JMIR Med Inform. 2025 Aug 6;13:e66727. doi: 10.2196/66727.

ABSTRACT

BACKGROUND: The assessment of osteonecrosis of the femoral head (ONFH) often presents challenges in accuracy and efficiency. Traditional methods rely on imaging studies and clinical judgment, prompting the need for advanced approaches. This study aims to use deep learning algorithms to enhance disease assessment and prediction in ONFH, optimizing treatment strategies.

OBJECTIVE: The primary objective of this research is to analyze pathological images of ONFH using advanced deep learning algorithms to evaluate treatment response, vascular reconstruction, and disease progression. By identifying the most effective algorithm, this study seeks to equip clinicians with precise tools for disease assessment and prediction.

METHODS: Magnetic resonance imaging (MRI) data from 30 patients diagnosed with ONFH were collected, totaling 1200 slices, which included 675 slices with lesions and 225 normal slices. The dataset was divided into training (630 slices), validation (135 slices), and test (135 slices) sets. A total of 10 deep learning algorithms were tested for training and optimization, and MobileNetV3_Large was identified as the optimal model for subsequent analyses. This model was applied for quantifying vascular reconstruction, evaluating treatment responses, and assessing lesion progression. In addition, a long short-term memory (LSTM) model was integrated for the dynamic prediction of time-series data.

RESULTS: The MobileNetV3_Large model demonstrated an accuracy of 96.5% (95% CI 95.1%-97.8%) and a recall of 94.8% (95% CI 93.2%-96.4%) in ONFH diagnosis, significantly outperforming DenseNet201 (87.3%; P<.05). Quantitative evaluation of treatment responses showed that vascularized bone grafting resulted in an average increase of 12.4 mm in vascular length (95% CI 11.2-13.6 mm; P<.01) and an increase of 2.7 in branch count (95% CI 2.3-3.1; P<.01) among the 30 patients. The model achieved an AUC of 0.92 (95% CI 0.90-0.94) for predicting lesion progression, outperforming traditional methods like ResNet50 (AUC=0.85; P<.01). Predictions were consistent with clinical observations in 92.5% of cases (24/26).

CONCLUSIONS: The application of deep learning algorithms in examining treatment response, vascular reconstruction, and disease progression in ONFH presents notable advantages. This study offers clinicians a precise tool for disease assessment and highlights the significance of using advanced technological solutions in health care practice.

PMID:40768653 | DOI:10.2196/66727

Sci Adv. 2025 Aug 8;11(32):eadr8901. doi: 10.1126/sciadv.adr8901. Epub 2025 Aug 6.

ABSTRACT

In this study, postimplantation human epiblast and amnion development are modeled using a stem cell-based embryoid system. A dataset of 3697 fluorescent images, along with tissue, cavity, and cell masks, is generated from experimental data. A computational pipeline analyzes morphological and marker expression features, revealing key developmental processes such as tissue growth, cavity expansion, and cell differentiation. To uncover hidden developmental dynamics, a deep manifold learning framework is introduced. This framework uses an autoencoder to project embryoid images into a twenty-dimensional (20D) latent space and models the dynamics using a mean-reverting stochastic process of mixed Gaussians. The approach accurately captures phenotypic changes observed at discrete experimental time points. Moreover, it enables the generation of artificial yet realistic embryoid images at finer temporal resolutions, providing deeper insights into the progression of early human development.

PMID:40768579 | DOI:10.1126/sciadv.adr8901

PLoS One. 2025 Aug 6;20(8):e0327315. doi: 10.1371/journal.pone.0327315. eCollection 2025.

