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We report a case of a Chinese girl who presented with multiple seizure types of epilepsy, followed by motor and intellectual regression, vision impairment, and cerebral and cerebellar atrophy. She carries an unreported compound heterozygous variant of the ASAH1 gene and is diagnosed with spinal muscular atrophy associated with progressive myoclonic epilepsy (SMA-PME), a disorder in which ceramide accumulation in lysosomes due to a decrease in acid ceramidase activity. This case suggests attention to this rare class of deceases involving both the central and peripheral nervous systems.

Abstract Helicobacter pylori (H. pylori) is a primary pathogen associated with gastrointestinal diseases, including gastric cancer. The increase in resistance to antibiotics, along with the adverse effects caused by complicated medication protocols, has made the eradication of H. pylori a more formidable challenge, necessitating alternative therapeutics. Herein, a targeted nanoplatform is reported based on sonodynamic therapy, the chitosan‐conjugated fucose loaded with indocyanine green (ICG@FCS). It penetrates the gastric mucosa and homes in on H. pylori through dual targeting mechanisms: molecular via fucose and physical via ultrasound. Upon ultrasound activation, it generates singlet oxygen, effectively attacking planktonic bacteria, disrupting biofilms, and facilitating the clearance of intracellular bacteria by promoting autophagy, including multidrug‐resistant strains. The ICG@FCS nanoplatform minimally affects the gut microbiota and aids in gastric mucosa repair. a holistic integrative H. pylori therapy strategy is proposed that targets eradication while preserving gastrointestinal health. This strategy emphasizes the importance of maintaining patient health while eradicating the pathogen. This advancement is set to refine the comprehensive antibacterial approach, offering a promising horizon in the ongoing battle against antibiotic resistance and more effective gastric cancer prevention strategies.

Helicobacter pylori (H. pylori) infection remains the leading cause of gastric adenocarcinoma, and its eradication primarily relies on the prolonged and intensive use of two antibiotics. However, antibiotic resistance has become a compelling health issue, leading to H. pylori eradication treatment failure worldwide. Additionally, the powerlessness of antibiotics against biofilms, as well as intracellular H. pylori and the long-term damage of antibiotics to the intestinal microbiota, have also created an urgent demand for antibiotic-free approaches. Herein, we describe an antibiotic-free, multifunctional copper-organic framework (HKUST-1) platform encased in a lipid layer comprising phosphatidic acid (PA), rhamnolipid (RHL), and cholesterol (CHOL), enveloped in chitosan (CS), and loaded in an ascorbyl palmitate (AP) hydrogel: AP@CS@Lip@HKUST-1. This platform targets inflammatory sites where H. pylori aggregates through electrostatic attraction. Then, hydrolysis by matrix metalloproteinases (MMPs) releases CS-encased nanoparticles, disrupting bacterial urease activity and membrane integrity. Additionally, RHL disperses biofilms, while PA promotes lysosomal acidification and activates host autophagy, enabling clearance of intracellular H. pylori. Furthermore, AP@CS@Lip@HKUST-1 alleviates inflammation and enhances mucosal repair through delayed Cu2+ release while preserving the intestinal microbiota. Collectively, this platform presents an advanced therapeutic strategy for eradicating persistent H. pylori infection without inducing drug resistance.

As a critical component of the engine, the failure of aviation fuel pumps can lead to serious safety accidents, necessitating the development of effective maintenance programs. Fault Tree Analysis (FTA) has a clear structure and strong interpretability in maintenance decision making. However, it heavily relies on expert knowledge, which is subject to uncertainty and incoherence. Therefore, this paper proposes the NOA (Nutcracker Optimization Algorithm)–GNN (Graph Neural Network) model to enhance the accuracy and robustness of FTA by mitigating the uncertainty and inconsistency in expert knowledge. The NOA algorithm efficiently searches the solution space to identify globally optimal solutions. An FTA-TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) maintenance decision-making framework has also been developed. By integrating FTA with TOPSIS, the proposed method provides a comprehensive and systematic approach that combines qualitative and quantitative analyses, thereby improving the effectiveness and reliability of maintenance decision making.

