Identification of platelet-related subtypes and diagnostic markers in pediatric Crohn’s disease based on WGCNA and machine learning

BackgroundThe incidence of pediatric Crohn’s disease (PCD) is increasing worldwide every year. The challenges in early diagnosis and treatment of PCD persist due to its inherent heterogeneity. This study’s objective was to discover novel diagnostic markers and molecular subtypes aimed at enhancing the prognosis for patients suffering from PCD.MethodsCandidate genes were obtained from the GSE117993 dataset and the GSE93624 dataset by weighted gene co-expression network analysis (WGCNA) and differential analysis, followed by intersection with platelet-related genes. Based on this, diagnostic markers were screened by five machine learning algorithms. We constructed predictive models and molecular subtypes based on key markers. The models were evaluated using the GSE101794 dataset as the validation set, combined with receiver operating characteristic curves, decision curve analysis, clinical impact curves, and calibration curves. In addition, we performed pathway enrichment analysis and immune infiltration analysis for different molecular subtypes to assess their differences.ResultsThrough WGCNA and differential analysis, we successfully identified 44 candidate genes. Following this, employing five machine learning algorithms, we ultimately narrowed it down to five pivotal markers: GNA15, PIK3R3, PLEK, SERPINE1, and STAT1. Using these five key markers as a foundation, we developed a nomogram exhibiting exceptional performance. Furthermore, we distinguished two platelet-related subtypes of PCD through consensus clustering analysis. Subsequent analyses involving pathway enrichment and immune infiltration unveiled notable disparities in gene expression patterns, enrichment pathways, and immune infiltration landscapes between these subtypes.ConclusionIn this study, we have successfully identified five promising diagnostic markers and developed a robust nomogram with high predictive efficacy. Furthermore, the recognition of distinct PCD subtypes enhances our comprehension of potential pathogenic mechanisms and paves the way for future prospects in early diagnosis and personalized treatment

The Survey of H5N1 Flu Virus in Wild Birds in 14 Provinces of China from 2004 to 2007

The highly pathogenic H5N1 avian influenza emerged in the year 1996 in Asia, and has spread to Europe and Africa recently. At present, effective monitoring and data analysis of H5N1 are not sufficient in Chinese mainland.)) were obviously higher than those in other 13 provinces. The results of sequence analysis indicated that the 17 strains isolated from wild birds were distributed in five clades (2.3.1, 2.2, 2.5, 6, and 7), which suggested that genetic diversity existed among H5N1 viruses isolated from wild birds. The five isolates from Qinghai came from one clade (2.2) and had a short evolutionary distance with the isolates obtained from Qinghai in the year 2005.We have measured the prevalence of H5N1 virus in 56 species of wild birds in 14 provinces of China. Continuous monitoring in the field should be carried out to know whether H5N1 virus can be maintained by wild birds

A multi-targeted approach to suppress tumor-promoting inflammation

Cancers harbor significant genetic heterogeneity and patterns of relapse following many therapies are due to evolved resistance to treatment. While efforts have been made to combine targeted therapies, significant levels of toxicity have stymied efforts to effectively treat cancer with multi-drug combinations using currently approved therapeutics. We discuss the relationship between tumor-promoting inflammation and cancer as part of a larger effort to develop a broad-spectrum therapeutic approach aimed at a wide range of targets to address this heterogeneity. Specifically, macrophage migration inhibitory factor, cyclooxygenase-2, transcription factor nuclear factor-κB, tumor necrosis factor alpha, inducible nitric oxide synthase, protein kinase B, and CXC chemokines are reviewed as important antiinflammatory targets while curcumin, resveratrol, epigallocatechin gallate, genistein, lycopene, and anthocyanins are reviewed as low-cost, low toxicity means by which these targets might all be reached simultaneously. Future translational work will need to assess the resulting synergies of rationally designed antiinflammatory mixtures (employing low-toxicity constituents), and then combine this with similar approaches targeting the most important pathways across the range of cancer hallmark phenotypes