ABSTRACT

Land cover and plant species identification using satellite images and deep learning approaches have recently been a widely addressed area of research. However, mangroves, a specific species that have significantly declined in quantity and quality worldwide despite their numerous benefits, have not been the subject of attention. The novelty of this research is to deal with this species based on an advanced deep learning solution (a proposed ensemble U-Net model) and a high-resolution Planet satellite imagery (5 m x 5 m) in a case study of Ngoc Hien district, Ca Mau province, Vietnam. Twelve single U-Net backbone models were trained, and three quantitative metrics (Intersection over Union, F1-score, and Overall Accuracy) were used to evaluate. The findings indicate that three out of twelve models (MobileNet, SEResNeXt-101 and Efficientnet-B7) experienced the most efficient assessment results for identifying all classes, in which the MobileNet model was the best. These models were applied for the ensemble model's development. The ensemble model's quantitative assessment metrics increased considerably by about 3-10% compared to the single-component models. The IoU, F1-score, and OA values of this model were 80.08%, 95.82%, and 95.90%, respectively. Three classes of mangrove species (Avicennia alba, Rhizophora apiculate, and mixed mangroves) in the ensemble model had more uniform assessment results. In conclusion, to achieve optimal classification outcomes, a land-cover map comprising mangrove species is possibly established using the proposed ensemble model, while a distribution map of mangrove species enables to be developed using the MobileNet model.

PMID:40768506 | DOI:10.1371/journal.pone.0327315

PLoS One. 2025 Aug 6;20(8):e0321239. doi: 10.1371/journal.pone.0321239. eCollection 2025.

ABSTRACT

Mass spectrometry (MS) analysis plays a crucial role in the biomedical field; however, the high dimensionality and complexity of MS data pose significant challenges for feature extraction and classification. Deep learning has become a dominant approach in data analysis, and while some deep learning methods have achieved progress in MS classification, their feature representation capabilities remain limited. Most existing methods rely on single-channel representations, which struggle to effectively capture structural information within MS data. To address these limitations, we propose a Multi-Channel Embedding Representation Module (MSMCE), which focuses on modeling inter-channel dependencies to generate multi-channel representations of raw MS data. Additionally, we implement a feature fusion mechanism by concatenating the initial encoded representation with the multi-channel embeddings along the channel dimension, significantly enhancing the classification performance of subsequent models. Experimental results on four public datasets demonstrate that the proposed MSMCE module not only achieves substantial improvements in classification performance but also enhances computational efficiency and training stability, highlighting its effectiveness in raw MS data classification and its potential for robust application across diverse datasets.

PMID:40768503 | DOI:10.1371/journal.pone.0321239

IEEE Trans Neural Syst Rehabil Eng. 2025 Aug 6;PP. doi: 10.1109/TNSRE.2025.3596488. Online ahead of print.

ABSTRACT

OBJECTIVES: After stroke, hand function assessments are used as outcome measures to evaluate new rehabilitation therapies, but do not reflect true performance in natural environments. Wearable (egocentric) cameras provide a way to capture hand function information during activities of daily living (ADLs). However, while clinical assessments involve observing multiple functional tasks, existing deep learning methods developed to analyze hands in egocentric video are only capable of considering single ADLs. This study presents a novel multi-video architecture that processes multiple task videos to make improved estimations about hand impairment.

METHODS: An egocentric video dataset of ADLs performed by stroke survivors in a home simulation lab was used to develop single and multi-input video models for binary impairment classification. Using SlowFast as a base feature extractor, late fusion (majority voting, fully-connected network) and intermediate fusion (concatenation, Markov chain) were investigated for building multi-video architectures.

RESULTS: Through evaluation with Leave-One-Participant-Out-Cross-Validation, using intermediate concatenation fusion to build multi-video models was found to achieve the best performance out of the fusion techniques. The resulting multi-video model for cropped inputs achieved an F1-score of 0.778±0.129 and significantly outperformed its single-video counterpart (F1-score of 0.696±0.102). Similarly, the multi-video model for full-frame inputs (F1-score of 0.796±0.102) significantly outperformed its single-video counterpart (F1-score of 0.708±0.099).

CONCLUSION: Multi-video architectures are beneficial for estimating hand impairment from egocentric video after stroke.

SIGNIFICANCE: The proposed deep learning solution is the first of its kind in multi-video analysis, and opens the door to further applications in automating other multi-observation assessments for clinical use.

PMID:40768474 | DOI:10.1109/TNSRE.2025.3596488

IEEE J Biomed Health Inform. 2025 Aug 6;PP. doi: 10.1109/JBHI.2025.3595697. Online ahead of print.

