Adv Sci (Weinh). 2025 May 24:e03135. doi: 10.1002/advs.202503135. Online ahead of print.

ABSTRACT

The precise prediction of transcription factor binding sites (TFBSs) is crucial in understanding gene regulation. In this study, deepTFBS, a comprehensive deep learning (DL) framework that builds a robust DNA language model of TF binding grammar for accurately predicting TFBSs within and across plant species is presented. Taking advantages of multi-task DL and transfer learning, deepTFBS is capable of leveraging the knowledge learned from large-scale TF binding profiles to enhance the prediction of TFBSs under small-sample training and cross-species prediction tasks. When tested using available information on 359 Arabidopsis TFs, deepTFBS outperformed previously described prediction strategies, including position weight matrix, deepSEA and DanQ, with a 244.49%, 49.15%, and 23.32% improvement of the area under the precision-recall curve (PRAUC), respectively. Further cross-species prediction of TFBS in wheat showed that deepTFBS yielded a significant PRAUC improvement of 30.6% over these three baseline models. deepTFBS can also utilize information from gene conservation and binding motifs, enabling efficient TFBS prediction in species where experimental data availability is limited. A case study, focusing on the WUSCHEL (WUS) transcription factor, illustrated the potential use of deepTFBS in cross-species applications, in our example between Arabidopsis and wheat. deepTFBS is publically available at https://github.com/cma2015/deepTFBS.

PMID:40411397 | DOI:10.1002/advs.202503135

Magn Reson Med. 2025 May 24. doi: 10.1002/mrm.30565. Online ahead of print.

ABSTRACT

PURPOSE: Although clinical 7T MRI offers various advantages compared to lower field strengths, achieving spatially uniform flip angle distributions remains a challenge. Sampling Perfection with Application optimized Contrast using different flip angle Evolution (SPACE) sequences employing a long train of refocusing pulses with varying flip angles pose a particular challenge in that regard. In this study, we investigate scalable dynamic parallel transmission (pTx) pulses to achieve homogeneous 3D high-resolution SPACE brain imaging at 7T.

METHODS: Non-parametrized and scalable dynamic pTx pulses were designed for excitation, refocusing and inversion in SPACE sequences by using fast online customization (FOCUS). First, a database of B0 and multi-channel B 1 + $$ {\mathrm{B}}_1^{+} $$ maps were used for optimizing universal pulses and parameters for flip angle homogeneity under strict specific absorption rate (SAR) constraints. During each new examination, B0 and B 1 + $$ {\mathrm{B}}_1^{+} $$ maps were acquired as additional calibration step and pTx pulses were tailored to the subject. For scalability, a symmetry condition was enforced. T1, T2, fluid-attenuated inversion recovery (FLAIR) and double inversion recovery (DIR) SPACE images were acquired in five healthy subjects at 7T using the proposed FOCUS pulses and conventional circularly polarized (CP) pulses for comparison.

RESULTS: Improved SNR and better image homogeneity were observed in every image acquired with FOCUS pulses in comparison to CP. Quantitative analysis showed a significant reduction in the coefficient of variation (COV) of image intensities in the cerebellum, a region notably affected by B 1 + $$ {\mathrm{B}}_1^{+} $$ inhomogeneities across all contrasts. FLAIR images, for example, exhibited a 46% COV reduction.

CONCLUSION: Individually optimized dynamic pTx pulses for 3D high-resolution SPACE imaging delivered clinically acceptable image homogeneity, enabling the application of widely used clinical contrasts at 7T.

PMID:40411368 | DOI:10.1002/mrm.30565

J Sci Food Agric. 2025 May 24. doi: 10.1002/jsfa.14378. Online ahead of print.

ABSTRACT

BACKGROUND: Computer vision and the use of image-based solutions are gaining traction as non-destructive food assessment methods because of the low costs of computational equipment. Research conducted on the development of wheat classification models has been based on limited data and a smaller number of classes compared to the availability of wheat varieties. To assess the applicability of convolutional neural network (CNN) models, the present study prepared multi-view images of 124 wheat varieties. Using deep learning (DL) methods, a four-layered CNN model was developed from scratch, and popular architectures, DenseNet201, MobileNet and InceptionV3 were trained using transfer learning.

