Sci Rep. 2025 Mar 31;15(1):10956. doi: 10.1038/s41598-024-81478-9.

ABSTRACT

To address the limitations of traditional partial discharge (PD) detection methods for switchgear, which fail to meet the requirements for real-time monitoring, rapid assessment, sample fusion, and joint analysis in practical applications, a joint PD recognition method of switchgear based on edge computing and deep learning is proposed. An edge collaborative defect identification architecture for switchgear is constructed, which includes the terminal device side, terminal collection side, edge-computing side, and cloud-computing side. The PD signal of switchgear is extracted based on UHF sensor and broadband pulse current sensor on the terminal collection side. Multidimensional features are obtained from these signals and a high-dimensional feature space is constructed based on feature extraction and dimensionality reduction on the edge-computing side. On the cloud side, the deep belief network (DBN)-based switchgear PD defect identification method is proposed and the PD samples acquired on the edge side are transmitted in real time to the cloud for training. Upon completion of the training, the resulting model is transmitted back to the edge side for inference, thereby facilitating real-time joint analysis of PD defects across multiple switchgear units. Verification of the proposed method is conducted using PD samples simulated in the laboratory. The results indicate that the DBN proposed in this paper can recognize PDs in switchgear with an accuracy of 88.03%, and under the edge computing architecture, the training time of the switchgear PD defect type classifier can be reduced by 44.28%, overcoming the challenges associated with traditional diagnostic models, which are characterized by long training durations, low identification efficiency, and weak collaborative analysis capabilities.

PMID:40164608 | DOI:10.1038/s41598-024-81478-9

Acad Radiol. 2025 Mar 30:S1076-6332(25)00206-5. doi: 10.1016/j.acra.2025.03.011. Online ahead of print.

ABSTRACT

RATIONALE AND OBJECTIVES: Coronary chronic total occlusion (CTO) and subtotal occlusion (STO) pose diagnostic challenges, differing in treatment strategies. Artificial intelligence and radiomics are promising tools for accurate discrimination. This study aimed to develop deep learning (DL) and radiomics models using coronary computed tomography angiography (CCTA) to differentiate CTO from STO lesions and compare their performance with that of the conventional method.

MATERIALS AND METHODS: CTO and STO were identified retrospectively from a tertiary hospital and served as training and validation sets for developing and validating the DL and radiomics models to distinguish CTO from STO. An external test cohort was recruited from two additional tertiary hospitals with identical eligibility criteria. All participants underwent CCTA within 1 month before invasive coronary angiography.

RESULTS: A total of 581 participants (mean age, 50 years ± 11 [SD]; 474 [81.6%] men) with 600 lesions were enrolled, including 403 CTO and 197 STO lesions. The DL and radiomics models exhibited better discrimination performance than the conventional method, with areas under the curve of 0.908 and 0.860, respectively, vs. 0.794 in the validation set (all p＜0.05), and 0.893 and 0.827, respectively, vs. 0.746 in the external test set (all p＜0.05).

CONCLUSIONS: The proposed CCTA-based DL and radiomics models achieved efficient and accurate discrimination of coronary CTO and STO.

PMID:40164533 | DOI:10.1016/j.acra.2025.03.011

Open Heart. 2025 Mar 31;12(1):e003185. doi: 10.1136/openhrt-2025-003185.

ABSTRACT

BACKGROUND: Atrial fibrillation (AF) is a common but often undiagnosed condition, increasing the risk of stroke and heart failure. Early detection is crucial, yet traditional methods struggle with AF's transient nature. This study investigates how augmenting ECG data with heart rate variability (HRV) and demographic data (age and sex) can improve AF detection.

