Clinical features and risk factors of plastic bronchitis caused by Mycoplasma pneumoniae pneumonia in children.

BACKGROUND: We analyzed the clinical characteristics of children with plastic bronchitis (PB) caused by Mycoplasma pneumoniae (MP) and explored its risk factors. METHODS: We prospectively analyzed clinical data of children with MP pneumonia (MPP) treated with fiberoptic bronchoscopy (FB). Patients were classified into a PB and non-PB group. General information, clinical manifestations, laboratory tests, results of computed tomography scan, and FB findings were compared between groups. We conducted statistical analysis of risk factors for developing PB. RESULTS: Of 1169 children who had MPP and were treated with FB, 133 and 1036 were in the PB and non-PB groups, respectively. There were no significant differences in sex, age, and incident season between groups (P > 0.05). The number of children in the PB group decreased during the COVID-19 pandemic. Compared with children in the non-PB group, those in the PB group had longer duration of hospitalization, increased levels of neutrophil (N), C-reactive protein (CRP), procalcitonin (PCT), D-dimer, lactate dehydrogenase (LDH), alanine transaminase (ALT) and aspartate transaminase (AST); lower levels of lymphocyte (L) and platelet (PLT); and higher incidence of lack of appetite, decreased breath sounds, single lobar infiltrate, pleural effusion, pericardial effusion, mucosal erosion and/or necrosis, and bronchial embolization. L levels and pleural effusion were identified as risk factors in multivariate logistic regression. CONCLUSIONS: Children with PB caused by MPP had a strong and local inflammatory response. L levels and pleural effusion were independent risk factors of PB with MPP in children. Our findings will help clinicians identify potential PB in pediatric patients for early and effective intervention.

Morphological component analysis under non-convex smoothing penalty framework for gearbox fault diagnosis.

The sparse representation methodology has been identified to be a promising tool for gearbox fault diagnosis. The core is how to precisely reconstruct the fault signal from noisy monitoring signals. The non-convex penalty has the ability to induce sparsity more efficiently than convex penalty. However, the introduction of non-convex penalty usually influences the convexity of the model, resulting in the unstable or sub-optimal solution. In this paper, we propose the non-convex smoothing penalty framework (NSPF) and combine it with morphological component analysis (MCA) for gearbox fault diagnosis. The proposed NSPF is a unify penalty construction framework, which contains many classical penalty while a new set of non-convex smoothing penalty functions can be generated. These non-convex penalty can guarantee the convexity of the objective function while enhancing the sparsity, thus the global optimal solution can be acquired. The simulation and engineering experiments validate that the NSPF enjoys more reconstruction precision compared to the existing penalties.

Editorial for Special Issue: Machine Health Monitoring and Fault Diagnosis Techniques.

Machine health monitoring and fault diagnosis have played crucial roles in automatic and intelligent industrial plants [...].

Exploring the essence of compound fault diagnosis: A novel multi-label domain adaptation method and its application to bearings.

Compound fault diagnosis in essence is a fundamental but difficult problem to be solved. The separation and extraction of compound fault features remain great challenges in industrial applications due to the lack of labeled fault data. This paper proposes a novel multi-label domain adaptation method applicable to compound fault diagnosis of bearings. Firstly, multi-layer domain adaptation is designed based on a fault feature extractor with customized residual blocks. In that way, features from discrepant domain can be transformed into domain-invariant features. Furthermore, a multi-label classifier is applied to decompose compound fault features into corresponding single fault feature, and diagnoses them separately. The application on bearing datasets demonstrates that the proposed method could enhance the detachable degree of compound faults and achieve greater diagnostic performance than other existing methods.

Radiomics based on readout-segmented echo-planar imaging (RS-EPI) diffusion-weighted imaging (DWI) for prognostic risk stratification of patients with rectal cancer: a two-centre, machine learning study using the framework of predictive, preventive, and personalized medicine.

Background: Currently, the rate of recurrence or metastasis (ROM) remains high in rectal cancer (RC) patients treated with the standard regimen. The potential of diffusion-weighted imaging (DWI) in predicting ROM risk has been reported, but the efficacy is insufficient. Aims: This study investigated the potential of a new sequence called readout-segmented echo-planar imaging (RS-EPI) DWI in predicting the ROM risk of patients with RC using machine learning methods to achieve the principle of predictive, preventive, and personalized medicine (PPPM) application in RC treatment. Methods: A total of 195 RC patients from two centres who directly received total mesorectal excision were retrospectively enrolled in our study. Machine learning methods, including recursive feature elimination (RFE), the synthetic minority oversampling technique (SMOTE), and the support vector machine (SVM) classifier, were used to construct models based on clinical-pathological factors (clinical model), radiomic features from RS-EPI DWI (radiomics model), and their combination (merged model). The Harrell concordance index (C-index) and the area under the time-dependent receiver operating characteristic curve (AUC) were calculated to evaluate the predictive performance at 1 year, 3 years, and 5 years. Kaplan‒Meier analysis was performed to evaluate the ability to stratify patients according to the risk of ROM. Findings: The merged model performed well in predicting tumour ROM in patients with RC at 1 year, 3 years, and 5 years in both cohorts (AUC = 0.887/0.813/0.794; 0.819/0.795/0.783) and was significantly superior to the clinical model (AUC = 0.87 [95% CI: 0.80-0.93] vs. 0.71 [95% CI: 0.59-0.81], p = 0.009; C-index = 0.83 [95% CI: 0.76-0.90] vs. 0.68 [95% CI: 0.56-0.79], p = 0.002). It also had a significant ability to differentiate patients with a high and low risk of ROM (HR = 12.189 [95% CI: 4.976-29.853], p < 0.001; HR = 6.427 [95% CI: 2.265-13.036], p = 0.002). Conclusion: Our developed merged model based on RS-EPI DWI accurately predicted and effectively stratified patients with RC according to the ROM risk at an early stage with an individualized profile, which may be able to assist physicians in individualizing the treatment protocols and promote a meaningful paradigm shift in RC treatment from traditional reactive medicine to PPPM. Supplementary Information: The online version contains supplementary material available at 10.1007/s13167-022-00303-3.

