Photooxidation triggered ultralong afterglow in carbon nanodots.

It remains a challenge to obtain biocompatible afterglow materials with long emission wavelengths, durable lifetimes, and good water solubility. Herein we develop a photooxidation strategy to construct near-infrared afterglow carbon nanodots with an extra-long lifetime of up to 5.9 h, comparable to that of the well-known rare-earth or organic long-persistent luminescent materials. Intriguingly, size-dependent afterglow lifetime evolution from 3.4 to 5.9 h has been observed from the carbon nanodots systems in aqueous solution. With structural/ultrafast dynamics analysis and density functional theory simulations, we reveal that the persistent luminescence in carbon nanodots is activated by a photooxidation-induced dioxetane intermediate, which can slowly release and convert energy into luminous emission via the steric hindrance effect of nanoparticles. With the persistent near-infrared luminescence, tissue penetration depth of 20 mm can be achieved. Thanks to the high signal-to-background ratio, biological safety and cancer-specific targeting ability of carbon nanodots, ultralong-afterglow guided surgery has been successfully performed on mice model to remove tumor tissues accurately, demonstrating potential clinical applications. These results may facilitate the development of long-lasting luminescent materials for precision tumor resection.

Multiphoton excited singlet/triplet mixed self-trapped exciton emission.

Multiphoton excited luminescence is of paramount importance in the field of optical detection and biological photonics. Self-trapped exciton (STE) emission with self-absorption-free advantages provide a choice for multiphoton excited luminescence. Herein, multiphoton excited singlet/triplet mixed STE emission with a large full width at half-maximum (617 meV) and Stokes shift (1.29 eV) has been demonstrated in single-crystalline ZnO nanocrystals. Temperature dependent steady state, transient state and time-resolved electron spin resonance spectra demonstrate a mixture of singlet (63%) and triplet (37%) mixed STE emission, which contributes to a high photoluminescence quantum yield (60.5%). First-principles calculations suggest 48.34 meV energy per exciton stored by phonons in the distorted lattice of excited states, and 58 meV singlet-triplet splitting energy for the nanocrystals being consistent with the experimental measurements. The model clarifies long and controversial debates on ZnO emission in visible region, and the multiphoton excited singlet/triplet mixed STE emission is also observed.

Fiber SPR biosensor sensitized by MOFs for MUC1 protein detection.

The concentration of tumor markers is low, which needs a highly sensitive, stable and fast detection method. In this paper, we proposed and demonstrated a U-shape fiber SPR biosensor sensitized by MOFs materials. The surface of the U-shape SPR sensor was modified with MOFs materials to enhance the sensitivity, and the nucleic acid aptamer was immobilized on the sensor surface because of the biocompatibility of MOFs materials. By the high specificity of the nucleic acid aptamer, the MUC1 protein was recognized and detected. The testing results indicate that the sensor has a logarithmic linear response in the MUC1 protein concentration detection range of 1 pg/ml-100 µg/ml, its sensitivity and detection limit are 5.33 nm/log(µg/ml) and 0.16 pg/ml respectively. After being sensitized by MOFs, the detection sensitivity of the sensor can be increased by 1.62 times，the LOD can be decreased by 0.75 times. The sensor has high sensitivity and specificity, which has broad application prospects in clinical detection of tumor markers.

Metal-Halide Perovskite Nanocrystal Superlattice: Self-Assembly and Optical Fingerprints.

Self-assembly of nanocrystals into superlattices is a fascinating process that not only changes geometric morphology, but also creates unique properties that considerably enrich the material toolbox for new applications. Numerous studies have driven the blossoming of superlattices from various aspects. These include precise control of size and morphology, enhancement of properties, exploitation of functions, and integration of the material into miniature devices. The effective synthesis of metal-halide perovskite nanocrystals has advanced research on self-assembly of building blocks into micrometer-sized superlattices. More importantly, these materials exhibit abundant optical features, including highly coherent superfluorescence, amplified spontaneous laser emission, and adjustable spectral redshift, facilitating basic research and state-of-the-art applications. This review summarizes recent advances in the field of metal-halide perovskite superlattices. It begins with basic packing models and introduces various stacking configurations of superlattices. The potential of multiple capping ligands is also discussed and their crucial role in superlattice growth is highlighted, followed by detailed reviews of synthesis and characterization methods. How these optical features can be distinguished and present contemporary applications is then considered. This review concludes with a list of unanswered questions and an outlook on their potential use in quantum computing and quantum communications to stimulate further research in this area.

