers to compute the cosine similarity between features and representative vectors of each class.During the knowledge transfer process,the classifiers in this model have a specific goal;they not only seek to maximise the entropy of the target domain samples but also aim to minimise the entropy of the target domain samples when they are misclassified,thus resolving the trade-off in traditional models where robustness is improved at the expense of accuracy.Experimental results indicate that the diagnostic accuracy of this model reaches 96%,with an approximately 8%improvement over existing methods due to the inner optimisation approach.This model provides an effective and feasible solution for the efficient monitoring and diagnosis of blast furnace processes.
基金supported by Innovative Human Resource Development for Local Intellectualization Programthrough the Institute of Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)(IITP-2025-RS-2022-00156360).
文摘The classification of respiratory sounds is crucial in diagnosing and monitoring respiratory diseases.However,auscultation is highly subjective,making it challenging to analyze respiratory sounds accurately.Although deep learning has been increasingly applied to this task,most existing approaches have primarily relied on supervised learning.Since supervised learning requires large amounts of labeled data,recent studies have explored self-supervised and semi-supervised methods to overcome this limitation.However,these approaches have largely assumed a closedset setting,where the classes present in the unlabeled data are considered identical to those in the labeled data.In contrast,this study explores an open-set semi-supervised learning setting,where the unlabeled data may contain additional,unknown classes.To address this challenge,a distance-based prototype network is employed to classify respiratory sounds in an open-set setting.In the first stage,the prototype network is trained using labeled and unlabeled data to derive prototype representations of known classes.In the second stage,distances between unlabeled data and known class prototypes are computed,and samples exceeding an adaptive threshold are identified as unknown.A new prototype is then calculated for this unknown class.In the final stage,semi-supervised learning is employed to classify labeled and unlabeled data into known and unknown classes.Compared to conventional closed-set semisupervised learning approaches,the proposed method achieved an average classification accuracy improvement of 2%–5%.Additionally,in cases of data scarcity,utilizing unlabeled data further improved classification performance by 6%–8%.The findings of this study are expected to significantly enhance respiratory sound classification performance in practical clinical settings.
文摘Exercise is a therapeutic approach in cancer treatment,providing several benefits.Moreover,exercise is associated with a reduced risk for developing a range of cancers and for their recurrence,as well as with improving survival,even though the underlying mechanisms remain unclear.Preclinical and clinical evidence shows that the acute effects of a single exercise session can suppress the growth of various cancer cell lines in vitro.This suppression is potentially due to altered concentrations of hormones(e.g.,insulin)and cytokines(e.g.,tumor necrosis factor alpha and interleukin 6)after exercise.These factors,known to be involved in tumorigenesis,may explain why exercise is associated with reduced cancer incidence,recurrence,and mortality.However,the effects of short-(<8 weeks)and long-term(≥8 weeks)exercise programs on cancer cells have been reported with mixed results.Although more research is needed,it appears that interventions incorporating both exercise and diet seem to have greater inhibitory effects on cancer cell growth in both apparently healthy subjects as well as in cancer patients.Although speculative,these suppressive effects on cancer cells may be driven by changes in body weight and composition as well as by a reduction in low-grade inflammation often associated with sedentary behavior,low muscle mass,and excess fat mass in cancer patients.Taken together,such interventions could alter the systemic levels of suppressive circulating factors,leading to a less favorable environment for tumorigenesis.While regular exercise and a healthy diet may establish a more cancer-suppressive environment,each acute bout of exercise provides a further“dose”of anticancer medicine.Therefore,integrating regular exercise could potentially play a significant role in cancer management,highlighting the need for future investigations in this promising area of research.
基金Research on the Construction of a Course Ideological and Political Education System and Evaluation Framework for the“Traditional Chinese Medicine Internal Medicine”Course(Project No.:2025J0459)Open Research Fund Program of Yunnan Key Laboratory of Integrated Traditional Chinese(Project No.:CWCD2023-002,CWCD2023-003&CWCD2023-009)+2 种基金Western Medicine for Chronic Disease in Prevention and TreatmentYunnan 047 Key Laboratory of Yi Dai Medicine and Yi Medicine(Project No.:2024SS24047)Yunnan 025 Key Laboratory of Yi Dai Medicine and Yi Medicine(Project No.:2024SS24025)。
文摘Standardized residency training programs primarily focus on developing clinical diagnostic and treatment skills,often allocating limited time to research activities.However,enhancing research skills is of paramount importance for residents,as it fosters critical thinking,problem-solving abilities,and a deeper understanding of applying scientific principles to clinical practice.This paper explores the necessity and significance of integrating research training into residency programs,emphasizing its role in cultivating well-rounded physicians capable of advancing medical knowledge.This study proposes a competency-based research training model that encompasses research literacy,study design,biostatistics,and scientific writing.Additionally,online asynchronous training modules,robust mentorship,and balanced time management strategies are recommended to enhance residents’research engagement without compromising clinical training.By implementing these measures,residency programs can improve residents’research capabilities,contributing to both individual professional growth and the broader advancement of medical science.
