Brain Res. 2025 Feb 2:149484. doi: 10.1016/j.brainres.2025.149484. Online ahead of print.

ABSTRACT

Accurate recognition and classification of motor imagery electroencephalogram (MI-EEG) signals are crucial for the successful implementation of brain-computer interfaces (BCI). However, inherent characteristics in original MI-EEG signals, such as nonlinearity, low signal-to-noise ratios, and large individual variations, present significant challenges for MI-EEG classification using traditional machine learning methods. To address these challenges, we propose an automatic feature extraction method rooted in deep learning for MI-EEG classification. First, original MI-EEG signals undergo noise reduction through discrete wavelet transform and common average reference. To reflect the regularity and specificity of brain neural activities, a convolutional neural network (CNN) is used to extract the time-domain features of MI-EEG. We also extracted spatial features to reflect the activity relationships and connection states of the brain in different regions. This process yields time series containing spatial information, focusing on enhancing crucial feature sequences through talking-heads attention. Finally, more abstract spatial-temporal features are extracted using a temporal convolutional network (TCN), and classification is done through a fully connected layer. Validation experiments based on the BCI Competition IV-2a dataset show that the enhanced EEG model achieves an impressive average classification accuracy of 85.53% for each subject. Compared with CNN, EEGNet, CNN-LSTM and EEG-TCNet, the classification accuracy of this model is improved by 11.24%, 6.90%, 11.18% and 6.13%, respectively. Our work underscores the potential of the proposed model to enhance intention recognition in MI-EEG significantly.

PMID:39904453 | DOI:10.1016/j.brainres.2025.149484

Tuberc Respir Dis (Seoul). 2025 Feb 4. doi: 10.4046/trd.2024.0166. Online ahead of print.

ABSTRACT

BACKGROUND: Computed tomography (CT) scans are used to assess emphysema, a significant phenotype of chronic obstructive pulmonary disease (COPD), but variability in CT protocols and devices across the hospitals may affect accuracy. This study aims to perform kernel conversion among different CT settings and to evaluate differences in the correlation between emphysema index before and after kernel conversion, as well as clinical measures in COPD patients.

METHODS: The data were extracted from the Korea COPD Subgroup Study database, involving 484 COPD patients with CT scan images. These were processed with kernel conversion. Emphysema extent was quantified as the percentage of low-attenuation areas (%LAA-950) by deep learning-based program. The correlation between %LAA-950 and clinical parameters, such as lung function tests, the modified Medical Research Council (mMRC), six-minute walking distance (6MWD), COPD assessment test (CAT), and the St. George's Respiratory Questionnaire for COPD (SGRQ-c), were analyzed. These values were then compared across different CT settings.

RESULTS: A total of 484 participants were included. Compared to before, kernel conversion reduced the variance in %LAA-950 values (before vs. after: 12.6±11.0 vs. 8.8±11.9). After kernel conversion, %LAA-950 showed moderate correlations with forced expiratory volume in one second (r = -0.41), residual volume/total lung capacity (r = 0.42), mMRC (r = 0.25), CAT score (r = 0.12), SGRQ-c (r = 0.21), and 6MWD (r = 0.15), all of which improved compared to the unconverted dataset (all, P<0.01).

CONCLUSION: CT images processed with kernel conversion improve the correlation between emphysema extent and clinical parameters in COPD.

PMID:39904364 | DOI:10.4046/trd.2024.0166

Nurse Educ Today. 2025 Jan 31;147:106600. doi: 10.1016/j.nedt.2025.106600. Online ahead of print.

ABSTRACT

OBJECTIVE: This umbrella review of reviews aims to synthesise current evidence on AI's utilisation in developing education within health sciences disciplines.

DESIGN: An umbrella review of reviews, review of reviews, based on Joanna Briggs Institute guidelines.

DATA SELECTION: CINAHL, ERIC(ProQuest), PubMed, Scopus, and Medic were systematically searched in December 2023 with no time limit. The inclusion and exclusion criteria were defined according to the PCC framework: Participants(P), Concept(C), and Context (C). Two independent researchers screened 6304 publications, and 201 reviews were selected in the full-text phase.

DATA EXTRACTION: All the reviews that met inclusion criteria were included in the analysis. The reference lists of included reviews were also searched. Included reviews were quality appraised. The results were analysed with narrative synthesis.

