Cancer Radiother. 2025 Apr 30;29(2):104622. doi: 10.1016/j.canrad.2025.104622. Online ahead of print.

ABSTRACT

PURPOSE: Lung cancers associated with interstitial lung disease are challenging to diagnose and manage. We investigated the prevalence of interstitial lung disease among consecutively irradiated cancer patients, and the tolerance and prognosis of patients with or without interstitial lung disease after thoracic radiotherapy.

MATERIAL AND METHODS: This bicentric study was designed as a case-control study of patients with interstitial lung disease prior to radiotherapy (cases) and controls without interstitial lung disease. Patients were irradiated with curative intent for localized, locally advanced or oligometastatic non-small cell lung cancer. Consecutive lung cancer patients undergoing curative radiotherapy between January 2018 and December 2020 had centralized review of their baseline and 6-month CT scans by a multidisciplinary board. Functional evaluation, radiological scores, clinical toxicities, best objective response, progression-free survival and overall survival were assessed.

RESULTS: Twelve cases were detected out of 166 patients (7.2 %), including six diagnosed a posteriori by central review (50 %). Initial patient, tumour and lung cancer treatment characteristics were similar between cases and controls except for performance status (P=0.004), Kazerooni scores of fibrosis and ground glass patterns (P<0.001). Cases and controls underwent three-dimensional radiotherapy in 0 and 37 (24.2 %), intensity-modulated radiotherapy in eight (66.7 %) and 60 (39.2 %), stereotactic body radiotherapy in four (33.3 %) and 56 (36.6 %), respectively (P=0.079). Grade≥2 pneumonitis occurred in 41.7 % of cases versus 11 %, of controls (P=0.01). Hospitalization rates were 16 % in cases versus 2 % in controls and one case died of lung toxicity. Best objective response was worse for cases (P=0.046). Median progression-free survival was 9.35 months for cases and 18.56 months for controls. Median overall survival was 17 months for cases and not reached for controls (P=0.002). Sex, tumour stage, histology, and interstitial pulmonary fibrosis were prognostic factors for overall survival on univariate analysis.

CONCLUSION: Interstitial lung disease was present in 7 % of the patients with lung cancer. Patients with interstitial lung disease had higher risks of toxicity events and poorer prognosis, suggesting the lungs should be assessed carefully and that specific management strategies are warranted.

PMID:40311519 | DOI:10.1016/j.canrad.2025.104622

J Cyst Fibros. 2025 May 1:S1569-1993(25)01462-6. doi: 10.1016/j.jcf.2025.04.007. Online ahead of print.

ABSTRACT

BACKGROUND: Inhaled antibiotics are frequently used as chronic Pseudomonas aeruginosa (Pa) suppressive therapy among people with cystic fibrosis (PwCF). However, their use might increase the risk of developing treatment-emergent respiratory organisms. This study aimed to describe the proportion of PwCF utilizing inhaled antibiotics, determine factors associated with inhaled antibiotic prescription, and determine if chronic inhaled antibiotic use is associated with an increased risk of Aspergillus fumigatus, Stenotrophomonas maltophilia, or Achromobacter spp.

METHODS: This retrospective cohort study utilized Canadian CF Registry data. Pa status (chronic, intermittent, and negative) was defined per calendar year. The risk of developing A. fumigatus, S. maltophilia, or Achromobacter spp was compared between PwCF prescribed versus not prescribed inhaled antibiotics, adjusting for confounding by indication using inverse probability of treatment weighting.

RESULTS: This analysis included data from 2800 PwCF. >75 % of PwCF with chronic Pa were prescribed inhaled antibiotics, while up to 13 % of PwCF negative for Pa received inhaled antibiotics during the study period. There was an increased risk of developing A. fumigatus among PwCF with intermittent Pa (HR 1.43, 95 %CI; 1.08-1.88; p = 0.01) and who were Pa negative (HR 2.44, 95 %CI; 1.65-3.61; p < 0.001), but not for PwCF with chronic Pa (HR 1.36, 95 %CI; 0.94-1.95; p = 0.10). No association was seen between inhaled antibiotics and developing either S. maltophilia or Achromobacter spp.

