J Autoimmun. 2025 Aug 8;156:103469. doi: 10.1016/j.jaut.2025.103469. Online ahead of print.

ABSTRACT

Autoimmune diseases, such as type 1 diabetes (T1D) and Hashimoto's thyroiditis (HT), are often studied from an immune perspective with less focus on the target tissue responses. Target tissues, however, are key to disease and engage in a harmful crosstalk with the immune system contributing to their own destruction. We presently integrated transcriptomic data from the target tissues of six autoimmune/inflammatory diseases affecting β-cells (T1D and type 2 diabetes), thyroid (HT), brain (multiple sclerosis and Alzheimer's disease) or the joints (rheumatoid arthritis), using both bulk and single-cell/nucleus RNA-sequencing (sc/snRNA-seq) approaches. Common upregulated pathways were associated with innate/adaptive immunity, antigen presentation and interferon (IFN) signaling. The role of IFNs was confirmed by RNA-seq in human insulin-producing EndoC-βH1 cells and stem cell-derived thyroid follicle cells exposed to IFNα or IFNγ. Commonly upregulated inflammatory gene signatures were explored, and fibroblast growth factor receptor (FGFR) inhibitors emerged as a potential strategy to counteract these inflammatory transcriptional signatures. The effects of the FGFR1 inhibitor PD173074 on IFN-induced immune related genes were evaluated in EndoC-βH1 cells, stem cell-derived islets and adult human islets. We validated the FGFR inhibitor PD173074 as a promising drug for preserving expression of β-cell protective genes (PDL1 and HLA-E) while reducing HLA class I expression and β-cell recognition by diabetogenic pre-proinsulin-specific CD8+ T-cells. In conclusion, we integrated transcriptomic data from the target tissues of autoimmune and inflammatory/degenerative diseases and departing from these data identified the potential beneficial effects of FGFR inhibitors in T1D.

PMID:40782630 | DOI:10.1016/j.jaut.2025.103469

Lancet. 2025 Aug 9;406(10503):650-664. doi: 10.1016/S0140-6736(25)01072-4.

ABSTRACT

Advancements over the past decade in understanding vesicular monoamine transporter 2 (VMAT2) inhibitors highlight their key role in the treatment of movement and neuropsychiatric disorders. VMAT2 is crucial for packaging neurotransmitters such as serotonin, dopamine, and norepinephrine into synaptic vesicles, facilitating their release and reuptake in synaptic transmission. VMAT2 inhibitors, such as tetrabenazine, deutetrabenazine, and valbenazine, show therapeutic efficacy in managing hyperkinetic movement disorders, including Huntington's disease, tardive dyskinesia, and Tourette's syndrome. These inhibitors modulate excessive synaptic activity by reducing neurotransmitter storage and release. Genetic variations, particularly in the cytochrome P450 enzyme family, influence VMAT2 inhibitor metabolism, necessitating personalised dosing to optimise efficacy and minimise adverse events. Recent studies have provided further structural insights into VMAT2 inhibition mechanisms, paving the way for the development of inhibitors with enhanced potency and selectivity. Leveraging pharmacogenetics for precision medicine and exploring VMAT2 inhibition in broader therapeutic contexts could revolutionise treatment frameworks for neurological and psychiatric conditions.

PMID:40783291 | DOI:10.1016/S0140-6736(25)01072-4

Eur J Pharmacol. 2025 Aug 7:178049. doi: 10.1016/j.ejphar.2025.178049. Online ahead of print.

ABSTRACT

BACKGROUND: Genetic factors affect DOAC pharmacokinetics and efficacy in atrial fibrillation (AF) patients, yet no pharmacogenomic guidelines exist. This study aims to assess their impact on supporting personalized therapy.

METHODS: Following PRISMA-2020 (PROSPERO: CRD42024592412), a systematic review analysed 31 studies with 8,558 AF patients across Asia and Europe. Structural Equation Modelling (SEM) in R Studio (v2024.12) assessed 331 genotypes affecting pharmacokinetics and clinical events. Monte Carlo simulations, pathway enrichment, PCA clustering, and logistic regression further refined the analysis.

