Front Microbiol. 2025 Apr 28;16:1568162. doi: 10.3389/fmicb.2025.1568162. eCollection 2025.

ABSTRACT

The potential of phosphate-solubilizing microbes (PSMs) to enhance plant phosphorus uptake and reduce fertilizer dependency remains underutilized. This is partially attributable to frequent biofertilizer-farming system misalignments that reduce efficacy, and an incomplete understanding of underlying mechanisms. This study explored the seed microbiomes of nine Australian lucerne cultivars to identify and characterize high-efficiency PSMs. From a library of 223 isolates, 94 (42%) exhibited phosphate solubilization activity on Pikovskaya agar, with 15 showing high efficiency (PSI > 1.5). Genomic analysis revealed that the "high-efficiency" phosphate-solubilizing microbes belonged to four genera (Curtobacterium, Pseudomonas, Paenibacillus, Pantoea), including novel strains and species. However, key canonical genes, such as pqq operon and gcd, did not reliably predict phenotype, highlighting the limitations of in silico predictions. Mutagenesis of the high-efficiency isolate Pantoea rara Lu_Sq_004 generated mutants with enhanced and null solubilization phenotypes, revealing the potential role of "auxiliary" genes in downstream function of solubilization pathways. Inoculation studies with lucerne seedlings demonstrated a significant increase in shoot length (p < 0.05) following treatment with the enhanced-solubilization mutant, indicating a promising plant growth-promotion effect. These findings highlight the potential of more personalized "system-appropriate" biofertilizers and underscore the importance of integrating genomic, phenotypic, and in planta analyses to validate function. Further research is required to investigate links between genomic markers and functional outcomes to optimize the development of sustainable agricultural inputs.

PMID:40356655 | PMC:PMC12066602 | DOI:10.3389/fmicb.2025.1568162

Elife. 2025 May 13;14:e79517. doi: 10.7554/eLife.79517.

ABSTRACT

Bacterial persistence is a phenomenon in which a small fraction of isogenic bacterial cells survives a lethal dose of antibiotics. Although the refractoriness of persistent cell populations has classically been attributed to growth-inactive cells generated before drug exposure, evidence is accumulating that actively growing cell fractions can also generate persister cells. However, single-cell characterization of persister cell history remains limited due to the extremely low frequencies of persisters. Here, we visualize the responses of over one million individual cells of wildtype Escherichia coli to lethal doses of antibiotics, sampling cells from different growth phases and culture media into a microfluidic device. We show that when cells sampled from exponentially growing populations were treated with ampicillin or ciprofloxacin, most persisters were growing before antibiotic treatment. Growing persisters exhibited heterogeneous survival dynamics, including continuous growth and fission with L-form-like morphologies, responsive growth arrest, or post-exposure filamentation. Incubating cells under stationary phase conditions increased both the frequency and the probability of survival of non-growing cells to ampicillin. Under ciprofloxacin, however, all persisters identified were growing before the antibiotic treatment, including samples from post-stationary phase culture. These results reveal diverse persister cell dynamics that depend on antibiotic types and pre-exposure history.

PMID:40356339 | DOI:10.7554/eLife.79517

Front Pharmacol. 2025 Apr 28;16:1563238. doi: 10.3389/fphar.2025.1563238. eCollection 2025.

ABSTRACT

BACKGROUND: Insulin glargine is a long-acting drug and the first synthetic insulin to mimic human metabolism. The safety of insulin glargine in the real world remains to be further investigated. This study aims to analyze insulin glargine-related adverse events (ADEs) to guide its safe clinical use.

METHODS: This study collected ADE reports from the FDA Adverse Event Reporting System (FAERS) between the first quarter of 2004 and the third quarter of 2024, where insulin glargine was identified as the primary suspect drug. Four disproportionate analytical methods were employed to analyze positive signals for drug-related ADEs, including the Reporting Odds Ratio (ROR), Proportional Reporting Ratio (PRR), Bayesian Confidence Propagation Neural Network (BCPNN), and Multi-item Gamma Poisson Shrinker (MGPS). The study also describes the time to onset of ADEs and uses the Weibull distribution to analyze the temporal trend of ADEs occurrence over time.