METHODS: We analysed 35 634 12-lead ECG recordings from three public databases (China Physiological Signal Challenge-Extra, PTB-XL and Georgia), each with physician-validated AF labels. A range of convolutional neural network models, including AlexNet, VGG-16, ResNet and transformers, were tested for AF prediction, enriched with HRV and demographic data to explore the effectiveness of the multimodal approach. Each data modality (ECG, HRV and demographic) was assessed for its contribution to model performance using fivefold cross-validation. Performance improvements were evaluated across key metrics, and saliency maps were generated to provide further insights into model behaviour and identify critical features in AF detection.

RESULTS: Integrating HRV and demographic data with ECG substantially improved performance. AlexNet and VGG-16 outperformed more complex models, achieving AUROC of 0.9617 (95% CI 0.95 to 0.97) and 0.9668 (95% CI 0.96 to 0.97), respectively. Adding HRV data showed the most significant improvement in sensitivity, with AlexNet increasing from 0.9117 to 0.9225 and VGG-16 from 0.9216 to 0.9225. Combining both HRV and demographic data led to further improvements, with AlexNet achieving a sensitivity of 0.9225 (up from 0.9192 with HRV) and VGG-16 reaching 0.9113 (up from 0.9097 with HRV). The combination of HRV and demographic data resulted in the highest gains in sensitivity and area under the receiver operating characteristic curve. Saliency maps confirmed the models identified key AF features, such as the absence of the P-wave, validating the multimodal approach.

CONCLUSIONS: AlexNet and VGG-16 excelled in AF detection, with HRV data improving sensitivity, and demographic data providing additional benefits. These results highlight the potential of multimodal approaches, pending further clinical validation.

PMID:40164487 | DOI:10.1136/openhrt-2025-003185

Semin Musculoskelet Radiol. 2025 Apr;29(2):221-235. doi: 10.1055/s-0045-1802660. Epub 2025 Mar 31.

ABSTRACT

Spondyloarthritis (SpA) encompasses inflammatory disorders affecting the axial skeleton, with sacroiliitis as a hallmark feature of axial SpA (axSpA). Imaging plays a vital role in early diagnosis and disease monitoring. Magnetic resonance imaging (MRI) is the preferred modality for detecting early inflammatory changes in axSpA, whereas structural lesions are better visualized using computed tomography (CT). However, synthetic computed tomography (sCT), a technique that generates CT-like images from MRI data, including deep learning methods, zero echo time, ultrashort echo time, and gradient-recalled echo sequences, has emerged as an innovative tool. It offers detailed anatomical resolution without ionizing radiation and combines the advantages of both, MRI and CT, by enabling the simultaneous evaluation of inflammatory and structural lesions. This review explores the potential role of MRI-based sCT in assessing structural changes in the sacroiliac joints, particularly in the context of axSpA, discussing conventional imaging and highlighting the potential of sCT to enhance early detection and monitoring of sacroiliitis.

PMID:40164079 | DOI:10.1055/s-0045-1802660

Comput Biol Med. 2025 Mar 30;190:110093. doi: 10.1016/j.compbiomed.2025.110093. Online ahead of print.

ABSTRACT

BACKGROUND: Major bleeding is a severe complication in critically ill medical patients, resulting in significant morbidity, mortality, and healthcare costs. This study aims to assess the incidence and risk factors for major bleeding in hospitalised medical patients using a Natural Language Processing (NLP) model.

METHODS: We conducted a retrospective, cross-sectional observational study using electronic health records of adult patients admitted through the Emergency Department at Odense University Hospital from January 2017 to December 2022. Major bleeding during admission was identified and validated using a natural language model, with events classified according to current guidelines. Risk factors, including demographics, comorbidities, and biochemical values at admission, were evaluated. Two risk assessment models (RAMs) were developed using Cox proportional hazards regression. Validation included, bootstrapping, K-fold cross validation, and cluster analyses.

