Diagnostics (Basel). 2025 Apr 19;15(8):1041. doi: 10.3390/diagnostics15081041.

ABSTRACT

Background/Objectives: Multiple sclerosis (MS) is an autoimmune disease that damages the myelin sheath of the central nervous system, which includes the brain and spinal cord. Although MS lesions in the brain are more frequently investigated, MS lesions in the cervical spinal cord (CSC) can be much more specific for the diagnosis of the disease. Furthermore, as lesion burden in the CSC is directly related to disease progression, the presence of lesions in the CSC may help to differentiate MS from other neurological diseases. Methods: In this study, two novel deep learning models based on fractal architectures are proposed for the automatic detection and segmentation of MS lesions in the CSC by improving the convolutional and connection structures used in the layers of the U-Net architecture. In our previous study, we introduced the FractalSpiNet architecture by incorporating fractal convolutional block structures into the U-Net framework to develop a deeper network for segmenting MS lesions in the CPC. In this study, to improve the detection of smaller structures and finer details in the images, an attention mechanism is integrated into the FractalSpiNet architecture, resulting in the Att-FractalSpiNet model. In addition, in the second hybrid model, a fractal convolutional block is incorporated into the skip connection structure of the U-Net architecture, resulting in the development of the Con-FractalU-Net model. Results: Experimental studies were conducted using U-Net, FractalSpiNet, Con-FractalU-Net, and Att-FractalSpiNet architectures to detect the CSC region and the MS lesions within its boundaries. In segmenting the CSC region, the proposed Con-FractalU-Net architecture achieved the highest Dice Similarity Coefficient (DSC) score of 98.89%. Similarly, in detecting MS lesions within the CSC region, the Con-FractalU-Net model again achieved the best performance with a DSC score of 91.48%. Conclusions: For segmentation of the CSC region and detection of MS lesions, the proposed fractal-based Con-FractalU-Net and Att-FractalSpiNet architectures achieved higher scores than the baseline U-Net architecture, particularly in segmenting small and complex structures.

PMID:40310404 | DOI:10.3390/diagnostics15081041

Hepatology. 2025 May 1. doi: 10.1097/HEP.0000000000001346. Online ahead of print.

ABSTRACT

BACKGROUND: Metabolic dysfunction-associated steatotic liver disease (MASLD) affects more than 30% of the world's population and is associated with multisystemic comorbidities. We combined multidimensional OMICs approaches to explore the feasibility of using high-throughput targeted circulating proteomics to track systemic organ damage and infer the underlying molecular mechanisms.

METHODS: We evaluated a 92-plex panel of prioritized proteins with pathophysiological relevance to organ damage in serum samples of patients using in-depth phenotyping. We included proteomic data from 60,042 individuals in the discovery and replication stages using diverse study designs and cross-proteomic platforms. We used deconvolution strategies to investigate whether the affected liver participated in the expression of biomarkers of organ damage. To assess cell type-specific transcriptional changes in the selected target, we used liver organoid data.

FINDINGS: The implicated proteins, including ADGRG1 (GPR56), are deregulated in patients who are at-risk of progressive disease and significant fibrosis. ADGRG1 was validated as a surrogate for organ damage, as it was associated with increased risk of end-stage liver disease, moderate but clinically significant risk of death, chronic obstructive pulmonary disease and ischaemic heart disease over a 16-year follow-up, regardless of the subject's MASLD status. ADGRG1 liver expression mirrors the circulation pattern. Mechanistic insights show that ADGRG1 shifts its transcriptional profile from low expression to upregulation in cells of the fibrotic and inflammatory niche in response to injury.

CONCLUSIONS: Our study provides a framework for potential mechanisms associated with systemic diseases that facilitates holistic management by stratifying patients with MASLD into subclasses at-risk of extrahepatic manifestations.

PMID:40310655 | DOI:10.1097/HEP.0000000000001346

mBio. 2025 May 1:e0201924. doi: 10.1128/mbio.02019-24. Online ahead of print.