RESULTS: Dabigatran metabolism is influenced by ABCB1 and CES1 polymorphisms, with ABCB1 (rs1045642, AAAG), CES1 (rs761128900, T), and CES1 (rs71647871, AA/AG) linked to low bleeding risk (<30%), ensuring stable efficacy. Apixaban's safety is associated with ABCB1 (rs2032582, AA), ABCB1 (rs1045642, CT/CT), and PXR haplotype1B, minimizing bleeding/stroke risk (<20%). Rivaroxaban effectiveness is enhanced by ABCG2 (rs546230660, AG), ABCB1 (c.61236, CT), CYP3A4 (rs4646440, WT), and CYP3A4 (rs55808883, CT), reducing stroke/bleeding risk (<20%). Edoxaban clearance depends on SLCO1B1 (rs11045879, TCCC) and SLCO1B1 (c.388A>G, rs12317268, AGGG), maintaining stable plasma levels (stroke/bleeding risk <40%). SEM results (CFI = 0.997, TLI = 0.990, RMSEA = 0.037, SRMR = 0.031) confirm model stability, logistic regression (AUC= 0.996) reinforcing findings. PCA clustering identified low risk (stable drug levels, minimal bleeding risk) and moderate risk (suboptimal pharmacokinetics, higher stroke/bleeding risks) 69.7% of the total variance.

CONCLUSION: genetics significantly influences DOAC pharmacokinetics, particularly for dabigatran and apixaban, where stroke and bleeding risks correlate with Cmin and Cmax. Pharmacogenomic profiling and therapeutic drug monitoring are crucial for optimizing therapy, especially in high-risk patients.

PMID:40783160 | DOI:10.1016/j.ejphar.2025.178049

Drug Metab Pharmacokinet. 2025 May 29;64:101496. doi: 10.1016/j.dmpk.2025.101496. Online ahead of print.

ABSTRACT

Although the United States Food and Drug Administration has disclosed a list of drugs with pharmacogenomic biomarkers for drug labeling, there is limited information regarding pharmacogenomic-associated drugs in Japan. Such associations include genetic variants of uridine diphosphate glucuronosyltransferase 1A1 for irinotecan, nudix hydrolase 15 for thiopurine drugs, and cytochrome P450 (P450) 2C9 for siponimod. The effects of such genetic variants on drug concentrations are similar to those from drug interactions. Because of race and dosage differences, the relevance of pharmacogenomic associations in Asian populations requires confirmation. This white paper proposes that in vitro pharmacogenomic information can be used to predict human pharmacokinetics and to describe in drug labels the changes in blood concentrations by genetic variants. For P450 variants CYP2C9∗3, CYP2C19∗2, CYP2C19∗3, CYP2D6∗10, and CYP3A4∗16, we propose using the enzymatic activity parameters obtained from in vitro functional analysis of the drug-metabolizing enzymes for multiple substrate drugs to predict the effects of these variants on human pharmacokinetics. Consequently, in patients prescribed only a single drug, anything more than a "moderate effect" on plasma exposure should be mentioned as a caution in the drug labels; such effects are likely caused by enzyme polymorphisms resulting in similar effects to drug-drug interactions.

PMID:40782573 | DOI:10.1016/j.dmpk.2025.101496

J Cyst Fibros. 2025 Aug 8:S1569-1993(25)01526-7. doi: 10.1016/j.jcf.2025.07.003. Online ahead of print.

ABSTRACT

BACKGROUND: Cystic Fibrosis (CF) Centers worldwide have reported healthcare-associated outbreaks of nontuberculous mycobacteria (NTM). We report a retrospective investigation of shared Mycobacterium abscessus strains among people with cystic fibrosis (pwCF) receiving care at Dell Children's/Ascension combined Pediatric and Adult CF Program (DCMC).

METHODS: Whole genome sequencing (WGS) was used to identify genetically similar isolates among 167 NTM isolates from 57 pwCF. Epidemiological investigation, respiratory and environmental isolate comparisons, and watershed mapping were performed.