RESULTS OF DATA SYNTHESIS: Seven reviews published between 2019 and 2023 were selected for analysis. Five key domains were identified: robotics, machine learning and deep learning, big data, immersive technologies, and natural language processing. Robotics enhances practical medical, dental and nursing education training. Machine learning personalises learning experiences and improves diagnostic skills. Immersive technologies provide interactive simulations for practical training.

CONCLUSION: This umbrella review of reviews highlights the potential of AI in health sciences education and the need for continued investment in AI technologies and ethical frameworks to ensure effective and equitable integration into educational practices.

PMID:39904286 | DOI:10.1016/j.nedt.2025.106600

Open Biol. 2025 Feb;15(2):240291. doi: 10.1098/rsob.240291. Epub 2025 Feb 5.

ABSTRACT

Peroxisomes are essential organelles involved in critical metabolic processes in animals such as fatty acid oxidation, ether phospholipid production and reactive oxygen species detoxification. We have generated transgenic Drosophila melanogaster models expressing fluorescent reporters for the selective autophagy of peroxisomes, a process known as pexophagy. We show that these reporters are colocalized with a peroxisomal marker and that they can reflect pexophagy induction by iron chelation and inhibition by depletion of the core autophagy protein Atg5. Using light sheet microscopy, we have been able to obtain a global overview of pexophagy levels across the entire organism at different stages of development. Tissue-specific control of pexophagy is exemplified by areas of peroxisome abundance but minimal pexophagy, observed in clusters of oenocytes surrounded by epithelial cells where pexophagy is much more evident. Enhancement of pexophagy was achieved by feeding flies with the iron chelator deferiprone, in line with past results using mammalian cells. Specific drivers were used to visualize pexophagy in neurons, and to demonstrate that specific depletion in the larval central nervous system of Hsc70-5, the Drosophila homologue of the chaperone HSPA9/mortalin, led to a substantial elevation in pexophagy.

PMID:39904371 | DOI:10.1098/rsob.240291

Immunity. 2025 Jan 31:S1074-7613(25)00032-9. doi: 10.1016/j.immuni.2025.01.007. Online ahead of print.

ABSTRACT

Activation of the NLRP3 inflammasome has been implicated in the pathogenesis of Alzheimer's disease (AD) via the release of IL-1β and ASC specks. However, whether NLRP3 is involved in pathways beyond this remained unknown. Here, we found that Aβ deposition in vivo directly triggered NLRP3 activation in APP/PS1 mice, which model many features of AD. Loss of NLRP3 increased glutamine- and glutamate-related metabolism and increased expression of microglial Slc1a3, which was associated with enhanced mitochondrial and metabolic activity. The generation of α-ketoglutarate during this process impacted cellular function, including increased clearance of Aβ peptides as well as epigenetic and gene transcription changes. This pathway was conserved between murine and human cells. Critically, we could mimic this effect pharmacologically using NLRP3-specific inhibitors, but only with chronic NLRP3 inhibition. Together, these data demonstrate an additional role for NLRP3, where it can modulate mitochondrial and metabolic function, with important downstream consequences for the progression of AD.

PMID:39904338 | DOI:10.1016/j.immuni.2025.01.007

Eur J Hosp Pharm. 2025 Feb 4:ejhpharm-2024-004414. doi: 10.1136/ejhpharm-2024-004414. Online ahead of print.

ABSTRACT

OBJECTIVE: Drug-related problems (DRPs) are a frequent reason for visits to the emergency department (ED). However, data about the characteristics associated with early revisits are limited. We aimed to identify clinical phenotype clusters of patients admitted to emergency rooms due DRPs to identify those patients with the highest risk of new visits.

METHODS: We included consecutive patients admitted to EDs due DRPs (February 2021 to December 2022), including DRP admissions in 2023 as validation cohort. We employed K-means clustering to group patients according to adjusted morbidity groups (GMA), age, and number of drugs at admission. To determine the optimal number of cluster centres, we used the elbow method. The impact of 30-day revisits in each cluster was assessed.