Observation of chiral and slow plasmons in twisted bilayer graphene.

Moiré superlattices have led to observations of exotic emergent electronic properties such as superconductivity and strong correlated states in small-rotation-angle twisted bilayer graphene (tBLG)1,2. Recently, these findings have inspired the search for new properties in moiré plasmons. Although plasmon propagation in the tBLG basal plane has been studied by near-field nano-imaging techniques3-7, the general electromagnetic character and properties of these plasmons remain elusive. Here we report the direct observation of two new plasmon modes in macroscopic tBLG with a highly ordered moiré superlattice. Using spiral structured nanoribbons of tBLG, we identify signatures of chiral plasmons that arise owing to the uncompensated Berry flux of the electron gas under optical pumping. The salient features of these chiral plasmons are shown through their dependence on optical pumping intensity and electron fillings, in conjunction with distinct resonance splitting and Faraday rotation coinciding with the spectral window of maximal Berry flux. Moreover, we also identify a slow plasmonic mode around 0.4 electronvolts, which stems from the interband transitions between the nested subbands in lattice-relaxed AB-stacked domains. This mode may open up opportunities for strong light-matter interactions within the highly sought after mid-wave infrared spectral window8. Our results unveil the new electromagnetic dynamics of small-angle tBLG and exemplify it as a unique quantum optical platform.

Increased serum levels of macrophage migration inhibitory factor in autism spectrum disorders.

OBJECTIVE: Macrophage migration inhibitory factor (MIF) has been suggested as a pivotal regulator of innate immunity and inflammatory. The aim of this study was to measure serum circulating levels of MIF in relation to the degree of the severity of autism spectrum disorders (ASD). METHODS: One hundred and two Chinese children with ASD and same their age-sex matched typical development children were included. Concentrations of MIF were tested by Quantikine Human MIF Immunoassay. Serum levels of homocysteine (HCY), C-reactive protein (CRP) and serum Interleukin 6 (IL-6) were also tested. The influence of serum levels of MIF on ASD risk and ASD severity were performed by binary logistic regression analysis. RESULTS: The serum levels of MIF in the children with ASD (24.7 ± 08.9 ng/ml) were significantly higher than those of control subjects (18.3 ± 5.5 ng/ml) (t = 6.134, P < 0.001). Levels of MIF increased with increasing severity of ASD as defined by the CARS score (P < 0.001). In multivariate model, MIF was associated with an increased risk of ASD (OR 1.11, 95% CI: 1.05-1.17; P < 0.001). MIF improved the combined model (HCY/CRP/IL-6) to predict ASD (P < 0.001). At admission, 68 children (66.7%) had a severe autism. In these children, the mean serum level of MIF was higher than in those children with mild to moderate autism (28.1 ± 8.5 ng/ml VS. 17.9 ± 4.7 ng/ml; t = 6.482, P < 0.001). In multivariate model, MIF was still associated with an increased risk of severe ASD (OR: 1.15, 95% CI: 1.04-1.19; P < 0.001). MIF improved the combined model (HCY/CRP/IL-6) to predict severe ASD (P < 0.001). CONCLUSIONS: These results identify high serum MIF levels are associated with severity of ASD. Further study is warranted on the precise involvement of MIF in ASD, and the mechanism by which MIF contributes to ASD pathogenesis.

The Protective Effects of Terpinen-4-ol on LPS-Induced Acute Lung Injury via Activating PPAR-γ.

Terpinen-4-ol, the major constituent of tea tree oil, has been reported to have anti-inflammatory effect. However, whether terpinen-4-ol could attenuate LPS-induced inflammation in lung tissues remains unclear. In the present study, we aimed to investigate the protective effects of terpinen-4-ol on LPS-induced acute lung injury (ALI) in mice. Terpinen-4-ol could inhibit LPS-induced ALI as confirmed by the decreased lung histopathological changes, MPO activity, and lung W/D ratio caused by terpinen-4-ol. The production of TNF-α and IL-1ß in the BALF was suppressed by the treatment of terpinen-4-ol. Western blot analysis showed that terpinen-4-ol significantly attenuated LPS-induced phosphorylation of IκBα and NF-κB p65. Furthermore, the expression of PPAR-γ was dose-dependently upregulated by the treatment of terpinen-4-ol. In conclusion, the results of this study indicated that terpinen-4-ol inhibited LPS-induced ALI via activating PPAR-γ, which subsequently attenuated LPS-induced NF-κB activation and inflammatory response.