RESULTS: Of the 46,439 eligible patients, 1246 (2.7 %) experienced major bleeding. Risk factors for major bleeding included older age, male sex, alcohol consumption, higher systolic blood pressure, lower haemoglobin, and higher creatinine. RAM 1, which included biochemical data and comorbidities, demonstrated robust predictive performance (Harrell's C-statistic = 0.726). RAM 2, a simplified model without comorbidities, maintained similar predictive accuracy (C-statistic = 0.721), indicating its potential utility in clinical settings with limited resources for detailed patient histories. Results were consistent throughout validation.

CONCLUSION: This study highlights the incidence and risk factors of major bleeding in medical patients, emphasizing the predictive value of routinely measured biochemical markers. Furthermore, it shows the applicability of NLP models in identifying bleeding episodes in EHR text.

PMID:40164027 | DOI:10.1016/j.compbiomed.2025.110093

Neuroscience. 2025 Mar 29:S0306-4522(25)00266-0. doi: 10.1016/j.neuroscience.2025.03.064. Online ahead of print.

ABSTRACT

Drug repositioning or drug reprofiling, involves identifying novel indications for approved and previously abandoned drugs in the treatment of other diseases. The traditional drug discovery process is tedious, time-consuming, risky, and challenging. Fortunately, the inception of the drug repositioning concept has expedited the process by using compounds with established safety profiles in humans, and thereby significantly reducing costs. Alzheimer's disease (AD) is a severe neurological disorder characterized by progressive degeneration of the brain with limited and less effective therapeutic interventions. Researchers have attempted to identify potential treatment of AD from existing drug however, the success of drug repositioning strategy in AD remains uncertain. This article briefly discusses the importance and effectiveness of drug repositioning strategies, the major obstacles in the development of drugs for Alzheimer's disease (AD), approaches to address these challenges, and the role of machine learning in identifying early markers of AD for improved management.

PMID:40164279 | DOI:10.1016/j.neuroscience.2025.03.064

Commun Biol. 2025 Mar 31;8(1):530. doi: 10.1038/s42003-025-07959-3.

ABSTRACT

The presence of pre-existing or acquired drug-resistant cells within the tumor often leads to tumor relapse and metastasis. Single-cell RNA sequencing (scRNA-seq) enables elucidation of the subtle differences in drug responsiveness among distinct cell subpopulations within tumors. A few methods have employed scRNA-seq data to predict the drug response of individual cells to date, but their performance is far from satisfactory. In this study, we propose SSDA4Drug, a semi-supervised few-shot transfer learning method for inferring drug-resistant cancer cells. SSDA4Drug extracts pharmacogenomic features from both bulk and single-cell transcriptomic data using semi-supervised adversarial domain adaptation. This allows us to transfer knowledge of drug sensitivity from bulk-level cell lines to single cells. We conduct extensive performance evaluation experiments across multiple independent scRNA-seq datasets, demonstrating SSDA4Drug's superior performance over current state-of-the-art methods. Remarkably, with only one or two labeled target-domain samples, SSDA4Drug significantly boosts the predictive performance of single-cell drug responses. Moreover, SSDA4Drug accurately recapitulates the temporally dynamic changes of drug responses during continuous drug exposure of tumor cells, and successfully identifies reversible drug-responsive states in lung cancer cells, which initially acquire resistance through drug exposure but later restore sensitivity during drug holidays. Also, our predicted drug responses consistently align with the developmental patterns of drug sensitivity observed along the evolutionary trajectory of oral squamous cell carcinoma cells. In addition, our derived SHAP values and integrated gradients effectively pinpoint the key genes involved in drug resistance in prostate cancer cells. These findings highlight the exceptional performance of our method in determining single-cell drug responses. This powerful tool holds the potential for identifying drug-resistant tumor cell subpopulations, paving the way for advancements in precision medicine and novel drug development.

PMID:40164749 | DOI:10.1038/s42003-025-07959-3

BMJ Open Respir Res. 2025 Mar 31;12(1):e002546. doi: 10.1136/bmjresp-2024-002546.