ABSTRACT

Medical imaging, particularly retinal fundus photography, plays a crucial role in early disease detection and treatment for various ocular disorders. However, the development of robust diagnostic systems using deep learning remains constrained by the scarcity of expertly annotated data, which is time-consuming and expensive. Self-Supervised Learning (SSL) has emerged as a promising solution, but existing models fail to effectively incorporate critical domain knowledge specific to retinal anatomy. This potentially limits their clinical relevance and diagnostic capability. We address this issue by introducing an anatomically aware SSL framework that strategically integrates domain expertise through specialized masking of vital retinal structures during pretraining. Our approach leverages vessel and optic disc segmentation maps to guide the SSL process, enabling the development of clinically relevant feature representations without extensive labeled data. The framework combines a Vision Transformer with dual-masking strategies and anatomically informed loss functions to preserve structural integrity during feature learning. Comprehensive evaluation across multiple datasets demonstrates our method's competitive performance in diverse retinal disease classification tasks, including diabetic retinopathy grading, glaucoma detection, age-related macular degeneration identification, and multi-disease classification. The evaluation results establish the effectiveness of anatomically-aware SSL in advancing automated retinal disease diagnosis while addressing the fundamental challenge of limited labeled medical data.

PMID:40768461 | DOI:10.1109/JBHI.2025.3595697

IEEE J Biomed Health Inform. 2025 Aug 6;PP. doi: 10.1109/JBHI.2025.3595781. Online ahead of print.

ABSTRACT

As the population of older adults continues growing, so will the need for cost-effective approaches to early dementia detection. Deep learning approaches using patient speech samples show promising results. This systematic review examines studies utilizing speech-based deep learning for dementia diagnosis with the objective of identifying best practices for future data-driven dementia research. Studies researching speech-based deep learning for dementia were obtained from PubMed, Wiley Library, Science Direct, IEEE, Web of Science, Google Scholar, and arXiv. 80 studies were reviewed. Studies were analyzed in terms of model architecture and performance, speech features employed, and databases used. We observed that transformer-based approaches were most frequent, achieving an average accuracy of 85.71%, and that linguistic features outperform acoustic features. Our review identified several limitations, including a lack of dataset diversity, inconsistent classification of dementia severity levels across studies, and variability in how sample sizes and model performance metrics (e.g., accuracy, sensitivity, specificity) are reported. These inconsistencies hinder direct comparisons between studies and limit the reproducibility of findings. Still, our findings suggest that incorporation of transformers into current speech-based deep learning models can further improve detection of cognitive impairment. Consideration of our observations in future data-driven dementia research will lead to advancements in the development of diagnostic decision support systems for clinical practice.

PMID:40768460 | DOI:10.1109/JBHI.2025.3595781

IEEE Trans Pattern Anal Mach Intell. 2025 Aug 6;PP. doi: 10.1109/TPAMI.2025.3596462. Online ahead of print.

ABSTRACT

Adversarial patches present significant challenges to the robustness of deep learning models, making the development of effective defenses become critical for real-world applications. This paper introduces DIFFender, a novel DIFfusion-based DeFender framework that leverages the power of a text-guided diffusion model to counter adversarial patch attacks. At the core of our approach is the discovery of the Adversarial Anomaly Perception (AAP) phenomenon, which enables the diffusion model to accurately detect and locate adversarial patches by analyzing distributional anomalies. DIFFender seamlessly integrates the tasks of patch localization and restoration within a unified diffusion model framework, enhancing defense efficacy through their close interaction. Additionally, DIFFender employs an efficient few-shot prompt-tuning algorithm, facilitating the adaptation of the pre-trained diffusion model to defense tasks without the need for extensive retraining. Our comprehensive evaluation, covering image classification and face recognition tasks, as well as real-world scenarios, demonstrates DIFFender's robust performance against adversarial attacks. The framework's versatility and generalizability across various settings, classifiers, and attack methodologies mark a significant advancement in adversarial patch defense strategies. Except for the popular visible domain, we have identified another advantage of DIFFender: its capability to easily expand into the infrared domain. Consequently, we demonstrate the good flexibility of DIFFender, which can defend against both infrared and visible adversarial patch attacks alternatively using a universal defense framework.

PMID:40768456 | DOI:10.1109/TPAMI.2025.3596462

Sci Transl Med. 2025 Aug 6;17(810):eadt1452. doi: 10.1126/scitranslmed.adt1452. Epub 2025 Aug 6.