A general and simple method for evaluating the electrical transport performance of graphene by the van der Pauw-Hall measurement.

Expected for many promising applications in the field of electronics and optoelectronics, a reliable method for the characterization of graphene electrical transport properties is desired to predict its device performance or provide feedback for its synthesis. However, the commonly used methods of extracting carrier mobility from graphene field effect transistor or Hall-bar is time consuming, expensive, and significantly affected by the device fabrication process other than graphene itself. Here we reported a general and simple method to evaluate the electrical transport performance of graphene by the van der Pauw-Hall measurement. By annealing graphene in vacuum to remove the adsorbed dopants and then exposing it in ambient surroundings, carrier mobility as a function of density can be measured with the increase of carrier density due to the dopant re-adsorption from the surroundings. Further, the relationship between the carrier mobility and density can be simply fitted with a power equation to the first level approximation, with which any pair of measured carrier mobility and density can be normalized to an arbitrary carrier density for comparison. We experimentally demonstrated the reliability of the method, which is much simpler than making devices and may promote the standard making for graphene characterization.

Association of TGFBR2 rs6785358 Polymorphism with Increased Risk of Congenital Ventricular Septal Defect in a Chinese Population.

Transforming growth factor beta receptor 2 (TGFBR2) plays a central role in normal heart development, and we investigated whether TGFBR2 polymorphism confers the risk of congenital ventricular septal defect (CVSD). The case-control study included 115 CVSD children and 188 healthy children in a Chinese population. TGFBR2 rs6785358 polymorphism was genotyped with polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP). Enzyme-linked immunoassay (ELISA) was used to detect serum TGFBR2 levels. The genotype and allele frequency of TGFBR2 rs6785358 were significantly higher in the CVSD group than in the controls (all P < 0.05). The G allele carriers were associated with increased CVSD risk compared with the A allele carriers in CVSD group (OR 3.503, 95 % CI 2.670-4.596). Stratified analysis by gender revealed that the TGFBR2 rs6785358 genotype and allele frequency were significantly different between the CVSD case and controls, in both the male subgroup and the female subgroup (all P < 0.001). The G allele carriers were more susceptible to CVSD risk than the A allele carriers in both the male subgroup (OR 9.096, 95 % CI 5.398-15.33) and the female subgroup (OR 3.148, 95 % CI 1.764-5.618). Logistic regression analysis revealed that age, gender and genotype were associated with the risk of CVSD (all P < 0.05). The study findings revealed that TGFBR2 rs6785358 polymorphism contributes to CVSD susceptibility, and the G allele may increase the risk of CVSD.

Wayside bearing fault diagnosis based on a data-driven Doppler effect eliminator and transient model analysis.

A fault diagnosis strategy based on the wayside acoustic monitoring technique is investigated for locomotive bearing fault diagnosis. Inspired by the transient modeling analysis method based on correlation filtering analysis, a so-called Parametric-Mother-Doppler-Wavelet (PMDW) is constructed with six parameters, including a center characteristic frequency and five kinematic model parameters. A Doppler effect eliminator containing a PMDW generator, a correlation filtering analysis module, and a signal resampler is invented to eliminate the Doppler effect embedded in the acoustic signal of the recorded bearing. Through the Doppler effect eliminator, the five kinematic model parameters can be identified based on the signal itself. Then, the signal resampler is applied to eliminate the Doppler effect using the identified parameters. With the ability to detect early bearing faults, the transient model analysis method is employed to detect localized bearing faults after the embedded Doppler effect is eliminated. The effectiveness of the proposed fault diagnosis strategy is verified via simulation studies and applications to diagnose locomotive roller bearing defects.

A Doppler transient model based on the laplace wavelet and spectrum correlation assessment for locomotive bearing fault diagnosis.

The condition of locomotive bearings, which are essential components in trains, is crucial to train safety. The Doppler effect significantly distorts acoustic signals during high movement speeds, substantially increasing the difficulty of monitoring locomotive bearings online. In this study, a new Doppler transient model based on the acoustic theory and the Laplace wavelet is presented for the identification of fault-related impact intervals embedded in acoustic signals. An envelope spectrum correlation assessment is conducted between the transient model and the real fault signal in the frequency domain to optimize the model parameters. The proposed method can identify the parameters used for simulated transients (periods in simulated transients) from acoustic signals. Thus, localized bearing faults can be detected successfully based on identified parameters, particularly period intervals. The performance of the proposed method is tested on a simulated signal suffering from the Doppler effect. Besides, the proposed method is used to analyze real acoustic signals of locomotive bearings with inner race and outer race faults, respectively. The results confirm that the periods between the transients, which represent locomotive bearing fault characteristics, can be detected successfully.