RESULTS: The proposed CNN model, DenseNet201, MobileNet and InceptionV3 models achieved classification accuracies of 95.40%, 92.41%, 90.54% and 83.47%, respectively, and they were found to be both promising and successful. Despite the challenges related to high computational resource demands, the newly proposed CNN model outperformed the pretrained models. It can be inferred that the multi-view, large-image dataset contributed significantly to the model's success in achieving promising accuracy in the challenging task of classifying 124 wheat varieties.

CONCLUSION: The present study recommends further fine-tuning of hyperparameters to improve the accuracy of the proposed CNN model and to identify better configurations. Besides, other popular models should be evaluated. Moreover, by freezing specific early layers, fine-tuning should be performed to maximize accuracy. Additionally, the image datasets used will be publicly available to allow researchers to discover new methodologies to classify wheat varieties. © 2025 The Author(s). Journal of the Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

PMID:40411235 | DOI:10.1002/jsfa.14378

Artif Cells Nanomed Biotechnol. 2025 Dec;53(1):231-243. doi: 10.1080/21691401.2025.2506591. Epub 2025 May 23.

ABSTRACT

The unknown pathogenic mechanisms of Alzheimer's disease (AD) make treatment challenging. Neuroimaging genetics offers a method for identifying disease biomarkers for early diagnosis, but traditional approaches struggle with complex non-linear, multimodal and multi-expression data. However, traditional association analysis methods face challenges in handling nonlinear, multimodal and multi-expression data. Therefore, a multimodal attention fusion deep self-restructuring presentation (MAFDSRP) model is proposed to solve the above problem. First, multimodal brain imaging data are processed through a novel histogram-matching multiple attention mechanisms to dynamically adjust the weight of each input brain image data. Simultaneous, the genetic data are preprocessed to remove low-quality samples. Subsequently, the genetic data and fused neuroimaging data are separately input into the self-reconstruction network to learn the nonlinear relationships and perform subspace clustering at the top layer of the network. Finally, the learned genetic data and fused neuroimaging data are analysed through expression association analysis to identify AD-related biomarkers. The identified biomarkers underwent systematic multi-level analysis, revealing biomarker roles at molecular, tissue and functional levels, highlighting processes like inflammation, lipid metabolism, memory and emotional processing linked to AD. The experimental results show that MAFDSRP achieved 0.58 in association analysis, demonstrating its great potential in accurately identifying AD-related biomarkers.

PMID:40411137 | DOI:10.1080/21691401.2025.2506591

Am J Physiol Cell Physiol. 2025 May 24. doi: 10.1152/ajpcell.00022.2025. Online ahead of print.

ABSTRACT

Idiopathic pulmonary fibrosis (IPF) is a fatal aging-related disease characterized by aberrant lung remodeling and progressive scarring, leading to organ failure and death. Current FDA approved anti-fibrotic treatments are unable to reverse established disease, highlighting the need for innovative therapeutic approaches targeting novel pathways and cell types. Mounting evidence, including our own, has recently highlighted the pathogenic role of aging-related endothelial abnormalities, including vascular inflammation and oxidative stress, in the progression of lung fibrosis, offering new therapeutic opportunities to block IPF progression. Unexplored, however, are the modalities to restore vascular abnormalities associated with progressive lung fibrosis, representing a critical gap to effective treatments for IPF. In this study, we demonstrate that circulating extracellular vesicles (cEVs) isolated from young mice are capable of reversing the aging-associated transcriptional alterations of the pulmonary vasculature, reducing transcripts associated with innate immunity, oxidative stress and senescence, while simultaneously increasing transcripts linked to endothelial identity. Using the bleomycin model of persistent lung fibrosis in aged mice, we then demonstrate that the pre-treatment with cEVs improves the vascular response to injury and attenuates lung fibrosis progression, as demonstrated by reduced lung collagen content and preserved vascular network and lung architecture. These findings support the efficacy of interventions targeting endothelial aging-associated transcriptional alterations, such as young cEV delivery, in mitigating pulmonary fibrosis progression in animal models of persistent fibrosis and indicate the potential benefits of combined therapies that simultaneously address vascular and non-vascular aspects of IPF.

PMID:40411768 | DOI:10.1152/ajpcell.00022.2025

STAR Protoc. 2025 May 22;6(2):103838. doi: 10.1016/j.xpro.2025.103838. Online ahead of print.

