Pharmacol Res. 2025 Mar 4:107684. doi: 10.1016/j.phrs.2025.107684. Online ahead of print.

NO ABSTRACT

PMID:40049427 | DOI:10.1016/j.phrs.2025.107684

Drug Resist Updat. 2025 Feb 28;80:101222. doi: 10.1016/j.drup.2025.101222. Online ahead of print.

ABSTRACT

BACKGROUND: Patients with breast cancer (BC) who benefit from the PD-1/PD-L1 inhibitor (PDi) is limited, necessitating novel strategies to improve immunotherapy efficacy of BC. Here we aimed to investigate the inhibitory effects of flaxseed lignans (FL) on the biological behaviors of BC and evaluate the roles of FL in enhancing the anticancer effects of PDi.

METHODS: HPLC was used to detect the content of enterolactone (ENL), the bacterial transformation product of FL. Transcript sequencing was performed and identified CD38 as a downstream target gene of ENL. CD38-overexpressing cells were constructed and cell proliferation, colony formation, wound healing and transwell assays were used to assess the function of ENL/CD38 axis on BC cells in vitro. Multiplexed immunohistochemistry (mIHC) and CyTOF were used to detect the changes of the tumor immune microenvironment (TIM). 16S rDNA sequencing was used to explore the changes of gut microbiota in mice. A series of in vivo experiments were conducted to investigate the anticancer effects and mechanisms of FL and PDi.

RESULTS: FL was converted to ENL by gut microbiota and FL administration inhibited the progression of BC. ENL inhibited the malignant behaviors of BC by downregulating CD38, a key gene associated with immunosuppression and PD-1/PD-L1 blockade resistance. The mIHC assay revealed that FL administration enhanced CD3+, CD4+ and CD8+ cells and reduced F4/80+ cells in TIM. CyTOF confirmed the regulatory effects of FL and FL in combination with PDi (FLcPDi) on TIM. In addition, 16S rDNA analysis demonstrated that FLcPDi treatment significantly elevated the abundance of Akkermansia and, importantly, Akkermansia administration enhanced the response to PDi in mice treated with antibiotics.

CONCLUSIONS: The FL/ENL/CD38 axis inhibited BC progression. FL enhanced the anticancer effects of PDi by modulating gut microbiota and host immunity.

PMID:40048957 | DOI:10.1016/j.drup.2025.101222

Ital J Pediatr. 2025 Mar 6;51(1):67. doi: 10.1186/s13052-025-01904-0.

ABSTRACT

BACKGROUND: Currently, there is a lack of data concerning the organization and characteristics of Italian pediatric physiotherapy units for the treatment of patients with chronic lung diseases, especially those with rare conditions such as Primary Ciliary Dyskinesia (PCD) and non-Cystic Fibrosis bronchiectasis (NCFB).

METHODS: A national descriptive study based on a survey questionnaire was conducted. The questionnaire consisted of three different sections: distribution and characteristics of the centres, services provided by respiratory therapists, physiotherapists' perception of the unit. The survey was distributed to all healthcare providers via an online platform, and a descriptive data analysis was performed.

RESULTS: The survey had a response rate of 97.5% with twenty-nine responses collected. The centers are heterogeneously distributed: thirteen in the northern regions, eight in the central regions and eight in the southern regions. Of the 29 centers with a physiotherapy unit, 19 had a specialized respiratory therapy unit. Respiratory therapy was provided in different care settings: regular wards (28/29 centers, 97%), outpatient service (29/29 centers, 100%), and intensive or semi-intensive care units (17/29 centers, 59%). The interventions provided by respiratory therapists involved more than just airway clearance (29/29). More specific interventions, such as pulmonary function tests (23/29), functional tests (27/29), educational training (26/29), management of workout exercise programs (25/29) and interventions developed in collaboration with physicians such as non-invasive ventilation (NIV) (23/29) and oxygen titration (21/29) are performed. It is interesting to note that therapists are also involved in various activities, such as telemedicine, physiotherapists' research projects, and supporting alongside physicians, for the prescription at home of medical devices. Perception of the unit was also evaluated.