Wild Bird Migration across the Qinghai-Tibetan Plateau: A Transmission Route for Highly Pathogenic H5N1

BACKGROUND: Qinghai Lake in central China has been at the center of debate on whether wild birds play a role in circulation of highly pathogenic avian influenza virus H5N1. In 2005, an unprecedented epizootic at Qinghai Lake killed more than 6000 migratory birds including over 3000 bar-headed geese (Anser indicus). H5N1 subsequently spread to Europe and Africa, and in following years has re-emerged in wild birds along the Central Asia flyway several times. METHODOLOGY/PRINCIPAL FINDINGS: To better understand the potential involvement of wild birds in the spread of H5N1, we studied the movements of bar-headed geese marked with GPS satellite transmitters at Qinghai Lake in relation to virus outbreaks and disease risk factors. We discovered a previously undocumented migratory pathway between Qinghai Lake and the Lhasa Valley of Tibet where 93% of the 29 marked geese overwintered. From 2003-2009, sixteen outbreaks in poultry or wild birds were confirmed on the Qinghai-Tibet Plateau, and the majority were located within the migratory pathway of the geese. Spatial and temporal concordance between goose movements and three potential H5N1 virus sources (poultry farms, a captive bar-headed goose facility, and H5N1 outbreak locations) indicated ample opportunities existed for virus spillover and infection of migratory geese on the wintering grounds. Their potential as a vector of H5N1 was supported by rapid migration movements of some geese and genetic relatedness of H5N1 virus isolated from geese in Tibet and Qinghai Lake. CONCLUSIONS/SIGNIFICANCE: This is the first study to compare phylogenetics of the virus with spatial ecology of its host, and the combined results suggest that wild birds play a role in the spread of H5N1 in this region. However, the strength of the evidence would be improved with additional sequences from both poultry and wild birds on the Qinghai-Tibet Plateau where H5N1 has a clear stronghold

A Fault Feature Extraction Method for Rolling Bearing Based on Pulse Adaptive Time-Frequency Transform

Shock pulse method is a widely used technique for condition monitoring of rolling bearing. However, it may cause erroneous diagnosis in the presence of strong background noise or other shock sources. Aiming at overcoming the shortcoming, a pulse adaptive time-frequency transform method is proposed to extract the fault features of the damaged rolling bearing. The method arranges the rolling bearing shock pulses extracted by shock pulse method in the order of time and takes the reciprocal of the time interval between the pulse at any moment and the other pulse as all instantaneous frequency components in the moment. And then it visually displays the changing rule of each instantaneous frequency after plane transformation of the instantaneous frequency components, realizes the time-frequency transform of shock pulse sequence through time-frequency domain amplitude relevancy processing, and highlights the fault feature frequencies by effective instantaneous frequency extraction, so as to extract the fault features of the damaged rolling bearing. The results of simulation and application show that the proposed method can suppress the noises well, highlight the fault feature frequencies, and avoid erroneous diagnosis, so it is an effective fault feature extraction method for the rolling bearing with high time-frequency resolution

Modeling and Experiment of a V-Shaped Piezoelectric Energy Harvester

Vibration-based energy harvesting technology is the most promising method to solve the problems of self-powered wireless sensor nodes, but most of the vibration-based energy harvesters have a rather narrow operation bandwidth and the operation frequency band is not convenient to adjust when the ambient frequency changes. Since the ambient vibration may be broadband and changeable, a novel V-shaped vibration energy harvester based on the conventional piezoelectric bimorph cantilevered structure is proposed, which successfully improves the energy harvesting efficiency and provides a way to adjust the operation frequency band of the energy harvester conveniently. The electromechanical coupling equations are established by using Euler-Bernoulli equation and piezoelectric equation, and then the coupled circuit equation is derived based on the series connected piezoelectric cantilevers and Kirchhoff's laws. With the above equations, the output performances of V-shaped structure under different structural parameters and load resistances are simulated and discussed. Finally, by changing the angle θ between two piezoelectric bimorph beams and the load resistance, various comprehensive experiments are carried out to test the performance of this V-shaped energy harvester under the same excitation. The experimental results show that the V-shaped energy harvester can not only improve the frequency response characteristic and the output performance of the electrical energy, but also conveniently tune the operation bandwidth; thus it has great application potential in actual structure health monitoring under variable working condition