RESULTS: This study included 97,350 ADE reports, containing 228,258 ADEs, and identified 130 ADEs with positive signal. The study confirmed several known ADEs, such as hypoglycemia, injection site pain and acquired lipodystrophy. Additionally, several unexpected ADEs were identified, including pancreatic neoplasm, medullary thyroid cancer, and bone marrow tumor cell infiltration. 28.13% of ADEs occurred within the first month. The Weibull distribution indicated that the occurrence of ADEs decreased over time.

CONCLUSION: This study explored the real-world safety of insulin glargine and revealed several unexpected ADEs. These findings provide new insights into the safety profile of insulin glargine for clinicians."

PMID:40356973 | PMC:PMC12066629 | DOI:10.3389/fphar.2025.1563238

J Gastroenterol Hepatol. 2025 May 13. doi: 10.1111/jgh.17003. Online ahead of print.

ABSTRACT

PURPOSE: This study aimed to evaluate the prognostic effect of the cumulative dose (CD) of regorafenib on survival in patients with metastatic colorectal cancer (mCRC).

MATERIALS AND METHODS: This retrospective study utilized the Taipei Medical University Clinical Research Database for analysis. Patients aged ≥ 20 years with mCRC who were prescribed regorafenib between January 2014 and December 2021 were identified and then divided into low- and high-CD groups (≤ 4200 mg vs. > 4200 mg). Overall survival (OS), time-to-treatment discontinuation (TTD), and the incidence of five common adverse events were compared between groups. In addition, natural cubic splines were employed to examine the non-linear relationship between cumulative doses and survival in the multivariate Cox regression model.

RESULTS: A total of 259 patients were enrolled, with 130 in the low-CD group and 129 in the high-CD group; the median OS was 4.6 months and 9.8 months, respectively (p < 0.01). The median TTD was 51.5 days for the low-CD group and 72.0 days for the high-CD group (p < 0.01). No significant difference in drug-related adverse events was observed between groups. In the multivariate Cox analysis, a CD ≤ 4200 mg was a negative prognostic factor (hazard ratio 1.41 [95% confidence interval 1.08-1.84], p = 0.01). In addition, patients on a dose range between 4368 and 5376 mg exhibited minimal mortality risk.

CONCLUSION: The cumulative doses of regorafenib > 4200 mg were associated with improved survival. The suggested optimal dose range serves as a reference for dose modification in clinical practice.

PMID:40356543 | DOI:10.1111/jgh.17003

J Immunother Cancer. 2025 May 12;13(5):e011545. doi: 10.1136/jitc-2025-011545.

ABSTRACT

BACKGROUND: Immune checkpoint inhibitors (ICIs) have led to enduring responses in subsets of patients with cancer. However, these responses carry the risk of immune-related adverse events (irAEs), which can diminish the overall benefit of ICI treatment. While associations between irAE development and overall survival have been increasingly documented, there is a need for further understanding of these connections in large prospective real-world cohorts.

METHODS: The Resistance Drivers for Immuno-Oncology Patients Interrogated by Harmonized Molecular Datasets (RADIOHEAD) study, a pan-tumor, prospective cohort of 1,070 individuals undergoing standard of care first-line ICI treatment, aims to identify factors driving irAEs and clinical response. Clinical data and longitudinal blood samples were collected prospectively at multiple time points from 49 community-based oncology clinics across the USA. Structured, harmonized clinical data underwent unbiased statistical analysis to uncover predictors of real-world overall survival (rwOS) and risk factors for irAEs.

