Association between Smoking Habit Changes and the Risk of Myocardial Infarction in Ischemic Stroke Patients: A Nationwide Cohort Study.

BACKGROUND: Smoking is a well-known risk factor for cardiovascular diseases, including myocardial infarction (MI) and ischemic stroke (IS). While the relationship between smoking and the risk of cardiovascular diseases is established, the impact of changing smoking habits post-IS on the risk of subsequent MI remains unclear. This study aims to elucidate the effects of alterations in smoking behavior following an IS diagnosis on the likelihood of experiencing an MI. METHODS: Utilizing data from the Korean National Health Insurance Services Database, this nationwide population-based cohort study included 199,051 participants diagnosed with IS between January 2010 and December 2016. Smoking status was categorized based on changes in smoking habits before and after IS diagnosis. The association between changes in smoking behavior and the risk of subsequent MI was analyzed using multivariable Cox proportional hazard regression models. RESULTS: During a median follow-up of 4.17 person-years, a total of 5,734 (2.88%) patients were diagnosed with MI after IS. Smoking quitters (2.93%) or former smokers (2.47%) have a similar or lower rate of MI than the average, even if they have smoked cigarettes, while sustained smokers (3.46%) or new smokers (3.81%) have much higher rates of MI. Among sustained and new smokers, the risk of incident MI was significantly higher than never smokers (new smoker adjusted HR [aHR]: 1.496, 95% CI: 1.262-1.774; sustained smoker aHR: 1.494, 95% CI: 1.361-1.641). Also, among the study participants, approximately two-thirds continued smoking after their IS diagnosis. CONCLUSION: Changing smoking habits after an IS diagnosis significantly influences the risk of subsequent MI. Specifically, continuing or starting to smoke after an IS diagnosis is associated with a higher risk of MI. These results underscore the importance of targeted smoking cessation interventions for stroke patients to reduce the risk of subsequent MI.

Multi-modal deep learning from imaging genomic data for schizophrenia classification.

Background: Schizophrenia (SZ) is a psychiatric condition that adversely affects an individual's cognitive, emotional, and behavioral aspects. The etiology of SZ, although extensively studied, remains unclear, as multiple factors come together to contribute toward its development. There is a consistent body of evidence documenting the presence of structural and functional deviations in the brains of individuals with SZ. Moreover, the hereditary aspect of SZ is supported by the significant involvement of genomics markers. Therefore, the need to investigate SZ from a multi-modal perspective and develop approaches for improved detection arises. Methods: Our proposed method employed a deep learning framework combining features from structural magnetic resonance imaging (sMRI), functional magnetic resonance imaging (fMRI), and genetic markers such as single nucleotide polymorphism (SNP). For sMRI, we used a pre-trained DenseNet to extract the morphological features. To identify the most relevant functional connections in fMRI and SNPs linked to SZ, we applied a 1-dimensional convolutional neural network (CNN) followed by layerwise relevance propagation (LRP). Finally, we concatenated these obtained features across modalities and fed them to the extreme gradient boosting (XGBoost) tree-based classifier to classify SZ from healthy control (HC). Results: Experimental evaluation on clinical dataset demonstrated that, compared to the outcomes obtained from each modality individually, our proposed multi-modal approach performed classification of SZ individuals from HC with an improved accuracy of 79.01%. Conclusion: We proposed a deep learning based framework that selects multi-modal (sMRI, fMRI and genetic) features efficiently and fuse them to obtain improved classification scores. Additionally, by using Explainable AI (XAI), we were able to pinpoint and validate significant functional network connections and SNPs that contributed the most toward SZ classification, providing necessary interpretation behind our findings.

Spiky tubular nanoparticles with low protein corona can realize efficient and non-destructive penetration through endothelial barrier.

