ObjectiveTo systematically review the clinical features of chronic fatigue syndrome (CFS) cases with pathogens infection. MethodsWe electronically searched databases including VIP, WanFang Data, CNKI, CBM, PubMed, MEDLINE, EMbase, The Cochrane Library, Web of Science, Elsevier and Google Scholar from 1994 to 2014 for CFS-related studies. Two reviewers independently screened literature and extracted data. Then we systematically reviewed and analyzed the information on demographic characteristics, clinical manifestations, types of infected pathogens, and results of some biochemical examinations. ResultsA total of 84 studies (case reports and case series) involving 2 565 CFS cases from 18 countries were included. The major infected pathogens of included CFS cases were mycoplasma, EB virus, intestinal virus, Bernat rickettsia, human-herpes virus, and Gram-negative intestinal bacteria. Fifty-seven studies reported that there might be associations between the pathogenic infection and CFS pathogenesis. Although there were different types of CFS-related pathogens, almost all the studies inferred that pathogens infection linked with immune dysfunction, which might cause CFS symptoms. ConclusionThere may be associations between the pathogenic infection and CFS pathogenesis.
Fatigue is an exhaustion state caused by prolonged physical work and mental work, which can reduce working efficiency and even cause industrial accidents. Fatigue is a complex concept involving both physiological and psychological factors. Fatigue can cause a decline of concentration and work performance and induce chronic diseases. Prolonged fatigue may endanger life safety. In most of the scenarios, physical and mental workloads co-lead operator into fatigue state. Thus, it is very important to study the interaction influence and its neural mechanisms between physical and mental fatigues. This paper introduces recent progresses on the interaction effects and discusses some research challenges and future development directions. It is believed that mutual influence between physical fatigue and mental fatigue may occur in the central nervous system. Revealing the basal ganglia function and dopamine release may be important to explore the neural mechanisms between physical fatigue and mental fatigue. Future effort is to optimize fatigue models, to evaluate parameters and to explore the neural mechanisms so as to provide scientific basis and theoretical guidance for complex task designs and fatigue monitoring.
ObjectiveTo investigate the fatigue of asthma patients, and to analyze its influencing factors, and provide a reference for clinical intervention.MethodsThe convenience sampling method was adopted to select asthma patients who were in clinic of the First Affiliated Hospital of Guangxi Medical University from November 2018 to March 2019. The patients’ lung function were measured. And questionnaires were conducted, including general data questionnaire, Chinese version of Checklist Individual Strength-Fatigue, Asthma Control Test, Chinese version of Self-rating Depression Scale. Relevant data were collected for multiple stepwise linear regression analysis.ResultsFinally, 120 patients were enrolled. The results of multiple stepwise linear regression analysis showed that age, education level, place of residence, time period of frequent asthma symptoms, degree of small airway obstruction, Asthma Control Test score and degree of depression were the influencing factors of fatigue in asthma patients (P≤0.05). Multivariate linear stepwise regression analysis showed that degree of small airway obstruction, degree of depression and time period of frequent asthma symptoms were the main influencing factors of fatigue in asthma patients, which could explain 51.8% of the variance of fatigue (ΔR2=0.518).ConclusionsThe incidence of fatigue in asthma patients is at a relatively high level. Medical staff should pay attention to the symptoms of fatigue in asthma patients. For asthma patients, it is recommended to strengthen standardized diagnosis and treatment, reduce the onset of symptoms at night and eliminate small airway obstruction. Psychological intervention methods are needed to improve patients’ depression, reduce fatigue symptoms, and improve quality of life.