CONCLUSIONS: The involved centers are heterogeneous in terms of distribution and treatments offered. The role of respiratory physiotherapists still seems to be fragmented. This first descriptive analysis of the physiotherapy units and the main differences between centers opens queries on the clinical approaches used for pediatric patients with PCD in terms of respiratory physiotherapy. However,in response to evolving treatment needs, a more specialized and standardized approach to patient care is required.

PMID:40050996 | DOI:10.1186/s13052-025-01904-0

J Allergy Clin Immunol Pract. 2025 Mar;13(3):720. doi: 10.1016/j.jaip.2025.01.008.

NO ABSTRACT

PMID:40049792 | DOI:10.1016/j.jaip.2025.01.008

J Allergy Clin Immunol Pract. 2025 Mar;13(3):720-721. doi: 10.1016/j.jaip.2025.01.009.

NO ABSTRACT

PMID:40049791 | DOI:10.1016/j.jaip.2025.01.009

BMC Med Inform Decis Mak. 2025 Mar 6;25(1):113. doi: 10.1186/s12911-025-02924-w.

ABSTRACT

BACKGROUND: Dementia is a neurological syndrome marked by cognitive decline. Alzheimer's disease (AD) and frontotemporal dementia (FTD) are the common forms of dementia, each with distinct progression patterns. Early and accurate diagnosis of dementia cases (AD and FTD) is crucial for effective medical care, as both conditions have similar early-symptoms. EEG, a non-invasive tool for recording brain activity, has shown potential in distinguishing AD from FTD and mild cognitive impairment (MCI).

METHODS: This study aims to develop a deep learning-based classification system for dementia by analyzing EEG derived scout time-series signals from deep brain regions, specifically the hippocampus, amygdala, and thalamus. Scout time series extracted via the standardized low-resolution brain electromagnetic tomography (sLORETA) technique are utilized. The time series is converted to image representations using continuous wavelet transform (CWT) and fed as input to deep learning models. Two high-density EEG datasets are utilized to validate the efficacy of the proposed method: the online BrainLat dataset (128 channels, comprising 16 AD, 13 FTD, and 19 healthy controls (HC)) and the in-house IITD-AIIA dataset (64 channels, including subjects with 10 AD, 9 MCI, and 8 HC). Different classification strategies and classifier combinations have been utilized for the accurate mapping of classes in both data sets.

RESULTS: The best results were achieved using a product of probabilities from classifiers for left and right subcortical regions in conjunction with the DenseNet model architecture. It yield accuracies of 94.17 % and 77.72 % on the BrainLat and IITD-AIIA datasets, respectively.

CONCLUSIONS: The results highlight that the image representation-based deep learning approach has the potential to differentiate various stages of dementia. It pave the way for more accurate and early diagnosis, which is crucial for the effective treatment and management of debilitating conditions.

PMID:40050853 | DOI:10.1186/s12911-025-02924-w

BMC Med Imaging. 2025 Mar 6;25(1):77. doi: 10.1186/s12880-025-01616-1.

ABSTRACT

Automated segmentation of retinal blood vessels in fundus images plays a key role in providing ophthalmologists with critical insights for the non-invasive diagnosis of common eye diseases. Early and precise detection of these conditions is essential for preserving vision, making vessel segmentation crucial for identifying vascular diseases that pose a threat to vision. However, accurately segmenting blood vessels in fundus images is challenging due to factors such as significant variability in vessel scale and appearance, occlusions, complex backgrounds, variations in image quality, and the intricate branching patterns of retinal vessels. To overcome these challenges, the Unified Gated Swin Transformer with Multi-Feature Full Fusion (UGS-M3F) model has been developed as a powerful deep learning framework tailored for retinal vessel segmentation. UGS-M3F leverages its Unified Multi-Context Feature Fusion (UM2F) and Gated Boundary-Aware Swin Transformer (GBS-T) modules to capture contextual information across different levels. The UM2F module enhances the extraction of detailed vessel features, while the GBS-T module emphasizes small vessel detection and ensures extensive coverage of large vessels. Extensive experimental results on publicly available datasets, including FIVES, DRIVE, STARE, and CHAS_DB1, show that UGS-M3F significantly outperforms existing state-of-the-art methods. Specifically, UGS-M3F achieves a Dice Coefficient (DC) improvement of 2.12% on FIVES, 1.94% on DRIVE, 2.52% on STARE, and 2.14% on CHAS_DB1 compared to the best-performing baseline. This improvement in segmentation accuracy has the potential to revolutionize diagnostic techniques, allowing for more precise disease identification and management across a range of ocular conditions.