CONCLUSIONS: Inhaled antibiotic use among Canadian PwCF was associated with an increased risk of A. fumigatus acquisition but not S. maltophilia or Achromobacter spp. Prospective studies are needed to better define this association.

PMID:40312233 | DOI:10.1016/j.jcf.2025.04.007

BMC Med Imaging. 2025 May 1;25(1):142. doi: 10.1186/s12880-025-01682-5.

ABSTRACT

BACKGROUND: The adrenal glands are small retroperitoneal organs, few reference standards exist for adrenal CT measurements in clinical practice. This study aims to develop a deep learning (DL) model for automated adrenal gland segmentation on non-contrast CT images, and to conduct a preliminary large-scale study on age-related volume changes in normal adrenal glands using the model output values.

METHODS: The model was trained and evaluated on a development dataset of annotated non-contrast CT scans of bilateral adrenal glands, utilizing nnU-Net for segmentation task. The ground truth was manually established by two experienced radiologists, and the model performance was assessed using the Dice similarity coefficient (DSC). Additionally, five radiologists provided annotations on a subset of 20 randomly selected cases to measure inter-observer variability. Following validation, the model was applied to a large-scale normal adrenal glands dataset to segment adrenal glands.

RESULTS: The DL model development dataset contained 1301 CT examinations. In the test set, the median DSC scores for the segmentation model of left and right adrenal glands were 0.899 and 0.904 respectively, and in the independent test set were 0.900 and 0.896. Inter-observer DSC for radiologist manual segmentation did not differ from automated machine segmentation (P = 0.541). The large-scale normal adrenal glands dataset contained 2000 CT examinations, the graph shows that adrenal gland volume increases first and then decreases with age.

CONCLUSION: The developed DL model demonstrates accurate adrenal gland segmentation, and enables a comprehensive study of age-related adrenal gland volume variations.

PMID:40312690 | DOI:10.1186/s12880-025-01682-5

BMC Med Imaging. 2025 May 1;25(1):144. doi: 10.1186/s12880-025-01663-8.

ABSTRACT

BACKGROUND: Magnetic resonance imaging (MRI) is an essential tool for medical diagnosis. However, artifacts may degrade images obtained through MRI, especially owing to patient movement. Existing methods that mitigate the artifact problem are subject to limitations including extended scan times. Deep learning architectures, such as U-Net, may be able to address these limitations. Optimizing deep learning networks with batch normalization (BN) and dropout layers enhances their convergence and accuracy. However, the influence of this strategy on U-Net has not been explored for artifact removal.

METHODS: This study developed a U-Net-based regression network for the removal of motion artifacts and investigated the impact of combining BN and dropout layers as a strategy for this purpose. A Transformer-based network from a previous study was also adopted for comparison. In total, 1200 images (with and without motion artifacts) were used to train and test three variations of U-Net.

RESULTS: The evaluation results demonstrated a significant improvement in network accuracy when BN and dropout layers were implemented. The peak signal-to-noise ratio of the reconstructed images was approximately doubled and the structural similarity index was improved by approximately 10% compared with those of the artifact images.

CONCLUSIONS: Although this study was limited to phantom images, the same strategy may be applied to more complex tasks, such as those directed at improving the quality of MR and CT images. We conclude that the accuracy of motion artifact removal can be improved by integrating BN and dropout layers into a U-Net-based network, with due consideration of the correct location and dropout rate.

PMID:40312665 | DOI:10.1186/s12880-025-01663-8

Sci Rep. 2025 May 1;15(1):15295. doi: 10.1038/s41598-025-98033-9.