RESULTS: WGS analysis revealed four M. abscessus clusters, two ssp. abscessus and two ssp. massiliense. One subject was infected with two distinct clustered M. abscessus (ssp. abscessus and ssp. massiliense). Epidemiologic investigation demonstrated opportunities for healthcare-associated transmission within all clusters. Two ssp. massiliense subject pairs had healthcare overlaps and high genomic relatedness, including one cohabitating sibling pair. M. abscessus recovered from DCMC revealed genetic similarity to a respiratory isolate from one patient who was never exposed to the hospital environment.

CONCLUSIONS: We identified shared M. abscessus strains via genomic analysis among pwCF at DCMC. None of the clustered patient isolates matched hospital environmental isolates at the genomic level. One hospital environmental isolate had genomic similarity to a respiratory isolate of M. abscessus, but the epidemiologic investigation revealed no evidence of subject exposure to the hospital setting. One ssp. massiliense subject pair had the same level of pangenome relatedness as the sibling pair and epidemiological investigation revealed overlap in the clinic, supporting healthcare-associated person-to-person transmission among the pair within a cluster. One pwCF had polyclonal clustered infections, suggesting multiple environmental sources of acquisition outside the healthcare environment.

PMID:40783340 | DOI:10.1016/j.jcf.2025.07.003

J Robot Surg. 2025 Aug 10;19(1):472. doi: 10.1007/s11701-025-02644-3.

ABSTRACT

Vascular contrast enhancement is crucial for early disease diagnosis and surgical precision in robotic surgery imaging. Traditional white-light imaging often fails to distinguish blood vessels due to the spectral similarity between vessels and surrounding tissues. Although techniques like narrow-band imaging improve contrast, they require specialized hardware and exhibit inconsistent performance across different surgical environments. To address these limitations, we propose StarVasc, a novel lightweight framework for unsupervised vascular contrast enhancement tailored for robotic surgical vision systems. StarVasc leverages an unpaired learning strategy based on a compact generative adversarial network. The generator incorporates a star operation module, enabling hyper-dimensional feature expansion. This operation implicitly maps input images into an exponentially high-dimensional nonlinear feature space, facilitating efficient representation of fine-grained vascular structures without increasing model complexity. In addition, we design a Spectral Feature Enhancement Module (SFEM) to further refine vascular detail. Acting as a narrow-band feature extractor, SFEM implicitly learns spectral cues without requiring hyperspectral input. It operates in a self-supervised reconstruction paradigm, ensuring that the extracted features are semantically aligned with vascular structures. Integrated within an encoder-decoder architecture, SFEM enhances vessel clarity and edge continuity in the output images. Extensive experiments demonstrate that StarVasc consistently outperforms both traditional enhancement techniques and recent deep learning methods across no-reference quality metrics and visual evaluations. Without relying on specialized hardware, StarVasc provides an adaptive, clinically viable solution for real-time vascular enhancement in robotic surgical imaging, contributing to improved visual perception and surgical safety in automated or robot-assisted interventions.

PMID:40783657 | DOI:10.1007/s11701-025-02644-3

Sci Rep. 2025 Aug 9;15(1):29173. doi: 10.1038/s41598-025-14985-y.

ABSTRACT

Accurate athlete pose estimation in basketball is crucial for game analysis, player training, and tactical decision-making. However, existing pose estimation methods struggle to effectively address common challenges in basketball, such as motion blur, occlusions, and complex backgrounds. To tackle these issues, this paper proposes a basketball action pose estimation framework, which first leverages a multi-dimensional data stream network to extract spatial, temporal, and contextual information separately. Specifically, the spatial stream branch aims to extract multi-scale features and captures the spatial pose information of players in single-frame images through feature fusion and spatial attention mechanisms. The temporal stream branch merges feature maps with adjacent frames, effectively capturing player motion information across consecutive frames. The context stream branch generates a global context feature vector that encodes the entire image, offering a holistic perspective for pose estimation. Subsequently, we designed a feature fusion module that integrates early fusion, late fusion, and hybrid fusion strategies to fully utilize multi-modal information. Finally, we introduced a stage-wise streaming training module that progressively enhances the model's accuracy and generalization ability through three stages. Experimental results demonstrate that the proposed framework significantly improves the accuracy and robustness of basketball action pose estimation, particularly excelling in scenarios with high dynamics and complex backgrounds.