Epidermal growth factor receptor in asthma: A promising therapeutic target?

Activation of the epidermal growth factor receptor (EGFR) pathway is involved in the pathogenesis of asthma. Although decades of intensive research have focused on the role of EGFR in asthma, the specific mechanisms and pathways of EGFR signaling remain unclear. Various reports have indicated that inhibition of EGFR improves the pathological features in asthma models. However, extending these experimental findings to clinical applications is difficult. Several measures can be adopted to promote clinical application of EGFR inhibitors. This review focuses on the role of EGFR in the pathogenesis of asthma and the development of a potentially novel therapeutic target for asthma.

Bioinformatic analysis and preliminary validation of potential therapeutic targets for COVID-19 infection in asthma patients.

BACKGROUND: Severe acute respiratory syndrome coronavirus 2 causes coronavirus disease 19 (COVID-19). The number of confirmed cases of COVID-19 is also rapidly increasing worldwide, posing a significant challenge to human safety. Asthma is a risk factor for COVID-19, but the underlying molecular mechanisms of the asthma-COVID-19 interaction remain unclear. METHODS: We used transcriptome analysis to discover molecular biomarkers common to asthma and COVID-19. Gene Expression Omnibus database RNA-seq datasets (GSE195599 and GSE196822) were used to identify differentially expressed genes (DEGs) in asthma and COVID-19 patients. After intersecting the differentially expressed mRNAs, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to identify the common pathogenic molecular mechanism. Bioinformatic methods were used to construct protein-protein interaction (PPI) networks and identify key genes from the networks. An online database was used to predict interactions between transcription factors and key genes. The differentially expressed long noncoding RNAs (lncRNAs) in the GSE195599 and GSE196822 datasets were intersected to construct a competing endogenous RNA (ceRNA) regulatory network. Interaction networks were constructed for key genes with RNA-binding proteins (RBPs) and oxidative stress-related proteins. The diagnostic efficacy of key genes in COVID-19 was verified with the GSE171110 dataset. The differential expression of key genes in asthma was verified with the GSE69683 dataset. An asthma cell model was established with interleukins (IL-4, IL-13 and IL-17A) and transfected with siRNA-CXCR1. The role of CXCR1 in asthma development was preliminarily confirmed. RESULTS: By intersecting the differentially expressed genes for COVID-19 and asthma, 393 common DEGs were obtained. GO and KEGG enrichment analyses of the DEGs showed that they mainly affected inflammation-, cytokine- and immune-related functions and inflammation-related signaling pathways. By analyzing the PPI network, we obtained 10 key genes: TLR4, TLR2, MMP9, EGF, HCK, FCGR2A, SELP, NFKBIA, CXCR1, and SELL. By intersecting the differentially expressed lncRNAs for COVID-19 and asthma, 13 common differentially expressed lncRNAs were obtained. LncRNAs that regulated microRNAs (miRNAs) were mainly concentrated in intercellular signal transduction, apoptosis, immunity and other related functional pathways. The ceRNA network suggested that there were a variety of regulatory miRNAs and lncRNAs upstream of the key genes. The key genes could also bind a variety of RBPs and oxidative stress-related genes. The key genes also had good diagnostic value in the verification set. In the validation set, the expression of key genes was statistically significant in both the COVID-19 group and the asthma group compared with the healthy control group. CXCR1 expression was upregulated in asthma cell models, and interference with CXCR1 expression significantly reduced cell viability. CONCLUSIONS: Key genes may become diagnostic and predictive biomarkers of outcomes in COVID-19 and asthma. Video Abstract.