ABSTRACT

BACKGROUND: A person's beliefs about treatment influence their engagement and adherence to that treatment. The Necessity-Concerns Framework suggests that adherence is influenced by a person's judgement of their own need for treatment (necessity beliefs) and concerns about the potential adverse consequences of taking the treatment. This study was conducted to explore the Necessity-Concerns Framework for elexacaftor-tezacaftor-ivacaftor (ETI) therapy (Kaftrio) in adults with cystic fibrosis (CF).

METHODS: A total of 64 adults with CF were maintained on ETI therapy as part of their routine CF care, and completed the Beliefs about Medicines Questionnaire. Patient demographics, lung function, body mass index and quality of life using the Cystic Fibrosis Questionnaire Revised were collected as part of routine clinical care. Duration of ETI therapy along with medicines possession ratio was recorded.

RESULTS: Patients reported strong beliefs about the necessity of ETI therapy. The majority of patients (78%) reported low concerns about ETI therapy while 22% of patients reported high concerns. A small number of patients (n=4) had concerns which were stronger than their beliefs about necessity.

DISCUSSION: Patients reported strong beliefs in the necessity of ETI therapy. Although concerns were lower, a significant proportion of the sample had strong concerns about their ETI therapy. By being aware of people with CF's necessity and concerns beliefs around ETI therapy clinical teams will be better armed to engage them in treatment decisions and support optimal adherence.

PMID:40164471 | DOI:10.1136/bmjresp-2024-002546

Respir Physiol Neurobiol. 2025 Mar 29:104423. doi: 10.1016/j.resp.2025.104423. Online ahead of print.

ABSTRACT

Physical activity is a leading trigger of dyspnea in chronic cardiopulmonary diseases. Recently, there has been a renewed interest in uncovering the mechanisms underlying this distressing symptom. We start by articulating a conceptual framework linking cardiorespiratory abnormalities with the central perception of undesirable respiratory sensations during exercise. We specifically emphasize that exertional dyspnea ultimately reflects an imbalance between (high) demand and (low) capacity. As such, the symptom arises in the presence of a heightened inspiratory neural drive - the will to breathe - secondary to a) increased ventilatory output relative to the instantaneous ventilatory capacity (excessive breathing) and/or b) its impeded translation into the act of breathing due to constraints on tidal volume expansion (constrained breathing). In patients with chronic obstructive pulmonary disease (COPD), asthma, cystic fibrosis, and interstitial lung disease (ILD), constrained breathing assumes a more dominant role as the disease progresses. Excessive breathing due to heightened wasted ventilation in the physiological dead space is particularly important in the initial stages of COPD, while alveolar hyperventilation has a major contributory role in hypoxemic patients with ILD. Hyperventilation is also a leading driver of dyspnea in heart failure (HF) with reduced ejection fraction (EF), while high physiological dead space is the main underlying mechanism in HF with preserved EF. Similarly, wasted ventilation in poorly perfused lung tissue dominates the scene in pulmonary vascular disease. New artificial intelligence-based approaches to expose the contribution of excessive and constrained breathing may enhance the yield of cardiopulmonary exercise testing in investigating exertional dyspnea in these patients.

PMID:40164293 | DOI:10.1016/j.resp.2025.104423

Biochemistry (Mosc). 2025 Jan;90(Suppl 1):S214-S232. doi: 10.1134/S0006297924603496.

ABSTRACT

Mycobacterium abscessus is a non-tuberculosis fast-growing mycobacterium that has recently become a serious concern due to its rapidly increasing prevalence worldwide, mainly in individuals with a high susceptibility to pulmonary infections, for example, patients with cystic fibrosis, bronchiectasis, chronic obstructive pulmonary disease, and previous tuberculosis infection. According to present estimations, at least 20% of patients with cystic fibrosis are infected with M. abscessus. This bacterium is extremely resistant to most drugs, leading to a severe and difficult-to-treat infection. That is why M. abscessus, previously classified as a low-virulent opportunistic pathogen, is now reconsidered as a true pathogenic bacterium. There are no effective drugs for successful M. abscessus infection therapy, as well as no vaccines to prevent its spread. This review focuses on the molecular mechanisms ensuring M. abscessus resistance to immune response and its ability to survive in the aggressive intracellular environment of human immune cells, and describes virulence factors that can serve as potential targets for the development of innovative therapeutic approaches to combat the spread of infections caused by M. abscessus.