RESULTS: 1611 patients (mean (SD) age 75.0 (15.1) years) were included. We identified six clusters, with 30-day revisits rates ranging from 14.8% to 24.5%. The main groups of drugs implicated in the DRP episodes were diuretics (190 patients; 11.8%). The most common DRP diagnoses were constipation (191; 11.9%) and gastrointestinal bleeding (158; 9.8%). Six clusters of patients were identified. Significantly higher 30-day revisits in patients identified in cluster 4 (24.5% vs 17.5%; p=0.007). The highest revisit rate was observed in the cluster including patients with a higher number of drugs and GMA status.

CONCLUSIONS: Patients admitted to the ED due DRPs exhibit varying revisit rates across different clinical phenotypes. K-means clustering aids in identifying patients who derive the greatest rates of readmission, and is a useful tool to prioritise interventions in these units.

PMID:39904593 | DOI:10.1136/ejhpharm-2024-004414

Eur J Hosp Pharm. 2025 Feb 4:ejhpharm-2024-004463. doi: 10.1136/ejhpharm-2024-004463. Online ahead of print.

NO ABSTRACT

PMID:39904591 | DOI:10.1136/ejhpharm-2024-004463

Drug Ther Bull. 2025 Feb 4:dtb-2023-000059. doi: 10.1136/dtb.2023.000059. Online ahead of print.

ABSTRACT

Cancer treatment is rapidly evolving and this review provides healthcare professionals who are not specialists in cancer therapeutics with a broad overview of the role of cancer systemic therapy, with a particular focus on chemotherapy.Historically, the majority of cytotoxic chemotherapy was used in patients with incurable or metastatic disease with the goal of disease control and symptom palliation. Now, with the advent of more effective, targeted systemic therapies (incorporating both cytotoxic and non-cytotoxic agents), systemic therapies are being used in more diverse treatment settings, both to increase the likelihood of cure and to induce prolonged disease remission.Chemotherapy (henceforth referring specifically to cytotoxic chemotherapy) remains important for the treatment of many cancer types. This article will review the principles of chemotherapy and the first-line systemic treatment paradigm of different cancer types. The potential toxicities of chemotherapy will also be described.

PMID:39904574 | DOI:10.1136/dtb.2023.000059

Cancer Res. 2025 Feb 4. doi: 10.1158/0008-5472.CAN-24-2289. Online ahead of print.

ABSTRACT

Non-coding RNA transcribed from active enhancers, known as enhancer RNA (eRNA), is a critical element in gene regulation with a highly specific expression pattern in the regulatory networks of tumor-infiltrating cells. Therefore, eRNA signatures could potentially be applied to represent anti-tumor immune cells and to improve cancer immunotherapy. Herein, we identified thousands of eRNAs that were significantly correlated with infiltrating immune cell abundance in more than 10,000 patient samples across a variety of cancer types. The expression of these eRNAs was mediated by transcription factors with high expression in anti-tumor immune cells, as identified through single-cell assays. An eRNA immunotherapy signature (eRIS) developed using the anti-tumor eRNAs was highly associated with the objective response rate (ORR) of immunotherapy and was elevated in patients who benefited from immune checkpoint blockade (ICB) treatment. In comparison with a signature based on protein-coding genes, the eRIS was more effective in predicting the response to immunotherapy. Integration of the eRIS with pharmacogenomic data revealed hundreds of anti-cancer drugs that have the potential to enhance immunotherapy efficacy. Finally, treatment of a mouse model of IDH mutant glioma with the histone deacetylase inhibitor vorinostat improved the effects of anti-PD-1 immunotherapy through increased abundance of infiltrating immune cells. Taken together, this study developed an eRIS with demonstrated efficacy in predicting immunotherapy response and used the eRIS to identify a series of effective combination drugs, thus highlighting the clinical utility of the eRIS in immunotherapy enhancement.

PMID:39903841 | DOI:10.1158/0008-5472.CAN-24-2289

Elife. 2025 Feb 4;13:RP97577. doi: 10.7554/eLife.97577.