ABSTRACT

Each year in the United States, ~50% of adults ≥18 years old are vaccinated against influenza viruses, with protective efficacy averaging 40.5% over the past 20 years. To model annual seasonal influenza, a cohort of 74 adults, who were unscreened for preexisting A/H1N1 immunity and half of whom were recently immunized with licensed QIV (mean of 64 days), were challenged with A/H1N1 influenza virus. Transcriptomic, proteomic, and VDJ repertoire analyses were performed on nasal and peripheral blood samples from participants to identify nasal mucosal and systemic immune responses that correlated with viral shedding and immune correlates of protection. Viral-shedding participants showed increased T cell, but not B cell, VDJ diversity with expansion of low-frequency B cell clones postchallenge, including broadly neutralizing motifs. Nonshedding participants demonstrated decreased clonality and increased richness of B and T cell VDJ clones, increased preinoculation nasal mucosal immune gene and serum protein expression, and increased ex vivo peripheral blood mononuclear cell responses. Nasal mucosal responses in participants shedding virus for 2 or more days showed higher early viral loads and exhibited stronger induction of antiviral responses compared with those in participants who shed virus for 1 day. Last, participants with a single day of viral shedding were three times more likely to be female. These data shed light on the complex immune responses in the nasal mucosa and the periphery after influenza vaccination and infection, which will be critical for next-generation vaccine development.

PMID:40768601 | DOI:10.1126/scitranslmed.adt1452

Plant Biotechnol J. 2025 Aug 6. doi: 10.1111/pbi.70289. Online ahead of print.

ABSTRACT

Nitrogen availability remains a principal constraint to crop productivity. Plants cannot directly assimilate the abundant nitrogen available in our atmosphere; instead, they rely on the uptake of inorganic forms of nitrogen, such as ammonium and nitrate from the soil. Nitrogen is a limiting nutrient in wheat production, and wheat yields are very responsive to nitrogen fertilisation. Only diazotrophic bacteria can convert atmospheric nitrogen to ammonia via biological nitrogen fixation (BNF), and although improving BNF in wheat has been a longstanding objective, there have been no descriptions of successful modification of wheat crops showing increased BNF in the literature. Here we describe the use of polycistronic multiplexed CRISPR to modify the flavone biosynthetic pathway of hexaploid wheat (Triticum aestivum) plants, generating DNA-edited plants with increased apigenin content. The apigenin-enriched plants exude apigenin into the soil, inducing the colonisation of the roots and subsequent formation of biofilms in soil by diazotrophic bacteria. The low permeability of the biofilm to oxygen protected the bacterial nitrogenase and stimulated BN. Under nitrogen-limiting conditions, apigenin-enriched wheat lines exhibited increased nitrogen content, improved photosynthetic performance, and higher grain yield relative to wild-type controls. This work demonstrates the feasibility of engineering associative BNF in cereals via metabolic reprogramming of root exudation, offering a sustainable route to reduce dependence on synthetic nitrogen fertilisers.

PMID:40768387 | DOI:10.1111/pbi.70289

Respir Investig. 2025 Aug 5;63(5):1002-1011. doi: 10.1016/j.resinv.2025.07.023. Online ahead of print.

ABSTRACT

Immune checkpoint inhibitors (ICIs) have changed cancer treatment, evoking durable responses in various cancers. However, their immune-mediated mechanisms can lead to unique toxicities known as immune-related adverse events (irAEs), among which ICI-related pneumonitis (ICI-P) is a critical concern owing to its potential severity and impact on treatment continuity. This review provides a comprehensive overview of ICI-P, covering its epidemiology, pathophysiology, clinical features, diagnosis, risk factors, and treatments. ICI-P is more frequently observed in real-world settings than in clinical trials, especially in patients with risk factors such as pre-existing interstitial lung disease. The pathogenesis of ICI-P involves Th1/Th17 inflammation and autoantibody production, but the exact mechanisms remain unclear. An organizing pneumonia-like pattern is a characteristic radiological finding on chest computed tomography. Diagnosis can be supported by the evaluation of biomarkers like Krebs von den Lungen-6 and surfactant protein-D. High-dose corticosteroids are the standard treatment, although optimal regimens remain under investigation. Relapse is not uncommon, particularly in cases with an organizing pneumonia-like pattern or prolonged exposure to ICIs. Resuming ICIs after ICI-P is associated with a marked risk of relapse and must be carefully considered. Notably, immune modulation by ICIs may persist after discontinuation, potentially increasing susceptibility to drug-induced pneumonitis from subsequent therapies. As the use of ICIs expands, enhanced recognition, timely intervention, and individualized management of ICI-P are essential. Future strategies incorporating biomarkers, real-world data, and artificial intelligence may further improve outcomes for patients with ICI-P.