ABSTRACT

Complex spatial transcriptomics (ST) data analysis can be challenging due to uneven sampling, sparsity, and ambiguous tissue boundaries. Here, we present a protocol for deciphering complex ST data using STMiner. We describe steps for installing STMiner, loading ST data into STMiner, and identifying spatially variable genes. We then detail procedures for determining gene sets associated with the structure of interest and obtaining spatial expression patterns. This protocol can be applied to varying resolutions and platforms, without additional reference data. For complete details on the use and execution of this protocol, please refer to Sun et al.1.

PMID:40411788 | DOI:10.1016/j.xpro.2025.103838

Mol Ecol Resour. 2025 May 24:e14122. doi: 10.1111/1755-0998.14122. Online ahead of print.

ABSTRACT

Numerous specimens stored in natural history collections have been involuntarily preserved together with their associated microbiomes. We propose exploiting century-old soils occasionally found on the roots of herbarium plants to assess the diversity of ancient soil microbial communities originally associated with these plants. We extracted total DNA and sequenced libraries produced from rhizospheric soils and roots of four plants preserved in herbaria for more than 120 years in order to characterise the preservation and taxonomic diversity that can be recovered in such contexts. Extracted DNA displayed typical features of ancient DNA, with cytosine deamination at the ends of fragments predominantly shorter than 50 bp. When compared to extant microbiomes, herbarium microbial communities clustered with soil communities and were distinct from communities from other environments. Herbarium communities also displayed biodiversity features and assembly rules typical of soil and plant-associated ones. Soil communities were richer than root-associated ones with which they shared most taxa. Regarding community turnover, we detected collection site, soil versus root and plant species effects. Eukaryotic taxa that displayed a higher abundance in roots were mostly plant pathogens that were not identified among soil-enriched ones. Conservation of these biodiversity features and assembly rules in herbarium-associated microbial communities indicates that herbarium-extracted DNA might reflect the composition of the original plant-associated microbial communities and that preservation in herbaria seemingly did not dramatically alter these characteristics. Using this approach, it should be possible to investigate historical soils and herbarium plant roots to explore the diversity and temporal dynamics of soil microbial communities.

PMID:40411280 | DOI:10.1111/1755-0998.14122

BMC Pulm Med. 2025 May 23;25(1):259. doi: 10.1186/s12890-025-03719-5.

ABSTRACT

BACKGROUND: Cryptogenic organizing pneumonia (COP), a rare interstitial lung disease, can mimic community-acquired pneumonia (CAP), often leading to delayed diagnosis. This case highlights the importance of recognizing COP in elderly patients and brings attention to pulmonary embolism (PE) as a potential but underrecognized complication associated with glucocorticosteroid therapy, providing novel insights into hypercoagulability risks during treatment.

CASE PRESENTATION: An 80-year-old woman from Xinjiang presented with a 4-week history of cough, dyspnea, and weight loss that was unresponsive to antibiotics. Chest Computed tomography (CT) revealed bilateral subpleural consolidations with air bronchograms. Bronchoscopy ruled out infection, and a multidisciplinary evaluation confirmed COP based on clinical, radiological, and pathological correlation. Oral prednisone at 0.75 mg/kg/day led to symptom resolution within 14 days. However, during steroid tapering (10% weekly reduction), she developed hypoxemia at 3 months. CT angiography revealed segmental PE, despite the absence of conventional thrombosis risk factors. Treatment with anticoagulation and continued glucocorticoid therapy resulted in full recovery after 6 months.

CONCLUSIONS: Clinicians should consider COP in elderly patients with pneumonia unresponsive to antibiotics, confirm the diagnosis through biopsy, and remain vigilant for hypercoagulable states during glucocorticoid tapering. Anticoagulation should be tailored even in the absence of traditional thrombosis risk factors. The temporal association between steroid tapering and PE suggests that glucocorticoids may modulate endothelial function and coagulation pathways, highlighting the need for mechanistic studies to inform thromboembolic surveillance in COP management.

PMID:40410735 | DOI:10.1186/s12890-025-03719-5

J Biomed Semantics. 2025 May 23;16(1):10. doi: 10.1186/s13326-025-00331-8.