PMID:40164160 | DOI:10.1134/S0006297924603496

Int Endod J. 2025 Mar 31. doi: 10.1111/iej.14231. Online ahead of print.

ABSTRACT

The integration of artificial intelligence (AI) into education is transforming learning across various domains, including dentistry. Endodontic education can significantly benefit from AI chatbots; however, knowledge gaps regarding their potential and limitations hinder their effective utilization. This narrative review aims to: (A) explain the core functionalities of AI chatbots, including their reliance on natural language processing (NLP), machine learning (ML), and deep learning (DL); (B) explore their applications in endodontic education for personalized learning, interactive training, and clinical decision support; (C) discuss the challenges posed by technical limitations, ethical considerations, and the potential for misinformation. The review highlights that AI chatbots provide learners with immediate access to knowledge, personalized educational experiences, and tools for developing clinical reasoning through case-based learning. Educators benefit from streamlined curriculum development, automated assessment creation, and evidence-based resource integration. Despite these advantages, concerns such as chatbot hallucinations, algorithmic biases, potential for plagiarism, and the spread of misinformation require careful consideration. Analysis of current research reveals limited endodontic-specific studies, emphasizing the need for tailored chatbot solutions validated for accuracy and relevance. Successful integration will require collaborative efforts among educators, developers, and professional organizations to address challenges, ensure ethical use, and establish evaluation frameworks.

PMID:40164964 | DOI:10.1111/iej.14231

Behav Res Methods. 2025 Mar 31;57(5):129. doi: 10.3758/s13428-025-02645-y.

ABSTRACT

Deep learning methods have significantly advanced the field of gaze estimation, yet the development of these algorithms is often hindered by a lack of appropriate publicly accessible training datasets. Moreover, models trained on the few available datasets often fail to generalize to new datasets due to both discrepancies in hardware and biological diversity among subjects. To mitigate these challenges, the research community has frequently turned to synthetic datasets, although this approach also has drawbacks, such as the computational resource and labor-intensive nature of creating photorealistic representations of eye images to be used as training data. In response, we introduce "Light Eyes" (LEyes), a novel framework that diverges from traditional photorealistic methods by utilizing simple synthetic image generators to train neural networks for detecting key image features like pupils and corneal reflections, diverging from traditional photorealistic approaches. LEyes facilitates the generation of synthetic data on the fly that is adaptable to any recording device and enhances the efficiency of training neural networks for a wide range of gaze-estimation tasks. Presented evaluations show that LEyes, in many cases, outperforms existing methods in accurately identifying and localizing pupils and corneal reflections across diverse datasets. Additionally, models trained using LEyes data outperform standard eye trackers while employing more cost-effective hardware, offering a promising avenue to overcome the current limitations in gaze estimation technology.

PMID:40164925 | DOI:10.3758/s13428-025-02645-y

Drug Deliv Transl Res. 2025 Mar 31. doi: 10.1007/s13346-025-01840-w. Online ahead of print.