PMID:40768795 | DOI:10.1016/j.resinv.2025.07.023

PLoS One. 2025 Aug 6;20(8):e0329636. doi: 10.1371/journal.pone.0329636. eCollection 2025.

ABSTRACT

BACKGROUND: Flurbiprofen, as a widely used nonsteroidal anti-inflammatory drug (NSAID), is commonly employed to relieve mild to moderate pain and inflammation. Understanding its adverse reactions in real-world usage is of significant importance.

METHODS: Reports of all adverse drug events (ADEs) related to flurbiprofen were extracted from the FAERS database, covering the period from Q1 2004 to Q3 2024. These reports were standardized and analyzed using various signal quantification techniques, including Reporting Odds Ratios (ROR), Proportional Reporting Ratios (PRR), Bayesian Confidence Propagation Neural Network (BCPNN), and Multi-item Gamma Poisson Shrinkage (MGPS). Finally, the association between flurbiprofen and ADEs as well as clinical medical events was assessed.

RESULTS: A total of 275 cases from the target population were identified in the FAERS database, with 788 instances of adverse events (AEs) occurring across 46 organ systems. We identified not only some common adverse reactions listed in the drug's package insert, such as acute kidney injury, nausea and vomiting, and facial edema, but also significant signals that were not mentioned in the package insert, including Dysphonia, Drug abuse, and Pancreatitis acute. The median time to onset of flurbiprofen-related AEs was 1 day (interquartile range [IQR] 0-5 days), with most AEs occurring within the first month of flurbiprofen use.

CONCLUSION: This study confirmed some common adverse reactions listed in the flurbiprofen drug package insert and identified significant unexpected adverse reactions. These findings can assist clinicians in conducting more comprehensive clinical monitoring when using the drug, thereby ensuring patient safety during treatment.

PMID:40768481 | DOI:10.1371/journal.pone.0329636

Curr Osteoporos Rep. 2025 Aug 6;23(1):34. doi: 10.1007/s11914-025-00928-z.

ABSTRACT

PURPOSE OF REVIEW: Calcification, the deposition of phosphate-calcium crystals, is essential for the development and function of mineralized tissues. When dysregulated, it can cause harmful effects. This review focuses on the critical role of the balance between phosphate (Pi) and pyrophosphate (PPi) in maintaining healthy mineralization and explains how disruptions in this balance contribute to rare calcifying disorders.

RECENT FINDINGS: Studies have identified key regulators of PPi production and Pi generation. Recent research on rare calcifying diseases and animal models has revealed how Pi/PPi imbalances lead to ectopic calcification in soft tissues, driving disease progression. The balance of Pi/PPi is vital for bone health and preventing pathological calcification. Disruptions in this equilibrium contribute to rare diseases. Understanding these mechanisms, supported by preclinical models, opens potential therapeutic avenues to restore balance and mitigate the impact of these diseases.

PMID:40768170 | DOI:10.1007/s11914-025-00928-z

J Genet Couns. 2025 Aug;34(4):e70085. doi: 10.1002/jgc4.70085.