ABSTRACT

Progression through the G1 phase of the cell cycle is the most highly regulated step in cellular division. We employed a chemogenetic approach to discover novel cellular networks that regulate cell cycle progression. This approach uncovered functional clusters of genes that altered sensitivity of cells to inhibitors of the G1/S transition. Mutation of components of the Polycomb Repressor Complex 2 rescued proliferation inhibition caused by the CDK4/6 inhibitor palbociclib, but not to inhibitors of S phase or mitosis. In addition to its core catalytic subunits, mutation of the PRC2.1 accessory protein MTF2, but not the PRC2.2 protein JARID2, rendered cells resistant to palbociclib treatment. We found that PRC2.1 (MTF2), but not PRC2.2 (JARID2), was critical for promoting H3K27me3 deposition at CpG islands genome-wide and in promoters. This included the CpG islands in the promoter of the CDK4/6 cyclins CCND1 and CCND2, and loss of MTF2 lead to upregulation of both CCND1 and CCND2. Our results demonstrate a role for PRC2.1, but not PRC2.2, in antagonizing G1 progression in a diversity of cell linages, including chronic myeloid leukemia (CML), breast cancer, and immortalized cell lines.

PMID:39903505 | DOI:10.7554/eLife.97577

PLoS Pathog. 2025 Feb 4;21(2):e1012784. doi: 10.1371/journal.ppat.1012784. Online ahead of print.

ABSTRACT

Aspergillus fumigatus (Af) is a major mould pathogen found ubiquitously in the air. It commonly infects the airways of people with cystic fibrosis (CF) leading to Aspergillus bronchitis or allergic bronchopulmonary aspergillosis. Resident alveolar macrophages and recruited neutrophils are important first lines of defence for clearance of Af in the lung. However, their contribution to the inflammatory phenotype in CF during Af infection is not well understood. Here, utilising CFTR deficient mice we describe a hyperinflammatory phenotype in both acute and allergic murine models of pulmonary aspergillosis. We show that during aspergillosis, CFTR deficiency leads to increased alveolar macrophage death and persistent inflammation of the airways in CF, accompanied by impaired fungal control. Utilising CFTR deficient murine cells and primary human CF cells we show that at a cellular level there is increased activation of NFκB and NFAT in response to Af which, as in in vivo models, is associated with increased cell death and reduced fungal control. Taken together, these studies indicate that CFTR deficiency promotes increased activation of inflammatory pathways, the induction of macrophage cell death and reduced fungal control contributing to the hyper-inflammatory of pulmonary aspergillosis phenotypes in CF.

PMID:39903773 | DOI:10.1371/journal.ppat.1012784

Pediatr Pulmonol. 2024 Jun;59(6):1661-1676. doi: 10.1002/ppul.26970. Epub 2024 Apr 12.

ABSTRACT

BACKGROUND: Cystic fibrosis (CF) is a life-shortening multisystem genetic disease. Although progressive pulmonary disease is the predominant cause of morbidity and mortality, improvements in treatment for CF-related lung disease, with associated increase in longevity, have increased the prevalence of extrapulmonary manifestations1.

METHODS: To discuss these issues, a multidisciplinary meeting of international leaders and experts in the field was convened in November 2021 at the Shaping Initiatives and Future Trends Symposium with the goal of highlighting shifting management paradigms in CF. The main topics covered were: (1) nutrition and obesity, (2) exocrine pancreas, (3) CF-related diabetes, (4) CF liver disease, (5) CF-related bone disease, and (6) post-lung transplant care. This document summarizes the proceedings, highlighting the key priorities and important research questions that were discussed.

RESULTS: Improved life expectancy, the advent of cystic fibrosis transmembrane conductance regulator modulators, and the increasing appreciation of the heterogeneity or spectrum of disease are leading to a shift in management for patients with cystic fibrosis. Care should be individualized to ensure that increased longevity is accompanied by improved extra-pulmonary care and reduced morbidity.

PMID:39903130 | DOI:10.1002/ppul.26970

Evol Comput. 2025 Feb 4:1-27. doi: 10.1162/evco_a_00367. Online ahead of print.