RESULTS: Across 1,070 participants' treatment courses, RADIOHEAD accumulated over 4,500 clinical data points. Patients experiencing any irAE (25.4%, n=272) exhibited significantly improved rwOS in the pan-tumor cohort (n=1,028, HR=0.41, 95% CI=(0.31, 0.55)). This association persisted when adjusting for age and metastatic disease in multivariate time-dependent Cox proportional hazard analysis, and was consistent across major tumor subtypes, including lung cancer and melanoma. Skin and endocrine irAEs of any grade were strongly associated with improved rwOS (Cox proportional hazard analysis, skin, p=2.03e-05; endocrine, p=0.0006). In this real-world cohort, the irAE rate appeared lower than those reported in clinical trials. Patients receiving corticosteroids prior to initiation of ICI treatment had significantly worse survival outcomes than non-users (HR 1.37, p=0.0054), with a stronger association with systemic steroid use (HR 1.75, p=0.0022). The risk of irAE was increased by exposure to combination immunotherapy relative to monotherapy (OR 4.17, p=2.8e-7), zoster vaccine (OR 2.4, p=5.2e-05), and decreased by prior chemotherapy (OR 1.69, p=0.0005).

CONCLUSION: The RADIOHEAD cohort is a well-powered, real-world cohort that clearly demonstrates the association between irAE development with improved response and baseline steroid use with worse response to ICI treatment after adjustment for survival bias.

PMID:40355283 | DOI:10.1136/jitc-2025-011545

Eur Radiol. 2025 May 12. doi: 10.1007/s00330-025-11655-5. Online ahead of print.

ABSTRACT

OBJECTIVE: To develop a novel MRI-based deep learning (DL) diagnostic model, utilizing multicenter large-sample data, for the preoperative differentiation of sinonasal inverted papilloma (SIP) from SIP-transformed squamous cell carcinoma (SIP-SCC).

METHODS: This study included 568 patients from four centers with confirmed SIP (n = 421) and SIP-SCC (n = 147). Deep learning models were built using T1WI, T2WI, and CE-T1WI. A combined model was constructed by integrating these features through an attention mechanism. The diagnostic performance of radiologists, both with and without the model's assistance, was compared. Model performance was evaluated through receiver operating characteristic (ROC) analysis, calibration curves, and decision curve analysis (DCA).

RESULTS: The combined model demonstrated superior performance in differentiating SIP from SIP-SCC, achieving AUCs of 0.954, 0.897, and 0.859 in the training, internal validation, and external validation cohorts, respectively. It showed optimal accuracy, stability, and clinical benefit, as confirmed by Brier scores and calibration curves. The diagnostic performance of radiologists, especially for less experienced ones, was significantly improved with model assistance.

CONCLUSIONS: The MRI-based deep learning model enhances the capability to predict malignant transformation of sinonasal inverted papilloma before surgery. By facilitating earlier diagnosis and promoting timely pathological examination or surgical intervention, this approach holds the potential to enhance patient prognosis.

KEY POINTS: Questions Sinonasal inverted papilloma (SIP) is prone to malignant transformation locally, leading to poor prognosis; current diagnostic methods are invasive and inaccurate, necessitating effective preoperative differentiation. Findings The MRI-based deep learning model accurately diagnoses malignant transformations of SIP, enabling junior radiologists to achieve greater clinical benefits with the assistance of the model. Clinical relevance A novel MRI-based deep learning model enhances the capability of preoperative diagnosis of malignant transformation in sinonasal inverted papilloma, providing a non-invasive tool for personalized treatment planning.

PMID:40355636 | DOI:10.1007/s00330-025-11655-5

Sci Rep. 2025 May 12;15(1):16418. doi: 10.1038/s41598-025-94127-6.

ABSTRACT

Detecting and classifying arrhythmias is essential in diagnosing cardiovascular diseases. However, current deep learning-based classification methods often encounter difficulties in effectively integrating both the morphological and temporal features of Electrocardiograms (ECGs). To address this challenge, we propose a Convolutional Neural Network (CNN) that incorporates mixed scales and hierarchical features combined with the Lead Encoder Attention (LEA) mechanism for multi-lead ECG classification. We validated the performance of our proposed method using the intrapatient approach of the MIT-BIH Arrhythmia (MIT-BIH-AR) Database and the interpatient approach of the Chinese Cardiovascular Disease Database (CCDD). Our model achieves an Accuracy (Acc) of 99.5% for the classification of normal and abnormal heartbeats in the MIT-BIH-AR database. Our method achieves a TPR95 (NPV under the condition of True Positive Rate being equal to 95 percent) of 78.5% and an Acc of 88.5% when classifying normal and abnormal ECG records from over 150,000 ECG records in the CCDD. The cross-dataset experimental results also confirm the model's strong generalization capability.