ABSTRACT

Parenting a child with a rare undiagnosed genetic condition can impact psychological well-being, including anxiety and health-related quality-of-life. We conducted a multi-site quantitative survey with parents to understand which parent and child characteristics are predictive of poorer psychological outcomes. 1366 surveys were sent out across seven NHS Trusts in England; 383 were returned and included in analysis (27% response rate). We used the GAD-7 to measure parents' generalized anxiety and the PedsQL Family Impact Module (FIM) to measure self-reported physical, emotional, social, and cognitive functioning (the health-related quality-of-life [HRQOL] summary score), communication, worry, daily activities, and family relationships (the family functioning [FF] summary score). Participant characteristics included: the 6-item Brief Resilience Scale to measure parental resilience, a bespoke single question to assess parents' tolerance for uncertainty, the EQ-5D-Y-3L to measure child health-related quality-of-life, two bespoke questions to assess the perceived seriousness/consequences of the child's condition, and standard characteristics questions (e.g., age, ethnicity, education, income). Overall, parental anxiety was low (mean = 5.31; SD = 5.82, range 0-21), although 21.9% had moderate (11.4%) or severe (10.5%) anxiety. A multivariable analysis indicated that higher anxiety scores were significantly associated with younger parental age (p = 0.010), lower education attainment (0.004), lower resilience (p = 0.049), and lower tolerance for uncertainty (p = 0.021). FIM total scores ranged from 0 to 100 (mean = 53.68, SD 20.45). Parents scored lowest on the subscale daily activities (43.68), worry (47.29), communication (51.31), and physical functioning (52.45). Family functioning summary scores were significantly lower for parents of children with developmental disorders compared to other conditions (p = 0.016). Multivariable analysis identified that lower scores (reflecting poorer outcomes) were significantly associated with lower parental resilience and lower tolerance for uncertainty (p < 0.001, respectively). Our findings highlight the significant psychological burden parenting a child with a rare undiagnosed condition can have on some parents and the importance of developing tailored support strategies.

PMID:40767117 | DOI:10.1002/jgc4.70085

Phytother Res. 2025 Aug 6. doi: 10.1002/ptr.8367. Online ahead of print.

ABSTRACT

The increasing prevalence of inflammatory diseases in the respiratory tract worldwide has raised concerns, and due to its high prevalence and poor prognosis, it remains a clinical focus and research hotspot. These inflammatory diseases include airway inflammation, asthma, bacterial antigens-induced tonsil epithelial inflammation, chronic obstructive pulmonary disease (COPD), cystic fibrosis (CF), COVID-19, acute lung injury, and lung cancer. This review summarizes the relevant molecular mechanisms of inflammatory diseases in the respiratory tract and the progress of natural bioactive components in inflammatory diseases in the respiratory tract. The natural bioactive components have good therapeutic or intervention effects on inflammatory airway diseases in vitro, in vivo, and in clinical trials. The information on inflammatory diseases in the respiratory tract and natural bioactive ingredients in anti-inflammatory diseases were collected from famous literature databases such as Web of Science, PubMed, and Google Scholar, with keywords including bioactive components, inflammatory diseases, respiratory tract, and so forth. The bioactive phytochemicals, such as curcumin, ginsenoside, safranal, melatonin, could improve inflammatory diseases through the regulation of PI3K/Akt, NF-κB, NRF2/HO-1, MAPK, cAMP-PKA, and MEK/ERK Signaling pathways. Further high-quality studies are still needed to firmly establish the clinical efficacy of bioactive ingredients. This review provides new insight for future research on functional food or drug-lead compound development on natural products improving inflammatory diseases in the respiratory tract.

PMID:40767628 | DOI:10.1002/ptr.8367

J Asthma. 2025 Aug 6:1-12. doi: 10.1080/02770903.2025.2494233. Online ahead of print.

ABSTRACT

OBJECTIVE: Age of asthma onset is critical for determining heterogeneous asthma phenotypes. How onset and duration affect therapeutic response is not well understood. Phase 3 QUEST (NCT02414854) and open-label extension TRAVERSE (NCT02134028) studies demonstrated dupilumab's efficacy up to three years in patients ≥12 years with uncontrolled, moderate-to-severe asthma. We assessed how age of asthma onset and asthma duration affect clinical efficacy of dupilumab in patients with moderate-to-severe type 2 inflammatory asthma.

METHODS: This post hoc analysis included patients with type 2 asthma from QUEST who enrolled in TRAVERSE. Annualized severe exacerbation rates (AER), change from parent study baseline (PSBL) in pre-bronchodilator forced expiratory volume in 1 s (FEV1), and five-item Asthma Control Questionnaire (ACQ-5) score were assessed according to asthma age of onset (<18 years, 18-40 years, >40 years) and duration (<20 years, ≥20 years).