ABSTRACT

BACKGROUND: Vaccines are crucial for preventing infectious diseases; however, they may also be associated with adverse events (AEs). Conventional analysis of vaccine AEs relies on manual review and assignment of AEs to terms in terminology or ontology, which is a time-consuming process and constrained in scope. This study explores the potential of using Large Language Models (LLMs) and LLM text embeddings for efficient and comprehensive vaccine AE analysis.

RESULTS: We used Llama-3 LLM to extract AE information from FDA-approved vaccine package inserts for 111 licensed vaccines, including 15 influenza vaccines. Text embeddings were then generated for each vaccine's AEs using the nomic-embed-text and mxbai-embed-large models. Llama-3 achieved over 80% accuracy in extracting AE text from vaccine package inserts. To further evaluate the performance of text embedding, the vaccines were clustered using two clustering methods: (1) LLM text embedding-based clustering and (2) ontology-based semantic similarity analysis. The ontology-based method mapped AEs to the Human Phenotype Ontology (HPO) and Ontology of Adverse Events (OAE), with semantic similarity analyzed using Lin's method. Text embeddings were generated for each vaccine's AE description using the LLM nomic-embed-text and mxbai-embed-large models. Compared to the semantic similarity analysis, the LLM approach was able to capture more differential AE profiles. Furthermore, LLM-derived text embeddings were used to develop a Lasso logistic regression model to predict whether a vaccine is "Live" or "Non-Live". The term "Non-Live" refers to all vaccines that do not contain live organisms, including inactivated and mRNA vaccines. A comparative analysis showed that, despite similar clustering patterns, the nomic-embed-text model outperformed the other. It achieved 80.00% sensitivity, 83.06% specificity, and 81.89% accuracy in a 10-fold cross-validation. Many AE patterns, with examples demonstrated, were identified from our analysis with AE LLM embeddings.

CONCLUSION: This study demonstrates the effectiveness of LLMs for automated AE extraction and analysis, and LLM text embeddings capture latent information about AEs, enabling more comprehensive knowledge discovery. Our findings suggest that LLMs demonstrate substantial potential for improving vaccine safety and public health research.

PMID:40410898 | DOI:10.1186/s13326-025-00331-8

Sci Rep. 2025 May 23;15(1):17949. doi: 10.1038/s41598-025-02802-5.

ABSTRACT

Oral cancer though preventable, shows high mortality and affect the overall quality of life when detected in late stages. Screening techniques that enable early diagnosis are the need of the hour. The present work aims to evaluate the effectiveness of AI screening tools in the diagnosis of OPMDs and Oral cancers via native or web-application (cloud) using smart phone devices. We trained and tested two deep learning models namely DenseNet201 and FixCaps using 518 images of the oral cavity. While DenseNet201 is a pre-trained model, we modified the FixCaps model from capsule network and trained it ground up. Standardized protocols were used to annotate and classify the lesions (suspicious vs. non-suspicious). In terms of model performance, DenseNet201 achieved an F1 score of 87.50% and AUC of 0.97; while FixCaps exhibited F1 score of 82.8% and AUC of 0.93. DenseNet201 model (20 M) serves as a robust screening model (accuracy 88.6%) that can be hosted on a web-application in the cloud servers; while the adapted FixCaps model with its low parameter size of 0.83 M exhibits comparable accuracy (83.8%) allowing easy transitioning into a native phone-based screening application.

PMID:40410364 | DOI:10.1038/s41598-025-02802-5

Eur Spine J. 2025 May 23. doi: 10.1007/s00586-025-08948-0. Online ahead of print.

ABSTRACT

PURPOSE: When spine fractures involve posterior wall damage, they pose a heightened risk of instability, consequently influencing treatment strategies. To enhance early diagnosis and refine treatment planning for these fractures, we implemented a radiomics analysis using deep learning techniques, based on both anteroposterior and lateral plain X-ray images.

METHODS: Retrospective data were collected for 130 patients with spine fractures who underwent anteroposterior and lateral imaging at two centers (Center 1, training cohort; Center 2, validation cohort) between January 2010 and June 2024. The Vision Transformer (ViT) technique was employed to extract imaging features. The features selected through multiple methods were then used to construct a machine learning model using NaiveBayes and Support Vector Machine (SVM). The model's performance was evaluated using the area under the curve (AUC) metric.

