microRNA (miR) has been shown to be involved in the treatment of diseases such as osteoarthritis (OA). This study aims to investigate the role of miR‐206 in regulating insulin‐like growth factor‐1 ...(IGF‐1) in chondrocyte autophagy and apoptosis in an OA rat model via the phosphoinositide 3‐kinase (P13K)/protein kinase B (AKT)‐mechanistic target of rapamycin (mTOR) signaling pathway. Wistar rats were used to establish the OA rat model, followed by the observation of histopathological changes, Mankin score, and the detection of IGF‐1‐positive expression and tissue apoptosis. The underlying regulatory mechanisms of miR‐206 were analyzed in concert with treatment by an miR‐206 mimic, an miR‐206 inhibitor, or small interfering RNA against IGF‐1 in chondrocytes isolated from OA rats. Then, the expression of miR‐206, IGF‐1, and related factors in the signaling pathway, cell cycle, and apoptosis, as well as inflammatory factors, were determined. Subsequently, chondrocyte proliferation, cell cycle distribution, apoptosis, autophagy, and autolysosome were measured. OA articular cartilage tissue exhibited a higher Mankin score, promoted cell apoptotic rate, increased expression of IGF‐1, Beclin1, light chain 3 (LC3), Unc‐51‐like autophagy activating kinase 1 (ULK1), autophagy‐related 5 (Atg5), caspase‐3, and Bax, yet exhibited decreased expression of miR‐206, P13K, AKT, mTOR, and Bcl‐2. Besides, miR‐206 downregulated the expression of IGF‐1 and activated the P13K/AKT signaling pathway. Moreover, miR‐206 overexpression and IGF‐1 silencing inhibited the interleukins levels (IL‐6, IL‐17, and IL‐18), cell apoptotic rate, the formation of autolysosome, and cell autophagy while promoting the expression of IL‐1β and cell proliferation. The findings from our study provide a basis for the efficient treatment of OA by investigating the inhibitory effects of miR‐206 on autophagy and apoptosis of articular cartilage in OA via activating the IGF‐1‐mediated PI3K/AKT‐mTOR signaling pathway.
The findings from our study provide a basis for the efficient treatment of OA by investigating the inhibitory effects of miR‐206 on autophagy and apoptosis of articular cartilage in OA via activating the IGF‐1‐mediated PI3K/AKT‐mTOR signaling pathway.
•The prevalence of anxiety and depression symptom was 7.7% and 12.2%, respectively.•Having confirmed and suspected cases in family members or relatives was associated with the higher risk of ...depression symptom.•Self-reported health condition was strongly associated with risk of anxiety and depression symptom.
: Although studies have suggested experiencing the epidemic of severe infectious diseases increased the prevalence of mental health problems, the association between COVID-19 epidemic and risk of anxiety and depression symptom in college students in China was unclear.
: A large cross-sectional online survey with 44,447 college students was conducted in Guangzhou, China. The Zung's Self-rating Anxiety Scale (SAS) and the Center for Epidemiologic Studies Depression Scale (CES-D Scale) were used to define the anxiety and depression symptom, respectively. Multivariable logistic regression models were used to analyze the association between COVID-19 epidemic and risk of anxiety and depression symptom.
: The prevalence of anxiety and depression symptom was 7.7% (95% confidence interval CI: 7.5%, 8.0%) and 12.2% (95%CI: 11.9%, 12.5%), respectively. Compared with students who reported have not infected or suspected cases in family members and relatives, students who reported having confirmed (OR=4.06; 95%CI: 1.62, 10.19; P = 0.003), and suspected (OR=2.11; 95%CI: 1.11, 4.00; P = 0.023) cases in family members and relatives had higher risk of depression symptom. Additionally, the proportions of students with anxiety and depression symptom reported more demand of psychological knowledge and interventions than those without (P<0.001).
: All the data in this study was collected through online questionnaire, and we did not evaluate the reliability and validity.
: The prevalence of anxiety and depression symptom was relatively low in college students, but the COVID-19 epidemic-related factors might be associated with higher depression symptom risk.
The vibration signals of faulty rotating machinery are typically nonstationary, nonlinear, and mixed with abundant compounded background noise. To extract the potential excitations from the observed ...rotating machinery, signal demodulation and time-frequency analysis are indispensable. This work proposes a novel particle swarm optimization-based variational mode decomposition method, which adopts the minimum mean envelope entropy to optimize the parameters (<inline-formula><tex-math notation="LaTeX">\alpha</tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">K</tex-math></inline-formula>) in the existing variational mode decomposition. The proposed fault-detection framework separated the observed vibration signals into a series of intrinsic modes. A certain number of the intrinsic modes are then selected by means of the Hilbert transform-based square envelope spectral kurtosis. Subsequently, in this study, the feature representations were reconstructed via the selected intrinsic modes; then, the envelope spectra of the real faulty conditions were generated in the rotating machinery. To verify the performance of the proposed method, a testbed platform of a gearbox with a combination of different faults was implemented. The experimental results demonstrated that the proposed method represented the patterns of the fault frequency more explicitly than the available empirical mode decomposition, the local mean decomposition, and the wavelet package transform method.