ABSTRACT

Effective teacher performance evaluation is important for enhancing the quality of educational systems. This study presents a novel approach that integrates deep learning and metaheuristics to assess the pedagogical quality of English as a foreign language (EFL) instruction in a classroom setting. A comprehensive index framework is developed, comprising five primary dimensions: instructional design, instructional materials, teaching methods and approaches, teaching effectiveness, and classroom management. Each dimension is further divided into secondary indicators that capture specific aspects of teaching quality, including pronunciation, content coverage, lesson objectives, and student engagement. The proposed approach uses a convolutional neural network (CNN) architecture optimized by a modified virus colony search (VCS) algorithm to analyze audio and video recordings of classroom interactions. The results demonstrate that the VCS/CNN algorithm can accurately evaluate EFL instruction based on multiple criteria and indicators, outperforming existing methods in terms of accuracy, robustness, flexibility, and efficiency. This study contributes to the development of a reliable and efficient teacher evaluation framework that can provide timely feedback, identify teacher strengths and weaknesses, and inform areas for professional development. The proposed approach has the potential to improve the quality of EFL instruction and administration by enhancing teacher performance and student learning outcomes.

PMID:40312557 | DOI:10.1038/s41598-025-98033-9

NPJ Digit Med. 2025 May 1;8(1):235. doi: 10.1038/s41746-025-01663-6.

ABSTRACT

Laparoscopic pancreatic surgery remains highly challenging due to the complexity of the pancreas and surrounding vascular structures, with risk of injuring critical blood vessels such as the Superior Mesenteric Vein (SMV)-Portal Vein (PV) axis and splenic vein. Here, we evaluated the High Resolution Network (HRNet)-Full Convolutional Network (FCN) model for its ability to accurately identify vascular contours and improve surgical safety. Using 12,694 images from 126 laparoscopic distal pancreatectomy (LDP) videos and 35,986 images from 138 Whipple procedure videos, the model demonstrated robust performance, achieving a mean Dice coefficient of 0.754, a recall of 85.00%, and a precision of 91.10%. By combining datasets from LDP and Whipple procedures, the model showed strong generalization across different surgical contexts and achieved real-time processing speeds of 11 frames per second during surgery process. These findings highlight HRNet-FCN's potential to recognize anatomical landmarks, enhance surgical precision, reduce complications, and improve laparoscopic pancreatic outcomes.

PMID:40312536 | DOI:10.1038/s41746-025-01663-6

Sci Rep. 2025 May 1;15(1):15284. doi: 10.1038/s41598-025-00259-0.

ABSTRACT

Deep neural networks are used to accurately detect, estimate, and predict human body poses in images or videos through deep learning-based human pose estimation. However, traditional multi-person pose estimation methods face challenges due to partial occlusions and overlaps between multiple human bodies and body parts. To address these issues, we propose EE-YOLOv8, a human pose estimation network based on the YOLOv8 framework, which integrates Efficient Multi-scale Receptive Field (EMRF) and Expanded Feature Pyramid Network (EFPN). First, the EMRF module is employed to further enhance the model's feature representation capability. Second, the EFPN optimizes cross-level information exchange and improves multi-scale data integration. Finally, Wise-IoU replaces the traditional Intersection over Union (IoU) to improve detection accuracy through precise overlap measurement between predicted and ground-truth bounding boxes. We evaluate EE-YOLOv8 on the MS COCO 2017 dataset. Compared to YOLOv8-Pose, EE-YOLOv8 achieves an AP of 89.0% at an IoU threshold of 0.5 (an improvement of 3.3%) and an AP of 65.6% over the IoU range of 0.5-0.95 (an improvement of 5.8%). Therefore, EE-YOLOv8 achieves the highest accuracy while maintaining the lowest parameter count and computational complexity among all analyzed algorithms. These results demonstrate that EE-YOLOv8 exhibits superior competitiveness compared to other mainstream methods.

PMID:40312474 | DOI:10.1038/s41598-025-00259-0

Sci Rep. 2025 May 1;15(1):15279. doi: 10.1038/s41598-025-97879-3.