Construction of a diagnostic signature and immune landscape of pulmonary arterial hypertension.

Background: Molecular biomarkers are widely used for disease diagnosis and exploration of pathogenesis. Pulmonary arterial hypertension (PAH) is a rapidly progressive cardiopulmonary disease with delayed diagnosis. Studies were limited regarding molecular biomarkers correlated with PAH from a broad perspective. Methods: Two independent microarray cohorts comprising 73 PAH samples and 36 normal samples were enrolled in this study. The weighted gene co-expression network analysis (WGCNA) was performed to identify the key modules associated with PAH. The LASSO algorithm was employed to fit a diagnostic model. The latent biology mechanisms and immune landscape were further revealed via bioinformatics tools. Results: The WGCNA approach ultimately identified two key modules significantly associated with PAH. For genes within the two models, differential expression analysis between PAH and normal samples further determined nine key genes. With the expression profiles of these nine genes, we initially developed a PAH diagnostic signature (PDS) consisting of LRRN4, PI15, BICC1, PDE1A, TSHZ2, HMCN1, COL14A1, CCDC80, and ABCB1 in GSE117261 and then validated this signature in GSE113439. The ROC analysis demonstrated outstanding AUCs with 0.948 and 0.945 in two cohorts, respectively. Besides, patients with high PDS scores enriched plenty of Th17 cells and neutrophils, while patients with low PDS scores were dramatically related to mast cells and B cells. Conclusion: Our study established a robust and promising signature PDS for diagnosing PAH, with key genes, novel pathways, and immune landscape offering new perspectives for exploring the molecular mechanisms and potential therapeutic targets of PAH.

Fiber SPR two-dimensional micro displacement sensor based on the coaxial double waveguide with a conical structure.

Fiber SPR micro displacement sensor cannot be used for two-dimensional displacement sensing at present. In this paper, we proposed and demonstrated a fiber SPR two-dimensional micro displacement sensor based on the coaxial double waveguide with a conical structure. The coaxial double waveguide is fused into a cone as the light injection fiber, and two different forms of outgoing light fields can be obtained through two cores of the fiber. The horn shaped light field emitted by the ring core of the coaxial double waveguide can cooperate with the sensing fiber to realize the micro displacement sensing in the x-axis direction. And the straight beam emitted by the middle core of the coaxial double waveguide can cooperate with the sensing fiber to realize the micro displacement sensing in the y-axis direction. Through simulation analysis and experimental test, its average wavelength sensitivity and light intensity sensitivity of the x-axis displacement are 0.0537nm/µm and 0.000124a.u./µm, respectively. And that of the y-axis displacement are 0.315nm/µm and 0.00277a.u./µm, respectively. The proposed fiber sensor realizes the two-dimensional displacement sensing based on SPR, which can be widely used in the fields of two-dimensional micro displacement measurement and two-dimensional position precision positioning.

Single-mode fiber curvature sensor based on SPR.

A fiber surface plasmon resonance (SPR) sensor is widely used in high-sensitivity refractive index measurement, but there is less research on curvature measurement. In this paper, a single-mode fiber curvature sensor based on SPR is designed and fabricated. By employing bending, the transmitted light in the fiber core leaks into the cladding. A 50 nm gold film is coated outside the cladding, and the evanescent field of the cladding after bending contacts the gold film to cause SPR. When the curvature changes, the coupled cladding mode and intensity are different; that is, the SPR incident angle and evanescent field intensity are different, so as to realize the dual parameters of SPR resonance wavelength and depth of the resonance valley changing with curvature. By experiments, the influence of different cutoff wavelengths of single-mode fiber on the performance of the sensor is studied. The testing results indicate that with the decrease in cutoff wavelength of the single-mode fiber, the valley depth sensitivity of the sensor increases, and the half height width (FWHM) decreases. When the cutoff wavelength of the single-mode fiber is 630 nm, the valley depth sensitivity of the sensor is 0.0088a.u/m-1, the wavelength sensitivity is 0.26nm/m-1, and the average FWHM is only 21 nm. The proposed single-mode fiber curvature sensor based on SPR has a narrow FWHM and an opening threshold. It can also realize no opening threshold by introducing a coreless fiber, which provides a new solution, to the best of our knowledge, for the diversified detection of fiber SPR sensors.