RESULTS: 12 features were selected to form the deep learning features. The SVM model using a combination of anteroposterior and lateral plain images showed good performance in both centers with a high AUC for predicting spine fractures with posterior wall injury (Center 1, AUC: 0.909, 95% CI: 0.763-1.000; Center 2, AUC: 0.837, 95% CI: 0.678-0.996). The SVM model based on the combined images outperformed both the individual position images and a spine surgeon with 3 years of clinical experience in classification performance.

CONCLUSIONS: Our study demonstrates that a radiomic model created by integrating anteroposterior and lateral plain X-ray images of the spine can more effectively predict spine fractures with posterior wall injury, aiding clinicians in making accurate diagnoses and treatment decisions.

PMID:40410361 | DOI:10.1007/s00586-025-08948-0

Sci Rep. 2025 May 23;15(1):18046. doi: 10.1038/s41598-025-99192-5.

ABSTRACT

In recent years, the application of Deep Learning techniques has shown remarkable success in various computer vision tasks, paving the way for their deployment in extraterrestrial exploration. Transfer learning has emerged as a powerful strategy for addressing the scarcity of labeled data in these novel environments. This paper represents one of the first efforts in evaluating the feasibility of employing adapters toward efficient transfer learning for rock segmentation in extraterrestrial landscapes, mainly focusing on lunar and martian terrains. Our work suggests that the use of adapters, strategically integrated into a pre-trained backbone model, can be successful in reducing both bandwidth and memory requirements for the target extraterrestrial device. In this study, we considered two memory-saving strategies: layer fusion (to reduce to zero the inference overhead) and an "adapter ranking" (to also reduce the transmission cost). Finally, we evaluate these results in terms of task performance, memory, and computation on embedded devices, evidencing trade-offs that open the road to more research in the field. The code will be open-sourced upon acceptance of the article.

PMID:40410339 | DOI:10.1038/s41598-025-99192-5

Eur Radiol. 2025 May 23. doi: 10.1007/s00330-025-11694-y. Online ahead of print.

ABSTRACT

OBJECTIVES: Pancreatic cancer treatment plans involving surgery and/or chemotherapy are highly dependent on disease stage. However, current staging systems are ineffective and poorly correlated with survival outcomes. We investigate how artificial intelligence (AI) can enhance prognostic accuracy in pancreatic cancer by integrating multiple data sources.

MATERIALS AND METHODS: Patients with histopathology and/or radiology/follow-up confirmed pancreatic ductal adenocarcinoma (PDAC) from a Dutch center (2004-2023) were included in the development cohort. Two additional PDAC cohorts from a Dutch and Spanish center were used for external validation. Prognostic models including clinical variables, contrast-enhanced CT images, and a combination of both were developed to predict high-risk short-term survival. All models were trained using five-fold cross-validation and assessed by the area under the time-dependent receiver operating characteristic curve (AUC).

RESULTS: The models were developed on 401 patients (203 females, 198 males, median survival (OS) = 347 days, IQR: 171-585), with 98 (24.4%) short-term survivors (OS < 230 days) and 303 (75.6%) long-term survivors. The external validation cohorts included 361 patients (165 females, 138 males, median OS = 404 days, IQR: 173-736), with 110 (30.5%) short-term survivors and 251 (69.5%) longer survivors. The best AUC for predicting short vs. long-term survival was achieved with the multi-modal model (AUC = 0.637 (95% CI: 0.500-0.774)) in the internal validation set. External validation showed AUCs of 0.571 (95% CI: 0.453-0.689) and 0.675 (95% CI: 0.593-0.757).

CONCLUSION: Multimodal AI can predict long vs. short-term survival in PDAC patients, showing potential as a prognostic tool in clinical decision-making.

KEY POINTS: Question Prognostic tools for pancreatic ductal adenocarcinoma (PDAC) remain limited, with TNM staging offering suboptimal accuracy in predicting patient survival outcomes. Findings The multimodal AI model demonstrated improved prognostic performance over TNM and unimodal models for predicting short- and long-term survival in PDAC patients. Clinical relevance Multimodal AI provides enhanced prognostic accuracy compared to current staging systems, potentially improving clinical decision-making and personalized management strategies for PDAC patients.