ABSTRACT

Sponge-associated microbes play fundamental roles in regulating their hosts' physiology, yet their contribution to sexual reproduction has been largely overlooked. Most studies have concentrated on the proportion of the microbiome transmitted from parents to offspring, providing little evidence of the putative microbial role during gametogenesis in sponges. Here, we use 16S rRNA gene analysis to assess whether the microbial composition of five gonochoristic sponge species differs between reproductive and non-reproductive individuals and correlate these changes with their gametogenic stages. In sponges with mature oocytes, reproductive status did not influence either beta or alpha microbial diversity. However, in two of the studied species, Geodia macandrewii and Petrosia ficiformis, which presented oocytes at the previtellogenic stage, significant microbial composition changes were detected between reproductive and non-reproductive individuals. These disparities were primarily driven by differentially abundant taxa affiliated with the Nitrososphaeria archaeal class in both species. We speculate that the previtellogenic stages are more energetically demanding, leading to microbial changes due to the phagocytosis of microbes to meet nutritional demands during this period. Supporting our hypothesis, we observed significant transcriptomic differences in G. macandrewii, mainly associated with the immune system, indicating potential changes in the sponge's recognition system. Overall, we provide new insights into the possible roles of sponge microbiomes during reproductive periods, potentially uncovering critical interactions that support reproductive success.

IMPORTANCE: Our research explores the fascinating relationship between sponges and their resident microbes, focusing specifically on how these microbes might influence sponge reproduction. Sponges are marine animals known for their complex and beneficial partnerships with various microbes. While previous studies have mainly looked at how these microbes are passed from parent sponges to their offspring, our study is among the first to examine how microbial communities change during the different stages of sponge reproduction. By analyzing the microbial composition in five sponge species, we discovered that significant changes occur in species with premature oocytes, suggesting that microbes may play a crucial role in providing the necessary nutrients during early egg development. This work not only enhances our understanding of sponge biology but also opens up new avenues for studying how microbes support the reproductive success of their hosts in marine environments.

PMID:40310091 | DOI:10.1128/mbio.02019-24

Physiol Plant. 2025 May-Jun;177(3):e70245. doi: 10.1111/ppl.70245.

ABSTRACT

The rising frequency of extreme climate events requires sustainable strategies to secure food production. Environmental stress impacts seed germination and seedling development, posing a significant agricultural challenge. To address this, we developed and applied iron-based nanoparticles (Bio-NPs) synthesized through green biosynthesis from Haematococcus pluvialis, a microalga rich in antioxidants like astaxanthin. These Bio-NPs, approximately 21 nm in diameter and characterized by a negative surface charge, were used as priming agents for maize seeds. Their effects on physiological traits were analyzed with multispectral imaging, showing enhanced normalized difference vegetation index (NDVI) and chlorophyll levels in maize seedlings, highlighting Bio-NPs as effective biostimulants. Among the tested concentrations, 6 mM Bio-NPs yielded the most substantial improvements in seedling health compared to unprimed and hydro-primed groups. Importantly, in vitro studies confirmed that Bio-NPs had no harmful effects on beneficial bacteria and fungi of agronomic importance, underscoring their safety. Although the exact biological pathways responsible for these enhancements are yet to be fully understood, further research into plant responses to Bio-NPs could yield new insights into plant biostimulation. Bio-NPs thus hold promises for strengthening seedling resilience under extreme environmental scenarios, currently observed due to global climate change, offering a safe, sustainable approach to agricultural enhancement. By leveraging microalgae-based biostimulants, this work advances seed priming technology, fostering crop resilience and supporting environmentally friendly agricultural practices.

PMID:40309930 | DOI:10.1111/ppl.70245

Nucleic Acids Res. 2025 May 1:gkaf373. doi: 10.1093/nar/gkaf373. Online ahead of print.

ABSTRACT

As part of the Library of Integrated Network-Based Cellular Signatures (LINCS) NIH initiative, 248 human cell lines were profiled with the L1000 assay to measure the effect of 33 621 small molecules and 7508 single-gene CRISPR knockouts. From this massive dataset, we computed 1.678 million sets of up- and down-regulated genes. These gene sets are served for search by the LINCS L1000 Signature Search (L2S2) web server application. With L2S2, users can identify small molecules and single gene CRISPR KOs that produce gene expression profiles similar or opposite to their submitted single or up/down gene sets. L2S2 also includes a consensus search feature that ranks perturbations across all cellular contexts, time points, and concentrations. To demonstrate the utility of L2S2, we crossed the L2S2 gene sets with gene sets collected for the RummaGEO resource. The analysis identified clusters of differentially expressed genes that match drug classes, tissues, and diseases, pointing to many opportunities for drug repurposing and drug discovery. Overall, the L2S2 web server application can be used to further the development of personalized therapeutics while expanding our understanding of complex human diseases. The L2S2 web server application is available at https://l2s2.maayanlab.cloud.