ABSTRACT

In many recent works,the potential of Exploratory Landscape Analysis (ELA) features to numerically characterize single-objective continuous optimization problems has been demonstrated. These numerical features provide the input for all kinds of machine learning tasks in the domain of continuous optimization problems, ranging, i.a., from High-level Property Prediction to Automated Algorithm Selection and Automated Algorithm Configuration. Without ELA features, analyzing and understanding the characteristics of single-objective continuous optimization problems is - to the best of our knowledge - very limited. Yet, despite their usefulness, as demonstrated in several past works, ELA features suffer from several drawbacks. These include, in particular, (1.) a strong correlation between multiple features, as well as (2.) its very limited applicability to multiobjective continuous optimization problems. As a remedy, recent works proposed deep learning-based approaches as alternatives to ELA. In these works, among others point-cloud transformers were used to characterize an optimization problem's fitness landscape. However, these approaches require a large amount of labeled training data. Within this work, we propose a hybrid approach, Deep-ELA, which combines (the benefits of) deep learning and ELA features. We pre-trained four transformers on millions of randomly generated optimization problems to learn deep representations of the landscapes of continuous single- and multi-objective optimization problems. Our proposed framework can either be used out-of-the-box for analyzing single- and multiobjective continuous optimization problems, or subsequently fine-tuned to various tasks focusing on algorithm behavior and problem understanding.

PMID:39903851 | DOI:10.1162/evco_a_00367

ACS Nano. 2025 Feb 4. doi: 10.1021/acsnano.4c15364. Online ahead of print.

ABSTRACT

Understanding molecular dynamics at the nanoscale remains challenging due to limitations in the temporal resolution of current imaging techniques. Deep learning integrated with Single-Molecule Localization Microscopy (SMLM) offers opportunities to probe these dynamics. Here, we leverage this integration to reveal entangled polymer dynamics at a fast time scale, which is relatively poorly understood at the single-molecule level. We used Lambda DNA as a model system and modeled their entanglement using the self-avoiding wormlike chain model, generated simulated localizations along the contours, and trained the deep learning algorithm on these simulated images to predict chain contours from sparse localization data. We found that the localizations are heterogeneously distributed along the contours. Our assessments indicated that chain entanglement creates local diffusion barriers for switching buffer molecules, affecting the photoswitching kinetics of fluorescent dyes conjugated to the DNA molecules at discrete DNA segments. Tracking these segments demonstrated stochastic and subdiffusive migration of the entanglement loci. Our approach provides direct visualization of nanoscale polymer dynamics and local molecular environments previously inaccessible to conventional imaging techniques. In addition, our results suggest that the switching kinetics of the fluorophores in SMLM can be used to characterize nanoscopic local environments.

PMID:39903818 | DOI:10.1021/acsnano.4c15364

PLoS One. 2025 Feb 4;20(2):e0318550. doi: 10.1371/journal.pone.0318550. eCollection 2025.

ABSTRACT

Detecting cracks in asphalt concrete slabs is challenging due to environmental factors like lighting changes, surface reflections, and weather conditions, which affect image quality and crack detection accuracy. This study introduces a novel deep learning-based anomaly model for effective crack detection. A large dataset of panel images was collected and processed using denoising, standardization, and data augmentation techniques, with crack areas labeled via LabelImg software. The core model is an improved Xception network, enhanced with an adaptive activation function, dynamic attention mechanism, and multi-level residual connections. These innovations optimize feature extraction, enhance feature weighting, and improve information transmission, significantly boosting accuracy and robustness. The improved model achieves a 97.6% accuracy and a Matthews correlation coefficient of 0.98, remaining stable under varying lighting conditions. This method not only provides a fresh approach to crack detection but also greatly enhances detection efficiency.

PMID:39903732 | DOI:10.1371/journal.pone.0318550

PLoS One. 2025 Feb 4;20(2):e0317136. doi: 10.1371/journal.pone.0317136. eCollection 2025.

ABSTRACT

Adenomyosis and leiomyomas are common benign uterine disorders characterized by abnormal cellular proliferation. The BCL6 protein, a transcriptional repressor implicated in cell proliferation and oncogenesis, has been linked to the pathogenesis of endometriosis. This study investigates BCL6 expression in adenomyosis, leiomyomas, and normal myometrium using immunohistochemistry and deep learning neural networks. We analyzed paraffin blocks from total hysterectomies performed between 2009 and 2017, confirming diagnoses through pathological review. Immunohistochemistry was conducted using an automated system, and BCL6 expression was quantified using Fiji-ImageJ software. A supervised deep learning neural network was employed to classify samples based on DAB staining. Our results show that BCL6 expression is significantly higher in leiomyomas compared to adenomyosis and normal myometrium. No significant difference in BCL6 expression was observed between adenomyosis and controls. The deep learning neural network accurately classified samples with a high degree of precision, supporting the immunohistochemical findings. These findings suggest that BCL6 plays a role in the pathogenesis of leiomyomas, potentially contributing to abnormal smooth muscle cell proliferation. The study highlights the utility of automated immunohistochemistry and deep learning techniques in quantifying protein expression and classifying uterine pathologies. Future studies should investigate the expression of BCL6 in adenomyosis and endometriosis to further elucidate its role in uterine disorders.