PMID:40410330 | DOI:10.1007/s00330-025-11694-y

Sci Rep. 2025 May 23;15(1):18008. doi: 10.1038/s41598-025-02452-7.

ABSTRACT

Post-COVID-19, depression rates have risen sharply, increasing the need for early diagnosis using electroencephalogram (EEG) and deep learning. To tackle this, we developed a cloud-based computer-aided depression diagnostic (CCADD) system that utilizes EEG signals from local databases. This system was optimized through a series of experiments to identify the most accurate model. The experiments employed a pre-trained convolutional neural network, ResNet18, fine-tuned on time-frequency synchrosqueezed wavelet transform (SSWT) images derived from EEG signals. Various data augmentation methods, including image processing techniques and noises, were applied to identify the best model for CCADD. To offer this device with minimal electrodes, we aimed to balance high accuracy with fewer electrodes. Two publicly databases were evaluated using this approach. Dataset I included 31 individuals detected with major depressive disorder and a control class of 27 age-matched healthy subjects. Dataset II comprised 90 participants, with 45 diagnosed with depression and 45 healthy controls. The leave-subjects-out cross-validation method with 20 subjects was used to validate the proposed method. The highest average accuracies for the selected model are 98%, 97%, 91%, and 88% for the parietal and central lobes in Databases I and II, respectively. The corresponding highest f-scores are 96.27%, 94.87%, 90.56%, and 89.65%. The highest intra-database accuracy and F1-score are 75.10% and 73.56% when training with SSWT images from Database II and testing with parietal images from Database I. This study introduces a novel cloud-based model for depression detection, paving the way for effective diagnostic tools and potentially revolutionizing depression management.

PMID:40410314 | DOI:10.1038/s41598-025-02452-7

Ann Gen Psychiatry. 2025 May 23;24(1):32. doi: 10.1186/s12991-025-00568-8.

ABSTRACT

Adolescent suicide represents a critical global health issue. While research has identified numerous risk factors, the specific impact of parental dysfunction on adolescent suicide remains understudied, especially in Chinese contexts. This study explores how parental dysfunction manifests in suicide notes and affects adolescent mental health. We collected data from Chinese social media platforms using web crawlers, yielding 30 valid suicide notes for analysis. Using the AI-aided content analysis platform DiVoMiner®, we conducted high-frequency word and semantic network analyses. Our findings reveal that parents are a central concern for suicidal youth. We identified three primary patterns of parental dysfunction: excessive emphasis on instrumental goals, neglect of basic emotional needs, and inadequate protection from life traumas. These dysfunctions contribute to severe psychological distress, identity loss, and negative coping behaviors among youth. The research highlights two significant phenomena in contemporary Chinese family dynamics: the "short-sightedness" of prioritizing short-term instrumental goals over long-term social-emotional development, and the remarkably high prevalence of "lack of autonomy" in parenting approaches. Our study extends the literature by exploring mechanisms through which parental dysfunctions contribute to suicidal behaviors in young people. These findings emphasize the need for collaborative efforts among parents, educators, policymakers, and mental health professionals to foster nurturing environments characterized by emotional support, autonomy encouragement, and balanced academic expectations-all crucial for adolescent well-being.

PMID:40410879 | DOI:10.1186/s12991-025-00568-8

Eur Neuropsychopharmacol. 2025 May 22;96:17-27. doi: 10.1016/j.euroneuro.2025.05.004. Online ahead of print.