PMID:40355498 | DOI:10.1038/s41598-025-94127-6

Sci Rep. 2025 May 12;15(1):16392. doi: 10.1038/s41598-025-01015-0.

ABSTRACT

Epilepsy is a neurological disorder characterized by recurrent seizures caused by excessive electrical discharges in brain cells, posing significant diagnostic and therapeutic challenges. Dynamic brain network analysis via electroencephalography (EEG) has emerged as a powerful tool for capturing transient functional connectivity changes, offering advantages over static networks. In this study, we propose a Dynamic Temporal-Spatial Graph Attention Network (DTS-GAN) to address the limitations of fixed-topology graph models in analysing time-varying brain networks. By integrating graph signal processing with a hybrid deep learning framework, DTS-GAN collaboratively extracts spatiotemporal features through two key modules: an LSTM-based temporal encoder to model long-term dependencies in EEG sequences, and a dynamic graph attention network with probabilistic Gaussian connectivity, enabling adaptive learning of transient functional interactions across electrode nodes. Experiments on the TUSZ dataset demonstrate that DTS-GAN achieves 89-91% accuracy and a weighted F1-score of 87-91% in classifying seven seizure types, significantly outperforming baseline models. The multi-head attention mechanism and dynamic graph generation strategy effectively resolve the temporal variability of functional connectivity. These results highlight the potential of DTS-GAN in providing precise and automated seizure detection, serving as a robust tool for clinical EEG analysis.

PMID:40355495 | DOI:10.1038/s41598-025-01015-0

Sci Rep. 2025 May 12;15(1):16409. doi: 10.1038/s41598-025-00116-0.

ABSTRACT

Accurate cortical surface parcellation is essential for elucidating brain organizational principles, functional mechanisms, and the neural substrates underlying higher cognitive and emotional processes. However, the cortical surface is a highly folded complex geometry, and large regional variations make the analysis of surface data challenging. Current methods rely on geometric simplification, such as spherical expansion, which takes hours for spherical mapping and registration, a popular but costly process that does not take full advantage of inherent structural information. In this study, we propose an Attention-guided Deep Graph Convolutional network (ADGCN) for end-to-end parcellation on primitive cortical surface manifolds. ADGCN consists of a deep graph convolutional layer with a symmetrical U-shaped structure, which enables it to effectively transmit detailed information of the original brain map and learn the complex graph structure, help the network enhance feature extraction capability. What's more, we introduce the Squeeze and Excitation (SE) module, which enables the network to better capture key features, suppress unimportant features, and significantly improve parcellation performance with a small amount of computation. We evaluated the model on a public dataset of 100 artificially labeled brain surfaces. Compared with other methods, the proposed network achieves Dice coefficient of 88.53% and an accuracy of 90.27%. The network can segment the cortex directly in the original domain, and has the advantages of high efficiency, simple operation and strong interpretability. This approach facilitates the investigation of cortical changes during development, aging, and disease progression, with the potential to enhance the accuracy of neurological disease diagnosis and the objectivity of treatment efficacy evaluation.

PMID:40355465 | DOI:10.1038/s41598-025-00116-0

Sci Rep. 2025 May 12;15(1):16440. doi: 10.1038/s41598-025-00024-3.