PMID:40308216 | DOI:10.1093/nar/gkaf373

J Appl Toxicol. 2025 May 1. doi: 10.1002/jat.4803. Online ahead of print.

ABSTRACT

The prediction of chemical toxicity is crucial for applications in drug discovery, environmental safety, and regulatory assessments. This study aims to evaluate the performance of advanced deep learning architectures, TabNet and TabTransformer, in comparison to traditional machine learning methods, for predicting the toxicity of chemical compounds across 12 toxicological endpoints. The dataset consisted of 12,228 training and 3057 test samples, each characterized by 801 molecular descriptors representing chemical and structural features. Traditional machine learning models, including XGBoost, CatBoost, SVM, and a voting classifier, were paired with feature selection techniques such as principal component analysis (PCA), recursive feature elimination (RFE), and mutual information (MI). Advanced architectures, TabNet and TabTransformer, were trained directly on the full feature set without dimensionality reduction. Model performance was assessed using accuracy, F1-score, AUC-ROC, AUPR, and Matthews correlation coefficient (MCC), alongside SHAP analysis to interpret feature importance and enhance model transparency under class imbalance conditions. Cross-validation and test set evaluations ensured robust comparisons across all models and toxicological endpoints. TabNet and TabTransformer consistently outperformed traditional classifiers, achieving AUC-ROC values up to 96% for endpoints such as SR.ARE and SR.p53. TabTransformer showed the highest performance on complex labels, benefiting from self-attention mechanisms that captured intricate feature relationships, while TabNet achieved competitive outcomes with an efficient, dynamic feature selection. In addition to standard metrics, we reported AUPR and MCC to better evaluate model performance under class imbalance, with both models maintaining high scores across endpoints. Although traditional classifiers, particularly the voting classifier, performed well when combined with feature selection-achieving up to 94% AUC-ROC on SR.p53-they lagged behind the deep learning models in generalizability and feature interaction modeling. SHAP analysis further highlighted the interpretability of the proposed architectures by identifying influential descriptors such as VSAEstate6 and MoRSEE8. This study highlights the superiority of TabNet and TabTransformer in predicting chemical toxicity while ensuring interpretability through SHAP analysis. These models offer a promising alternative to traditional in vitro and in vivo approaches, paving the way for cost-effective and ethical toxicity assessments.

PMID:40309751 | DOI:10.1002/jat.4803

Diabetes Metab Syndr Obes. 2025 Apr 26;18:1311-1321. doi: 10.2147/DMSO.S506494. eCollection 2025.

ABSTRACT

PURPOSE: This study aims to develop and validate a deep learning-based automated diagnostic system that utilizes fluorescein angiography (FFA) images for the rapid and accurate diagnosis of diabetic retinopathy (DR) and its complications.

METHODS: We collected 19,031 FFA images from 2753 patients between June 2017 and March 2024 to construct and evaluate our analytical framework. The images were preprocessed and annotated for training and validating the deep learning model. The study employed a two-stage deep learning system: the first stage used EfficientNetB0 for a five-class classification task to differentiate between normal retinal conditions, various stages of DR, and post-laser treatment status; the second stage focused on images classified as abnormal in the first stage, further detecting the presence of diabetic macular edema (DME). Model performance was evaluated using multiple classification metrics, including accuracy, AUC, precision, recall, F1-score, and Cohen's kappa coefficient.

RESULTS: In the first stage, the model achieved an accuracy of 0.7036 and an AUC of 0.9062 on the test set, demonstrating high accuracy and discriminative ability. In the second stage, the model achieved an accuracy of 0.7258 and an AUC of 0.7530, performing well. Additionally, through Grad-CAM (gradient-weighted class activation mapping), we visualized the most influential image regions in the model's decision-making process, enhancing the model's interpretability.

CONCLUSION: This study successfully developed an end-to-end DR diagnostic system based on the EfficientNetB0 model. The system not only automates the grading of FFA images but also detects DME, significantly reducing the time required for image interpretation by clinicians and providing an effective tool to improve the efficiency and accuracy of DR diagnosis.

PMID:40309724 | PMC:PMC12042962 | DOI:10.2147/DMSO.S506494

Front Artif Intell. 2025 Apr 16;8:1522730. doi: 10.3389/frai.2025.1522730. eCollection 2025.