ABSTRACT

Despite the rapid development of nanoparticle (NP)-based drug delivery systems, intravenous delivery of drugs to the brain remains a major challenge due to various biological barriers. To achieve therapeutic effects, NP-encapsulated drugs must avoid accumulation in off-target organs and selectively deliver to the brain, successfully cross the blood-brain barrier (BBB), and reach the target cells in the brain. Conjugating receptor-specific ligands to the surface of NPs is a promising technique for engineering NPs to overcome these barriers. Specifically, peptides as brain-targeting ligands have been of increasing interest given their ease of synthesis, low cytotoxicity, and strong affinity to target proteins. The success of peptides as targeting ligands is largely due to the diverse strategies of designing and modifying peptides with favorable properties, including membrane permeability and multi-receptor targeting. Here, we review the design and implementation of peptide-functionalized NP systems for neurological disease applications. We also explore advances in rational peptide design strategies for brain targeting, including using generative deep-learning models to computationally design new peptides.

PMID:40164912 | DOI:10.1007/s13346-025-01840-w

Surg Endosc. 2025 Mar 31. doi: 10.1007/s00464-025-11685-6. Online ahead of print.

ABSTRACT

BACKGROUND: Robotic-assisted minimally invasive esophagectomy (RAMIE) is a complex surgical procedure for treating esophageal cancer. Artificial intelligence (AI) is an uprising technology with increasing applications in the surgical field. This scoping review aimed to assess the current AI applications in RAMIE, with a focus on intraoperative video analysis.

METHODS: To identify all articles utilizing AI in RAMIE, a comprehensive literature search was performed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analysis for scoping reviews of Medline and Embase databases and the Cochrane Library. Two independent reviewers assessed articles for quality and inclusion.

RESULTS: One hundred and seventeen articles were identified, of which four were included in the final analysis. Results demonstrated that the main AI applications in RAMIE were intraoperative video assessment and the evaluation of surgical technical skills to evaluate surgical performance. AI was also used for surgical phase recognition to support clinical decision-making through intraoperative guidance and identify key anatomical landmarks. Various deep-learning networks were used to generate AI models, and there was a strong emphasis on using high-quality standardized video frames.

CONCLUSIONS: The use of AI in RAMIE, especially in intraoperative video analysis and surgical phase recognition, is still a relatively new field that should be further explored. The advantages of using AI algorithms to evaluate intraoperative videos in an automated manner may be harnessed to improve technical performance and intraoperative decision-making, achieve a higher quality of surgery, and improve postoperative outcomes.

PMID:40164839 | DOI:10.1007/s00464-025-11685-6

J Imaging Inform Med. 2025 Mar 31. doi: 10.1007/s10278-025-01481-y. Online ahead of print.

ABSTRACT

The rapid advancement of artificial intelligence techniques, particularly deep learning, has transformed medical imaging. This paper presents a comprehensive review of recent research that leverage vision transformer (ViT) models for medical image classification across various disciplines. The medical fields of focus include breast cancer, skin lesions, magnetic resonance imaging brain tumors, lung diseases, retinal and eye analysis, COVID-19, heart diseases, colon cancer, brain disorders, diabetic retinopathy, skin diseases, kidney diseases, lymph node diseases, and bone analysis. Each work is critically analyzed and interpreted with respect to its performance, data preprocessing methodologies, model architecture, transfer learning techniques, model interpretability, and identified challenges. Our findings suggest that ViT shows promising results in the medical imaging domain, often outperforming traditional convolutional neural networks (CNN). A comprehensive overview is presented in the form of figures and tables summarizing the key findings from each field. This paper provides critical insights into the current state of medical image classification using ViT and highlights potential future directions for this rapidly evolving research area.

PMID:40164818 | DOI:10.1007/s10278-025-01481-y

Sci Rep. 2025 Mar 31;15(1):10990. doi: 10.1038/s41598-025-96024-4.