PMID:40783613 | DOI:10.1038/s41598-025-14985-y

Sci Rep. 2025 Aug 9;15(1):29224. doi: 10.1038/s41598-025-14471-5.

ABSTRACT

Brain stroke is an illness which affects almost every age group, particularly people over 65. There are two significant kinds of strokes: ischemic and hemorrhagic strokes. Blockage of brain vessels causes an ischemic stroke, while cracks in blood vessels in or around the brain cause a hemorrhagic stroke. In the prompt analysis of brain stroke, patients can live an easier life. Recognizing strokes using medical imaging is crucial for early diagnosis and treatment planning. Conversely, access to innovative imaging methods is restricted, particularly in emerging states, so it is challenging to analyze brain stroke cases of disabled people appropriately. Hence, the development of more accurate, faster, and more reliable diagnostic models for the timely recognition and efficient treatment of ischemic stroke is greatly needed. Artificial intelligence technologies, primarily deep learning (DL), have been widely employed in medical imaging, utilizing automated detection methods. This paper presents an Enhanced Brain Stroke Detection and Classification using Artificial Intelligence with Feature Fusion Technologies (EBSDC-AIFFT) model. This paper aims to develop an enhanced brain stroke detection system for individuals with disabilities, utilizing biomedical images to improve diagnostic accuracy. Initially, the image pre-processing stage involves various steps, including resizing, normalization, data augmentation, and data splitting, to enhance image quality. In addition, the EBSDC-AIFFT model combines the Inception-ResNet-v2 model, the convolutional block attention module-ResNet18 method, and the multi-axis vision transformer technique for feature extraction. Finally, the variational autoencoder (VAE) model is implemented for the classification process. The performance validation of the EBSDC-AIFFT technique is performed under the brain stroke CT image dataset. The comparison study of the EBSDC-AIFFT technique demonstrated a superior accuracy value of 99.09% over existing models.

PMID:40783612 | DOI:10.1038/s41598-025-14471-5

Sci Rep. 2025 Aug 9;15(1):29147. doi: 10.1038/s41598-025-13080-6.

ABSTRACT

Cancer of bone marrow is classified as Acute Lymphoblastic Leukemia (ALL), an abnormal growth of lymphoid progenitor cells. It affects both children and adults and is the most predominant form of infantile cancer. Currently, there has been significant growth in the identification and therapy of acute lymphoblastic leukemia. Therefore, a method is required that is capable to accurately assessing risk by an appropriate treatment strategy that takes into account all relevant clinical, morphological, cytogenetic, and molecular aspects. However, to enhance survival and quality of life for those afflicted by this aggressive haematological malignancy, more research and clinical trials are required to address the issues associated with resistance, relapse, and long-term toxicity. Consequently, a deep optimized Convolutional Neural Network (CNN) has been proposed for the early diagnosis and detection of ALL. The design of the deep optimized CNN model consisted of five convolutional blocks with thirteen convolutional layers and five max pool layers. The proposed deep optimized CNN model is tuned using the hyperparameters such as 30 epochs, batch size 32 and optimizers, namely Adam and Adamax. Out of the two optimizers, the proposed deep optimized CNN model has outperformed using Adam optimizer with the points of accuracy and precision as 0.96 and 0.95, respectively.

PMID:40783578 | DOI:10.1038/s41598-025-13080-6

Nat Commun. 2025 Aug 9;16(1):7365. doi: 10.1038/s41467-025-62459-6.