ABSTRACT

INTRODUCTION: Accurate segmentation of anatomical structures in chest X-ray images remains challenging, especially for regions with low contrast and overlapping structures. This limitation significantly affects the diagnosis of cardiothoracic diseases. Existing deep learning methods often struggle with preserving structural boundaries, leading to segmentation artifacts.

METHODS: To address these challenges, I propose a novel segmentation approach that integrates contour detection techniques with the U-net deep learning architecture. Specifically, the method employs Sobel and Scharr edge detection filters to enhance structural boundaries in chest X-ray images before segmentation. The pipeline involves pre-processing using contour detection, followed by segmentation with a U-net model trained to identify lungs, heart, and clavicles.

RESULTS: Experimental evaluation demonstrated that using edge-enhancing filters, particularly the Sobel operator, leads to a marked improvement in segmentation accuracy. For lung segmentation, the model achieved an accuracy of 99.26%, a Dice coefficient of 98.88%, and a Jaccard index of 97.54%. Heart segmentation results included 99.47% accuracy and 94.14% Jaccard index, while clavicle segmentation reached 99.79% accuracy and 89.57% Jaccard index. These results consistently outperform the baseline U-net model without edge enhancement.

DISCUSSION: The integration of contour detection methods with the U-net model significantly improves the segmentation quality of complex anatomical regions in chest X-rays. Among the tested filters, the Sobel operator proved to be the most effective in enhancing boundary information and reducing segmentation artifacts. This approach offers a promising direction for more accurate and robust computer-aided diagnosis systems in radiology.

PMID:40309721 | PMC:PMC12040918 | DOI:10.3389/frai.2025.1522730

Front Neurosci. 2025 Apr 16;19:1577029. doi: 10.3389/fnins.2025.1577029. eCollection 2025.

ABSTRACT

The hierarchical modular functional structure in the human brain has not been adequately depicted by conventional functional magnetic resonance imaging (fMRI) acquisition techniques and traditional functional connectivity reconstruction methods. Fortunately, rapid advancements in fMRI scanning techniques and deep learning methods open a novel frontier to map the spatial hierarchy within Brain Connectivity Networks (BCNs). The novel multiband multi-echo (MBME) fMRI technique has increased spatiotemporal resolution and peak functional sensitivity, while the advanced deep linear model (multilayer-stacked) named DEep Linear Matrix Approximate Reconstruction (DELMAR) enables the identification of hierarchical features without extensive hyperparameter tuning. We incorporate a multi-echo blood oxygenation level-dependent (BOLD) signal and DELMAR for denoising in its first layer, thereby eliminating the need for a separate multi-echo independent component analysis (ME-ICA) denoising step. Our results demonstrate that the DELMAR/Denoising/Mapping strategy produces more accurate and reproducible hierarchical BCNs than traditional ME-ICA denoising followed by DELMAR. Additionally, we showcase that MBME fMRI outperforms multiband (MB) fMRI in terms of hierarchical BCN mapping accuracy and precision. These reproducible spatial hierarchies in BCNs have significant potential for developing improved fMRI diagnostic and prognostic biomarkers of functional connectivity across a wide range of neurological and psychiatric disorders.

PMID:40309655 | PMC:PMC12040835 | DOI:10.3389/fnins.2025.1577029

J Healthc Inform Res. 2025 Mar 6;9(2):174-190. doi: 10.1007/s41666-025-00192-x. eCollection 2025 Jun.

ABSTRACT

Abnormal serum sodium levels are a common and severe complication in stroke patients, significantly increasing mortality risk and prolonging ICU stays. Accurate real-time prediction of serum sodium fluctuations is crucial for optimizing clinical interventions. However, existing predictive models face limitations in handling complex dynamic features and long time series data, making them less effective in guiding individualized treatment. To address this challenge, this study developed a deep learning model based on a multi-head attention mechanism to enable real-time prediction of serum sodium concentrations and provide personalized intervention recommendations for ICU stroke patients. This study utilized publicly available MIMIC-III (n = 2346) and MIMIC-IV (n = 896) datasets, extracting time series data from 10 key clinical indicators closely associated with serum sodium levels. To address the complexity of long time series data, a moving sliding window sub-sampling segmentation method was employed, effectively transforming extensive sequences into more manageable inputs while preserving critical temporal dependencies. By leveraging advanced mathematical modeling, meaningful insights were extracted from sparse and irregular time series data. The resulting time-feature fusion multi-head attention (TFF-MHA) model underwent rigorous validation using public datasets and demonstrated superior performance in predicting both serum sodium values and corresponding intervention measures compared to existing models. This study contributes to the field of healthcare informatics by introducing an innovative, data-driven approach for dynamic serum sodium prediction and intervention recommendation, providing a valuable clinical decision-support tool for optimizing sodium management strategies in critically ill stroke patients.