ABSTRACT

Monocular image depth estimation is crucial for indoor scene reconstruction, and it plays a significant role in optimizing building energy efficiency, indoor environment modeling, and smart space design. However, the small depth variability of indoor scenes leads to weakly distinguishable detail features. Meanwhile, there are diverse types of indoor objects, and the expression of the correlation among different objects is complicated. Additionally, the robustness of recent models still needs further improvement given these indoor environments. To address these problems, a detail‒semantic collaborative network (DSCNet) is proposed for monocular depth estimation of indoor scenes. First, the contextual features contained in the images are fully captured via the hierarchical transformer structure. Second, a detail‒semantic collaborative structure is established, which establishes a selective attention feature map to extract details and semantic information from feature maps. The extracted features are subsequently fused to improve the perception ability of the network. Finally, the complex correlation among indoor objects is addressed by aggregating semantic and detailed features at different levels, and the model accuracy is effectively improved without increasing the number of parameters. The proposed model is tested on the NYU and SUN datasets. The proposed approach produces state-of-the-art results compared with the 14 performance results of recent optimal methods. In addition, the proposed approach is fully discussed and analyzed in terms of stability, robustness, ablation experiments and availability in indoor scenes.

PMID:40164814 | DOI:10.1038/s41598-025-96024-4

Nat Neurosci. 2025 Mar 31. doi: 10.1038/s41593-025-01905-6. Online ahead of print.

ABSTRACT

Natural spoken communication happens instantaneously. Speech delays longer than a few seconds can disrupt the natural flow of conversation. This makes it difficult for individuals with paralysis to participate in meaningful dialogue, potentially leading to feelings of isolation and frustration. Here we used high-density surface recordings of the speech sensorimotor cortex in a clinical trial participant with severe paralysis and anarthria to drive a continuously streaming naturalistic speech synthesizer. We designed and used deep learning recurrent neural network transducer models to achieve online large-vocabulary intelligible fluent speech synthesis personalized to the participant's preinjury voice with neural decoding in 80-ms increments. Offline, the models demonstrated implicit speech detection capabilities and could continuously decode speech indefinitely, enabling uninterrupted use of the decoder and further increasing speed. Our framework also successfully generalized to other silent-speech interfaces, including single-unit recordings and electromyography. Our findings introduce a speech-neuroprosthetic paradigm to restore naturalistic spoken communication to people with paralysis.

PMID:40164740 | DOI:10.1038/s41593-025-01905-6

Sci Rep. 2025 Mar 31;15(1):11006. doi: 10.1038/s41598-025-95267-5.

ABSTRACT

Prostate cancer (PCa) diagnosis faces significant challenges due to its complex pathological characteristics and insufficient pathologist resources. While deep learning-based image analysis (DLIA) shows promise in enhancing diagnostic accuracy, its application to radical prostatectomy (RP) specimens remains underexplored. In this study, we evaluated the clinical feasibility and prognostic value of a DLIA algorithm for Gleason grading and tumor quantification on whole RP specimens. Using 29,646 digitized H&E-stained slides from 992 patients who underwent RP, we compared the case-level algorithm results with pathologist assessments for the International Society of Urological Pathology grade groups (GG), tumor volumes (TV), and percent tumor volumes (PTV). We also evaluated their prognostic performance in predicting biochemical progression-free survival (BPFS). Pathologists identified cancer in 986 cases and assigned GG in 980, while the DLIA algorithm identified cancer and assigned GG to all cases without omission. DLIA-assigned GG showed fair concordance with pathologist assessments (linear-weighted Cohen's kappa: 0.374) and demonstrated similar efficacy in predicting BPFS (c-index: 0.644 for DLIA vs. 0.654 for pathologists; p = 0.52). In tumor quantification, DLIA-measured TV and PTV were strongly correlated with pathologist-based measurements (Pearson's correlation coefficient: 0.830 and 0.846, respectively), but showed stronger efficacy in BPFS prediction, with c-index values of 0.657 and 0.672 compared to 0.622 and 0.641, respectively. Incorporating DLIA-derived PTV into the CAPRA-S score significantly improved its predictive accuracy for BCR (p = 0.006), increasing the c-index from 0.704 to 0.715. Our findings indicate that DLIA algorithms can enhance the accuracy of Gleason grading and tumor quantification in RP specimens, providing valuable support in clinical decision-making for PCa management.