ABSTRACT

A substantial portion of lung cancer-associated genetic elements in East Asian populations remains unidentified, underscoring the need for large-scale genome-wide studies, particularly on non-coding regulation. We conducted a whole genome sequencing (WGS)-based genome-wide scan in 13,722 Chinese individuals to identify regulatory elements associated with lung cancer. We verified common-variant-based loci by meta-analysis across the available East Asian studies. Integrating a genome-transcriptome reference panel of lung tissue in 297 Chinese, we bridged the variant-lung cancer associations, highlighting genes including TP63 and DCBLD1. Implementing the STAAR pipeline for rare variant aggregate analysis, we identified and replicated novel genes, including PARPBP, PLA2G4C, and RITA1 in the context of non-coding regulation. Adapting a deep learning-based approach, potential upstream regulators such as TP53, MYC, ZEB1, and NFKB1 were revealed for the lung cancer-associated genes. These findings offered crucial insights into the non-coding regulation for the etiology of lung cancer, providing additional potential targets for intervention.

PMID:40783572 | DOI:10.1038/s41467-025-62459-6

BMC Psychol. 2025 Aug 9;13(1):888. doi: 10.1186/s40359-025-03197-8.

ABSTRACT

The rapid evolution of internet technologies has emphasized the importance of integrating psychological education with moral dilemmas across diverse sectors. This paper investigates the interrelationship between psychological education and moral reasoning, proposing that this integration represents a pivotal approach for fostering effective educational strategies. Leveraging deep learning models, we aim to enhance both the scientific rigor and theoretical understanding of how these two domains intersect. Initially, the paper addresses inherent value challenges within psychological and moral dilemma analyses, setting the stage for deeper exploration. Subsequently, fundamental algorithms underpinning deep learning neural networks are introduced, illustrating their potential applications in studying the integration of psychological and moral values. Various features of this integration are discussed, highlighting their contributions to elucidating and interpreting complex value issues. Moreover, optimization functions pertinent to deep learning are examined, alongside their practical implications for enhancing educational practices. An empirical study is conducted to evaluate the impact of psychological feature analysis on addressing value issues through the lens of psychological education. Utilizing advanced deep learning techniques, our experimental investigation reveals significant improvements in understanding and resolving these value issues. Key findings underscore the positive influence of incorporating psychological feature analysis into educational frameworks, particularly in contexts involving moral dilemmas. These insights pave the way for more effective, integrative approaches to education, promoting holistic development and ethical reasoning among learners.

PMID:40783551 | DOI:10.1186/s40359-025-03197-8

BMC Sports Sci Med Rehabil. 2025 Aug 9;17(1):234. doi: 10.1186/s13102-025-01284-2.

ABSTRACT

This study presents a deep learning-based approach for the automated detection of Quiet Eye (QE) durations from electrooculography (EOG) signals in archery. QE-the final fixation or tracking of the gaze before executing a motor action-is a critical factor in precision sports. Traditional detection methods, which rely on expert evaluations, are inherently subjective, time-consuming, and inconsistent. To overcome these limitations, EOG data were collected from 10 licensed archers during controlled shooting sessions and preprocessed using a wavelet transform and a Butterworth bandpass filter for noise reduction. We implemented and compared a traditional model (SVM) and five deep learning models-CNN + LSTM, CNN + GRU, Transformer, UNet, and 1D CNN-for QE detection. The CNN + LSTM model achieved the highest accuracy (95%), followed closely by CNN + GRU (93%), demonstrating superior performance in capturing both spatial and temporal dependencies in the EOG signals. Although Transformer-based and UNet models performed competitively, they exhibited lower precision in distinguishing QE periods. The performance of the traditional model was inferior to deep learning approaches. These results indicate that deep learning provides an effective and scalable solution for objective QE analysis, substantially reducing the dependence on expert annotations. This automated approach can enhance sports training by offering real-time, data-driven feedback to athletes and coaches. Furthermore, the methodology holds promise for broader applications in cognitive and motor skill assessments across various domains. Future work will focus on expanding the dataset, enabling real-time deployment, and evaluating model generalizability across different skill levels and sports disciplines.

PMID:40783550 | DOI:10.1186/s13102-025-01284-2

BMC Med Inform Decis Mak. 2025 Aug 9;25(1):297. doi: 10.1186/s12911-025-03144-y.

ABSTRACT

BACKGROUND: Chronic wounds (CWs) represent a significant and growing challenge in healthcare due to their prolonged healing times, complex management, and associated costs. Inadequate wound assessment by healthcare professionals (HCPs), often due to limited training and high clinical workload, contributes to suboptimal treatment and increased risk of complications. This study aimed to develop an artificial intelligence (AI)-powered wound assessment tool, integrated into a mobile application, to support HCPs in diagnosis, monitoring, and clinical decision-making.