文摘Purpose We aimed to determine:(a)the chronic effects of interval training(IT)combined with blood flow restriction(BFR)on physiological adaptations(aerobic/anaerobic capacity and muscle responses)and performance enhancement(endurance and sprints),and(b)the influence of participant characteristics and intervention protocols on these effects.Methods Searches were conducted in PubMed,Web of Science(Core Collection),Cochrane Library(Embase,ClinicalTrials.gov,and International Clinical Trials Registry Platform),and Chinese National Knowledge Infrastructure on April 2,with updates on October 17,2024.Pooled effects for each outcome were summarized using Hedge's g(g)through meta-analysis-based random effects models,and subgroup and regression analyses were used to explore moderators.Results A total of 24 studies with 621 participants were included.IT combined with BFR(IT+BFR)significantly improved maximal oxygen uptake(VO2_(max))(g=0.63,I^(2)=63%),mean power during the Wingate 30-s test(g=0.70,I^(2)=47%),muscle strength(g=0.88,I^(2)=64%),muscle endurance(g=0.43,I^(2)=0%),time to fatigue(g=1.26,I^(2)=86%),and maximal aerobic speed(g=0.74,I^(2)=0%)compared to IT alone.Subgroup analysis indicated that participant characteristics including training status,IT intensity,and IT modes significantly moderated VO2_(max)(subgroup differences:p<0.05).Specifically,IT+BFR showed significantly superior improvements in VO2_(max)compared to IT alone in trained individuals(g=0.76)at supra-maximal intensity(g=1.29)and moderate intensity(g=1.08)as well as in walking(g=1.64)and running(g=0.63)modes.Meta-regression analysis showed cuff width(β=0.14)was significantly associated with VO2_(max)change,identifying 8.23 cm as the minimum threshold required for significant improvement.Subgroup analyses regarding muscle strength did not reveal any significant moderators.Conclusion IT+BFR enhances physiological adaptations and optimizes aspects of endurance performance,with moderators including training status,IT protocol(intensity,mode,and type),and cuff width.This intervention addresses various IT-related challenges and provides tailored protocols and benefits for diverse populations.
基金Teaching and Research Project of Anhui Urban Management Vocational College(Project No.:2024kfkc001)。
文摘This paper reports a case of cerebral stem infarction with quadriplegia and complete dependence on daily life.The course of the disease lasted more than 7 months.Frenchay's improved articulation Disorder Assessment Form has been assessed as severe articulation disorder.The patient has significantly improved his speech function and quality of life after systematic head control training,respiratory function training,articulation motor training,and articulation training.In the course of treatment,emphasis was placed on head postural control training and respiratory function training,and emphasis was placed on the strength and coordination training of articulatory organs,and the results were remarkable.After the patient was discharged from the hospital,the follow-up of basic daily life communication was not limited.
基金supported by a grant from University of Social Welfare and Rehabilitation Sciences for the research expenses.
文摘Background Schizophrenia is characterised by pervasive cognitive deficits that significantly impair daily functioning and quality of life.Pharmacological treatments have limited efficacy in addressing these deficits,highlighting the need for adjunctive interventions like computerised cognitive training(CCT).Aims This study aimed to evaluate the effects of a 30-session CCT programme on mental well-being and cognitive performance in individuals with schizophrenia.Additionally,it assessed the usability and acceptability of CCT in this population.Methods A double-blind,randomised clinical trial was conducted with 54 participants assigned to intervention and control groups.Cognitive and mental health outcomes were assessed using validated tools such as the Depression Anxiety Stress Scale 21,the Warwick-Edinburgh Mental Wellbeing Scale and the Cambridge Neuropsychological Test Automated Battery.Usability was measured with the System Usability Scale(SUS).Assessments were conducted at baseline,post-intervention and 3 months post-follow-up.Results The CCT intervention significantly improved mental well-being,reduced stress and enhanced working memory(paired associate learning,spatial working memory and spatial span)compared with controls.However,no significant effects were observed for anxiety,depression or executive function.Usability scores were high(SUS=83.51),and compliance rates were strong(92.7%),indicating favourable participant engagement.Conclusion CCT demonstrated potential as an adjunctive treatment for schizophrenia,with significant improvements in targeted cognitive and mental health domains.The high usability and compliance rates support its feasibility for broader implementation.Further research is needed to optimise protocols and explore long-term benefits.CCT offers a promising approach to addressing mental health and cognitive challenges in schizophrenia,particularly for stress and working memory.Its usability and acceptability suggest it could be seamlessly integrated into clinical practice.