Upon intravascular applications, i.e., cancer treatment, nanoparticles (NPs) are required to deliver through blood circulation, sustain serum protein interactions, before they penetrate the blood vessels and reach targeted sites for payload drug release. For a delivery process as such, it is elusive and difficult to comprehend the morphological change of NP surface and evaluate associated effects on its targeted delivery. Herein, we used silica NPs with different surface modifications to demonstrate the morphological impact of NPs during the application of the NP-blood protein interaction, vascular endothelial cell penetration, subsequent targeted delivery and photodynamic therapy efficacy, and pursue high drug-load NPs with surface designs. Compared to solid and mesoporous NPs, we found the spiky tubular NPs reserved the NPs' antifouling properties (or shedding of "protein corona"), promoted better endothelial penetration and less destruction in vitro and in vivo. Such effects could be attributed to their spiky surface structures, which can limit the NP-protein interaction area and promote the NP-protein steric hindrance. Further in molecular simulations, we determined that the spiky tubular morphological modification on NPs enhanced the interaction free energy and lowered the amino acids number and the subsequent frequency in contacting with VE-cadherin of vascular endothelia. As a result, the spiky tubular NPs demonstrated its advantages in mitigating damages to VE-cadherin stability and endothelial cell integrity. Exploiting such spiky tubular surface modification, we can improve the NP delivery efficiency and prohibit the leakiness of vascular endothelia, helping address challenges faced by tumor migration in nanomedicine applications for cancer therapy.

Self-Driven, Monopolar Electrohydrodynamic Printing via Dielectric Nanoparticle Layer.

Electrohydrodynamic printing holds both ultrahigh-resolution fabrication capability and unmatched ink-viscosity compatibility yet fails on highly insulating thick/irregular substrates. Herein, we proposed a single-potential driven electrohydrodynamic printing process with submicrometer resolution on arbitrary nonconductive targets, regardless of their geometric shape or sizes, via precoating with an ultrathin dielectric nanoparticle layer. Benefiting from the favorable Maxwell-Wagner polarization, the reversely polarized spot brought about a significant drop (∼57% for ceramics) in the operation voltage as its induced electric field and a negligible residual charge accumulation. Thus, ordered micro/nanostructures with line widths down to 300 nm were directly written at a stage speed as low as 5 mm/s, and silver features with width of ∼2 µm or interval of ∼4 µm were achieved on insulating substrates separately. Flexible sensors and curved heaters were then high-precision printed and demonstrated successfully, presenting this technique with huge potential for fabricating flexible/conformal electronics on arbitrary 3D structures.

The Prediction of Recombination Hotspot Based on Automated Machine Learning.

Meiotic recombination plays a pivotal role in genetic evolution. Genetic variation induced by recombination is a crucial factor in generating biodiversity and a driving force for evolution. At present, the development of recombination hotspot prediction methods has encountered challenges related to insufficient feature extraction and limited generalization capabilities. This paper focused on the research of recombination hotspot prediction methods. We explored deep learning-based recombination hotspot prediction and scrutinized the shortcomings of prevalent models in addressing the challenge of recombination hotspot prediction. To addressing these deficiencies, an automated machine learning approach was utilized to construct recombination hotspot prediction model. The model combined sequence information with physicochemical properties by employing TF-IDF-Kmer and DNA composition components to acquire more effective feature data. Experimental results validate the effectiveness of the feature extraction method and automated machine learning technology used in this study. The final model was validated on three distinct datasets and yielded accuracy rates of 97.14%, 79.71%, and 98.73%, surpassing the current leading models by 2%, 2.56%, and 4%, respectively. In addition, we incorporated tools such as SHAP and AutoGluon to analyze the interpretability of black-box models, delved into the impact of individual features on the results, and investigated the reasons behind misclassification of samples. Finally, an application of recombination hotspot prediction was established to facilitate easy access to necessary information and tools for researchers. The research outcomes of this paper underscore the enormous potential of automated machine learning methods in gene sequence prediction.

Research progress on the structure, function, and use of angiogenin in malignant tumours.

Angiogenin (ANG) is a specialised secreted ribonuclease, also known as RNase5, that is widely expressed in vertebrates. ANG dysregulation is closely associated with the development of breast, nasopharyngeal, and lung cancers. In recent years, studies have found that ANG not only induces neovascularisation by activating endothelial cells, but also plays a regulatory role in the plasticity of cancer cells. Cellular plasticity plays pivotal roles in cancer initiation, progression, migration, therapeutic resistance, and relapse. Therefore, it is a promising biomarker for cancer diagnosis, prognostic evaluation, and therapy. This review summarises the current knowledge regarding the roles and clinical applications of ANG in cancer development and progression.

Transient charge-driven 3D conformal printing via pulsed-plasma impingement.