METHODS: A multicenter observational study was conducted across three healthcare institutions in Western Switzerland. Researchers compiled a hybrid dataset of approximately 4,000 wound images through both retrospective extraction from clinical records and prospective collection using a standardized mobile application. The prospective data included high-resolution images, short videos, and 3D scans, along with structured clinical metadata. Retrospective data were anonymized and manually annotated by wound care experts. All images were labeled for wound segmentation and tissue classification to train and validate deep learning models.

RESULTS: The resulting dataset represented a broad spectrum of wound types (acute and chronic), anatomical locations, skin tones, and healing stages. The AI-based wound segmentation model, developed using the Deeplabv3 + architecture with a ResNet50 backbone, achieved a DICE score of 92% and an Intersection-over-Union (IOU) score of 85%. Tissue classification yielded a preliminary mean DICE score of 78%, although accuracy varied across tissue types, especially fibrin and necrosis. The models were optimized for mobile implementation through quantization, achieving real-time inference with an average processing time of 0.3 seconds and only a 0.3% performance reduction. The dual approach to data collection, prospective and retrospective-ensured both image standardization and real-world variability, enhancing the model's generalizability.

CONCLUSIONS: This study laid the foundation for an AI-driven digital tool to assist clinical wound assessment and education. The integration of robust datasets and AI models demonstrated the potential to improve diagnostic precision, support personalized care, and reduce wound-related healthcare costs. Although challenges remained, particularly in tissue classification, this work highlighted the promise of AI in transforming wound care and advancing clinical training.

TRIAL REGISTRATION: Not applicable.

PMID:40783534 | DOI:10.1186/s12911-025-03144-y

NPJ Breast Cancer. 2025 Aug 9;11(1):88. doi: 10.1038/s41523-025-00797-w.

ABSTRACT

Complete tumour removal is vital in curative breast cancer (BCa) surgery to prevent recurrence. Recently, [18F]FDG micro-PET-CT of lumpectomy specimens has shown promise for intraoperative margin assessment (IMA). To aid interpretation, we trained a 2D Residual U-Net to delineate invasive carcinoma of no special type in micro-PET-CT lumpectomy images. We collected 53 BCa lamella images from 19 patients with true histopathology-defined tumour segmentations. Group five-fold cross-validation yielded a dice similarity coefficient of 0.71 ± 0.20 for segmentation. Afterwards, an ensemble model was generated to segment tumours and predict margin status. Comparing predicted and true histopathological margin status in a separate set of 31 micro-PET-CT lumpectomy images of 31 patients achieved an F1 score of 84%, closely matching the mean performance of seven physicians who manually interpreted the same images. This model represents an important step towards a decision-support system that enhances micro-PET-CT-based IMA in BCa, facilitating its clinical adoption.

PMID:40783490 | DOI:10.1038/s41523-025-00797-w

BMJ Case Rep. 2025 Aug 9;2009:bcr0920080905. doi: 10.1136/bcr.09.2008.0905.

ABSTRACT

Here we describe a case of a secondary bronchiolitis obliterans organizing pneumonia (BOOP), which was associated with repeated respiratory infections caused by carbamazepine (CBZ)- induced hypogammaglobulinaemia. A 49-year-old woman had been treated with CBZ (400 mg/day). Two and a half years later, she developed of dyspnea with productive cough and high-grade fever. Chest roentgenogram and computed tomography showed bilateral infiltrates in lower lung fields. Her laboratory findings revealed severe hypogammaglobulinaemia, suggesting that an immune system disorder caused pulmonary infection. Histological examination by trans-bronchial lung biopsy (TBLB) demonstrated that many foamed alveolar macrophages were obstructing the alveolar ducts and adjacent alveoli, suggesting BOOP. After cessation of CBZ, the hypogammaglobulinaemia and chest roentgenogram findings markedly improved. The present case suggests that CBZ may have some adverse effects on the immune system and cause frequent airway infections, and that secondary BOOP could be induced by repeated infections caused by CBZ-induced hypogammaglobulinaemia.