Abstract Background The current state of knowledge regarding the efficacy of whole-body vibration (WBV) training for individuals with chronic kidney disease (CKD) is limited. To address this gap, the present study seeks to undertake a comprehensive systematic review and meta-analysis of clinical trials to evaluate the impact of WBV on physical function and quality of life outcomes in CKD patients. Methods A systematic search was performed on the PubMed, Embase, Web of Science, and Scopus databases from inception to March 2023 and updated in June 2023. The inclusion criteria comprised randomized controlled studies, quasi-experimental studies, and single-arm trials that evaluated the impact of WBV on physical function, encompassing cardiopulmonary fitness, muscle strength, mobility, and balance, in CKD patients. Adverse events that were included in the study reports were recorded. The pooled evidence was assessed using the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) method. Results Nine studies were identified, of which seven were included in the meta-analysis. The results of the meta-analysis indicated a statistically significant improvement in upper (mean difference: 3.45 kg; 95% confidence interval 1.61 to 5.29) and lower (standardized mean difference: 0.34, 95% confidence interval 0.08 to 0.59) extremity muscle strength in patients with CKD who underwent WBV training compared to baseline (low-level evidence). Furthermore, WBV training favored improved cardiorespiratory fitness, mobility, and balance function, but no statistical difference was observed. The impact of WBV training on quality of life in patients with CKD requires further validation. Notably, only one adverse event (nausea) was reported in the included studies. Conclusions WBV has demonstrated efficacy and feasibility in enhancing muscle strength among patients with CKD. However, further investigation is warranted to determine its potential for improving cardiorespiratory adaptations, mobility, balance function, and quality of life. Additionally, future research should prioritize comprehensive reporting of WBV protocols to establish an optimal training regimen for the CKD population.

In the process of rehabilitation treatment for stroke patients, rehabilitation evaluation is a significant part in rehabilitation medicine. Researchers intellectualized the evaluation of rehabilitation evaluation methods and proposed quantitative evaluation methods based on evaluation scales, without the clinical background of physiatrist. However, in clinical practice, the experience of physiatrist plays an important role in the rehabilitation evaluation of patients. Therefore, this paper designs a 5 degrees of freedom (DoFs) upper limb (UL) rehabilitation robot and proposes a rehabilitation evaluation model based on Belief Rule Base (BRB) which can add the expert knowledge of physiatrist to the rehabilitation evaluation. The motion data of stroke patients during active training are collected by the rehabilitation robot and signal collection system, and then the upper limb motor function of the patients is evaluated by the rehabilitation evaluation model. To verify the accuracy of the proposed method, Back Propagation Neural Network (BPNN) and Support Vector Machines (SVM) are used to evaluate. Comparative analysis shows that the BRB model has high accuracy and effectiveness among the three evaluation models. The results show that the rehabilitation evaluation model of stroke patients based on BRB could help physiatrists to evaluate the UL motor function of patients and master the rehabilitation status of stroke patients.

The prediction of the health status of critical components is an important influence in making accurate maintenance decisions for rotating equipment. Since vibration signals contain a large amount of fault information, they can more accurately describe the health status of critical components. Therefore, it is widely used in the field of rotating equipment health state prediction. However, there are two major problems in predicting the health status of key components based on vibration signals: (1) The working environment of rotating equipment is harsh, and if only one feature in the time or frequency domain is selected for fault analysis, it will be susceptible to harsh operating environments and cannot completely reflect the fault information. (2) The vibration signals are unlabeled time series data, which are difficult to accurately convert into the health status of key components. In order to solve the above problems, this paper proposes a combined prediction model combining a bidirectional long- and short-term memory network (BiLSTM), a self-organizing neural network (SOM) and particle swarm optimization (PSO). Firstly, the SOM is utilized to fuse the fault characteristics of multiple vibration signals of key components to obtain an indicator (HI) that can reflect the health status of rotating equipment and to also compensate for the vulnerability of single signal characteristics in the time or frequency domain to environmental influences. Secondly, the K-means clustering method is employed to cluster the health indicators and determine the health state, which solves the problem of determining the health of a component from unsupervised vibration signal data which is quite difficult. Finally, the particle swarm optimized BiLSTM model is used to predict the health state of key components and the bearing dataset from the IEEE PHM 2012 Data Challenge verifies the method’s effectiveness and validity.