Seamless integration of microstructures and circuits on three-dimensional (3D) complex surfaces is of significance and is catalyzing the emergence of many innovative 3D curvy electronic devices. However, patterning fine features on arbitrary 3D targets remains challenging. Here, we propose a facile charge-driven electrohydrodynamic 3D microprinting technique that allows micron- and even submicron-scale patterning of functional inks on a couple of 3D-shaped dielectrics via an atmospheric-pressure cold plasma jet. Relying on the transient charging of exposed sites arising from the weakly ionized gas jet, the specified charge is programmably deposited onto the surface as a virtual electrode with spatial and time spans of ~mm in diameter and ~µs in duration to generate a localized electric field accordantly. Therefore, inks with a wide range of viscosities can be directly drawn out from micro-orifices and deposited on both two-dimensional (2D) planar and 3D curved surfaces with a curvature radius down to ~1 mm and even on the inner wall of narrow cavities via localized electrostatic attraction, exhibiting a printing resolution of ~450 nm. In addition, several conformal electronic devices were successfully printed on 3D dielectric objects. Self-aligned 3D microprinting, with stacking layers up to 1400, is also achieved due to the electrified surfaces. This microplasma-induced printing technique exhibits great advantages such as ultrahigh resolution, excellent compatibility of inks and substrates, antigravity droplet dispersion, and omnidirectional printing on 3D freeform surfaces. It could provide a promising solution for intimately fabricating electronic devices on arbitrary 3D surfaces.

Age Encoded Adversarial Learning for Pediatric CT Segmentation.

Organ segmentation from CT images is critical in the early diagnosis of diseases, progress monitoring, pre-operative planning, radiation therapy planning, and CT dose estimation. However, data limitation remains one of the main challenges in medical image segmentation tasks. This challenge is particularly huge in pediatric CT segmentation due to children's heightened sensitivity to radiation. In order to address this issue, we propose a novel segmentation framework with a built-in auxiliary classifier generative adversarial network (ACGAN) that conditions age, simultaneously generating additional features during training. The proposed conditional feature generation segmentation network (CFG-SegNet) was trained on a single loss function and used 2.5D segmentation batches. Our experiment was performed on a dataset with 359 subjects (180 male and 179 female) aged from 5 days to 16 years and a mean age of 7 years. CFG-SegNet achieved an average segmentation accuracy of 0.681 dice similarity coefficient (DSC) on the prostate, 0.619 DSC on the uterus, 0.912 DSC on the liver, and 0.832 DSC on the heart with four-fold cross-validation. We compared the segmentation accuracy of our proposed method with previously published U-Net results, and our network improved the segmentation accuracy by 2.7%, 2.6%, 2.8%, and 3.4% for the prostate, uterus, liver, and heart, respectively. The results indicate that our high-performing segmentation framework can more precisely segment organs when limited training images are available.

Beneficial effects of the first case of washed microbiota transplantation for postorgasmic illness syndrome: a case report.

Introduction: Postorgasmic illness syndrome (POIS) is characterized by allergic symptoms and flu-like illness after ejaculation. There are still no effective treatments for POIS. Aim: To report the first case of washed microbiota transplantation (WMT) to treat patient with POIS. Methods: Data were collected from a patient with POIS who had received 3 courses of WMT: self-rating scale of POIS symptoms, Self-rating Anxiety Scale, Self-rating Depression Scale, and Symptom Checklist 90. The patient's stool samples for 16sDNA sequencing were collected 1 month after WMT. Results: POIS symptoms improved after WMT. Scores decreased from baseline after WMT: self-rating scale of POIS symptoms (before WMT, 16; after first, 16; after second, 8; after third, 9), Self-rating Anxiety Scale (45, 42.5, 37.5, 45), Self-rating Depression Scale (63.75, 58.75, 47.5, 50), and Symptom Checklist 90 (143, 140, 109, 149). Characteristics of the patient's gut microbiota changed. At the genus level, the relative abundance of beneficial bacteria increased, and some opportunistic pathogenic bacteria decreased. Conclusion: WMT may be an effective and safe choice for the treatment of patients with POIS by changing the gut microbiota of the host.

Prediction of lymph node metastasis in patients with papillary thyroid cancer based on radiomics analysis and intraoperative frozen section analysis: A retrospective study.