RESULTS: In all subgroups, treatment with dupilumab through QUEST and TRAVERSE progressively reduced AER (TRAVERSE Week 48-96 range, 0.160-0.333), increased pre-bronchodilator FEV1 (TRAVERSE Week 96 change from PSBL range, 0.20-0.44 L), and reduced ACQ-5 scores (TRAVERSE Week 48 change from PSBL range, -1.63 to -1.84). In patients who received placebo during QUEST, treatment with dupilumab in TRAVERSE improved AER, FEV1, and ACQ-5 in all subgroups.

CONCLUSIONS: In patients with uncontrolled, moderate-to-severe type 2 asthma, treatment with dupilumab provides sustained, long-term exacerbation rate reductions and improvements in lung function and asthma control, across all subgroups, with higher reductions in AER and improvements in pre-bronchodilator FEV1 seen in patients with later onset or longer duration.

PMID:40767333 | DOI:10.1080/02770903.2025.2494233

J Imaging Inform Med. 2025 Aug 6. doi: 10.1007/s10278-025-01622-3. Online ahead of print.

ABSTRACT

This study aimed to explore whether an artificial intelligence iterative reconstruction (AIIR) algorithm combined with low-dose aortic computed tomography angiography (CTA) demonstrates clinical effectiveness in assessing preoperative access for transcatheter aortic valve implantation (TAVI). A total of 109 patients were prospectively recruited for aortic CTA scans and divided into two groups: group A (n = 51) with standard-dose CT examinations (SDCT) and group B (n = 58) with low-dose CT examinations (LDCT). Group B was further subdivided into groups B1 and B2. Groups A and B2 used the hybrid iterative algorithm (HIR: Karl 3D), whereas Group B1 used the AIIR algorithm. CT attenuation and noise of different vessel segments were measured, and the contrast-to-noise ratio (CNR) and signal-to-noise ratio (SNR) were calculated. Two radiologists, who were blinded to the study details, rated the subjective image quality on a 5-point scale. The effective radiation doses were also recorded for groups A and B. Group B1 demonstrated the highest CT attenuation, SNR, and CNR and the lowest image noise among the three groups (p < 0.05). The scores of subjective image noise, vessel and non-calcified plaque edge sharpness, and overall image quality in Group B1 were higher than those in groups A and B2 (p < 0.001). Group B2 had the highest artifacts scores compared with groups A and B1 (p < 0.05). The radiation dose in group B was reduced by 50.33% compared with that in group A (p < 0.001). The AIIR algorithm combined with low-dose CTA yielded better diagnostic images before TAVI than the Karl 3D algorithm.

PMID:40768017 | DOI:10.1007/s10278-025-01622-3

Environ Monit Assess. 2025 Aug 6;197(9):982. doi: 10.1007/s10661-025-14420-9.

ABSTRACT

Crop classification during the early stages is challenging because of the striking similarity in spectral and texture features among various crops. To improve classification accuracy, this study proposes a novel fusion-based deep learning approach. The approach integrates textural and spectral features from a fused dataset generated by merging Landsat 8-9 and Sentinel-2A data using the Gram-Schmidt fusion approach. The textural features were extracted using the multi-patch Gray Level Co-occurrence Matrix (GLCM) technique. The spectral features, namely the Enhanced Vegetation Index (EVI) and Normalized Difference Vegetation Index (NDVI), were obtained using the spectral index method. The five machine learning methods (deep neural network, 1D convolutional neural network, decision tree, support vector machine, and random forest) were trained using textural and spectral parameters to develop classifiers. The proposed approach achieves promising results using deep neural network (DNN), with an accuracy of 0.89, precision of 0.88, recall of 0.91, and F1-score of 0.90. These results demonstrate the effectiveness of the fusion-based deep learning approach in enhancing classification accuracy for early-stage crops.

PMID:40767980 | DOI:10.1007/s10661-025-14420-9

J Cachexia Sarcopenia Muscle. 2025 Aug;16(4):e70021. doi: 10.1002/jcsm.70021.

ABSTRACT

BACKGROUND: AI-driven automated body composition analysis (BCA) may provide quantitative prognostic biomarkers derived from routine staging CTs. This two-centre study evaluates the prognostic value of these volumetric markers for overall survival in lung cancer patients.