基金the Talent Fund of Beijing Jiaotong University(Grant No.2024XKRC055).
文摘In recent years,railway construction in China has developed vigorously.With continuous improvements in the highspeed railway network,the focus is gradually shifting from large-scale construction to large-scale operations.However,several challenges have emerged within the high-speed railway dispatching and command system,including the heavy workload faced by dispatchers,the difficulty of quantifying subjective expertise,and the need for effective training of professionals.Amid the growing application of artificial intelligence technologies in railway systems,this study leverages Large Language Model(LLM)technology.LLMs bring enhanced intelligence,predictive capabilities,robust memory,and adaptability to diverse real-world scenarios.This study proposes a human-computer interactive intelligent scheduling auxiliary training system built on LLM technology.The system offers capabilities including natural dialogue,knowledge reasoning,and human feedback learning.With broad applicability,the system is suitable for vocational education,guided inquiry,knowledge-based Q&A,and other training scenarios.Validation results demonstrate its effectiveness in auxiliary training,providing substantial support for educators,students,and dispatching personnel in colleges and professional settings.
基金sponsored by the National Natural Science Foundation of China Grant No.62271302the Shanghai Municipal Natural Science Foundation Grant 20ZR1423500.
文摘Large amounts of labeled data are usually needed for training deep neural networks in medical image studies,particularly in medical image classification.However,in the field of semi-supervised medical image analysis,labeled data is very scarce due to patient privacy concerns.For researchers,obtaining high-quality labeled images is exceedingly challenging because it involves manual annotation and clinical understanding.In addition,skin datasets are highly suitable for medical image classification studies due to the inter-class relationships and the inter-class similarities of skin lesions.In this paper,we propose a model called Coalition Sample Relation Consistency(CSRC),a consistency-based method that leverages Canonical Correlation Analysis(CCA)to capture the intrinsic relationships between samples.Considering that traditional consistency-based models only focus on the consistency of prediction,we additionally explore the similarity between features by using CCA.We enforce feature relation consistency based on traditional models,encouraging the model to learn more meaningful information from unlabeled data.Finally,considering that cross-entropy loss is not as suitable as the supervised loss when studying with imbalanced datasets(i.e.,ISIC 2017 and ISIC 2018),we improve the supervised loss to achieve better classification accuracy.Our study shows that this model performs better than many semi-supervised methods.
基金Supported by the National Natural Science Foundation of China(No.62001313)the Key Project of Liaoning Provincial Department of Science and Technology(No.2021JH2/10300134,2022JH1/10500004)。
文摘In the realm of medical image segmentation,particularly in cardiac magnetic resonance imaging(MRI),achieving robust performance with limited annotated data is a significant challenge.Performance often degrades when faced with testing scenarios from unknown domains.To address this problem,this paper proposes a novel semi-supervised approach for cardiac magnetic resonance image segmentation,aiming to enhance predictive capabilities and domain generalization(DG).This paper establishes an MT-like model utilizing pseudo-labeling and consistency regularization from semi-supervised learning,and integrates uncertainty estimation to improve the accuracy of pseudo-labels.Additionally,to tackle the challenge of domain generalization,a data manipulation strategy is introduced,extracting spatial and content-related information from images across different domains,enriching the dataset with a multi-domain perspective.This papers method is meticulously evaluated on the publicly available cardiac magnetic resonance imaging dataset M&Ms,validating its effectiveness.Comparative analyses against various methods highlight the out-standing performance of this papers approach,demonstrating its capability to segment cardiac magnetic resonance images in previously unseen domains even with limited annotated data.
基金supported by a research grant from the Medical Research Program of Chongqing University Three Gorges Hospital(No.2022YJKYXM-017).