ABSTRACT

In the era of rapid societal modernization, the issue of crime stands as an intrinsic facet, demanding our attention and consideration. As our communities evolve and adopt technological advancements, the dynamic landscape of criminal activities becomes an essential aspect that requires careful examination and proactive approaches for public safety application. In this paper, we proposed a collaborative approach to detect crime patterns and criminal emotions with the aim of enhancing judiciary decision-making. For the same, we utilized two standard datasets - a crime dataset comprised of different features of crime. Further, the emotion dataset has 135 classes of emotion that help the AI model to efficiently find criminal emotions. We adopted a convolutional neural network (CNN) to get first trained on crime datasets to bifurcate crime and non-crime images. Once the crime is detected, criminal faces are extracted using the region of interest and stored in a directory. Different CNN architectures, such as LeNet-5, VGGNet, RestNet-50, and basic CNN, are used to detect different emotions of the face. The trained CNN models are used to detect criminal emotion and enhance judiciary decision-making. The proposed framework is evaluated with different evaluation metrics, such as training accuracy, loss, optimizer performance, precision-recall curve, model complexity, training time, and inference time. In crime detection, the CNN model achieves a remarkable accuracy of 92.45% and in criminal emotion detection, LeNet-5 outperforms other CNN architectures by offering an accuracy of 98.6%.

PMID:40312470 | DOI:10.1038/s41598-025-97879-3

Sci Rep. 2025 May 1;15(1):15267. doi: 10.1038/s41598-025-00065-8.

ABSTRACT

With the growing demand for contactless human-computer interaction in the smart home field, gesture recognition technology shows great market potential. In this paper, a sparse millimeter wave point cloud-based gesture recognition system, RaGeoSense, is proposed, which is designed for smart home scenarios. RaGeoSense effectively improves the recognition performance and system robustness by combining multiple advanced signal processing and deep learning methods. Firstly, the system adopts three methods, namely K-mean clustering straight-through filtering, frame difference filtering and median filtering, to reduce the noise of the raw millimeter wave data, which significantly improves the quality of the point cloud data. Subsequently, the generated point cloud data are processed with sliding sequence sampling and point cloud tiling to extract the spatio-temporal features of the action. To further improve the classification performance, the system proposes an integrated model architecture that combines GBDT and XGBoost for efficient extraction of nonlinear features, and utilizes LSTM gated loop units to classify the gesture sequences, thus realizing the accurate recognition of eight different one-arm gestures. The experimental results show that RaGeoSense performs well at different distances, angles and movement speeds, with an average recognition rate of 95.2%, which is almost unaffected by the differences in personnel and has a certain degree of anti-interference ability.

PMID:40312411 | DOI:10.1038/s41598-025-00065-8

BMC Med Inform Decis Mak. 2025 May 1;25(1):177. doi: 10.1186/s12911-025-02978-w.

ABSTRACT

Recent advances in artificial intelligence-based audio and speech processing have increasingly focused on the binary and multi-class classification of voice disorders. Despite progress, achieving high accuracy in multi-class classification remains challenging. This paper proposes a novel hybrid approach using a two-stage framework to enhance voice disorders classification performance, and achieve state-of-the-art accuracies in multi-class classification. Our hybrid approach, combines deep learning features with various powerful classifiers. In the first stage, high-level feature embeddings are extracted from voice data spectrograms using a pre-trained VGGish model. In the second stage, these embeddings are used as input to four different classifiers: Support Vector Machine (SVM), Logistic Regression (LR), Multi-Layer Perceptron (MLP), and an Ensemble Classifier (EC). Experiments are conducted on a subset of the Saarbruecken Voice Database (SVD) for male, female, and combined speakers. For binary classification, VGGish-SVM achieved the highest accuracy for male speakers (82.45% for healthy vs. disordered; 75.45% for hyperfunctional dysphonia vs. vocal fold paresis), while VGGish-EC performed best for female speakers (71.54% for healthy vs. disordered; 68.42% for hyperfunctional dysphonia vs. vocal fold paresis). In multi-class classification, VGGish-SVM outperformed other models, achieving mean accuracies of 77.81% for male speakers, 63.11% for female speakers, and 70.53% for combined genders. We conducted a comparative analysis against related works, including the Mel frequency cepstral coefficient (MFCC), MFCC-glottal features, and features extracted using the wav2vec and HuBERT models with SVM classifier. Results demonstrate that our hybrid approach consistently outperforms these models, especially in multi-class classification tasks. The results show the feasibility of a hybrid framework for voice disorder classification, offering a foundation for refining automated tools that could support clinical assessments with further validation.