All-fiber biological detection microfluidic chip based on space division and wavelength division multiplexing technologies.

To further reduce the size of a microfluidic detection chip and the sample consumption and to shorten the chip manufacturing cycle, an all-fiber SPR detection multichannel microfluidic chip was proposed and demonstrated in this paper. The microfluidic channel of the proposed chip was provided by the air channel of a double side-hole fiber, the detection unit was fabricated using a dumbbell fiber with a fiber core exposed to air, and the sensing probe was composed and packaged by fiber micro-processing technology. The internal double channels of the fiber constructed from double side-hole and dumbbell fibers can realize dual channel detection based on space division multiplexing. 30 nm silver and 50 nm gold films were respectively coated on the left and right sides of the dumbbell fiber, which can realize the dual channel simultaneous detection based on wavelength division multiplexing. We employed the proposed microfluidic chip to detect immunoglobulin G and dopamine molecules, where the average sensitivity is 0.252 nm (mg mL-1)-1 and 0.061 nm (µg mL-1)-1, and the LOD is 0.397 mg mL-1 and 1.639 µg mL-1, respectively. The microfluidic channel and detection unit of all-fiber multi-channel SPR detection microfluidic chip are provided by a soft and flexible fiber, which is compact in structure, flexible in fabrication and short in manufacturing cycle, making it possible for the microfluidic chip to enter the human body for detection and enabling a new approach for the fabrication of wearable detection microfluidic devices. This provides a new idea for the development of microfluidic chips.

Use of machine learning models to predict prognosis of combined pulmonary fibrosis and emphysema in a Chinese population.

BACKGROUND: Combined pulmonary fibrosis and emphysema (CPFE) is a novel clinical entity with a poor prognosis. This study aimed to develop a clinical nomogram model to predict the 1-, 2- and 3-year mortality of patients with CPFE by using the machine learning approach, and to validate the predictive ability of the interstitial lung disease-gender-age-lung physiology (ILD-GAP) model in CPFE. METHODS: The data of CPFE patients from January 2015 to October 2021 who met the inclusion criteria were retrospectively collected. We utilized LASSO regression and multivariable Cox regression analysis to identify the variables associated with the prognosis of CPFE and generate a nomogram. The Harrell's C index, the calibration curve and the area under the receiver operating characteristic (ROC) curve (AUC) were used to evaluate the performance of the nomogram. Then, we performed likelihood ratio test, net reclassification improvement (NRI), integrated discrimination improvement (IDI) and decision curve analysis (DCA) to compare the performance of the nomogram with that of the ILD-GAP model. RESULTS: A total of 184 patients with CPFE were enrolled. During the follow-up, 90 patients died. After screening out, diffusing lung capacity for carbon monoxide (DLCO), right ventricular diameter (RVD), C-reactive protein (CRP), and globulin were found to be associated with the prognosis of CPFE. The nomogram was then developed by incorporating the above five variables, and it showed a good performance, with a Harrell's C index of 0.757 and an AUC of 0.800 (95% CI 0.736-0.863). Moreover, the calibration plot of the nomogram showed good concordance between the prediction probabilities and the actual observations. The nomogram also improved the discrimination ability of the ILD-GAP model compared to that of the ILD-GAP model alone, and this was substantiated by the likelihood ratio test, NRI and IDI. The significant clinical utility of the nomogram was demonstrated by DCA. CONCLUSION: Age, DLCO, RVD, CRP and globulin were identified as being significantly associated with the prognosis of CPFE in our cohort. The nomogram incorporating the 5 variables showed good performance in predicting the mortality of CPFE. In addition, although the nomogram was superior to the ILD-GAP model in the present cohort, further validation is needed to determine the clinical utility of the nomogram.