PMID:39903727 | DOI:10.1371/journal.pone.0317136

PLoS One. 2025 Feb 4;20(2):e0318767. doi: 10.1371/journal.pone.0318767. eCollection 2025.

ABSTRACT

The frequent bending of shearer cables during operation often leads to mechanical fatigue, posing risks to equipment safety. Accurately predicting the mechanical properties of these cables under bending conditions is crucial for improving the reliability and service life of shearers. This paper proposes a shearer optical fiber cable mechanical characteristics prediction model based on Temporal Convolutional Network (TCN), Bidirectional Long Short-Term Memory (BiLSTM), and Squeeze-and-Excitation Attention (SEAttention), referred to as the TCN-BiLSTM-SEAttention model. This method leverages TCN's causal and dilated convolution operations to capture long-term sequential features, BiLSTM's bidirectional information processing to ensure the completeness of sequence information, and the SEAttention mechanism to assign adaptive weights to features, effectively enhancing the focus on key features. The model's performance is validated through comparisons with multiple other models, and the contributions of input features to the model's predictions are quantified using Shapley Additive Explanations (SHAP). By learning the stress variation patterns between the optical fiber, power conductor, and control conductor in the shearer cable, the model enables accurate prediction of the stress in other cable conductors based on optical fiber stress data. Experiments were conducted using a shearer optical fiber cable bending simulation dataset with traction speeds of 6 m/min, 8 m/min, and 10 m/min. The results show that, compared to other predictive models, the proposed model achieves reductions in Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE) to 0.0002, 0.0159, and 0.0126, respectively, with the coefficient of determination (R2) increasing to 0.981. The maximum deviation between predicted and actual values is only 0.86%, demonstrating outstanding prediction accuracy. SHAP feature analysis reveals that the control conductor features have the most substantial influence on predictions, with a SHAP value of 0.095. The research shows that the TCN-BiLSTM-SEAttention model demonstrates outstanding predictive capability under complex operating conditions, providing a novel approach for improving cable management and equipment safety through optical fiber monitoring technology in the intelligent development of coal mines, highlighting the potential of deep learning in complex mechanical predictions.

PMID:39903714 | DOI:10.1371/journal.pone.0318767

Biol Reprod. 2025 Feb 4:ioaf023. doi: 10.1093/biolre/ioaf023. Online ahead of print.

ABSTRACT

The number and distribution of follicles in each growth stage provides a reliable readout of ovarian health and function. Leveraging techniques for three-dimensional imaging of ovaries in toto has the potential to uncover total, accurate ovarian follicle counts. Due to the size and holistic nature of these images, counting oocytes is time consuming and difficult. The advent of machine-learning algorithms has allowed for the development of ultra-fast, automated methods to analyze microscopy images. In recent years, these pipelines have become more accessible to non-specialists. We used these tools to create OoCount, a high-throughput, open-source method for automatic oocyte segmentation and classification from fluorescent 3D microscopy images of whole mouse ovaries using a deep-learning convolutional neural network (CNN) based approach. We developed a fast tissue-clearing and imaging protocol to obtain 3D images of whole mount mouse ovaries. Fluorescently labeled oocytes from 3D images were manually annotated in Napari to develop a training dataset. This dataset was used to retrain StarDist using a CNN within DL4MicEverywhere to automatically label all oocytes in the ovary. In a second phase, we utilize Accelerated Pixel and Object Classification, a Napari plugin, to sort oocytes into growth stages. Here, we provide an end-to-end pipeline for producing high-quality 3D images of mouse ovaries and obtaining follicle counts and staging. We demonstrate how to customize OoCount to fit images produced in any lab. Using OoCount, we obtain accurate oocyte counts from each growth stage in the perinatal and adult ovary, improving our ability to study ovarian function and fertility.