PMID:40309130 | PMC:PMC12037442 | DOI:10.1007/s41666-025-00192-x

Appl Plant Sci. 2025 Feb 13;13(2):e70000. doi: 10.1002/aps3.70000. eCollection 2025 Mar-Apr.

ABSTRACT

PREMISE: Deep learning has become increasingly important in the analysis of digitized herbarium collections, which comprise millions of scans that provide valuable resources for studying plant evolution and biodiversity. However, leveraging deep learning algorithms to analyze these scans presents significant challenges, partly due to the heterogeneous nature of the non-plant material that forms the background of the scans. We hypothesize that removing such backgrounds can improve the performance of these algorithms.

METHODS: We propose a novel method based on deep learning to segment and generate plant masks from herbarium scans and subsequently remove the non-plant backgrounds. The semi-automatic preprocessing stages involve the identification and removal of non-plant elements, substantially reducing the manual effort required to prepare the training dataset.

RESULTS: The results highlight the importance of effective image segmentation, which achieved an F1 score of up to 96.6%. Moreover, when used in classification models for plant morphological trait identification, the images resulting from segmentation improved classification accuracy by up to 3% and F1 score by up to 7% compared to non-segmented images.

DISCUSSION: Our approach isolates plant elements in herbarium scans by removing background elements to improve classification tasks. We demonstrate that image segmentation significantly enhances the performance of plant morphological trait identification models.

PMID:40308899 | PMC:PMC12038731 | DOI:10.1002/aps3.70000

Biol Methods Protoc. 2025 Apr 17;10(1):bpaf028. doi: 10.1093/biomethods/bpaf028. eCollection 2025.

ABSTRACT

The application of machine learning (ML) techniques in predictive modelling has greatly advanced our comprehension of biological systems. There is a notable shift in the trend towards integration methods that specifically target the simultaneous analysis of multiple modes or types of data, showcasing superior results compared to individual analyses. Despite the availability of diverse ML architectures for researchers interested in embracing a multimodal approach, the current literature lacks a comprehensive taxonomy that includes the pros and cons of these methods to guide the entire process. Closing this gap is imperative, necessitating the creation of a robust framework. This framework should not only categorize the diverse ML architectures suitable for multimodal analysis but also offer insights into their respective advantages and limitations. Additionally, such a framework can serve as a valuable guide for selecting an appropriate workflow for multimodal analysis. This comprehensive taxonomy would provide a clear guidance and support informed decision-making within the progressively intricate landscape of biomedical and clinical data analysis. This is an essential step towards advancing personalized medicine. The aims of the work are to comprehensively study and describe the harmonization processes that are performed and reported in the literature and present a working guide that would enable planning and selecting an appropriate integrative model. We present harmonization as a dual process of representation and integration, each with multiple methods and categories. The taxonomy of the various representation and integration methods are classified into six broad categories and detailed with the advantages, disadvantages and examples. A guide flowchart describing the step-by-step processes that are needed to adopt a multimodal approach is also presented along with examples and references. This review provides a thorough taxonomy of methods for harmonizing multimodal data and introduces a foundational 10-step guide for newcomers to implement a multimodal workflow.

PMID:40308831 | PMC:PMC12043205 | DOI:10.1093/biomethods/bpaf028

Front Neurorobot. 2025 Apr 16;19:1558948. doi: 10.3389/fnbot.2025.1558948. eCollection 2025.

ABSTRACT

NeRF and its variants excel in novel view synthesis but struggle with scenes featuring specular highlights. To address this limitation, we introduce the Highlight Recovery Network (HRNet), a new architecture that enhances NeRF's ability to capture specular scenes. HRNet incorporates Swish activation functions, affine transformations, multilayer perceptrons (MLPs), and residual blocks, which collectively enable smooth non-linear transformations, adaptive feature scaling, and hierarchical feature extraction. The residual connections help mitigate the vanishing gradient problem, ensuring stable training. Despite the simplicity of HRNet's components, it achieves impressive results in recovering specular highlights. Additionally, a density voxel grid enhances model efficiency. Evaluations on four inward-facing benchmarks demonstrate that our approach outperforms NeRF and its variants, achieving a 3-5 dB PSNR improvement on each dataset while accurately capturing scene details. Furthermore, our method effectively preserves image details without requiring positional encoding, rendering a single scene in ~18 min on an NVIDIA RTX 3090 Ti GPU.