Commercial vehicle disk brakes operate at a high-temperature and in a heavy-load environment within the braking system. The primary cause of failure is the cracking of the brake disk. In order to study its fatigue damage and service life, a finite element model of disk brake fatigue life was established, and thermal stress coupling simulation analysis was carried out from a practical problem. Based on the temperature and stress fields of the brake disk under emergency braking conditions obtained from the simulation results, the effects of vehicle load, initial speed, temperature, and other factors on brake fatigue life are explored. The fatigue life of the hazardous node can be calculated using the Manson–Coffin model, and then the strain–life (ɛ–N) curve of the material can be fit at high temperature. The fatigue life of brake disks was predicted using the fatigue analysis software FE-SAFE and verified by testing. The results showed that the maximum stress on the surface of the disk brake was the same as the area of the minimum fatigue life, accurately analyzing the fatigue life of the region and predicting the location of fatigue cracks. The results of the research can provide a reference for the design of disk brake engineering and fatigue failure.

BackgroundLifestyle modification is the cornerstone of non-alcoholic fatty liver disease (NAFLD) prevention and treatment. Short video platforms can facilitate easier access to health information for patients, thereby influencing lifestyle changes. An increasing number of individuals rely on online platforms to acquire health-related information about NAFLD. However, the quality of information regarding NAFLD on these platforms remains unclear.ObjectiveThis study aimed to investigate the quality of information about NAFLD on TikTok.MethodsA total of 497 videos were retrieved from TikTok. The basic video information, including the video source, was extracted. Two independent raters evaluated the quality and reliability of the videos using the Global Quality Score system and a modified DISCERN tool. Propensity score matching (PSM) was used to compare video quality across sources.ResultsNAFLD-related videos on TikTok were divided into three groups according to the uploader: health professionals, medical institutions, and science bloggers. Overall, the quality of NAFLD videos on TikTok was not satisfactory. Before PSM, there were no significant differences in video quality or content between the three groups. After PSM, the quality of NAFLD videos from health professionals was significantly better than the videos created by other groups. Besides, the videos of health professionals outperformed those of medical institutions and science bloggers in terms of the definition of disease, risk factors, and treatment, but were inferior to those of medical institutions considering the symptoms and tests of NAFLD.ConclusionThe quality of NAFLD-related videos on TikTok needs improvement. Compared with videos created by science bloggers and medical institutions, videos from health professionals may provide accurate guidance on the treatment and prevention of NAFLD.

To develop innovative drug delivery carriers for controllable release and cancer-targeted delivery of therapeutic agents to accomplish efficient cancer chemotherapy. Herein we effectively fabricated CaCO3 primarily loaded biotin (BT) and directly the self-assembly of oxaliplatin (Pt (IV)) prodrugs form in liposomes. The acquired BT-Pt (IV)@PEG/CaCO3 with outstanding biological stability displays rapid pH-mediated degradations, thus allowing the effective pH-responsive delivery of BT. In vitro, anticancer assays proved that BT-Pt (IV)@PEG/CaCO3 effectively kills the thyroid cancer cells (B-CPAP and FTC-133). The biochemical staining assays investigated the morphological changes of thyroid cancer after treatment with nanoparticles. The DNA fragmentation of the cells was assessed by utilizing the comet assay. BT-Pt (IV)@PEG/CaCO3 increased ROS levels and caused mitochondrial membrane potential and DNA damage, which resulted in apoptosis. Due to its versatile drug-loading capability, this research demonstrates that CaCO3 liposomal formulation is a biocompatible and reliable substrate for establishing pH-mediated drug delivery methods and promising for possible therapeutic application.