Objectives To explore the quality of the reporting of randomized controlled trials (RCTs) of traditional Chinese medicine (TCM) for chronic fatigue syndrome (CFS).Methods We searched the Cochrane Central Register of Controlled Clinical Trials (CENTRAL) (The Cochrane Library, Issue 4, 2006), PubMed, EMbase, the Chinese Biomedical Database (CBMdisc), VIP Information, and China National Knowledge Infrastructure (CNKI) (from establishment to February 2007). We also checked the reference lists of included studies. The quality of the reporting of RCTs was assessed using the 22-item checklist of the CONSORT Statement and other self-established criteria. Results Thirty-eight RCTs were included. The word “randomization” was not present in any of the trials, and only 17 reports used a structured abstract. All trials did not report the scientific background and the rational for the trial, the estimation of the necessary sample size, the methods of allocation concealment and blinding, participant flow chart, ITT analysis, and ancillary analyses. Some authors misunderstood the diagnostic criteria and inclusion criteria, some selected inappropriate control interventions, and some did not clearly describe their statistical methods or used incorrect methods. All 38 trials reported positive outcomes, few reported adverse effects. No report included a general interpretation of the new trial’s results in the context of current evidence in their discussion section, and none mentioned the limitations of the study, the clinical and research implications or the external validity of the trial findings. Conclusion The overall reporting quality of RCTs of TCM for CFS is poor. Defects are found in each section of the reports. Researchers and journal editors should learn and use the principles and methods of evidence-based medicine—especially the use of a transparent prospective clinical trial register and the CONSORT Statement—to improve the design, conduct and report TCM trials.
ObjectiveTo investigate the methodological and reporting quality of clinical trials involving Xiaoyao San for chronic fatigue syndrome. MethodsWe searched PubMed, CBM, CNKI, VIP and WanFang Data to identify randomized controlled trials (RCTs) about Xiaoyao San for chronic fatigue syndrome. The methodological and reporting quality of included RCTs was respectively evaluated according to the assessment tool of risk of bias of the Cochrane Handbook 5.1.0 and the CONSORT 2010 statement, combined with complementary assessment by the characteristic indicators of traditional Chinese medicine (TCM). The methodological and reporting quality of included case series study was respectively assessed by the methods recommended by the Britain's National Institute for Clinical Excellence (NICE) and the STROBE statement. ResultsA total of 27 clinical trials were included, involving 11 RCTs and 16 case series studies. According to the assessment tool of risk of bias of the Cochrane Handbook, 54.5% of the RCTs performed proper random method, 9.1% conducted allocation concealment and blinding, 72.7% selected intention-to-treat (ITT) analysis without the report of loss to follow-up, and no RCT existed selective reports. Corresponding to the characteristic indicators of TCM, 54.5% of the RCTs did not conduct TCM syndrome diagnosis, the curative effect standard of TCM syndrome was discrepant, and no RCT was multi-center study. The CONSORT 2010 statement indicated that no RCT explained sample size estimation, implementation details of randomization, flow diagram of participant, use of ITT and clinical trial registration. According to the items recommended by Britain's NICE, 6.25% of the case series studies were multi-center, 81.25% did not report clear inclusion and exclusion criteria, and no case series study performed continuous patient recruitment and stratification analysis of outcome. The STROBE statement indicated that no case series study reported research design, sample size, flow chart, bias, limitations and generalizability. ConclusionThe quality of clinical trials about Xiaoyao San for chronic fatigue syndrome is still low in methodological and reporting aspects. It is suggested that the future clinical trials should be conducted with references of CONSORT statement and STROBE statement, to propel the modernization and internationalization of TCM.
The pace of modern life is accelerating, the pressure of life is gradually increasing, and the long-term accumulation of mental fatigue poses a threat to health. By analyzing physiological signals and parameters, this paper proposes a method that can identify the state of mental fatigue, which helps to maintain a healthy life. The method proposed in this paper is a new recognition method of psychological fatigue state of electrocardiogram signals based on convolutional neural network and long short-term memory. Firstly, the convolution layer of one-dimensional convolutional neural network model is used to extract local features, the key information is extracted through pooling layer, and some redundant data is removed. Then, the extracted features are used as input to the long short-term memory model to further fuse the ECG features. Finally, by integrating the key information through the full connection layer, the accurate recognition of mental fatigue state is successfully realized. The results show that compared with traditional machine learning algorithms, the proposed method significantly improves the accuracy of mental fatigue recognition to 96.3%, which provides a reliable basis for the early warning and evaluation of mental fatigue.