INTRODUCTION: To evaluate the diagnostic efficiency among the clinical model, the radiomics model and the nomogram that combined radiomics features, frozen section (FS) analysis and clinical characteristics for the prediction of lymph node (LN) metastasis in patients with papillary thyroid cancer (PTC). METHODS: A total of 208 patients were randomly divided into two groups randomly with a proportion of 7:3 for the training groups (n = 146) and the validation groups (n = 62). The Least Absolute Shrinkage and Selection Operator (LASSO) regression was used for the selection of radiomics features extracted from ultrasound (US) images. Univariate and multivariate logistic analyses were used to select predictors associated with the status of LN. The clinical model, radiomics model and nomogram were subsequently established by logistic regression machine learning. The area under the curve (AUC), sensitivity and specificity were used to evaluate the diagnostic performance of the different models. The Delong test was used to compare the AUC of the three models. RESULTS: Multivariate analysis indicated that age, size group, Adler grade, ACR score and the psammoma body group were independent predictors of lymph node metastasis (LNM). The results showed that in both the training and validation groups, the nomogram showed better performance than the clinical model, albeit not statistically significant (p > .05), and significantly outperformed the radiomics model (p < .05). However, the nomogram exhibits a slight improvement in sensitivity that could reduce the incidence of false negatives. CONCLUSION: We propose that the nomogram holds substantial promise as an effective tool for predicting LNM in patients with PTC.

Transcriptomic and functional analyzes reveal that the brassinosteroid insensitive 1 receptor (OsBRI1) regulates cold tolerance in rice.

Brassinosteroids (BR) play crucial roles in plant development and abiotic stresses in plants. Exogenous application of BR can significantly enhance cold tolerance in rice. However, the regulatory relationship between cold tolerance and the BR signaling pathway in rice remains largely unknown. Here, we characterized functions of the BR receptor OsBRI1 in response to cold tolerance in rice using its loss-of-function mutant (d61-1). Our results showed that mutant d61-1 was less tolerant to cold stress than wild-type (WT). Besides, d61-1 had lower levels than WT for some physiological parameters, including catalase activity (CAT), superoxide dismutase activity (SOD), peroxidase activity (POD), peroxidase activity (PRO), soluble protein, and soluble sugar content, while malondialdehyde content (MDA) and relative electrical conductivity (REC) levels in d61-1 were higher than those in WT plants. These results indicated that the loss of OsBRI1 function resulted in decreased cold tolerance in rice. In addition, we performed RNA sequencing (RNA-seq) of WT and d61-1 mutant under cold stress. Numerous common and unique differentially expressed genes (DEGs) with up- and down-regulation were observed in WT and d61-1 mutant. Some DEGs were expressed to various degrees, even opposite, between CK1 vs. T1 (WT) and CK2 vs. T2 (d61-1). Among these specific DEGs, some typical genes are involved in plant tolerance to cold stress. Through weighted correlation network analysis (WGCNA), 50 hub genes were screened in the turquoise and blue module. Many genes were involved in cold stress and plant hormone, such as Os01g0279800 (BRI1-associated receptor kinase 1 precursor), Os10g0513200 (Dwarf and tiller-enhancing 1, DTE1), Os02g0706400 (MYB-related transcription factor, OsRL3), etc. Differential expression levels of some genes were verified in WT and d61-1 under cold stress using qRT-PCR. These valuable findings and gene resources will be critical for understanding the regulatory relationships between cold stress tolerance and the BR signaling pathways in rice.

Bibliometric and visualized analysis of hydrogels in organoids research.

Over the past decades, there has been ongoing effort to develop complex biomimetic tissue engineering strategies for in vitro cultivation and maintenance of organoids. The defined hydrogels can create organoid models for various organs by changing their properties and various active molecules. An increasing number of researches has been done on the application of hydrogels in organoids, and a large number of articles have been published on the topic. Although there have been existing reviews describing the application of hydrogels in the field of organoids, there is still a lack of comprehensive studies summarizing and analyzing the overall research trends in this field. The citation can be used as an indicator of the scientific influence of an article in its field. This study aims to evaluate the application of hydrogels in organoids through bibliometric analysis, and to predict the hotspots and developing trends in this field.

MIBPred: Ensemble Learning-Based Metal Ion-Binding Protein Classifier.