ABSTRACT

Proteomics has been scarcely explored for predicting treatment outcomes in major depressive disorder (MDD), due to methodological challenges and costs. Predicting protein levels from genetic scores provides opportunities for exploratory studies and the selection of targeted panels. In this study, we examined the association between genetically predicted plasma proteins and treatment outcomes - including non-response, non-remission, and treatment-resistant depression (TRD) - in 3559 patients with MDD from four clinical samples. Protein levels were predicted from individual-level genotypes using genetic scores from the publicly available OmicsPred database, which estimated genetic scores based on genome-wide genotypes and proteomic measurements from the Olink and SomaScan platforms. Associations between predicted protein levels and treatment outcomes were assessed using logistic regression models, adjusted for potential confounders including population stratification. Results were meta-analysed using a random-effects model. The Bonferroni correction was applied. We analysed 257 proteins for Olink and 1502 for SomaScan; 111 proteins overlapped between the two platforms. Despite no association was significant after multiple-testing correction, many top results were consistent across phenotypes, in particular seven proteins were nominally associated with all the analysed outcomes (CHL1, DUSP13, EVA1C, FCRL2, KITLG, SMAP1, and TIM3/HAVCR2). Additionally, three proteins (CXCL6, IL5RA, and RARRES2) showed consistent nominal associations across both the Olink and SomaScan platforms. The convergence of results across phenotypes is in line with the hypothesis of the involvement of immune-inflammatory mechanisms and neuroplasticity in treatment response. These results can provide hints for guiding the selection of protein panels in future proteomic studies.

PMID:40408832 | DOI:10.1016/j.euroneuro.2025.05.004

Respir Med. 2025 May 21:108171. doi: 10.1016/j.rmed.2025.108171. Online ahead of print.

ABSTRACT

Cystic fibrosis (CF) is a genetic disease that often leads to progressive lung disease and lung transplantation. CF transmembrane conductance regulator modulators (CFTRm) improve lung function in people with CF. The US Scientific Registry of Transplant Recipients (SRTR) data were used to assess rates of lung transplant waitlisting, waitlist removal, pre-transplant mortality, and lung transplantation in people with CF (CF cohort) compared to those with other respiratory conditions (non-CF cohort) across three time periods: (i) prior to approval of any CFTRm (Pre-CFTRm era); (ii) from approval of ivacaftor to pre-approval of elexacaftor/tezacaftor/ivacaftor (Pre-ETI CFTRm era); and (iii) after approval of ETI (ETI era). Among the CF cohort, new waitlistings decreased by 78% in ETI era compared to Pre-ETI CFTRm era while rates increased in the non-CF cohort. Among the CF cohort, waitlist removal for improving condition increased 18-fold in ETI era compared to Pre-ETI CFTRm era; rates remained stable among the non-CF cohort. Lung transplants decreased by 72% in ETI era compared to pre-ETI CFTRm era; rates increased among the non-CF cohort. These results suggest the availability of ETI is associated with reductions in demand for lung transplants for people with CF, increasing availability of donor lungs for non-CF candidates.

PMID:40409741 | DOI:10.1016/j.rmed.2025.108171

Cancer Imaging. 2025 May 23;25(1):64. doi: 10.1186/s40644-025-00883-z.

ABSTRACT

BACKGROUND: Accurate differentiation between benign and malignant adnexal masses is crucial for patients to avoid unnecessary surgical interventions. Ultrasound (US) is the most widely utilized diagnostic and screening tool for gynecological diseases, with contrast-enhanced US (CEUS) offering enhanced diagnostic precision by clearly delineating blood flow within lesions. According to the Ovarian and Adnexal Reporting and Data System (O-RADS), masses classified as categories 4 and 5 carry the highest risk of malignancy. However, the diagnostic accuracy of US remains heavily reliant on the expertise and subjective interpretation of radiologists. Radiomics has demonstrated significant value in tumor differential diagnosis by extracting microscopic information imperceptible to the human eye. Despite this, no studies to date have explored the application of CEUS-based radiomics for differentiating adnexal masses. This study aims to develop and validate a multimodal US-based nomogram that integrates clinical variables, radiomics, and deep learning (DL) features to effectively distinguish adnexal masses classified as O-RADS 4-5.

METHODS: From November 2020 to March 2024, we enrolled 340 patients who underwent two-dimensional US (2DUS) and CEUS and had masses categorized as O-RADS 4-5. These patients were randomly divided into a training cohort and a test cohort in a 7:3 ratio. Adnexal masses were manually segmented from 2DUS and CEUS images. Using machine learning (ML) and DL techniques, five models were developed and validated to differentiate adnexal masses. The diagnostic performance of these models was evaluated using the area under the receiver operating characteristic (ROC) curve (AUC), accuracy, sensitivity, specificity, precision, and F1-score. Additionally, a nomogram was constructed to visualize outcome measures.