A novel O2- (2,4-dinitrophenyl) diazeniumdiolate inhibits hepatocellular carcinoma migration, invasion, and EMT through the Wnt/ß-catenin pathway.

Targeted Wnt/ß-catenin pathway is considered to be a promising therapy for cancer metastasis. The novel O2 -(2,4-dinitrophenyl) diazeniumdiolate (JS-K) plays a potent inhibitory role in the proliferation of cancers. In this study, HepG2 and SMMC7721 were used to clarify the efficacy of JS-K inhibition of HCC metastasis. JS-K significantly inhibited cell motility through a wound-healing assay and restrained cell migration and invasion at noncytotoxic concentrations. However, the inhibitory effects of migration and invasion were abolished after the addition of NO scavenger, Carboxy-PTIO. In addition, JS-K inhibited the Wnt/ß-catenin pathway by a decrease of p-GSK-3ß at Ser9, cytosolic ß-catenin, and nuclear ß-catenin accumulation whereas an increase of p-ß-catenin. Furthermore, the transcription regulators c-Myc, survivin, and Cyclin D1 were down-regulated after treating with JS-K. The inhibitory of the Wnt/ß-catenin pathway was reversed after the addition of Carboxy-PTIO or LiCl. Meanwhile, JS-K also inhibited the epithelial-mesenchymal transition (EMT)-mediated cell migration and invasion. The characteristics of the inhibition were reflected by the upregulation of E-cadherin whereas the downregulation of Vimentin, Snail, and Slug. Taking together, these results demonstrated that JS-K inhibited HepG2 and SMMC7721 cells migration and invasion by reversing EMT via the Wnt/ß-catenin pathway.

Predictors of Maternal Death Among Women With Pulmonary Hypertension in China From 2012 to 2020: A Retrospective Single-Center Study.

Background: Previous studies have suggested that pregnant women with pulmonary hypertension (PH) have high maternal mortality. However, indexes or factors that can predict maternal death are lacking. Methods: We retrospectively reviewed pregnant women with PH admitted for delivery from 2012 to 2020 and followed them for over 6 months. The patients were divided into two groups according to 10-day survival status after delivery. Predictive models and predictors for maternal death were identified using four machine learning algorithms: naïve Bayes, random forest, gradient boosting decision tree (GBDT), and support vector machine. Results: A total of 299 patients were included. The most frequent PH classifications were Group 1 PH (73.9%) and Group 2 PH (23.7%). The mortality within 10 days after delivery was 9.4% and higher in Group 1 PH than in the other PH groups (11.7 vs. 2.6%, P = 0.016). We identified 17 predictors, each with a P-value < 0.05 by univariable analysis, that were associated with an increased risk of death, and the most notable were pulmonary artery systolic pressure (PASP), platelet count, red cell distribution width, N-terminal brain natriuretic peptide (NT-proBNP), and albumin (all P < 0.01). Four prediction models were established using the candidate variables, and the GBDT model showed the best performance (F1-score = 66.7%, area under the curve = 0.93). Feature importance showed that the three most important predictors were NT-proBNP, PASP, and albumin. Conclusion: Mortality remained high, particularly in Group 1 PH. Our study shows that NT-proBNP, PASP, and albumin are the most important predictors of maternal death in the GBDT model. These findings may help clinicians provide better advice regarding fertility for women with PH.

High sensitivity fiber cladding SPR strain sensor based on V-groove structure.