ABSTRACT

Deforestation, urbanization, and climate change have significantly increased the risk of zoonotic diseases. Nipah virus (NiV) of Paramyxoviridae family and Henipavirus genus is transmitted by Pteropus bats. Climate-induced changes in bat migration patterns and food availability enhances the virus's adaptability, in turn increasing the potential for transmission and outbreak risk. NiV infection has high human fatality rate. With no antiviral drugs or vaccines available, exploring the complex machinery involved in viral RNA synthesis presents a promising target for therapy. Drug repurposing provides a fast-track approach by identifying existing drugs with potential to target NiV RNA-dependent RNA polymerase (L), bypassing the time-consuming process of developing novel compounds. To facilitate this, we developed an attention-based deep learning model that utilizes pharmacophore properties of the active sites and their binding efficacy with NiV L protein. Around 500 FDA-approved drugs were filtered and assessed for their ability to bind NiV L protein. Compared to the control Remdesivir, we identified Cangrelor, an antiplatelet drug for cardiovascular diseases, with stronger binding affinity to NiV L (glide score of -12.30 kcal/mol). Molecular dynamics simulations further revealed stable binding (RMSD of 3.54 Å) and a post-MD binding energy of -181.84 kcal/mol. The strong binding of Cangrelor is illustrated through trajectory analysis, principal component analysis, and solvent accessible surface area, further confirming the stable interaction with the active site of NiV RdRp. Cangrelor can interact with NiV L protein and may potentially interfere with its replication. These findings suggest that Cangrelor will be a potential drug candidate that can effectively interact with the NiV L protein and potentially disrupt the viral replication. Further in vivo studies are warranted to explore its potential as a repurposable antiviral drug.

PMID:40355437 | DOI:10.1038/s41598-025-00024-3

Med Phys. 2025 May 12. doi: 10.1002/mp.17883. Online ahead of print.

ABSTRACT

BACKGROUND: Deep learning networks map input data to output predictions by fitting network parameters using training data. However, applying a trained network to new, unseen inference data resembles an interpolation process, which may lead to inaccurate predictions if the training and inference data distributions differ significantly.

PURPOSE: This study aims to generally improve the prediction accuracy of deep learning networks on the inference case by bridging the gap between training and inference data.

METHODS: We propose an inference-specific learning strategy to enhance the network learning process without modifying the network structure. By aligning training data to closely match the specific inference data, we generate an inference-specific training dataset, enhancing the network optimization around the inference data point for more accurate predictions. Taking medical image auto-segmentation as an example, we develop an inference-specific auto-segmentation framework consisting of initial segmentation learning, inference-specific training data deformation, and inference-specific segmentation refinement. The framework is evaluated on public abdominal, head-neck, and pancreas CT datasets comprising 30, 42, and 210 cases, respectively, for medical image segmentation.

RESULTS: Experimental results show that our method improves the organ-averaged mean Dice by 6.2% (p-value = 0.001), 1.5% (p-value = 0.003), and 3.7% (p-value < 0.001) on the three datasets, respectively, with a more notable increase for difficult-to-segment organs (such as a 21.7% increase for the gallbladder [p-value = 0.004]). By incorporating organ mask-based weak supervision into the training data alignment learning, the inference-specific auto-segmentation accuracy is generally improved compared with the image intensity-based alignment. Besides, a moving-averaged calculation of the inference organ mask during the learning process strengthens both the robustness and accuracy of the final inference segmentation.

CONCLUSIONS: By leveraging inference data during training, the proposed inference-specific learning strategy consistently improves auto-segmentation accuracy and holds the potential to be broadly applied for enhanced deep learning decision-making.

PMID:40356014 | DOI:10.1002/mp.17883

BMC Med Educ. 2025 May 12;25(1):691. doi: 10.1186/s12909-025-07259-8.

ABSTRACT

BACKGROUND: Dentistry is shifting from traditional to digital practices owing to the rapid development of "artificial intelligence" (AI) technology in healthcare systems. The dental curriculum lacks the integration of emerging technologies such as AI, which could prepare students for the evolving demands of modern dental practice. This study aimed to assess dental faculty members' knowledge, awareness, and attitudes toward AI and provide consensus-based recommendations for increasing the adoption of AI in dental education and dental practice.

METHOD: This mixed-method study was conducted via a modified version of the General Attitudes toward Artificial Intelligence Scale (GAAIS) and Focus Group Discussions (FGD). Four hundred faculty members from both public and private dental colleges in Pakistan participated. The quantitative data were analyzed using SPSS version 23. Otter.ai was used to transcribe the data, followed by thematic analysis to generate codes, themes, and subthemes.