Fatigue driving is one of the leading causes of traffic accidents, posing a significant threat to drivers and road safety. Most existing methods focus on studying whole-brain multi-channel electroencephalogram (EEG) signals, which involve a large number of channels, complex data processing, and cumbersome wearable devices. To address this issue, this paper proposes a fatigue detection method based on frontal EEG signals and constructs a fatigue driving detection model using an asymptotic hierarchical fusion network. The model employed a hierarchical fusion strategy, integrating an attention mechanism module into the multi-level convolutional module. By utilizing both cross-attention and self-attention mechanisms, it effectively fused the hierarchical semantic features of power spectral density (PSD) and differential entropy (DE), enhancing the learning of feature dependencies and interactions. Experimental validation was conducted on the public SEED-VIG dataset. The proposed model achieved an accuracy of 89.80% using only four frontal EEG channels. Comparative experiments with existing methods demonstrate that the proposed model achieves high accuracy and superior practicality, providing valuable technical support for fatigue driving monitoring and prevention.
ObjectiveTo assess the fatigue in patients with obstructive sleep apnea hypopnea syndrome (OSAHS), and analyze the factors caused fatigue and the relationship between quality of life (QOL) and fatigue. MethodsOne hundred and sixty-nine patients with OSAHS and 78 subjects without OSAHS diagnosed by polysomnography (PSG) between December 2010 and March 2011 in West China Hospital were recruited in the study. Fatigue was assessed by using multidimensional fatigue inventory (MFI), excessive daytime sleepiness by Epworth sleepiness scale(ESS), QOL by functional outcomes of sleep questionnaire (FOSQ). ResultsFatigue in the patients with OSAHS was more severe than that of the controls (51.06±13.39 vs. 44.82±9.81, P < 0.001), but no difference was revealed in the patients with different degree of OSAHS. Fatigue was positively correlated with ESS score(r=0.210), total sleep time intervals(r=0.156), and the ratio of time of SpO2 below 90% in total sleep time(r=0.153)(P < 0.05), and was negatively correlated with the average oxygen saturation(r=-0.171, P < 0.05) and all subscales of FOSQ(P < 0.01). ConclusionsFatigue in patients with OSAHS is more severe than that of controls. Fatigue can significantly reduce QOL, and the impact is greater than that of excessive daytime sleepiness.
This study is aimed to investigate objective indicators of mental fatigue evaluation to improve the accuracy of mental fatigue evaluation. Mental fatigue was induced by a sustained cognitive task. The brain functional networks in two states (normal state and mental fatigue state) were constructed based on electroencephalogram (EEG) data. This study used complex network theory to calculate and analyze nodal characteristics parameters (degree, betweenness centrality, clustering coefficient and average path length of node), and served them as the classification features of support vector machine (SVM). Parameters of the SVM model were optimized by gird search based on 6-fold cross validation. Then, the subjects were classified. The results show that characteristic parameters of node of brain function networks can be divided into normal state and mental fatigue state, which can be used in the objective evaluation of mental fatigue state.
To evaluate the fatigue behavior of nitinol stents, we used the finite element method to simulate the manufacture processes of nitinol stents, including expanding, annealing, crimping, and releasing procedure in applications of the clinical treatments. Meanwhile, we also studied the effect of the crown area dimension of stent on strain distribution. We then applied a fatigue diagram to investigate the fatigue characteristics of nitinol stents. The results showed that the maximum strain of all three stent structures, which had different crown area dimensions under vessel loads, located at the transition area between the crown and the strut, but comparable deformation appeared at the inner side of the crown area center. The cause of these results was that the difference of the area moment of inertia determined by the crown dimension induced the difference of strain distribution in stent structure. Moreover, it can be drawn from the fatigue diagrams that the fatigue performance got the best result when the crown area dimension equaled to the intermediate value. The above results proved that the fatigue property of nitinol stent had a close relationship with the dimension of stent crown area, but there was no positive correlation.