In biological organisms, metal ion-binding proteins participate in numerous metabolic activities and are closely associated with various diseases. To accurately predict whether a protein binds to metal ions and the type of metal ion-binding protein, this study proposed a classifier named MIBPred. The classifier incorporated advanced Word2Vec technology from the field of natural language processing to extract semantic features of the protein sequence language and combined them with position-specific score matrix (PSSM) features. Furthermore, an ensemble learning model was employed for the metal ion-binding protein classification task. In the model, we independently trained XGBoost, LightGBM, and CatBoost algorithms and integrated the output results through an SVM voting mechanism. This innovative combination has led to a significant breakthrough in the predictive performance of our model. As a result, we achieved accuracies of 95.13% and 85.19%, respectively, in predicting metal ion-binding proteins and their types. Our research not only confirms the effectiveness of Word2Vec technology in extracting semantic information from protein sequences but also highlights the outstanding performance of the MIBPred classifier in the problem of metal ion-binding protein types. This study provides a reliable tool and method for the in-depth exploration of the structure and function of metal ion-binding proteins.

Effect of Compounded Aluminum Hydroxide Flame Retardants on the Flammability and Smoke Suppression Performance of Asphalt Binders.

Compounded aluminum hydroxide (ATH) flame retardants have been widely used for their low cost and environmentally friendly characteristics. However, previous research lacks a systematic and comprehensive comparison. In addition, the combustion characteristics and phase characterization of asphalt binders are not taken into account either. In this work, flame retardants, for instance, APP, Sb2O3, ZB, and LDHs, were compounded with ATH. The flame retardant behavior, together with the smoke suppression behavior, of asphalt binders with compounded flame retardants was determined by LOI and CCT. Furthermore, mechanisms on flame retardants were investigated. It was found that ATH compounded with ZB significantly reduced the heat smoke release and suppressed the formation of toxic volatiles during asphalt combustion. This was because ATH/ZB facilitated the formation of polyaromatic structures and improved the resistance of the char layer. ATH compounded with APP showed an antagonistic effect in the limiting oxygen test because the reaction between ATH and APP inhibited and delayed the decomposition of ATH during asphalt combustion with more aluminum phosphate presenting relatively poor barrier properties produced.

Research progress and application of single-cell sequencing in head and neck malignant tumors.

Single-cell sequencing (SCS) is a technology that separates thousands of cells from the organism and accurately analyzes the genetic material expressed in each cell using high-throughput sequencing technology. Unlike the traditional bulk sequencing approach, which can only provide the average value of a cell population and cannot obtain specific single-cell data, single-cell sequencing can identify the gene sequence and expression changes of a single cell, and reflects the differences between genetic material and protein between cells, and ultimately the role played by the tumor microenvironment. single-cell sequencing can further explore the pathogenesis of head and neck malignancies from the single-cell biological level and provides a theoretical basis for the clinical diagnosis and treatment of head and neck malignancies. This article will systematically introduce the latest progress and application of single-cell sequencing in malignant head and neck tumors.

Application of organoids in otolaryngology: head and neck surgery.

PURPOSE: The purpose of this review is to systematically summarize the application of organoids in the field of otolaryngology and head and neck surgery. It aims to shed light on the current advancements and future potential of organoid technology in these areas, particularly in addressing challenges like hearing loss, cancer research, and organ regeneration. METHODS: Review of current literature regrading organoids in the field of otolaryngology and head and neck surgery. RESULTS: The review highlights several advancements in the field. In otology, the development of organoid replacement therapies offers new avenues for treating hearing loss. In nasal science, the creation of specific organoid models aids in studying nasopharyngeal carcinoma and respiratory viruses. In head and neck surgery, innovative approaches for squamous cell carcinoma prediction and thyroid regeneration using organoids have been developed. CONCLUSION: Organoid research in otolaryngology-head and neck surgery is still at an early stage. This review underscores the potential of this technology in advancing our understanding and treatment of various conditions, predicting a transformative impact on future medical practices in these fields.

Nano-induced endothelial leakiness-reversing nanoparticles for targeting, penetration and restoration of endothelial cell barrier.