PMID:40312383 | DOI:10.1186/s12911-025-02978-w

BMC Genomics. 2025 May 1;26(1):432. doi: 10.1186/s12864-025-11588-9.

ABSTRACT

BACKGROUND: Accurate genetic risk prediction and understanding the mechanisms underlying complex diseases are essential for effective intervention and precision medicine. However, current methods often struggle to capture the intricate and subtle genetic interactions contributing to disease risk. This challenge may be further exacerbated by the curse of dimensionality when considering large-scale pairwise genetic combinations with limited samples. Overcoming these limitations could transform biomedicine by providing deeper insights into disease mechanisms, moving beyond black-box models and single-locus analyses, and enabling a more comprehensive understanding of cross-disease patterns.

RESULTS: We developed Ge-SAND (Genomic Embedding Self-Attention Neurodynamic Decoder), an explainable deep learning-driven framework designed to uncover complex genetic interactions at scales exceeding 106 in parallel for accurate disease risk prediction. Ge-SAND leverages genotype and genomic positional information to identify both intra- and interchromosomal interactions associated with disease phenotypes, providing comprehensive insights into pathogenic mechanisms crucial for disease risk prediction. Applied to simulated datasets and UK Biobank cohorts for Crohn's disease, schizophrenia, and Alzheimer's disease, Ge-SAND achieved up to a 20% improvement in AUC-ROC compared to mainstream methods. Beyond its predictive accuracy, through self-attention-based interaction networks, Ge-SAND provided insights into large-scale genotype relationships and revealed genetic mechanisms underlying these complex diseases. For instance, Ge-SAND identified potential genetic interaction pairs, including novel relationships such as ISOC1 and HOMER2, potentially implicating the brain-gut axis in Crohn's and Alzheimer's diseases.

CONCLUSION: Ge-SAND is a novel deep-learning approach designed to address the challenges of capturing large-scale genetic interactions. By integrating disease risk prediction with interpretable insights into genetic mechanisms, Ge-SAND offers a valuable tool for advancing genomic research and precision medicine.

PMID:40312319 | DOI:10.1186/s12864-025-11588-9

BMC Cancer. 2025 May 1;25(1):818. doi: 10.1186/s12885-025-14221-w.

ABSTRACT

BACKGROUND: Epidermal growth factor receptor (EGFR) mutations are present in 10-60% of all non-small cell lung cancer (NSCLC) patients and are associated with dismal prognosis. Lung cancer brain metastases (LCBM) are a common complication of lung cancer. Predictions of EGFR can help physicians in decision-making and, through optimizing treatment strategies, can result in more favorable outcomes. This systematic review and meta-analysis evaluated the predictive performance of machine learning (ML)-based models in EGFR status in NSCLC patients with brain metastasis.

METHODS: On December 20, 2024, the four electronic databases, Pubmed, Embase, Scopus, and Web of Science, were systematically searched. Studies that evaluated EGFR status in patients with brain metastasis from NSCLC were included.

RESULTS: Twenty studies with 3517 patients with 6205 NSCLC brain metastatic lesions were included. The majority of the best-performance models were ML-based (70%, 7/10), and deep learning (DL)-based models comprised 30% (6/20) of models. The area under the curve (AUC) and accuracy (ACC) of the best-performance models ranged from 0.765 to 1 and 0.69 to 0.93, respectively. The meta-analysis of the best-performance model revealed a pooled AUC of 0.91 (95%CI: 0.88-0.93) and ACC of 0.82 (95%CI: 0.79-0.86) along with a pooled sensitivity of 0.87 (95%CI: 0.83-0.9), specificity of 0.86 (95%CI: 0.79-0.9), and diagnostic odds ratio (DOR) of 35.2 (95%CI: 21.2-58.4). The subgroup analysis did not show significant differences between ML and DL models.