PMID:40050753 | DOI:10.1186/s12880-025-01616-1

BMC Med Imaging. 2025 Mar 6;25(1):78. doi: 10.1186/s12880-025-01610-7.

ABSTRACT

Deep learning methods have been migrated to rectal cancer staging as a classification process based on magnetic resonance images (MRIs). Typical approaches suffer from the imperceptible variation of images from different stage. The data augmentation also introduces scale invariance and rotation consistency problems after converting MRIs to 2D visible images. Moreover, the correctly labeled images are inadequate since T-staging requires pathological examination for confirmation. It is difficult for classification model to characterize the distinguishable features with limited labeled data. In this article, Laplace of Gaussian (LoG) filter is used to enhance the texture details of converted MRIs and we propose a new method named LoG-staging to predict the T stages of rectal cancer patients. We first use the LoG operator to clarify the fuzzy boundaries of rectal cancer cell proliferation. Then, we propose a new feature clustering method by leveraging the maximization of mutual information (MMI) mechanism which jointly learns the parameters of a neural network and the cluster assignments of features. The assignments are used as labels for the next round of training, which compensate the inadequacy of labeled training data. Finally, we experimentally verify that the LoG-staging is more accurate than the nonlinear dimensionality reduction in predicting the T stages of rectal cancer. We innovatively implement information bottleneck (IB) method in T-staging of rectal cancer based on image classification and impressive results are obtained.

PMID:40050741 | DOI:10.1186/s12880-025-01610-7

NPJ Digit Med. 2025 Mar 6;8(1):144. doi: 10.1038/s41746-025-01483-8.

ABSTRACT

The DESTINY-Breast04 trial has recently demonstrated survival benefits of trastuzumab-deruxtecan (T-DXd) in metastatic breast cancer patients with low Human Epidermal Growth Factor Receptor 2 (HER2) expression. Accurate differentiation of HER2 scores has now become crucial. However, visual immunohistochemistry (IHC) scoring is labour-intensive and prone to high interobserver variability, and artificial intelligence (AI) has emerged as a promising tool in diagnostic medicine. We conducted a diagnostic meta-analysis to evaluate AI's performance in classifying HER2 IHC scores, demonstrating high accuracy in predicting T-DXd eligibility, with a pooled sensitivity of 0.97 [95% CI 0.96-0.98] and specificity of 0.82 [95% CI 0.73-0.88]. Meta-regression revealed better performance with deep learning and patch-based analysis, while performance declined in externally validated and those utilising commercially available algorithms. Our findings indicate that AI holds promising potential in accurately identifying HER2-low patients and excels in distinguishing 2+ and 3+ scores.

PMID:40050686 | DOI:10.1038/s41746-025-01483-8

Sci Rep. 2025 Mar 6;15(1):7817. doi: 10.1038/s41598-025-92582-9.

ABSTRACT

This study presents a novel hybrid deep learning model for arrhythmia classification from electrocardiogram signals, utilizing the stockwell transform for feature extraction. As ECG signals are time-series data, they are transformed into the frequency domain to extract relevant features. Subsequently, a CNN is employed to capture local patterns, while a transformer architecture learns long-term dependencies. Unlike traditional CNN-based models that require R-peak detection, the proposed model operates without it and demonstrates superior accuracy and efficiency. The findings contribute to enhancing the accuracy of ECG-based arrhythmia diagnosis and are applicable to real-time monitoring systems. Specifically, the model achieves an accuracy of 97.8% on the Icentia11k dataset using four arrhythmia classes and 99.58% on the MIT-BIH dataset using five arrhythmia classes.

PMID:40050678 | DOI:10.1038/s41598-025-92582-9

Sci Rep. 2025 Mar 6;15(1):7876. doi: 10.1038/s41598-025-92594-5.