BackgroundHyperphosphatemia remains a major complication in patients with Continuous ambulatory peritoneal dialysis (CAPD) leading to increased morbidity and mortality. However, phosphorus management still has many challenges.ObjectiveThis study aimed to investigate the prevalence and factors of hyperphosphatemia among continuous ambulatory peritoneal dialysis patients in a tertiary public hospital in Shanghai, China.MethodsThe single-center cross-sectional study recruited end-stage renal failure patients who received continuous ambulatory peritoneal dialysis (CAPD) for at least 3 months. The participants aged 18–80 years had undergone CAPD between 1 July 2021 and 30 May 2022, in Shanghai, China.The patients’ sociodemographic, clinical, and laboratory data were collected prospectively from medical records and via face-to-face interviews. A sample size of convenience decides the sample size. This study used the information-motivation-behavioral (IMB) skills model as a theoretical framework. The questionnaire included knowledge and behavior of diet and medication in patients with hyperphosphatemia of chronic kidney disease, self-efficacy for managing chronic disease, and social support rating scale. Univariate analysis and binary logistic regression were performed to identify the influencing factors of hyperphosphatemia by SPPS 27.0.ResultsIn total, 141 CAPD patients (73% hyperphosphatemia) were included in the final analysis. In logistic regression analysis, dialysis vintage (OR: 0.975, 95%CI: 0.957–0.993), dialysis exchanges (OR: 0.317, 95%CI: 0.131–0.768), urine output (OR: 0.997, 95%CI: 0.995–0.999), serum albumin (OR: 1.166, 95%CI:1.008–1.349), serum creatinine (OR: 1.005, 95%CI: 1.001–1.008), hyperphosphatemia knowledge behavior score (OR: 0.888, 95%CI: 0.797–0.991), and social support level (OR: 0.841, 95%CI:0.765–0.925) were the influencing factors of hyperphosphatemia.ConclusionHyperphosphatemia is a frequent complication in CAPD patients. Dialysis vintage, dialysis exchanges, urine output, serum albumin, serum creatinine, hyperphosphatemia knowledge behavior, and social support were the associated factors of hyperphosphatemia in CAPD patients. It is crucial for healthcare providers to maintain phosphorus balance among CAPD patients using phosphorus management strategies.

BackgroundThe “pediatric inactivity triad” framework consists of three complex, interrelated conditions influencing physical inactivity and associated health risks. Evidence on the beneficial effects of physical activity in adults with chronic kidney disease (CKD) continues to grow, but few studies have explored the complex interactions behind inactivity in this population.ResultsBased on the “pediatric inactivity triad” framework and prior research, we would like to propose a new concept, the “adult inactivity triad” in CKD, including (1) exercise deficit disorder, (2) sarcopenia, and (3) physical illiteracy. Individuals can shift from “adult inactivity triad” to “adult activity triad” and move at different rates and directions along the arrows in each of the three components.ConclusionThis review explores and summarizes previous research on the three main adult inactivity triad components in the chronic kidney disease population.

Obesity is a metabolic disease associated with gut microbiota and low-grade chronic inflammation. Tremella fuciformis is a medicinal and edible fungus; polysaccharide (TP) is the main active component, which has a variety of biological activities, such as hypoglycemic and hypolipidemic. However, the anti-obesity effects and potential mechanisms of TP have never been reported. This study was conducted to elucidate the inhibitory effect of TP on high-fat diet (HFD)-induced obesity in mice. Mice were split into five groups: normal chow diet (NCD) group, NCD_TP_H group, HFD group, HFD_TP_L group and HFD_TP_H group. Our study showed that TP inhibited high-fat diet-induced weight gain and fat accumulation in mice and reduced blood glucose, hyperlipidemia and inflammation. TP also improved gut microbiota disorders by reducing the Firmicutes/Bacteroidetes ratio and modulating the relative abundance of specific gut microbiota. We also found that the anti-obesity and gut microbiota-modulating effects of TP could be transferred to HFD-fed mice via faecal microbiota transplantation (FMT), confirming that the gut microbiota was one of the targets of TP for obesity inhibition. Further studies showed that TP increased the production of short-chain fatty acids and the secretion of intestinal hormones. Our studies showed that TP inhibited obesity by modulating inflammation and the microbe-gut-brain axis, providing a rationale for developing TP to treat obesity and its complications.

Health state prediction is an effective way to improve the reliability for sensors. In the process of sensor degradation, it is difficult to obtain more effective monitoring data. And in the classification of health states, how to identify the adjacent state is also a problem. This paper proposed a health state prediction model based on belief rule base (BRB) and membership function (MF), which is called BRB-MF. In the model, BRB can make full use of expert knowledge and poor effective data. In the prediction results of BRB, it may be not completely logical or not entirely appropriate facing adjacent states of sensor. In order to solve the problem, MF is used to continue the analysis of the predicted results of BRB. In the BRB-MF model, the covariance matrix adaptation evolutionary strategies (CMA-ES) optimization algorithm is used to update the model parameters to make up for the uncertainty of expert knowledge. In the end, the brightness sensor of the rail vehicle LED lighting system is taken as a case study. The results show that the BRB-MF model can predict the health state of sensor with a high accuracy and a reasonable state.