PMID:39903695 | DOI:10.1093/biolre/ioaf023

Eur J Prev Cardiol. 2025 Feb 4:zwaf038. doi: 10.1093/eurjpc/zwaf038. Online ahead of print.

ABSTRACT

AIMS: Cardiovascular disease (CVD) is a leading global cause of mortality. Environmental factors are increasingly recognized as influential determinants of cardiovascular health. Nevertheless, a finer-grained understanding of the effects of the built environment remains crucial for comprehending CVD. We sought to investigate the relationship between built environment features, including residential greenspace and sidewalks, and cardiovascular risk using street-level imagery and deep learning techniques.

METHODS: This study employed Google Street View (GSV) imagery and deep learning techniques to analyze built environment features around residences in relation to major adverse cardiovascular events (MACE) risk. Data from a Northeast Ohio cohort were utilized. Various covariates, including socioeconomic and environmental factors, were incorporated in Cox Proportional Hazards models.

RESULTS: Of 49,887 individuals included, 2,083 experienced MACE over a median follow-up of 26.86 months. Higher tree-sky index and sidewalk presence were associated with reduced MACE risk (HR: 0.95, 95% CI: 0.91-0.99, and HR: 0.91, 95% CI: 0.87-0.96, respectively), even after adjusting for demographic, socioeconomic, environmental, and clinical factors.

CONCLUSIONS: Visible vertical greenspace and sidewalks, as discerned from street-level images using deep learning, demonstrated potential associations with cardiovascular risk. This innovative approach highlights the potential of deep learning to analyze built environments at scale, offering new avenues for public health research. Future research is needed to validate these associations and better understand the underlying mechanisms.

PMID:39903569 | DOI:10.1093/eurjpc/zwaf038

Eur Radiol. 2025 Feb 4. doi: 10.1007/s00330-025-11376-9. Online ahead of print.

ABSTRACT

OBJECTIVE: To directly compare coronary arterial stenosis evaluations by hybrid-type iterative reconstruction (IR), model-based IR (MBIR), deep learning reconstruction (DLR), and high-resolution deep learning reconstruction (HR-DLR) on coronary computed tomography angiography (CCTA) in both in vitro and in vivo studies.

MATERIALS AND METHODS: For the in vitro study, a total of three-vessel tube phantoms with diameters of 3 mm, 4 mm, and 5 mm and with simulated non-calcified stepped stenosis plaques with degrees of 0%, 25%, 50%, and 75% stenosis were scanned with area-detector CT (ADCT) and ultra-high-resolution CT (UHR-CT). Then, ADCT data were reconstructed using all methods, although UHR-CT data were reconstructed with hybrid-type IR, MBIR, and DLR. For the in vivo study, patients who had undergone CCTA at ADCT were retrospectively selected, and each CCTA data set was reconstructed with all methods. To compare the image noise and measurement accuracy at each of the stenosis levels, image noise, and inner diameter were evaluated and statistically compared. To determine the effect of HR-DLR on CAD-RADS evaluation accuracy, the accuracy of CAD-RADS categorization of all CCTAs was compared by using McNemar's test.

RESULTS: The image noise of HR-DLR was significantly lower than that of others on ADCT and UHR-CT (p < 0.0001). At a 50% and 75% stenosis level for each phantom, hybrid-type IR showed a significantly larger mean difference on ADCT than did others (p < 0.05). At in vivo study, 31 patients were included. Accuracy on HR-DLR was significantly higher than that on hybrid-type IR, MBIR, or DLR (p < 0.0001).

CONCLUSION: HR-DLR is potentially superior for coronary arterial stenosis evaluations to hybrid-type IR, MBIR, or DLR shown on CCTA.

KEY POINTS: Question How do coronary arterial stenosis evaluations by hybrid-type IR, MBIR, DLR, and HR-DLR compare to coronary CT angiography? Findings HR-DLR showed significantly lower image noise and more accurate coronary artery disease reporting and data system (CAD-RADS) evaluation than others. Clinical relevance HR-DLR is potentially superior to other reconstruction methods for coronary arterial stenosis evaluations, as demonstrated by coronary CT angiography results on ADCT and as shown in both in vitro and in vivo studies.

PMID:39903239 | DOI:10.1007/s00330-025-11376-9