RESULTS: The CEUS-based radiomics model outperformed the 2DUS model (AUC: 0.826 vs. 0.737). Similarly, the CEUS-based DL model surpassed the 2DUS model (AUC: 0.823 vs. 0.793). The ensemble model combining clinical variables, radiomics, and DL features achieved the highest AUC (0.929).

CONCLUSIONS: Our study confirms the effectiveness of CEUS-based radiomics for distinguishing adnexal masses with high accuracy and specificity using a multimodal US-based radiomics DL nomogram. This approach holds significant promise for improving the diagnostic precision of adnexal masses classified as O-RADS 4-5.

PMID:40410823 | DOI:10.1186/s40644-025-00883-z

BMC Med Inform Decis Mak. 2025 May 23;25(1):195. doi: 10.1186/s12911-025-02925-9.

ABSTRACT

Glaucoma is a group of serious eye diseases that can cause incurable blindness. Despite the critical need for early detection, over 60% of cases remain undiagnosed, especially in less developed regions. Glaucoma diagnosis is a costly task and some models have been proposed to automate diagnosis based on images of the retina, specifically the area known as the optic cup and the associated disc where retinal blood vessels and nerves enter and leave the eye. However, diagnosis is complicated because both normal and glaucoma-affected eyes can vary greatly in appearance. Some normal cases, like glaucoma, exhibit a larger cup-to-disc ratio, one of the main diagnostic criteria, making it challenging to distinguish between them. We propose a deep learning model with domain features (DLMDF) to combine unstructured and structured features to distinguish between glaucoma and physiologic large cups. The structured features were based upon the known cup-to-disc ratios of the four quadrants of the optic discs in normal, physiologic large cups, and glaucomatous optic cups. We segmented each cup and disc using a fully convolutional neural network and then calculated the cup size, disc size, and cup-to-disc ratio of each quadrant. The unstructured features were learned from a deep convolutional neural network. The average precision (AP) for disc segmentation was 98.52%, and for cup segmentation it was also 98.57%. Thus, the relatively high AP values enabled us to calculate the 15 reliable features from each segmented disc and cup. In classification tasks, the DLMDF outperformed other models, achieving superior accuracy, precision, and recall. These results validate the effectiveness of combining deep learning-derived features with domain-specific structured features, underscoring the potential of this approach to advance glaucoma diagnosis.

PMID:40410768 | DOI:10.1186/s12911-025-02925-9

Spine Deform. 2025 May 23. doi: 10.1007/s43390-025-01111-1. Online ahead of print.

ABSTRACT

PURPOSE: This review evaluates advances made in deep learning (DL) applications to automatic spinopelvic parameter estimation, comparing their accuracy to manual measurements performed by surgeons.

METHODS: The PubMed database was queried for studies on DL measurement of adult spinopelvic parameters between 2014 and 2024. Studies were excluded if they focused on pediatric patients, non-deformity-related conditions, non-human subjects, or if they lacked sufficient quantitative data comparing DL models to human measurements. Included studies were assessed based on model architecture, patient demographics, training, validation, testing methods, and sample sizes, as well as performance compared to manual methods.

RESULTS: Of 442 screened articles, 16 were included, with sample sizes ranging from 15 to 9,832 radiograph images and reporting interclass correlation coefficients (ICCs) of 0.56 to 1.00. Measurements of pelvic tilt, pelvic incidence, T4-T12 kyphosis, L1-L4 lordosis, and SVA showed consistently high ICCs (>0.80) and low mean absolute deviations (MADs <6°), with substantial number of studies reporting pelvic tilt achieving an excellent ICC of 0.90 or greater. In contrast, T1-T12 kyphosis and L4-S1 lordosis exhibited lower ICCs and higher measurement errors. Overall, most DL models demonstrated strong correlations (>0.80) with clinician measurements and minimal differences compared to manual references, except for T1-T12 kyphosis (average Pearson correlation: 0.68), L1-L4 lordosis (average Pearson correlation: 0.75), and L4-S1 lordosis (average Pearson correlation: 0.65).

CONCLUSION: Novel computer vision algorithms show promising accuracy in measuring spinopelvic parameters, comparable to manual surgeon measurements. Future research should focus on external validation, additional imaging modalities, and the feasibility of integration in clinical settings to assess model reliability and predictive capacity.

PMID:40410653 | DOI:10.1007/s43390-025-01111-1