The running gear is a vital component of a high-speed train to ensure operation safety. Accurately predicting the future health status of the running gear is significant to keep the reliability and safety of trains. It is difficult to predict the future health status based on the analytical model of the running gear system because of its complexity and coupling. Moreover, the fault data are a minor part of tremendous data in the running and monitoring process of a high-speed train, which obstructs accurately predicting the health status based on a data-driven method. To solve the above problems, this paper proposes a health status prediction method based on the belief rule base (BRB) for the running gear system. First, a failure mechanism is analyzed to confirm the fault characteristics, which can represent the health status of the running gear system. Second, in order to avoid the limitations of a single sensor acquisition, such as a lack of comprehensiveness and robustness, singular value decomposition is used to achieve multisensory information fusion. The fused features are used as the input to the health status prediction model. Data fusion is a way to improve the precision of the health status prediction in the model input. Then, this model based on the BRB is established using the fault data and expert knowledge. During the process of prediction, the subjectivity of experts makes the initial BRB imprecise, so a projection constrained covariance matrix adaptive evolution strategy algorithm is needed to optimize the initial parameters and improve the accuracy of the proposed model. Finally, a case study for the running gear system is carried out to verify the effectiveness and accuracy of the proposed model. The results show that the proposed model can help to accurately predict the health status of the running gear system.

The proper operation of running gears of a high-speed train is one of the key factors to ensure its safety and reliability. The diagnosis of the state of running gears of a high-speed train is one of the effective ways to improve its reliability. It is difficult to diagnose the running gears of a high-speed train accurately because of the characteristics of its complex-analytic structure, multiple types of monitoring feature data, and lack of effective failure mode data. Therefore, this paper proposes a fault diagnosis method for the running gears of a high-speed train based on a semi-quantitative information model. The relation between the effective data and expert knowledge is studied, and the state of the running gears of a high-speed train is rigorously analyzed. To reduce the data dimension and the diagnostic calculation time of the running gears of a high-speed train, the principal component analysis (PCA) is used to screen its key monitoring features. Then, based on the change of the feature quantity in the working process of the running gears of a high-speed train, the semi-quantitative information model of belief-rule-base (BRB) fault diagnosis is established. In the diagnosis process, the initial model parameters of BRB are determined by expert knowledge and they have certain subjectivity. To improve the accuracy of the model, the constrained covariance matrix adaptive evolutionary strategy (CMA-ES) algorithm is used to optimize the parameters of the initial BRB model to improve the validity and accuracy of the diagnosis. Finally, to verify the effectiveness of the proposed semi-quantitative information model, a set of real data of the running gears of a high-speed train is used as case studies.

With the growing demand for high safety in industrial system, fault diagnosis has attracted more and more attention. Currently, belief rule base (BRB) has shown an excellent performance in modeling complex system, where the expert knowledge is used effectively. Existing BRB models are assumed that the inputs of the attributes are independent and the attribute correlation is not taken into account. However, in some engineering system, there is an obvious correlation among these attributes. The correlated attributes may produce redundant information which limits the abilities of attributes to express the accurate information of system. In this paper, a new BRB model with considering attribute correlation (BRB-c) is proposed. Moreover, a decoupling matrix is introduced to eliminate the redundant information from the attributes. The initial parameters of the decoupling matrix are given according to the expert knowledge. And then, when the inputs of the attributes are available, the parameters in the decoupling matrix are trained by an optimization model. The projection covariance matrix adaption evolution strategy is chosen as an optimization algorithm. A practical case study about fault diagnosis of oil pipeline is conducted and the results show that the BRB-c model can diagnose the leak size and leak time of oil pipeline accurately, which can demonstrate that the proposed model can be widely applied in engineering for fault diagnosis.

Remaining life prediction is an effective way to optimize maintenance strategy and improve service life for light-emitting diode driving power in rail vehicle carriage. In this article, a Wiener process–based remaining life prediction method is proposed with the analysis of performance degradation data of light-emitting diode driving power in rail vehicle carriage. First, the temperature and humidity stress accelerated degradation tests are put forward in order to measure the output current of light-emitting diode driving power. Based on the output current, the accelerated degradation model is established. The drift and diffusion coefficients of the Wiener process are then obtained without prior information. Finally, the reliability of light-emitting diode driving power in rail vehicle carriage is assessed and the remaining lifetime is predicted after updating the degradation model parameters with Bayesian inference. The results show that the proposed method can improve the precision of assessment and reduce the uncertainty of prediction significantly. It also provides a potential solution for life prediction of other similar products.