PMID:40164701 | DOI:10.1038/s41598-025-95267-5

Mol Psychiatry. 2025 Mar 31. doi: 10.1038/s41380-025-02974-6. Online ahead of print.

ABSTRACT

Major depressive disorder (MDD) presents a substantial health burden with low treatment response rates. Predicting antidepressant efficacy is challenging due to MDD's complex and varied neuropathology. Identifying biomarkers for antidepressant treatment requires thorough analysis of clinical trial data. Multimodal neuroimaging, combined with advanced data-driven methods, can enhance our understanding of the neurobiological processes influencing treatment outcomes. To address this, we analyzed resting-state fMRI and EEG connectivity data from 130 patients treated with sertraline and 135 patients with placebo from the Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC) study. A deep learning framework was developed using graph neural networks to integrate data-augmented connectivity and cross-modality correlation, aiming to predict individual symptom changes by revealing multimodal brain network signatures. The results showed that our model demonstrated promising prediction accuracy, with an R2 value of 0.24 for sertraline and 0.20 for placebo. It also exhibited potential in transferring predictions using only EEG. Key brain regions identified for predicting sertraline response included the inferior temporal gyrus (fMRI) and posterior cingulate cortex (EEG), while for placebo response, the precuneus (fMRI) and supplementary motor area (EEG) were critical. Additionally, both modalities identified the superior temporal gyrus and posterior cingulate cortex as significant for sertraline response, while the anterior cingulate cortex and postcentral gyrus were common predictors in the placebo arm. Additionally, variations in the frontoparietal control, ventral attention, dorsal attention, and limbic networks were notably associated with MDD treatment. By integrating fMRI and EEG, our study established novel multimodal brain network signatures to predict individual responses to sertraline and placebo in MDD, providing interpretable neural circuit patterns that may guide future targeted interventions. Trial Registration: Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care for Depression (EMBARC) ClinicalTrials.gov Identifier: NCT#01407094.

PMID:40164695 | DOI:10.1038/s41380-025-02974-6

Sci Rep. 2025 Mar 31;15(1):11000. doi: 10.1038/s41598-025-95709-0.

ABSTRACT

Well log analysis is significant for hydrocarbon exploration, providing detailed insights into subsurface geological formations. However, gaps and inaccuracies in well log data, often due to equipment limitations, operational challenges, and harsh subsurface conditions, can introduce significant uncertainties in reservoir evaluation. Addressing these challenges requires effective methods for both synthetic data generation and precise imputation of missing data, ensuring data completeness and reliability. This study introduces a novel framework utilizing sequence-based generative adversarial networks (GANs) specifically designed for well log data generation and imputation. The framework integrates two distinct sequence-based GAN models: time series GAN (TSGAN) for generating synthetic well log data and sequence GAN (SeqGAN) for imputing missing data. Both models were tested on a dataset from the North Sea, Netherlands region. For the imputation task, the input comprises logs with missing values and the output is the corresponding imputed logs; for the synthetic data generation task, the input is complete real logs and the output is synthetic logs that mimic the statistical properties of the original data. All log measurements are normalized to a 0-1 range using min-max scaling, and error metrics are reported in these normalized units. Different sections of 5, 10, and 50 data points were used. Experimental results demonstrate that this approach achieves superior accuracy in filling data gaps compared to other deep learning models for spatial series analysis. The imputation method yielded [Formula: see text] values of 0.92, 0.86, and 0.57, with corresponding mean absolute percentage error (MAPE) values of 8.320, 0.005, and 166.6, and mean absolute error (MAE) values of 0.012, 0.002, and 0.03, respectively. The synthetic generation yielded [Formula: see text] of 0.92, MAE, of 0.35, and MRLE of 0.01. These results set a new benchmark for data integrity and utility in geosciences, particularly in well log data analysis.

PMID:40164658 | DOI:10.1038/s41598-025-95709-0