文摘Objective:The objective of this study is to develop and evaluate a structured clinical rotation-based emergency response training program to enhance nurses’emergency competence,theoretical knowledge,and technical skills.Methods:A comprehensive emergency training program was developed,and a randomized controlled trial was implemented from June 2022 to May 2023 at a tertiary general hospital in Chongqing,China.The study involved 214 nurses,with 106 participants in the intervention group receiving a 3-month innovative emergency response competence training and 108 in the control group undergoing conventional training.Postintervention assessments evaluated emergency response capabilities using the Emergency Response Ability Assessment Scale for Nurses in Public Health Emergencies,theoretical knowledge through a self-designed comprehensive theoretical assessment instrument,technical skills using a standardized skill assessment form,and training satisfaction through two distinct feedback instruments.Results:The emergency response ability scores were significantly higher in the intervention group compared to controls(3.99±0.18 vs.2.53±0.25,P<0.05).Theoretical assessment scores showed marked improvement in the intervention group versus the control group(85.31±4.40 vs.52.45±6.19,P<0.05).Technical skill performance was significantly better in the intervention group than that in controls(94.47±1.64 vs.86.39±2.36,P<0.05).Training satisfaction was higher among intervention group nurses compared to controls(4.53±0.23 vs.4.00±0.38,P<0.05),with nursing managers also reporting greater satisfaction with the intervention program versus conventional training(4.57±0.49 vs.3.92±0.79,P<0.05).Conclusion:The clinical rotation-based structured emergency response training program effectively enhances nurses’emergency competencies,theoretical knowledge,and technical skills.These findings provide both theoretical foundations and practical guidelines for developing emergency response and specialized nursing competence training programs.
文摘Endoscopic ultrasound(EUS)is an indispensable tool for the diagnosis and management of various diseases,particularly biliopancreatic disorders,as it provides detailed visualization of the gastrointestinal tract and surrounding structures.As the demand for diagnostic and interventional EUS procedures increases,ensuring high-quality training for endoscopists is essential to improve patient outcomes.This mini-review provides an overview of the current state of EUS training and emphasizes the importance of a structured approach that integrates theoretical knowledge and hands-on experience.We discuss different training methods,focusing on the main courses available worldwide,and highlight their advantages and limitations.In addition,we examine the challenges of training for diagnostic and interventional EUS,such as limited access to training centers and the need for personalized feedback.Overall,improving EUS training programs is essential to enhance physician skills and ensure this advanced technique is used safely and efficiently in clinical practice.
基金supported by Brazilian agencies CAPES(Finance Code 001)CNPq through PQ productivity scholarship.
文摘Here we compare the efficacy of anti-obesity drugs alone or combined with exercise training on body weight and exercise capacity of obese patients.Randomized clinical trials that assessed the impact of any anti-obesity drug alone or combined with exercise training on body weight,body fat,fat-free mass and cardiorespiratory fitness in obese patients were retrieved from Pubmed and EMBASE up to May 2024.Risk of bias assessment was performed with RoB 2.0,and the GRADE approach assessed the certainty of evidence(CoE)of each main outcome.We included four publications summing up 202 patients.Two publications used orlistat as an anti-obesity drug treatment,while the other two adopted GLP-1 receptor agonist(liraglutide or tirzepatide)as a pharmacotherapy for weight management.Orlistat combined with exercise was superior to change body weight(mean difference(MD):−2.27 kg;95%CI:−2.86 to−1.69;CoE:very low),fat mass(MD:−2.89;95%CI:−3.87 to−1.91;CoE:very low),fat-free mass(MD:0.56;95%CI:0.40–0.72;CoE:very low),and VO_(2)Peak(MD:2.64;95%CI:2.52–2.76;CoE:very low).GLP-1 receptor agonist drugs combined with exercise had a great effect on body weight(MD:−3.96 kg;95%CI:−5.07 to−2.85;CoE:low),fat mass(MD:−1.76;95%CI:−2.24 to−1.27;CoE:low),fat-free mass(MD:0.50;95%CI:−0.98 to 1.98;CoE:very low)and VO_(2)Peak(MD:2.47;95%CI:1.31–3.63;CoE:very low).The results reported here suggest that exercise training remains an important approach in weight management when combined with pharmacological treatment.
文摘Digital economy has become a new driving force for China’s economic growth,continuously boosting economic development and rapidly integrating into various fields of China’s economy and society.The advent of the digital economy era has reshaped the development pattern of the financial industry.The rapid development of financial technology has promoted the transformation of financial formats and put forward higher requirements for financial talent training in the new era.Digital finance is not only a key part of the transformation and upgrading of China’s financial industry but also an integral component of China’s modern financial ecosystem.Against the backdrop of the digital economy,cultivating digital finance talents in vocational colleges is of great significance to China’s economic development.This paper analyzes the predicaments faced in digital finance talent training,explores in depth the reform of digital finance talent training modes,and proposes policy suggestions for the digital finance talent training system based on the development characteristics of digital finance.