ABSTRACT

Whole-body bone scan (WBS) is usually used as the effective diagnostic method for early-stage and comprehensive bone metastases of breast cancer. WBS images with breast cancer bone metastasis have the characteristics of low resolution, small foreground, and multiple lesions, hindering the widespread application of deep learning-based models. Automatically detecting a large number of densely small lesions on low-resolution WBS images remains a challenge. We aim to develop a unified framework for detecting multiple densely bone metastases based on low-resolution WBS images. We propose a novel unified detection framework to detect multiple bone metastases based on WBS images. Considering the difficulties of feature extraction caused by low resolution and multiple lesions, we innovatively propose the plug-and-play position auxiliary extraction module and feature fusion module to enhance the ability of global information extraction. In order to accurately detect small metastases in WBS, we designed the self-attention transformer-based target detection head. This retrospective study included 512 patients with breast cancer bone metastases from Peking Union Medical College Hospital. The data type is whole-body bone scan image. For our study, the ratio of training set, validation set and test set is about 6:2:2. The benchmarks are four representative baselines, SSD, YOLOR, Faster_RCNN_R and Scaled-YOLOv4. The performance metrics are Average Precision (AP), Precision and Recall. The detection results obtained through the proposed method were assessed using the Bonferroni-adjusted Wilcoxon rank test. The significant level is adjusted according to different multiple comparisons. We conducted extensive experiments and ablation studies on a private dataset of breast cancer WBS and a public dataset of bone scans from West China Hospital to validate the effectiveness and generalization. Experiments were conducted to evaluate the effectiveness of our method. First, compared to different network architectures, our method obtained AP of 55.0 ± 6.4% (95% confidence intervals (CI) 49.9-60.1%, [Formula: see text]), which improved AP by 45.2% for the SSD baseline with AP 9.8 ± 2% (95% CI 8.1-11.4%). For the metric of recall, our method achieved the average of 54.3 ± 4.2% (95% CI 50.9-57.6%, [Formula: see text]), which has improved the recall values by 49.01% for the SSD model with 5.2 ± 12.7% (95% CI 10-21.3%). Second, we conducted ablation studies. On the private dataset, adding the detection head module and position auxiliary extraction module will increase the AP values by 14.03% (from 33.3 ± 2% to 47.6 ± 4.4%) and 19.3% (from 33.3 ± 2% to 52.6 ± 6.1%), respectively. In addition, the generalization of the method was also verified on the public dataset BS-80K from West China Hospital. Extensive experimental results have demonstrated the superiority and effectiveness of our method. To the best of our knowledge, our work is the first attempt for developing automatic detector considering the unique characteristics of low resolution, small foreground and multiple lesions of breast cancer WBS images. Our framework is tailored for whole-body WBS and can be used as a clinical decision support tool for early decision-making for breast cancer bone metastases.

PMID:40050676 | DOI:10.1038/s41598-025-92594-5

Nat Commun. 2025 Mar 6;16(1):2269. doi: 10.1038/s41467-025-57485-3.

ABSTRACT

Pharmacophores are abstractions of essential chemical interaction patterns, holding an irreplaceable position in drug discovery. Despite the availability of many pharmacophore tools, the adoption of deep learning for pharmacophore-guided drug discovery remains relatively rare. We herein propose a knowledge-guided diffusion framework for 'on-the-fly' 3D ligand-pharmacophore mapping, named DiffPhore. It leverages ligand-pharmacophore matching knowledge to guide ligand conformation generation, meanwhile utilizing calibrated sampling to mitigate the exposure bias of the iterative conformation search process. By training on two self-established datasets of 3D ligand-pharmacophore pairs, DiffPhore achieves state-of-the-art performance in predicting ligand binding conformations, surpassing traditional pharmacophore tools and several advanced docking methods. It also manifests superior virtual screening power for lead discovery and target fishing. Using DiffPhore, we successfully identify structurally distinct inhibitors for human glutaminyl cyclases, and their binding modes are further validated through co-crystallographic analysis. We believe this work will advance the AI-enabled pharmacophore-guided drug discovery techniques.

PMID:40050649 | DOI:10.1038/s41467-025-57485-3

Sci Rep. 2025 Mar 6;15(1):7900. doi: 10.1038/s41598-025-90415-3.