Abstract
This study utilizes digital image correlation (DIC) technology to measure the far-field displacements and strains of rock specimens during the entire loading and unloading. Through analyzing ...the distributions of strain, displacement and their variations per unit length at different stages, the variations of both length and migration velocity of the fracture process zone (FPZ) were studied, and the crack propagation was also investigated. In addition, the entire path of crack propagation was observed by scanning electron microscope (SEM). The results reveal that (1) the fractured ligament can be divided into three zones based on the displacement variation per unit length: intact zone, crack propagation zone, and FPZ. (2) The FPZ length reaches its maximum at the peak load and then decreases, and the minimum length even is only 1/3–1/2 of the maximum length. The FPZ migration velocity is − 48 to 1460 m/s. FPZ’s microscale features are intergranular microcracks, transgranular microcracks, cleavage, and debris on fracture surface and around main crack propagation path. (3) The crack propagation length during peak load to peak-post 90% accounts for more than 1/3–1/4 of the entire post-peak length. Crack propagation is alternating fast and slow, i.e., the velocity of crack propagation varies regularly in the range of 24–700 m/s. The region of crack initial propagation is more severely damaged compared to other propagation regions.
Accurate health evaluation is crucial to reliable operation of complex degradation systems. Although traditional machine learning methods such as artificial neural network (ANN) and support vector ...machine (SVM) have been used widely, state assessment schemes based on a single classification model still suffer from low multiclass classification efficiency, high variance, and deviation. To solve these problems, this article proposes a novel health evaluation method based on stacking ensemble learning and generalized multiclass support vector machine (GMSVM) algorithm. The proposed health evaluation framework includes three parts: 1) abnormal value elimination and missing value processing are applied for multiple sensor data; 2) statistical features are extracted from the observed data and the Pearson correlation coefficient is applied for feature selection; and 3) ensemble generalized multiclass support vector machines (EGMSVMs) are utilized to evaluate the health situation of a degradation system. Unlike the binary classifiers and deep-learning-based classifiers, EGMSVMs utilize the stacking-based method to combine several GMSVMs as submodels and random forest as a metamodel, and the metamodel ensembles the results of submodels to reach a satisfied performance. Compared to traditional SVM- and ANN-based algorithms, EGMSVMs, in processing multiclass problems, achieve high efficiency and, meanwhile, low variance and deviation. The proposed method is verified using a hydraulic test rig. The experimental results show the feasibility and applicability of the proposed health evaluation framework.
The data-driven fault indicator for rotating machinery is designed to reveal the possible fault scenarios from the observed statistical vibration signals. This study develops a novel ensemble extreme ...learning machine (EELM) network to replace the conventional layout by combining binary classifiers (e.g., binary relevance) for compound-fault diagnosis of rotating machinery. The proposed EELMs consist of two sub-networks, namely, the first extreme learning machine (ELM) for clustering, and the second for multi-label classification. The first network generates the Euclidean distance representations from each point to every centroid with unsupervised clustering, and the second identifies potential output tags through multiple-output-node multi-label learning. Compared to the existing multi-label classifiers (e.g., multi-label radial basis function, rank support vector machine, back-propagation multi-label learning, and binary classifiers with binary relevance), the theoretical verification reveals EELMs perform the best in hamming loss, one-error, training time, and achieves the best overall evaluation for the two real-world databases (e.g., Yeast and Image). Regarding the real test for the compound-fault diagnosis of rotating machinery, this paper applies the particle swarm optimization-based variational mode decomposition to decompose the raw vibration signals into a series of intrinsic modes, and selects ten time-domain indicators and five frequency-domain statistical characteristics for feature extraction. The experimental results illustrate that the EELM-based fault diagnosis method achieves the best overall performance.
Anthropogenic environments have been implicated in enrichment and exchange of antibiotic resistance genes and bacteria. Here we study the impact of confined and controlled swine farm environments on ...temporal changes in the gut microbiome and resistome of veterinary students with occupational exposure for 3 months. By analyzing 16S rRNA and whole metagenome shotgun sequencing data in tandem with culture-based methods, we show that farm exposure shapes the gut microbiome of students, resulting in enrichment of potentially pathogenic taxa and antimicrobial resistance genes. Comparison of students' gut microbiomes and resistomes to farm workers' and environmental samples revealed extensive sharing of resistance genes and bacteria following exposure and after three months of their visit. Notably, antibiotic resistance genes were found in similar genetic contexts in student samples and farm environmental samples. Dynamic Bayesian network modeling predicted that the observed changes partially reverse over a 4-6 month period. Our results indicate that acute changes in a human's living environment can persistently shape their gut microbiota and antibiotic resistome.