The health of a complex electromechanical system is dynamic and is accompanied by a full life cycle. Due to the complexity and coupling of complex electromechanical systems, the establishment of a dynamic and accurate model for the health state is difficult. A belief rule base (BRB) shows outstanding performance in modeling complex systems because it can combine both quantitative information and expert knowledge. In this paper, a double-layer BRB model is proposed to predict the health state of a complex electromechanical system. The two layers achieve different functions: BRB_layer1 is used to establish the dynamic change of the time series of features, BRB_layer2 is employed to combine the features for predicting the health state of the complex electromechanical system. During this process, the infinite irrelevance method is utilized for feature selection in reducing the scale of the BRB model. Considering the initial parameters are given by experts, which may have boundedness and may not be appropriate for engineering practice, the projection covariance matrix adaption evolution strategy (P-CMA-ES) is chosen as the optimization algorithm to train the initial parameters. To verify the rationality and effectiveness of the proposed model, the low-frequency vibration fault of a certain aero-engine is taken as an example. The results show that the proposed method can predict the health state of a complex electromechanical system precisely according to current and historical data.

Vibration signal processing is commonly used in the mechanical fault diagnosis. It contains abundant working status information. The vibration signal has some features such as non-linear and non-stationary. It has a lot of interference information. Fault information is vulnerable to the impact of the interference information. Empirical mode decomposition denoising method and kurtosis correlation threshold have been widely used in the field of fault diagnosis. But the method mainly depends on the subjective experience, the large number of attempts, and lack of adaptability. In this article, the signals are decomposed into several intrinsic mode functions adaptively with ensemble empirical mode decomposition. The intrinsic mode functions containing the main fault information are selected by the correlation coefficient to emphasize the fault feature and inhibit the normal information. Finally, the energy features of these intrinsic mode functions are taken as inputs of a neural network to identify the fault patterns of rolling bearing. The experiment shows that the neural network diagnosis method based on ensemble empirical mode decomposition has a higher fault recognition rate than based on empirical mode decomposition or wavelet packet method.

Fan is an important rotating part in turbofan engine gas-path. The health condition of fan has a great impact on the health status of the whole aero-engine. Based on belief-rule-base, a novel health estimation model is proposed for fan in turbofan engine gas-path. In this model, the health condition of fan is reflected by the observable information which can represent the system health. In the process of health estimation, the expert knowledge is used fully to improve the precision and speed of the estimation. In the initial health estimation model, some parameters given by expert may not be accurate. To obtain the accurate estimation result, an algorithm for updating the parameters is proposed based on differential evolution algorithm. In order to verify the feasibility and accuracy of the proposed model, back-propagation neural network is applied to comparison. The newly proposed model is applied to an actual test in the aero-engine test bed, which is used to testify the validity of the health estimation model. This model can also provide a reference for the health estimation of turbofan engine gas-path.

BACKGROUND: Fibroblast growth factor 21 (FGF-21) is a metabolic regulator with multiple beneficial effects on glucose homeostasis and lipid metabolism in animal models. The relationship between plasma levels of FGF-21 and coronary heart disease (CHD) in unknown. METHODOLOGY/PRINCIPAL FINDINGS: This study aimed to investigate the correlation of serum FGF-21 levels and lipid metabolism in the patients with coronary heart disease. We performed a logistic regression analysis of the relation between serum levels of FGF-21 and CHD patients with and without diabetes and hypertension. This study was conducted in the Departments of Endocrinology and Cardiovascular Diseases at two University Hospitals. Participants consisted of one hundred and thirty-five patients who have been diagnosed to have CHD and sixty-one control subjects. Serum FGF-21 level and levels of fasting blood glucose; triglyceride; apolipoprotein B100; HOMA-IR; insulin; total cholesterol; HDL-cholesterol; LDL-cholesterol; and C-reactive protein were measured. We found that median serum FGF-21 levels were significantly higher in CHD than that of control subjects (P