ABSTRACT

Cervical cancer (CC) is the leading cancer, which mainly affects women worldwide. It generally occurs from abnormal cell evolution in the cervix and a vital functional structure in the uterus. The importance of timely recognition cannot be overstated, which has led to various screening methods such as colposcopy, Human Papillomavirus (HPV) testing, and Pap smears to identify potential threats and enable early intervention. Early detection during the precancerous phase is crucial, as it provides an opportunity for effective treatment. The diagnosis and screening of CC depend on colposcopy and cytology models. Deep learning (DL) is an appropriate technique in computer vision, which has developed as a latent solution to increase the efficiency and accuracy of CC screening when equated to conventional clinical inspection models that are vulnerable to human error. This study presents a Leveraging Swin Transformer with an Ensemble of Deep Learning Model for Cervical Cancer Screening (LSTEDL-CCS) technique for colposcopy images. The presented LSTEDL-CCS technique aims to detect and classify CC on colposcopy images. Initially, the wiener filtering (WF) model is used for image pre-processing. Next, the swin transformer (ST) network is utilized for feature extraction. For the cancer detection process, the ensemble learning method is performed by employing three models, namely autoencoder (AE), bidirectional gated recurrent unit (BiGRU), and deep belief network (DBN). Finally, the hyperparameter tuning of the DL techniques is performed using the Pelican Optimization Algorithm (POA). A comprehensive experimental analysis is conducted, and the results are evaluated under diverse metrics. The performance validation of the LSTEDL-CCS methodology portrayed a superior accuracy value of 99.44% over existing models.

PMID:40050635 | DOI:10.1038/s41598-025-90415-3

Sci Data. 2025 Mar 6;12(1):390. doi: 10.1038/s41597-025-04701-w.

ABSTRACT

Delineating the extent of urban villages (UVs) is crucial for effective urban planning and management, as well as for providing targeted policy and financial support. Unlike field surveys, the interpretation of satellite imagery provides an efficient, near real-time, and objective means of mapping UV. However, current research efforts predominantly concentrate on individual cities, resulting in a scarcity of interpretable UV maps for numerous other cities. This gap in availability not only hinders public awareness of the distribution and evolution of UV but also limits the reliability and transferability of models due to the insufficient number and diversity of samples. To address this issue, we developed CUGUV, a benchmark dataset that includes a diverse collection of thousands of UV samples, carefully curated from 15 major cities across various geographical regions in China. The dataset can be accessed through this link: https://doi.org/10.6084/m9.figshare.26198093 . This dataset can serve as a foundation for evaluating and improving the robustness and transferability of models. Subsequently, we present an innovative framework that effectively integrates and learns from multiple data sources to better address the cross-city UV mapping task. Tests show that the proposed models achieve over 92% in overall accuracy, precision, and F1-scores, outperforming state-of-the-art models. This highlights the effectiveness of both the proposed dataset and model. This presented dataset and model bolsters our capability to better understand and accurately model these complex and diverse phenomena, ultimately leading to a notable improvement in the performance of large-scale UV mapping.

PMID:40050634 | DOI:10.1038/s41597-025-04701-w

Nat Commun. 2025 Mar 6;16(1):2251. doi: 10.1038/s41467-025-57516-z.

ABSTRACT

Frequency transfer is a key challenge in machine learning as it allows researchers to go beyond in-range analyses of spectrum properties towards out-of-the-range predictions. Traditionally, to predict properties at a specific frequency, targeted spectrum is included in training data for a deep neural network (DNN). However, due to limitations of measurement or computation source, training data at some frequencies are hardly accessible, especially for multi-physics problems. In this work, we propose a multi-physics deep learning framework (MDLF) consisting of a multi-fidelity DeepONet, a Euler latent dynamic network, and a data-analytical inversion network. Without the knowledge about multi-physics response, MDLF is successfully generalized to unseen frequency bands for both parametric and free-form metasurface by dynamically utilizing a Euler latent space and single-physics information. Moreover, an inversion method is introduced to incorporate hybrid a priori in inverse design of metasurface. Under EM-thermal coupling, we verify the proposed MDLF numerically and experimentally.

PMID:40050630 | DOI:10.1038/s41467-025-57516-z

Eur J Pharmacol. 2025 Mar 4:177461. doi: 10.1016/j.ejphar.2025.177461. Online ahead of print.