METHODS: Lung cancer cohorts from Hospital A (n = 3345, median age 65, 86% NSCLC, 40% M1, 40% female) and B (n = 1364, median age 66, 87% NSCLC, 37% M1, 38% female) underwent automated BCA of abdominal CTs ±60 days of primary diagnosis. A deep learning network segmented muscle, bone and adipose tissues (visceral = VAT, subcutaneous = SAT, intra-/intermuscular = IMAT and total = TAT) to derive three markers: Sarcopenia Index (SI = Muscle/Bone), Myosteatotic Fat Index (MFI = IMAT/TAT) and Abdominal Fat Index (AFI = VAT/SAT). Kaplan-Meier survival analysis, Cox proportional hazards modelling and machine learning-based survival prediction were performed. A survival model including clinical data (BMI, ECOG, L3-SMI, -SATI, -VATI and -IMATI) was fitted on Hospital A data and validated on Hospital B data.

RESULTS: In nonmetastatic NSCLC, high SI predicted longer survival across centres for males (Hospital A: 24.6 vs. 46.0 months; Hospital B: 13.3 vs. 28.9 months; both p < 0.001) and females (Hospital A: 37.9 vs. 53.6 months, p = 0.008; Hospital B: 23.0 vs. 28.6 months, p = 0.018). High MFI indicated reduced survival in males at both hospitals (Hospital A: 43.7 vs. 28.2 months; Hospital B: 28.8 vs. 14.3 months; both p ≤ 0.001) but showed center-dependent effects in females (significant only in Hospital A, p < 0.01). In metastatic disease, SI remained prognostic for males at both centres (p < 0.05), while MFI was significant only in Hospital A (p ≤ 0.001) and AFI only in Hospital B (p = 0.042). Multivariate Cox regression confirmed that higher SI was protective (A: HR 0.53, B: 0.59, p ≤ 0.001), while MFI was associated with shorter survival (A: HR 1.31, B: 1.12, p < 0.01). The multivariate survival model trained on Hospital A's data demonstrated prognostic differentiation of groups in internal (n = 209, p ≤ 0.001) and external (Hospital B, n = 361, p = 0.044) validation, with SI feature importance (0.037) ranking below ECOG (0.082) and M-status (0.078), outperforming all other features including conventional L3-single-slice measurements.

CONCLUSION: CT-based volumetric BCA provides prognostic biomarkers in lung cancer with varying significance by sex, disease stage and centre. SI was the strongest prognostic marker, outperforming conventional L3-based measurements, while fat-related markers showed varying associations. Our multivariate model suggests that BCA markers, particularly SI, may enhance risk stratification in lung cancer, pending centre-specific and sex-specific validation. Integration of these markers into clinical workflows could enable personalized care and targeted interventions for high-risk patients.

PMID:40767951 | DOI:10.1002/jcsm.70021

J Robot Surg. 2025 Aug 6;19(1):453. doi: 10.1007/s11701-025-02583-z.

ABSTRACT

To analyze the structural and temporal evolution of artificial intelligence (AI) and digital health applications in vascular surgery over the past two decades, identifying historical development trajectories, research focal points, and emerging frontiers. Publications on AI and digital health applications in vascular surgery were retrieved from WoSCC. Analyzed through CiteSpace and HistCite to track temporal development, thematic shifts, and innovation patterns within the domain. Active themes have emerged over time, with 123 related disciplines, 505 keywords, and 675 outbreak papers cited. Keyword clustering anchors seven emerging research subfields, namely #0 deep learning, #2 machine learning, #3 peripheral arterial disease, #4 renal cell carcinoma, #5 aortic aneurysm, #6 pulmonary embolism, #7nanocarrier. The alluvial map indicates that the most enduring research concepts within the domain include bypass, revascularisation, and others, while emerging keywords consist of chronic limb-threatening ischemia and peripheral vascular intervention, among others. Reference clustering identifies seven recent subfields of research: nephrectomy #0, force #1, artificial intelligence #2, navigation #4, prediction #5, augmented reality #9, and telemedicine #13. This study provides a comprehensive mapping of AI and digital health adoption in vascular surgery, delineating paradigm shifts from traditional surgical techniques to computational prediction models and intelligent intervention systems. The findings establish foundational references for prioritizing research investments and developing standardized evaluation metrics for emerging technologies.

PMID:40767924 | DOI:10.1007/s11701-025-02583-z