How to couple the light in the fiber core to the cladding is an urgent issue that need to be done for the fabrication of the fiber-cladding SPR sensor, and there is no report about the fiber SPR strain sensor. Hereby, we propose and demonstrate a high sensitivity fiber cladding SPR strain sensor based on V-groove structure. By CO2 laser, the V-groove is fabricated on the single-mode fiber, and the light in the fiber core is effectively coupled to the cladding. The cladding 2cm behind the V-groove is coated with sensing gold film, and a multimode fiber is spliced with the sensing probe to construct the novel fiber cladding SPR sensor. On the basis of the investigation of the effects of different V-groove depth, number and period on the performance of fiber SPR refractive index sensor, a high sensitivity strain SPR sensor is designed and fabricated by employing the characteristic that the V-groove will deform with strain. The testing results indicate that the average refractive index sensitivity of the sensor is 2896.4nm/RIU, and the strain wavelength sensitivity is 25.92pm/µÎµ which is much higher than that of the fiber interference and grating strain sensors, and the strain light intensity sensitivity is -4.4×10-4 a.u./µÎµ. The proposed fiber cladding SPR strain sensor has the advantages of simple structure and convenient manufacture, and can be used for working in a narrow space.

Laser Curve Extraction of Wheelset Based on Deep Learning Skeleton Extraction Network.

In this paper, a new algorithm for extracting the laser fringe center is proposed. Based on a deep learning skeleton extraction network, the laser stripe center can be extracted quickly and accurately. Skeleton extraction is the process of reducing the shape image to its approximate central axis representation while maintaining the image's topological and geometric shape. Skeleton extraction is an important step in topological and geometric shape analysis. According to the characteristics of the wheelset laser curve dataset, a new skeleton extraction network, a hierarchical skeleton network (LuoNet), is proposed. The proposed architecture has three levels of the encoder-decoder network, and YE Module interconnection is designed between each level of the encoder and decoder network. In the wheelset laser curve dataset, the F1_score can reach 0.714. Compared with the traditional laser curve center extraction algorithm, the proposed LuoNet algorithm has the advantages of short running time, high accuracy, and stable extraction results.

Prediction of long-term mortality by using machine learning models in Chinese patients with connective tissue disease-associated interstitial lung disease.

BACKGROUND: The exact risk assessment is crucial for the management of connective tissue disease-associated interstitial lung disease (CTD-ILD) patients. In the present study, we develop a nomogram to predict 3­ and 5-year mortality by using machine learning approach and test the ILD-GAP model in Chinese CTD-ILD patients. METHODS: CTD-ILD patients who were diagnosed and treated at the First Affiliated Hospital of Zhengzhou University were enrolled based on a prior well-designed criterion between February 2011 and July 2018. Cox regression with the least absolute shrinkage and selection operator (LASSO) was used to screen out the predictors and generate a nomogram. Internal validation was performed using bootstrap resampling. Then, the nomogram and ILD-GAP model were assessed via likelihood ratio testing, Harrell's C index, integrated discrimination improvement (IDI), the net reclassification improvement (NRI) and decision curve analysis. RESULTS: A total of 675 consecutive CTD-ILD patients were enrolled in this study, during the median follow-up period of 50 (interquartile range, 38-65) months, 158 patients died (mortality rate 23.4%). After feature selection, 9 variables were identified: age, rheumatoid arthritis, lung diffusing capacity for carbon monoxide, right ventricular diameter, right atrial area, honeycombing, immunosuppressive agents, aspartate transaminase and albumin. A predictive nomogram was generated by integrating these variables, which provided better mortality estimates than ILD-GAP model based on the likelihood ratio testing, Harrell's C index (0.767 and 0.652 respectively) and calibration plots. Application of the nomogram resulted in an improved IDI (3- and 5-year, 0.137 and 0.136 respectively) and NRI (3- and 5-year, 0.294 and 0.325 respectively) compared with ILD-GAP model. In addition, the nomogram was more clinically useful revealed by decision curve analysis. CONCLUSIONS: The results from our study prove that the ILD-GAP model may exhibit an inapplicable role in predicting mortality risk in Chinese CTD-ILD patients. The nomogram we developed performed well in predicting 3­ and 5-year mortality risk of Chinese CTD-ILD patients, but further studies and external validation will be required to determine the clinical usefulness of the nomogram.

LncRNA PVT1 Facilitates Proliferation, Migration and Invasion of NSCLC Cells via miR-551b/FGFR1 Axis.