ABSTRACT

INTRODUCTION: & Aim: Novel treatments for idiopathic pulmonary fibrosis (IPF) are needed urgently. A better understanding of the molecular pathways activated by TGFβ1 in human lung tissue may facilitate the development of more effective anti-fibrotic medications. This study utilized proteomic analysis to test the hypothesis that TGFβ1 induces pro-fibrotic effects on human lung parenchyma proteome, and to evaluate the viability of this model for testing novel therapeutic targets.

METHODS: Non-fibrotic human lung parenchymal tissue from 11 patients was cultured for 7 days in serum-free (SF) media supplemented with TGFβ1 (10 ng/mL) or vehicle control, and the putative antifibrotic KCa3.1 ion channel blocker senicapoc or vehicle control. The tissue was homogenized, digested for bottom-up proteomics, and analysed using liquid chromatography-tandem mass spectrometry (LC-MS/MS). Principal component analysis, differential expression analysis, pathway analysis, and drug repurposing analysis were performed.

RESULTS: TGFβ1 stimulation for 7 days induced a strong fibrotic protein response relevant to IPF pathology. A total of 2,391 proteins were quantified, 306 upregulated and 285 downregulated (FDR-adjusted p-value<0.05). Of these, 118 were upregulated and 28 downregulated at log2(FC)>0.58. These changes were attenuated by senicapoc (100 nM). Drug repurposing analysis identified 265 drugs predicted to inhibit the effects of TGFβ1 in this model. These included clotrimazole, a KCa3.1 blocker, and nintedanib, a drug licenced for the treatment of IPF, providing validation of this approach.

CONCLUSION: A pro-fibrotic proteome is induced in human lung parenchyma exposed to TGFβ1, sensitive to pharmacological intervention. This approach has the potential to enhance therapeutic drug screening for IPF treatment.

PMID:40049575 | DOI:10.1016/j.ejphar.2025.177461

Respir Med. 2025 Mar 4:108028. doi: 10.1016/j.rmed.2025.108028. Online ahead of print.

ABSTRACT

OBJECT: Idiopathic pulmonary fibrosis (IPF) is a chronic lung disease with characterized by progressive fibrosis. Galectin-3(Gal-3) is a B-galactoside binding lectin plays a central role in inflammation and fibrosis. In our study, we aimed to define levels of serum galectin-3 protein in IPF patients by comparing them with healthy subjects. We also aimed to show that galectin-3 concentrations can be used as a diagnostic and prognostic biomarker in the serum of IPF patients and that the use of galectin-3 inhibitors in combination with antifibrotic treatments may be useful in the therapeutic management of fibrosis.

METHODS: 44 patients with IPF and 35 control patients who were followed up in our outpatient clinic between 2016 and 2022 were evaluated, anamnesis, spirometric measurements and galectin-3 results were recorded. Patients were grouped according to their antifibrotic treatment.

RESULTS: The mean galectin-3 level in the patient group was 8.4 ng/ml and in the control group was 8.2 ng/ml. Serum levels were 8.9 ng/ml in pirfenidone users and 8.2 ng/ml in nintedanib users. Gal-3 was found to be higher in patients taking pirfenidone compared to nintedanib, but there was no statistically significant difference (p>0.05).

CONCLUSION: Galectin-3 levels were found to be slightly higher in IPF patients compared to healthy subjects. In addition, gal-3 levels decreased as the follow-up period increased in IPF patients in our study. Considering that the patients were receiving pirfenidone or nintedanib treatment during the follow-up period, it may be possible that galectin-3 levels decreased as exposure to these drugs increased. Further studies are needed to clarify these mechanisms.

PMID:40049461 | DOI:10.1016/j.rmed.2025.108028

Exp Cell Res. 2025 Mar 4:114494. doi: 10.1016/j.yexcr.2025.114494. Online ahead of print.