CONCLUSION: ML-based models demonstrated promising predictive outcomes in predicting EGFR status. Applying ML-based models in daily clinical practice can optimize treatment strategies and enhance clinical and radiological outcomes.

PMID:40312289 | DOI:10.1186/s12885-025-14221-w

Cell Signal. 2025 Apr 29:111830. doi: 10.1016/j.cellsig.2025.111830. Online ahead of print.

ABSTRACT

Senescence of alveolar epithelial cells (AEC) is a key event in the onset and progression of Idiopathic pulmonary fibrosis (IPF). The pathogenic mechanisms that underlie the effects of AEC senescence remain largely unexplained. Some age-related diseases have an etiology linked to mitochondrial dysfunction induced by excessive lysine succinylation (Ksucc). SIRT5 can remove excessive Ksucc levels to maintain mitochondrial homeostasis. Therefore, this study aimed to determine the effects of SIRT5-mediated de-Ksucc on mitochondrial function and pulmonary fibrosis after AEC senescence. We found AEC in the lungs derived from IPF patients exhibit a marked accumulation of dysmorphic and dysfunctional mitochondria and excessive Ksucc levels. These mitochondrial abnormalities in AEC of normal mice with advancing age were associated with the downregulation of SIRT5. Increased SIRT5 expression by LV-SIRT5pcDNA in senescent AEC sustains mitochondrial integrity and reduces fibrotic effects of AEC senescence in established bleomycin (BLM)-aging mouse model. The level of ITGB1 K238 was upregulation in senescent AEC, LV-SIRT5pcDNA down-regulates the Ksucc level of ITGB1 K238 blocking the activation of ITGB1/STAT3 signaling pathway associated pulmonary fibrosis. Collectively, our findings indicate excessive lysine succinylation (hyperKsucc) is a fundamental basis for mitochondrial dysfunction in pulmonary fibrosis induced by the AEC senescence and SIRT5 alleviates AEC senescence by stabilizing the mitochondrial function.

PMID:40311988 | DOI:10.1016/j.cellsig.2025.111830

Pharmacol Res. 2025 Apr 29:107756. doi: 10.1016/j.phrs.2025.107756. Online ahead of print.

ABSTRACT

Idiopathic pulmonary fibrosis (IPF) is one of the most common interstitial lung diseases with a high mortality rate. Calcaratarin D (CalD), a labdane diterpenoid, has been shown to possess anti-inflammatory properties. The present study evaluated the therapeutic potential of CalD in pulmonary fibrosis. A single dose of bleomycin (BLM, 2.5mg/kg) was instilled intratracheally in mice for up to 21 days to develop lung fibrosis. Oral CalD (50mg/kg) reduced BLM-induced inflammatory cell infiltration, especially pro-fibrotic Arg1-expressing interstitial macrophages in the bronchoalveolar lavage fluid. During the late fibrotic phase, CalD decreased BLM-induced mortality and body weight loss. In addition, CalD ameliorated lung histopathology, reduced collagen deposition and mucus hypersecretion, and improved lung functions in BLM-exposed mice. Furthermore, CalD modulated the levels of pro-inflammatory cytokines, chemokines, and growth factors in BAL fluid and lung tissues. In mouse lungs, BLM selectively upregulated Wnt10A level and promoted β-catenin nuclear translocation. CalD not only blocked Wnt10A/β-catenin signaling pathway but also reduced pro-fibrotic markers such as collagens, α-SMA and FHL2. In normal human lung fibroblasts, CalD inhibited TGF-β1-stimulated pro-fibrotic markers and Wnt/β-catenin signaling pathway by reducing Wnt10A production, upregulating endogenous Wnt antagonist DKK1 level, dephosphorylating Wnt ligand co-receptor LRP6, and preventing β-catenin and YAP/TAZ nuclear translocation. The antifibrotic action of CalD was shown to be dependent on its α,β-unsaturated γ-butyrolactone structure that is essential for CalD to form covalent interaction with cellular protein targets. Our results imply that CalD could be a novel antifibrotic agent for IPF, acting through blockade of the Wnt/β-catenin signaling pathway.