RESULTS: The majority of the faculty members was aware of the application of AI in daily life and learned about AI mainly from their colleagues and social media. Fewer than 20% of faculty members were aware of terms such as machine learning and deep learning. 81% of the participants acknowledged the need for and limited opportunities to learn about AI. Overall, the dental faculty demonstrated a generally positive attitude toward AI, with a mean score of 3.5 (SD ± 0.61). The benefits of AI in dentistry, the role of AI in dental education and research, and barriers to AI adoption and recommendations for AI integration in dentistry were the main themes identified from the FGD.

CONCLUSIONS: The dental faculty members showed general awareness and positive attitudes toward AI; however, their knowledge regarding advanced AI concepts such as machine learning and deep learning was limited. The major barriers identified in AI adoption are financial constraints, a lack of AI training, and ethical concerns for data management and academics. There is a need for targeted education initiatives, interdisciplinary and multi-institutional collaborations, the promotion of local manufacturing of such technologies, and robust policy initiatives by the governing body.

PMID:40355937 | DOI:10.1186/s12909-025-07259-8

J Transl Med. 2025 May 12;23(1):532. doi: 10.1186/s12967-025-06507-1.

ABSTRACT

BACKGROUND: Accurate assessment of expected survival in melanoma patients is crucial for treatment decisions. This study explores deep learning-based body composition analysis to predict overall survival (OS) using baseline Computed Tomography (CT) scans and identify fully volumetric, prognostic body composition features.

METHODS: A deep learning network segmented baseline abdomen and thorax CTs from a cohort of 495 patients. The Sarcopenia Index (SI), Myosteatosis Fat Index (MFI), and Visceral Fat Index (VFI) were derived and statistically assessed for prognosticating OS. External validation was performed with 428 patients.

RESULTS: SI was significantly associated with OS on both CT regions: abdomen (P ≤ 0.0001, HR: 0.36) and thorax (P ≤ 0.0001, HR: 0.27), with lower SI associated with prolonged survival. MFI was also associated with OS on abdomen (P ≤ 0.0001, HR: 1.16) and thorax CTs (P ≤ 0.0001, HR: 1.08), where higher MFI was linked to worse outcomes. Lastly, VFI was associated with OS on abdomen CTs (P ≤ 0.001, HR: 1.90), with higher VFI linked to poor outcomes. External validation replicated these results.

CONCLUSIONS: SI, MFI, and VFI showed substantial potential as prognostic factors for OS in malignant melanoma patients. This approach leveraged existing CT scans without additional procedural or financial burdens, highlighting the seamless integration of DL-based body composition analysis into standard oncologic staging routines.

PMID:40355935 | DOI:10.1186/s12967-025-06507-1

BMC Cardiovasc Disord. 2025 May 12;25(1):362. doi: 10.1186/s12872-025-04818-1.

ABSTRACT

BACKGROUND: Heart failure (HF) after acute myocardial infarction (AMI) is a leading cause of mortality and morbidity worldwide. Accurate prediction and early identification of HF severity are crucial for initiating preventive measures and optimizing treatment strategies. This study aimed to develop an interpretable artificial intelligence (AI) model for HF severity prediction using multidimensional clinical data.

METHODS: This study included data from 1574 AMI patients, including medical history, clinical features, physiological parameters, laboratory test, coronary angiography and echocardiography results. Both deep learning (TabNet, Multi-Layer Perceptron) and machine learning (Random Forest, XGboost) models were employed in constructing model. Additionally, the Shapley Additive Explanation (SHAP) method was used to elucidate clinical factors importance and enhance model interpretability. A web platform ( https://prediction-killip-gby.streamlit.app/ ) was also developed to facilitate clinical application.

RESULTS: Among the models, TabNet demonstrated the best performance, achieving an AUROC of 0.827 for KILLIP four-class classification and 0.831 for KILLIP binary classification. Key clinical factors such as GRACE score, NT-pro BNP, and TIMI score were highly correlated with KILLIP classification, aligning with established clinical knowledge.