PMID:40783246 | DOI:10.1136/bcr.09.2008.0905

Acta Biomater. 2025 Aug 7:S1742-7061(25)00591-4. doi: 10.1016/j.actbio.2025.08.010. Online ahead of print.

ABSTRACT

Idiopathic pulmonary fibrosis (IPF) is a progressive and incurable lung disease characterized by tissue scarring that disrupts gas exchange. Epithelial cell dysfunction, fibroblast activation, and excessive extracellular matrix deposition drive this pathology that ultimately leads to respiratory failure. Mechanistic studies have shown that repeated injury to alveolar epithelial cells initiates an aberrant wound-healing response by surrounding fibroblasts through secretion of mediators like transforming growth factor beta (TGF- β), yet the precise biological pathways contributing to disease progression are not fully understood. To better study these interactions there is a critical need for lung models that replicate the cellular heterogeneity, geometry, and biomechanics of the distal lung microenvironment. In this study, induced pluripotent stem cell-derived alveolar epithelial type II (iATII) cells and human pulmonary fibroblasts were arranged to replicate key features of human lung micro-architecture and embedded in soft or stiff poly(ethylene glycol) norbornene (PEG-NB) hydrogels that recapitulated the mechanical properties of healthy and fibrotic lung tissue, respectively. The co-cultured cells were then exposed to pro-fibrotic cytokines and growth factors. iATIIs and fibroblasts exhibited differentiation pathways and gene expression patterns consistent with trends observed during IPF progression in vivo. A design of experiments statistical analysis identified stiff hydrogels combined with pro-fibrotic biochemical cue exposure as the most effective condition tested in this study for modeling fibrosis in vitro. Finally, treatment with Nintedanib, one of only two Food and Drug Administration (FDA)-approved drugs for IPF, was assessed. Treatment reduced fibroblast activation, as indicated by downregulation of key activation genes, and upregulated several epithelial genes involved in alveolar repair. These findings demonstrate that human 3D co-culture models hold are a promising tool for advancing our understanding of IPF and identifying new therapeutic targets. STATEMENT OF SIGNIFICANCE: This study leverages advanced biomaterials and biofabrication techniques to engineer physiologically relevant, donor-specific, and sex-matched models of pulmonary fibrosis, addressing the critical need for pre-clinical therapeutic drug screening platforms. These human 3D lung models successfully replicated key features of fibrotic lung tissue. Tuning microenvironmental stiffness of 3D PEG-NB hydrogels to match fibrotic lung values and exposing human iATII cells and fibroblasts to pro-fibrotic biochemical cues recreated hallmark characteristics of in vivo fibrosis pathogenesis, including epithelial differentiation and loss, as well as fibroblast activation. The utility of these models was further validated by demonstrating responsiveness to Nintedanib, a clinically available treatment for IPF. These findings highlight the transformative potential of well-defined biomaterial-based 3D models for elucidating complex disease mechanisms and accelerating therapeutic drug discovery for chronic pulmonary diseases like idiopathic pulmonary fibrosis.

PMID:40782923 | DOI:10.1016/j.actbio.2025.08.010

Clinics (Sao Paulo). 2025 Aug 8;80:100732. doi: 10.1016/j.clinsp.2025.100732. Online ahead of print.

ABSTRACT

BACKGROUND: Research has shown that long noncoding RNAs (lncRNAs) play a role in Idiopathic Pulmonary Fibrosis (IPF), but their specific functions and patterns of expression are still unclear.

METHOD: A diagnostic study was conducted by utilizing analysis techniques. RNA sequencing (RNA-seq) data from 12 IPF patients and 5 controls was used to study lncRNA functions in IPF. The authors identified Differentially Expressed lncRNAs (DElncRNAs) and explored co-expression networks in a transient manner, as well as using Weighted Gene Co-expression Network Analysis (WGCNA) to identify modules associated with IPF.