PMID:40311955 | DOI:10.1016/j.phrs.2025.107756

Am J Pathol. 2025 Apr 29:S0002-9440(25)00140-3. doi: 10.1016/j.ajpath.2025.03.013. Online ahead of print.

ABSTRACT

Collagen VII is an essential anchoring protein in the basement membrane zone, maintaining the attachment of stratified and pseudostratified epithelia to the underlying interstitial matrix. However, collagen VII is largely unexplored in normal lungs and idiopathic pulmonary fibrosis (IPF), a disease characterized by excessive accumulation of extracellular matrix (ECM) and aberrant re-epithelialization of fibrotic lung parenchyma. Analysis of collagen VII mRNA and protein in IPF distal lungs demonstrated elevated levels compared to normal lungs. To investigate its cellular source and spatial distribution in lung tissue, immunohistochemistry, RNAscope in situ hybridization, and cell culture experiments in combination with analysis of public transcriptomic datasets were performed. In IPF lungs, collagen VII was abundant in pathological remodeled airways and honeycomb cysts, associated with increased basal cell populations. In contrast, in the control lungs, collagen VII was mainly localized in larger airways. RNA sequencing data revealed that epithelial basal cells and KRT5-/KRT17+ aberrant basaloid cells are the primary sources of COL7A1 expression. Furthermore, COL7A1 expression was found in mesenchymal subsets and both collagen VII mRNA and protein were observed in fibroblast foci, another histopathological feature of IPF. In vitro, COL7A1 expression was found to be increased in normal human lung fibroblasts treated with TGF-β1. These findings suggest that collagen VII could be involved in the process of abnormal re-epithelialization in lung fibrosis.

PMID:40311757 | DOI:10.1016/j.ajpath.2025.03.013

Lancet Respir Med. 2025 Apr 28:S2213-2600(25)00116-X. doi: 10.1016/S2213-2600(25)00116-X. Online ahead of print.

NO ABSTRACT

PMID:40311651 | DOI:10.1016/S2213-2600(25)00116-X

Lancet Respir Med. 2025 Apr 28:S2213-2600(25)00045-1. doi: 10.1016/S2213-2600(25)00045-1. Online ahead of print.

ABSTRACT

BACKGROUND: Rare pathogenic variants in telomere-related genes are associated with poorer clinical outcomes in idiopathic pulmonary fibrosis (IPF). We aimed to assess whether rare qualifying variants in monogenic adult-onset pulmonary fibrosis genes are associated with IPF survival. Using polygenic risk scores (PRS), we also evaluated the influence of common IPF risk variants in patients carrying the qualifying variants.

METHODS: We identified qualifying variants in telomere and non-telomere genes using whole-genome sequences from individuals clinically diagnosed with IPF and enrolled in the Pulmonary Fibrosis Foundation Patient Registry (PFFPR), a large multicentre, observational cohort study (March 29, 2016 to June 15, 2018, n=888). We also derived a PRS for IPF (PRS-IPF) from known common sentinel IPF variants. The primary outcome was the association between qualifying variants and survival. The secondary outcome was the association between qualifying variants and PRS-IPF. We used logistic regression models adjusted for sex, age at diagnosis, and principal components of genetic heterogeneity to examine the mutual relationship of qualifying variants and PRS-IPF. The association between qualifying variants and PRS-IPF with survival was tested using Cox proportional hazard models adjusted for baseline confounders. Validation of the results was sought in data from an independent multicentre, prospective, observational cohort study of IPF in the UK (PROFILE, May 17, 2010 to Sept 5, 2017, n=472), and results were meta-analysed under a fixed-effects model.