Nano-induced endothelial leakiness (NanoEL) can improve the ability of nanoparticles (NPs) to enter the tumor environment, nevertheless, it can inadvertently trigger adverse effects such as tumor metastasis. To overcome these concerns, it becomes important to develop a NPs design strategy that capitalizes on the NanoEL effect while averting unwanted side effects during the drug delivery process. Herein, we introduce the PLGA-ICG-PEI-Ang1@M NP which has a core comprising poly (lactic-co-glycolic acid) (PLGA) and the inner shell with a highly positively charged polyethyleneimine (PEI) and the anti-permeability growth factor Angiopoietin 1 (Ang1), while the outer shell is camouflaged with a Jurkat cell membrane. During the drug delivery process, our NPs exhibit their capability to selectively target and penetrate endothelial cell layers. Once the NPs penetrate the endothelial layer, the proton sponge effect triggered by PEI in the acidic environment surrounding the tumor site can rupture the cell membrane on the NPs' surface. This rupture, in turn, enables the positively charged Ang1 to be released due to the electrostatic repulsion from PEI and the disrupted endothelial layer can be restored. Consequently, the designed NPs can penetrate endothelial layers, promote the cell layer recovery, restrict the tumor metastasis, and facilitate efficient cancer therapy. STATEMENT OF SIGNIFICANCE.

Discussion on the Operation, Maintenance and Care Modes of Laboratory Animals Facilities / 实验动物与比较医学

With the rapid development of the biopharmaceutical industry in China, the laboratory animal sector has entered a stage of rapid growth, and the construction of facility operation systems has become increasingly automated and intelligent. Compared to traditional laboratory animal facilities, new facilities require a more specialized technical team for the maintenance of air supply and exhaust systems, air conditioning, automated control, and the entire barrier system. The Lingang Laboratory’s animal facilities accommodate both large and small animals for feeding and experimental purposes. The facility management team has summarized daily maintenance experiences and explored various operational and maintenance modes based on the characteristics of laboratory operation. After analyzing the advantages and disadvantages of three common modes, this paper provides new ideas for the management of these laboratory animal facilities, and offers guidance for peers in choosing the most appropriate professional maintenance mode.

PM2.5 induces lung inflammation and fibrosis via airway smooth muscle cell expression of the Wnt5a/JNK pathway.

Background: In recent years, particulate matter 2.5 (PM2.5) exposure has been considered a key dangerous factor in chronic obstructive pulmonary disease (COPD). The dysfunction of airway smooth muscle cells (ASMCs) facilitates lung inflammation and fibrosis in COPD. Therefore, we explored whether PM2.5 could promote the inflammatory response and fibrosis in ASMCs in vivo and in vitro via the wingless-related integration site 5a (Wnt5a)/c-Jun N-terminal kinase (JNK)/nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) pathway. Methods: Wnt5a expression in the bronchoalveolar lavage fluid (BALF) of COPD patients exposed to PM2.5 was measured by enzyme-linked immunosorbent assay (ELISA). Mice were intratracheally injected with PM2.5 and a Wnt5a antagonist (BOX5). ASMCs were transfected with Wnt5a small interfering RNA (siRNA), BOX5 and the JNK inhibitor SP600125 before PM2.5 stimulation. Hematoxylin and eosin (H&E) staining was performed to measure the inflammatory response and airway fibrosis. The production of Wnt5a/JNK/NF-KB pathway factors was analyzed by Western blotting. The secretion of interleukin-6 (IL-6), IL-8 and tumor necrosis factor-α (TNF-α) was measured by ELISA. The expression levels of alpha smooth muscle actin (α-SMA), collagen I and collagen III were assessed by quantitative real time polymerase chain reaction (qRT-PCR) and Western blotting. Results: We found that the increase in Wnt5a expression in the BALF of COPD patients was positively correlated with the levels of PM2.5 exposure. The Wnt5a/JNK/NF-κB pathway was activated in the lung samples of PM2.5-induced model mice and PM2.5-exposed ASMCs, which promoted the production of α-SMA, collagen I and collagen III and increased the secretion of IL-6, IL-8 and TNF-α. Furthermore, our results showed that BOX5 could prevent these effects. Wnt5a siRNA blocked the activation of the Wnt5a/JNK/NF-κB pathway and inhibited the effects of PM2.5 on fibrosis and inflammation in ASMCs. SP600125 blocked the phosphorylation of NF-κB and inhibited inflammation and fibrosis in PM2.5-exposed ASMCs. Conclusions: These findings suggest that PM2.5 stimulation of ASMCs induces pulmonary inflammatory factor expression and collagen deposition during COPD via the Wnt5a/JNK pathway, which indicates that modulating the Wnt5a/JNK pathway could be a promising therapeutic strategy for PM2.5-induced COPD.