RESULTS: The study found 541 differentially expressed lncRNAs in IPF, with 201 up-regulated and 340 down-regulated. DElncRNAs, especially the up-regulated, were significantly correlated with DEmRNAs at their expression levels. DEmRNAs showed extracellular matrix-related biological functions in addition to increased lncRNAs. WGCNA results demonstrated that Module Eigengene green (MEgreen) and MEred modules indicated the highest negative correlation significance with IPF phenotype, and eigengene patterns of both modules were downregulated in IPF samples. The authors identified six significant lncRNAs in these two modules, including FAM13A-AS1, RP11-180C16.1, MYO16-AS1, AC007278.2, BACH1-IT2, and RP11-153M7.5, and their co-expressed DE mRNAs were enriched in inflammatory response pathways. The authors used single-cell RNA sequencing (scRNA-seq) data to investigate dysregulated lncRNAs and their co-expressed mRNAs. The authors found that five DEmRNAs that were co-expressed with DElncRNAs exhibited dysregulated expression patterns in multiple cell types of the IPF samples.

CONCLUSION: LncRNAs are functionally active and potentially involved in the inflammatory response in pathological processes of IPF. It is also important to consider some specific lncRNAs as potential diagnostic biomarkers or therapeutic targets for preclinical and clinical studies with IPF in the future.

PMID:40782499 | DOI:10.1016/j.clinsp.2025.100732

Aging (Albany NY). 2025 Aug 8;17. doi: 10.18632/aging.206295. Online ahead of print.

ABSTRACT

Idiopathic pulmonary fibrosis (IPF) is a condition predominantly affecting the elderly and leading to a decline in lung function. Our study investigates the aging-related mechanisms in IPF using artificial intelligence (AI) approaches. We developed a pathway-aware proteomic aging clock using UK Biobank data and applied it alongside a specialized version of Precious3GPT (ipf-P3GPT) to demonstrate an AI-driven mode of IPF research. The aging clock shows great performance in cross-validation (R2=0.84) and its utility is validated in an independent dataset to show that severe cases of COVID-19 are associated with an increased aging rate. Computational analysis using ipf-P3GPT revealed distinct but overlapping molecular signatures between aging and IPF, suggesting that IPF represents a dysregulation rather than mere acceleration of normal aging processes. Our findings establish novel connections between aging biology and IPF pathogenesis while demonstrating the potential of AI-guided approaches in therapeutic development for age-related diseases.

PMID:40782333 | DOI:10.18632/aging.206295

Nat Commun. 2025 Aug 9;16(1):7373. doi: 10.1038/s41467-025-61699-w.

ABSTRACT

Monkeypox virus (MPXV) has re-emerged globally since May 2022, posing a significant public health threat. To address this, we develop two multivalent mRNA vaccine candidates-AAL, encoding three MPXV antigens, and AALI, which combines AAL with an immune-enhancing IFN-α protein. Both vaccines are delivered via mannose-modified lipid nanoparticles to target dendritic cells. Here we show that these vaccines elicit strong antibody responses against vaccinia virus and multiple MPXV clades, induce robust memory B-cell and T-cell responses, and promote dendritic cell maturation. In mouse challenge models, both vaccines provide protection against clade IIb MPXV and vaccinia virus, significantly reducing viral loads and preventing lung damage. Immune profiling reveals enhanced B- and T-cell receptor diversity and distinct CDR3 motifs post-vaccination. These findings demonstrate the potential of using mRNA-based multivalent vaccines as an effective strategy for preventing mpox and related Orthopoxvirus infections.

PMID:40783493 | DOI:10.1038/s41467-025-61699-w

Trends Parasitol. 2025 Aug 8:S1471-4922(25)00195-3. doi: 10.1016/j.pt.2025.07.006. Online ahead of print.

ABSTRACT

Parasitology has long relied on genomics and transcriptomics to explore gene function, diversity, and host-parasite interactions, yet functional insight often requires deeper molecular resolution. This forum highlights advances in proteomics, metabolomics, lipidomics, and emerging technologies. We advocate an integrative multiomics approach to better understand parasite biology in context.

PMID:40783337 | DOI:10.1016/j.pt.2025.07.006