FINDINGS: We included 888 patients from PFFPR and 472 from PROFILE, totalling 1360 participants. In the PFFPR, carriers of qualifying variants in monogenic adult-onset pulmonary fibrosis genes were associated with lower PRS-IPF (odds ratio 1·79 [95% CI 1·15-2·81]; p=0·010) and shorter survival (hazard ratio 1·53 [1·12-2·10]; p=7·33 × 10-3). Individuals with the lowest PRS-IPF also had worse survival (1·61 [1·25-2·07]; p=1·87 × 10-4). These findings were validated in PROFILE and the meta-analysis of the results showed a consistent direction of effect across both cohorts.

INTERPRETATION: We found non-additive effects between qualifying variants and common risk variants in IPF survival, suggesting distinct disease subtypes and raising the possibility of using PRS to guide sequencing prioritisation. Assessing the carrier status for qualifying variants and modelling PRS-IPF promises to further contribute to predicting disease progression among patients with IPF.

FUNDING: Instituto de Salud Carlos III; Instituto Tecnológico y de Eenergías Renovables; Cabildo Insular de Tenerife; Fundación DISA; National Heart, Lung, and Blood Institute of the US National Institutes of Health; and UK Medical Research Council.

PMID:40311650 | DOI:10.1016/S2213-2600(25)00045-1

Nat Methods. 2025 May 1. doi: 10.1038/s41592-025-02659-6. Online ahead of print.

ABSTRACT

Structural biology offers a window into the functionality of molecular machines in health and disease. A fundamental challenge lies in capturing both the high-resolution structural details and dynamic changes that are essential for function. The high-resolution methods of X-ray crystallography and electron cryo-microscopy (cryo-EM) are mainly used to study ensembles of static conformations but can also capture crucial dynamic transition states. Here, we review the latest strategies and advancements in time-resolved structural biology with a focus on capturing dynamic changes. We describe recent technology developments for time-resolved sample preparation and delivery in the cryo-EM and X-ray fields and explore how these technologies could mutually benefit each other to advance our understanding of biology at the molecular and atomic scales.

PMID:40312512 | DOI:10.1038/s41592-025-02659-6

Sci Rep. 2025 May 1;15(1):15298. doi: 10.1038/s41598-025-98923-y.

NO ABSTRACT

PMID:40312492 | DOI:10.1038/s41598-025-98923-y

Eur Urol Oncol. 2025 Apr 30:S2588-9311(25)00094-X. doi: 10.1016/j.euo.2025.03.016. Online ahead of print.

ABSTRACT

BACKGROUND AND OBJECTIVE: The interplay between prostate cancer and the tumor microenvironment is well documented and of primary importance in disease evolution. Herein, we investigated the prognostic value of tissue and urinary collagen-related molecular signatures in predicting biochemical recurrence (BCR) after radical prostatectomy (RP).

METHODS: A comprehensive analysis of 55 collagen-related features was conducted using transcriptomic datasets (n = 1393), with further validation at the proteomic level (n = 69). Additionally, a distinct cohort (n = 73) underwent a urine-based peptidomic analysis, culminating in the validation of a urine-derived prognostic model. Independent prognostic significance was assessed using Cox proportional hazards modeling, while the model's predictive performance was benchmarked against established clinical metrics.

KEY FINDINGS AND LIMITATIONS: An expression analysis of 55 collagen-related transcripts identified 11 transcripts significantly associated with BCR (C-index: 0.55-0.72, p < 0.002). Multivariable models incorporating these transcripts enhanced prognostic accuracy, surpassing clinical variables (C-index: 0.66-0.89, p < 0.002). Proteomic validation confirmed five key collagen proteins, while a urine-based collagen model (C-index: 0.73, 95% confidence interval: 0.62-0.85) demonstrated a strong prognostic potential, although limited by small patient numbers. Additionally, tissue collagen-based models predicted overall survival with a significant prognostic value (C-index: 0.59-0.70, p < 0.01).

CONCLUSIONS AND CLINICAL IMPLICATIONS: Collagen-based molecular signatures in both tissue and urine emerge as robust prognostic biomarkers for predicting BCR following RP.

PMID:40312179 | DOI:10.1016/j.euo.2025.03.016