Aims
To assess the associations of diabetes duration and glycaemic control (defined by plasma glycated haemoglobin HbA1c level) with the risks of cardiovascular disease (CVD) and all‐cause mortality ...and to determine whether the addition of either or both to the established CVD risk factors can improve predictions.
Materials and Methods
A total of 435 679 participants from the UK Biobank without CVD at baseline were included. Cox models adjusting for classic risk factors (sociodemographic and anthropometric characteristics, lipid profiles and medication use) were used, and predictive utility was determined by the C‐index and net reclassification improvement (NRI).
Results
Compared with participants without diabetes, participants with longer diabetes durations and poorer glycaemic control had a higher risk of fatal/nonfatal CVD. Among participants with diabetes, the fully‐adjusted hazard ratios (HRs) for diabetes durations of 5 to <10 years, 10 to <15 years and ≥15 years were 1.15 (95% confidence interval CI 0.99, 1.34), 1.50 (95% CI 1.26, 1.79) and 2.22 (95% CI 1.90, 2.58; P‐trend <0.01), respectively, compared with participants with diabetes durations <5 years. In addition, those with the longest disease duration (≥15 years) and poorer glycaemic control (HbA1c ≥64 mmol/mol 8%) had the highest risk of fatal/nonfatal CVD (HR 3.12, 95% CI 2.52, 3.86). Among participants with diabetes, the addition of both diabetes duration and glycaemic control levels significantly improved both the C‐index (change in C‐index +0.0254; 95% CI 0.0111, 0.0398) and the overall NRI for fatal/nonfatal CVD (0.0992; 95% CI 0.0085, 0.1755) beyond the use of the classic risk factors.
Conclusions
Both longer diabetes duration and poorer glycaemic control were associated with elevated risks of CVD and mortality. Clinicians should consider not only glycaemic control but also diabetes duration in CVD risk assessments for participants with diabetes.
The bearing is the core component of the gearbox transmission system. Once it is damaged during operation, it will cause the shutdown of the mechanical equipment for maintenance. It has important ...application significance to carry out fault detection and remaining useful life (RUL) prediction. Whereas, some bottlenecks, such as the noise interference of state characteristics, the excessive dependence of supervised learning on prior samples, and the practical RUL online calculation, restrict the industrial application of RUL prediction for rotating machinery equipment. To overcome the above problems, this paper introduces the discrete wavelet transform (DWT) to decrease the noise of the vibration acceleration signal obtained, and then uses the sliding average method to weaken the transient excitation. To make the state characteristics of the monitored bearing trendy, linear, and monotonic, this paper proposes a new set of state interpret indicators: energy and cumulative summation feature (CSF) to reflect the bearing health status. Based on the available bearing health information, the fault boundary threshold is established through the 3
σ
criteria, which serves as the basis for first predicting time (FPT) detection. Once the FPT point is determined, this paper applies CSF to replace the original vibration acceleration amplitude as the degradation indicator, which has better linearity and monotonicity than amplitude-based indicators, and which is conducive to the implementation of simple structure curve fitting to carry out the overall RUL prediction. Comparing with existing methods, such as relevance vector machine (RVM), deep belief network (DBN), and particle filtering (PF)-based methods, the experimental results demonstrate that the proposed method has the best RUL prediction efficiency and the fastest convergence.
Knee injury is known as a frequently occurred damage related to sports, which may affect the function of cartilage. This study aims to explore whether Insulin‐like growth factor 1 (IGF‐1) and bone ...morphogenetic protein‐7 (BMP‐7)‐modified bone‐marrow mesenchymal stem cells (BMSCs) affect the repair of cartilage damage found in the knee. Primarily, BMSCs were treated with a series of pEGFP‐C1, IGF‐1, and BMP‐7, followed by determination of IGF‐1 and BMP‐7 expression. A rabbit cartilage defect model was also established. Afterfward, cell morphology, viability, cartilage damage repair effect, and expression of collagen I and collagen II at the 6th and the 12th week were measured. BMSCs treated with pEGFP‐C1/IGF‐1, pEGFP‐C1/BMP‐7, and pEGFP‐C1/BMP‐7‐IGF‐1 exhibited elevated expression of BMP‐7 and IGF‐1. Besides, BMSCs in the P10 generation displayed decreased cell proliferation. Moreover, BMSCs treated with IGF‐1, BMP‐7, and IGF‐1‐BMP‐7 showed reduced histological score and collagen I expression while elevated collagen II expression, as well as better repair effect, especially in those treated with IGF‐1‐BMP‐7. Collectively, these results demonstrated a synergistic effect of IGF‐1 and BMP‐7 on the BMSC chondrogenic differentiation on the articular cartilage damage repair in the rabbit knees, highlighting its therapeutic potential for the treatment of articular cartilage damage.
These results demonstrate that IGF‐1 and BMP‐7 gene modification may contribute to the repair of cartilage damage in the knee by BMSCs.
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