BACKGROUND: Long non-coding RNA (lncRNA) plasmacytoma variant translocation 1 (PVT1) plays a crucial role in non-small cell lung cancer (NSCLC). Nonetheless, regulatory effects of PVT1 on functions of NSCLC cells remain blurry. METHODS: Relative expression levels of PVT1, miR-551b and FGFR1 mRNA in tumor tissues and cells were examined employing quantitative real-time polymerase chain reaction (qRT-PCR); CCK-8 and BrdU assays were utilized for measuring cell viability and proliferation of H1299 and A549 cells; cell migration and invasion were detected deploying Transwell assay; dual-luciferase assay was used for the validation of binding sequence between PVT1 and miR-551b. FGFR1 expression in protein level was quantified employing Western blot. RESULTS: PVT1 was highly expressed in NSCLC tissues and cell lines, whereas miR-551b expression was down-regulated. Overexpression of PVT1 potentiated viability, proliferation, migration and invasion of NSCLC cells while miR-551b inhibited the biological behaviors mentioned above. MiR-551b was predicted and then confirmed as a direct downstream target of PVT1. Meanwhile, a negative correlation was observed between PVT1 expression and miR-551b expression in NSCLC tissues. Besides, PVT1 could increase FGFR1 expression by repressing miR-551b expression. CONCLUSION: PVT1 promotes the proliferation, migration and invasion of NSCLC cells by indirectly mediating FGFR1 via targeting miR-551b.

Laser curve extraction of a train wheelset based on an encoder-decoder network.

An algorithm of laser curve segmentation for a train wheelset based on an encoder- decoder network is proposed. Aiming at the rich local features and simple semantic features of the train wheelset laser curve image, a neural network with shallow depth, high resolution, and good detail performance was designed. The proposed neural network makes full use of the dense connection mechanism and the upsampling module to enhance feature reuse and feature propagation. It can extract context semantic information at multiple scales with fewer parameters. Experimental results show that the encoder-decoder network has better performance than other neural networks in laser curve extraction of train wheelset. Based on the encoder-decoder neural network, mIOU, Recall, Accuracy, and F1_score of the laser curve dataset of the train wheelset, the score index reached 86.5%, 89.2%, 99.9%, and 85.0%, which can accurately extract the laser stripe of the train wheelset. Additionally, the encoder-decoder network can diminish the influence of noise on the extraction of laser fringes of a train wheelset to a certain extent. Therefore, it has good application in railway safety.

Tissue factor promotes airway pathological features through epithelial-mesenchymal transition of bronchial epithelial cells in mice with house dust mite-induced asthma.

It has recently been shown that expression levels of tissue factor (TF) are high in the serum and peripheral blood mononuclear cells of patients with asthma. However, whether TF impacts airway inflammation and remodelling in asthma remains unknown. The aim of this study was to investigate the effect of TF in asthma airway inflammation and remodelling using a house dust mite (HDM)-induced chronic asthma model and human bronchial epithelial (16HBE) cells. A chronic asthma model was constructed in BALB/c mice by the intranasal instillation of HDM. Mice were treated with short hairpin TF (shTF), and airway inflammation and remodelling features of asthma and epithelial-mesenchymal transition (EMT) were assessed. 16HBE cells were induced by transforming growth factor-ß1 (TGF-ß1) and HDM in the presence or absence of shTF; then, EMT markers and invasion and migration ability were determined. TF expression increased in the lung tissue and 16HBE cells when exposed to HDM. TF downregulation in the lung significantly reduced airway hyperresponsiveness, eosinophil inflammation, the EMT process, and levels of interleukin (IL)-4, IL-6, IL-13, and TGF-ß1 in bronchoalveolar lavage fluid of asthmatic mice. Moreover, TF downregulation inhibited migration and incursion and decreased the expression levels of fibronectin 1 and TGF-ß1, but increased the expression of E-cadherin in HDM- and TGF-ß1-stimulated 16HBE cells. These results demonstrated that TF promoted airway pathological features by enhancing the EMT of bronchial epithelial cells both in vitro and in mice with house dust mite-induced asthma.