Research on Zonal Disintegration Characteristics and Failure Mechanisms of Deep Tunnel in Jointed Rock Mass with Strength Reduction Method

To understand the fracture features of zonal disintegration and reveal the failure mechanisms of circle tunnels excavated in deep jointed rock masses, a series of three-dimensional heterogeneous models considering varying joint dip angles are established. The strength reduction method is embedded in the RFPA method to achieve the gradual fracture process, macro failure mode and safety factor, and to reproduce the characteristic fracture phenomenon of deep rock masses, i.e., zonal disintegration. The mechanical mechanisms and acoustic emission energy of surrounding rocks during the different stages of the whole formation process of zonal disintegration affected by different-dip-angle joints and randomly distributed joints are further discussed. The results demonstrate that the zonal disintegration process is induced by the stress redistribution, which is significantly different from the formation mechanism of traditional surrounding rock loose zone; the dip angle of joint set has a great influence on the stress buildup, stress shadow and stress transfer as well as the failure mode of surrounding rock mass; the existence of parallel and random joints lead the newly formed cracks near the tunnel surface to developing along their strikes; the random joints make the zonal disintegration pattern much more complex and affected by the regional joint composition. These will greatly improve our understanding of the zonal disintegration in deep engineering

A Novel Method for Mechanical Fault Diagnosis Based on Variational Mode Decomposition and Multikernel Support Vector Machine

A novel fault diagnosis method based on variational mode decomposition (VMD) and multikernel support vector machine (MKSVM) optimized by Immune Genetic Algorithm (IGA) is proposed to accurately and adaptively diagnose mechanical faults. First, mechanical fault vibration signals are decomposed into multiple Intrinsic Mode Functions (IMFs) by VMD. Then the features in time-frequency domain are extracted from IMFs to construct the feature sets of mixed domain. Next, Semisupervised Locally Linear Embedding (SS-LLE) is adopted for fusion and dimension reduction. The feature sets with reduced dimension are inputted to the IGA optimized MKSVM for failure mode identification. Theoretical analysis demonstrates that MKSVM can approximate any multivariable function. The global optimal parameter vector of MKSVM can be rapidly identified by IGA parameter optimization. The experiments of mechanical faults show that, compared to traditional fault diagnosis models, the proposed method significantly increases the diagnosis accuracy of mechanical faults and enhances the generalization of its application

Adaptive tacho information estimation for surveillance of rotatory machine under nonstationary conditions

International audienceRolling bearing faults are the leading causes of downtime in rotary machines. In recent years, numerous and various vibration-based approaches have been put forwarded for rolling bearing fault detection. In the vibration-based techniques, order tracking-based methods are considered as very effective techniques. In the current reported order tracking methods, auxiliary devices are essential to obtain the instantaneous angular speed (IAS) of the machine. Aiming at this shortcoming, estimating IAS from vibration signals has been studied and some tacho-less order tracking (TLOT) techniques have been put forwarded. However, the effectiveness of the current available TLOT algorithms rely on the manually selection of the initial parameters for IAS estimation, which bring about user-friendliness. In order to tackle the aforementioned obstacles, a novel adaptive tacho information estimation method based on nonlinear mode decomposition (NMD) is proposed. In the proposed method, the nonlinear mode decomposition (NMD) method is improved and its computational burden is reduced. And then, the tacho information is adaptively estimated. The vibration signal collected from an aircraft engine is used for signal analysis and the effectiveness of the proposed is successfully validated