ABSTRACT

BTBD8 contributes to the pathogenesis of inflammatory bowel disease through regulating intestinal barrier integrity and inflammation. However, its role in idiopathic pulmonary fibrosis (IPF) remains unknown. Here we investigated whether BTBD8 plays a role in bleomycin-induced pulmonary fibrosis. Pulmonary fibrosis was induced in wild-type (WT) and Btbd8 knockout (KO) mice by intratracheal instillation of bleomycin. The mice were sacrificed on day 7 or 12. Subsequently, the progression of bleomycin-induced pulmonary fibrosis was assessed. We found that Btbd8 KO mice are more susceptible to bleomycin-induced pulmonary fibrosis, with more severe body weight loss and pulmonary injury, increased collagen deposition and myofibroblast accumulation. We further demonstrated that BTBD8 functions in pulmonary fibroblasts to suppress the conversion of fibroblasts to myofibroblasts. Moreover, Btbd8 deficiency promotes the infiltration of inflammatory cells and the secretion of pro-inflammatory cytokines in IPF mouse model. These results highlight the critical role of BTBD8 in the pathogenesis of bleomycin-induced pulmonary fibrosis in mice, and suggest that BTBD8 may alleviate bleomycin-induced fibrosis by suppressing the differentiation of fibroblasts to myofibroblast, as well as inflammatory responses.

PMID:40049313 | DOI:10.1016/j.yexcr.2025.114494

Environ Int. 2025 Feb 28;197:109354. doi: 10.1016/j.envint.2025.109354. Online ahead of print.

ABSTRACT

Exposure to fine particulate matter (PM2.5) is associated with increased morbidity and mortality among patients with idiopathic pulmonary fibrosis (IPF). Pathological alterations in IPF typically originate in the subpleural regions of the lungs. However, it was unclear how PM2.5 affected subpleural pulmonary fibrosis. In this study, atmospheric PM2.5 and carbon blacks were utilized as representative particulate matter to investigate these effects. Mouse models and cell models were made to investigate macrophage chemotaxis changes under PM2.5 exposure in vivo and in vitro. The findings indicated that PM2.5 promoted macrophage aggregation in the subpleural region of lung and aggravated bleomycin-induced pulmonary fibrosis in mice. At the same time, we uncovered for the first time that PM2.5 exposure led to an upregulation of midkine, which subsequently enhanced the production of monocyte chemotactic protein-1 (MCP-1) through the cell surface receptor Syndecan 4 (SDC4) in pleural mesothelial cells (PMCs), thereby, inducing macrophage aggregation in subpleural region of lung. Furthermore, our results indicated that PM2.5 and bleomycin facilitated macrophage M1 polarization and the production of profibrotic inflammatory factors, culminating in fibrotic alterations in PMCs, lung fibroblasts, and alveolar epithelial cells. Finally, we demonstrated that inhibition of midkine ameliorated lung function and mitigated pulmonary fibrosis in vivo. In conclusion, our findings elucidated that midkine acted as a novel MCP-1 activator, mediating PM2.5-aggravated experimental pulmonary fibrosis, and suggested that the midkine/SDC4/MCP-1 signal should be a new therapeutic target for the treatment of PM2.5-related IPF.

PMID:40049042 | DOI:10.1016/j.envint.2025.109354

Eur J Med Chem. 2025 Mar 1;289:117463. doi: 10.1016/j.ejmech.2025.117463. Online ahead of print.

ABSTRACT

Idiopathic pulmonary fibrosis (IPF) is a deadly lung disease characterized by fibroblast proliferation, excessive extracellular matrix buildup, inflammation, and tissue damage, resulting in respiratory failure and death. Recent studies suggest that impaired interactions among epithelial, mesenchymal, immune, and endothelial cells play a key role in IPF development. Advances in bioinformatics have also linked epigenetics, which bridges gene expression and environmental factors, to IPF. Despite the incomplete understanding of the pathogenic mechanisms underlying IPF, recent preclinical studies have identified several novel epigenetic therapeutic targets, including DNMT, EZH2, G9a/GLP, PRMT1/7, KDM6B, HDAC, CBP/p300, BRD4, METTL3, FTO, and ALKBH5, along with potential small-molecule inhibitors relevant for its treatment. This review explores the pathogenesis of IPF, emphasizing epigenetic therapeutic targets and potential small molecule drugs. It also analyzes the structure-activity relationships of these epigenetic drugs and summarizes their biological activities. The objective is to advance the development of innovative epigenetic therapies for IPF.

PMID:40048798 | DOI:10.1016/j.ejmech.2025.117463