CONCLUSIONS: By leveraging easily accessible multidimensional data, this model enables accurate early prediction and personalized diagnosis of HF risk and severity following AMI. It supports early clinical intervention and improves patient outcomes, offering significant clinical application value.

CLINICAL TRIAL NUMBER: Not applicable.

PMID:40355836 | DOI:10.1186/s12872-025-04818-1

Aging Clin Exp Res. 2025 May 13;37(1):149. doi: 10.1007/s40520-025-03048-x.

ABSTRACT

BACKGROUND: Osteoporosis is often underdiagnosed due to limitations in traditional screening methods, leading to missed early intervention opportunities. AI-driven screening using chest radiographs could improve early detection, reduce fracture risk, and improve public health outcomes.

AIMS: To assess the cost-effectiveness of deep learning models (hereafter referred to as AI-driven) applied to chest radiographs for opportunistic osteoporosis screening in German women aged 50 and older.

METHODS: A decision tree and microsimulation Markov model were used to calculate the cost per quality-adjusted life year (QALY) gained (€2024) for screening with AI-driven chest radiographs followed by treatment, compared to no screening and treatment. Patient pathways were based on AI model accuracy and German osteoporosis guidelines. Women with a fracture risk below 5% received no treatment, those with 5-10% risk received alendronate, and women 65 + with a risk above 10% received sequential treatment starting with romosozumab. Data was validated by a German clinical expert, incorporating real-world treatment persistence, DXA follow-up rates, and treatment initiation. Sensitivity analyses assessed parameter uncertainty.

RESULTS: The cost per QALY gained from screening was €13,340, far below the typical cost-effectiveness threshold of €60,000. Optimizing follow-up, treatment initiation, and medication adherence further improved cost-effectiveness, with dominance achievable by halving medication non-persistence, and in women aged 50-64.

CONCLUSION: AI-driven chest radiographs for opportunistic osteoporosis screening is a cost-effective strategy for German women aged 50+, with the potential to significantly improve public health outcomes, reduce fracture burdens and address healthcare disparities. Policymakers and clinicians should consider implementing this scalable and cost-effective screening strategy.

PMID:40355760 | DOI:10.1007/s40520-025-03048-x

Nat Methods. 2025 May 12. doi: 10.1038/s41592-025-02698-z. Online ahead of print.

ABSTRACT

Light-field microscopy (LFM) and its variants have significantly advanced intravital high-speed 3D imaging. However, their practical applications remain limited due to trade-offs among processing speed, fidelity, and generalization in existing reconstruction methods. Here we propose a physics-driven self-supervised reconstruction network (SeReNet) for unscanned LFM and scanning LFM (sLFM) to achieve near-diffraction-limited resolution at millisecond-level processing speed. SeReNet leverages 4D information priors to not only achieve better generalization than existing deep-learning methods, especially under challenging conditions such as strong noise, optical aberration, and sample motion, but also improve processing speed by 700 times over iterative tomography. Axial performance can be further enhanced via fine-tuning as an optional add-on with compromised generalization. We demonstrate these advantages by imaging living cells, zebrafish embryos and larvae, Caenorhabditis elegans, and mice. Equipped with SeReNet, sLFM now enables continuous day-long high-speed 3D subcellular imaging with over 300,000 volumes of large-scale intercellular dynamics, such as immune responses and neural activities, leading to widespread practical biological applications.

PMID:40355725 | DOI:10.1038/s41592-025-02698-z

Sci Rep. 2025 May 12;15(1):16481. doi: 10.1038/s41598-025-01810-9.

ABSTRACT

This work proposes a personalized music learning platform model based on deep learning, aiming to provide efficient and customized learning recommendations by integrating audio, video, and user behavior data. This work uses Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) networks to extract audio and video features, while using multi-layer perceptrons to encode user behavior data. To further improve the recommendation accuracy, this work constructs a knowledge graph that integrates key entities and their relationships in the music field, and fuses them with the extracted feature vectors. The knowledge graph provides the platform with rich semantic information and relational data, helping the model better understand the correlation between user needs and music content, thereby improving the accuracy and personalization of recommendation results. Experimental analysis based on different datasets shows that the proposed music recommendation platform performs well in multiple key performance indicators. Especially under different TOP-K conditions, the accuracy reaches 0.90, significantly exceeding collaborative filtering and content-based recommendation methods. In addition, the platform can maintain high accuracy when processing sparse data, demonstrating stronger robustness and adaptability. The platform has significant advantages in overall performance, providing users with more reliable and efficient recommendation services.

PMID:40355699 | DOI:10.1038/s41598-025-01810-9

J Imaging Inform Med. 2025 May 12. doi: 10.1007/s10278-025-01534-2. Online ahead of print.

ABSTRACT

Pain assessment is a critical aspect of medical care, yet automated systems for clinical pain estimation remain rare. Tools such as the visual analog scale (VAS) are commonly used in emergency departments (EDs) but rely on subjective self-reporting, with pain intensity often fluctuating during triage. An effective automated system should utilize objective labels from healthcare professionals and identify key frames from video sequences for accurate inference. In this study, short video clips were treated as instance segments for the model, with ground truth (physician-rated VAS) provided at the video level. To address the weak label problem, we proposed flexible multiple instance learning approaches. Using a specialized loss function and sampling strategy, our instance-appraisable model, EDi Pain, was trained to estimate pain intensity while evaluating the significance of each instance segment. During inference, the VAS pain score for the entire video is derived from instance-level predictions. In retrospective analysis using the public UNBC-McMaster dataset, the EDi Pain model demonstrated competitive performance relative to prior studies, achieving strong performance in video-level pain intensity estimation, with a mean absolute error (MAE) of 1.85 and a Pearson correlation coefficient (PCC) of 0.63. Additionally, our model was validated on a prospectively collected dataset of 931 patients from National Taiwan University Hospital, yielding an MAE of 1.48 and a PCC of 0.22. In summary, we developed and validated a novel deep learning-based, instance-appraisable model for pain intensity estimation using facial videos. The EDi Pain model shows promise for real-time application in clinical settings, offering a more objective and dynamic approach to pain assessment.

PMID:40355693 | DOI:10.1007/s10278-025-01534-2

J Imaging Inform Med. 2025 May 12. doi: 10.1007/s10278-025-01529-z. Online ahead of print.

ABSTRACT

The purpose of this study was to investigate the utility of deep learning image reconstruction at medium and high intensity levels (DLIR-M and DLIR-H, respectively) for better delineation of liver metastases in pre-contrast and post-contrast CT, compared to conventional hybrid iterative reconstruction (IR) methods. Forty-one patients with liver metastases who underwent abdominal CT were studied. The raw data were reconstructed with three different algorithms: hybrid IR (ASiR-V 50%), DLIR-M (TrueFildelity-M), and DLIR-H (TrueFildelity-H). Three experienced radiologists independently rated the lesion conspicuity of liver metastases on a qualitative 5-point scale (score 1 = very poor; score 5 = excellent). The observers also selected each image series for pre- and post-contrast CT per patient that was considered most preferable for liver metastases assessment. For pre-contrast CT, lesion conspicuity scores for DLIR-H and DLIR-M were significantly higher than those for hybrid IR for two of the three observers, while there was no significant difference for one observer. For post-contrast CT, the lesion conspicuity scores for DLIR-H images were significantly higher than those for DLIR-M images for two of the three observers on post-contrast CT (Observer 1: DLIR-H, 4.3 ± 0.8 vs. DLIR-M, 3.9 ± 0.9, p = 0.0006; Observer 3: DLIR-H, 4.6 ± 0.6 vs. DLIR-M, 4.3 ± 0.6, p = 0.0013). For post-contrast CT, all observers most often selected DLIR-H as the best reconstruction method for the diagnosis of liver metastases. However, in the pre-contrast CT, there was variation among the three observers in determining the most preferred image reconstruction method, and DLIR was not necessarily preferred over hybrid IR for the diagnosis of liver metastases.

PMID:40355690 | DOI:10.1007/s10278-025-01529-z