PMID:40308477 | PMC:PMC12041011 | DOI:10.3389/fnbot.2025.1558948

Front Plant Sci. 2025 Apr 16;16:1561632. doi: 10.3389/fpls.2025.1561632. eCollection 2025.

ABSTRACT

INTRODUCTION: Detection of rice panicles and recognition of rice growth stages can significantly improve precision field management, which is crucial for maximizing grain yield. This study explores the use of deep learning on mobile phones as a platform for rice phenotype applications.

METHODS: An improved YOLOv8 model, named YOLO_Efficient Computation Optimization (YOLO_ECO), was proposed to detect rice panicles at the booting, heading, and filling stages, and to recognize growth stages. YOLO_ECO introduced key improvements, including the C2f-FasterBlock-Effective Multi-scale Attention (C2f-Faster-EMA) replacing the original C2f module in the backbone, adoption of Slim Neck to reduce neck complexity, and the use of a Lightweight Shared Convolutional Detection (LSCD) head to enhance efficiency. An Android application, YOLO-RPD, was developed to facilitate rice phenotype detection in complex field environments.

RESULTS AND DISCUSSION: The performance impact of YOLO-RPD using models with different backbone networks, quantitative models, and input image sizes was analyzed. Experimental results demonstrated that YOLO_ECO outperformed traditional deep learning models, achieving average precision values of 96.4%, 93.2%, and 81.5% at the booting, heading, and filling stages, respectively. Furthermore, YOLO_ECO exhibited advantages in detecting occlusion and small panicles, while significantly optimizing parameter count, computational demand, and model size. The YOLO_ECO FP32-1280 achieved a mean average precision (mAP) of 90.4%, with 1.8 million parameters and 4.1 billion floating-point operations (FLOPs). The YOLO-RPD application demonstrates the feasibility of deploying deep learning models on mobile devices for precision agriculture, providing rice growers with a practical, lightweight tool for real-time monitoring.

PMID:40308302 | PMC:PMC12040913 | DOI:10.3389/fpls.2025.1561632

Front Neurol. 2025 Apr 16;16:1532398. doi: 10.3389/fneur.2025.1532398. eCollection 2025.

ABSTRACT

OBJECTIVE: This study systematically reviewed deep learning (DL) applications in neurosurgical practice to provide a comprehensive understanding of DL in neurosurgery. The review process included a systematic overview of recent developments in DL technologies, an examination of the existing literature on their applications in neurosurgery, and insights into the future of neurosurgery. The study also summarized the most widely used DL algorithms, their specific applications in neurosurgical practice, their limitations, and future directions.

MATERIALS AND METHODS: An advanced search using medical subject heading terms was conducted in Medline (via PubMed), Scopus, and Embase databases restricted to articles published in English. Two independent neurosurgically experienced reviewers screened selected articles.

RESULTS: A total of 456 articles were initially retrieved. After screening, 162 were found eligible and included in the study. Reference lists of all 162 articles were checked, and 19 additional articles were found eligible and included in the study. The 181 included articles were divided into 6 categories according to the subspecialties: general neurosurgery (n = 64), neuro-oncology (n = 49), functional neurosurgery (n = 32), vascular neurosurgery (n = 17), neurotrauma (n = 9), and spine and peripheral nerve (n = 10). The leading procedures in which DL algorithms were most commonly used were deep brain stimulation and subthalamic and thalamic nuclei localization (n = 24) in the functional neurosurgery group; segmentation, identification, classification, and diagnosis of brain tumors (n = 29) in the neuro-oncology group; and neuronavigation and image-guided neurosurgery (n = 13) in the general neurosurgery group. Apart from various video and image datasets, computed tomography, magnetic resonance imaging, and ultrasonography were the most frequently used datasets to train DL algorithms in all groups overall (n = 79). Although there were few studies involving DL applications in neurosurgery in 2016, research interest began to increase in 2019 and has continued to grow in the 2020s.

CONCLUSION: DL algorithms can enhance neurosurgical practice by improving surgical workflows, real-time monitoring, diagnostic accuracy, outcome prediction, volumetric assessment, and neurosurgical education. However, their integration into neurosurgical practice involves challenges and limitations. Future studies should focus on refining DL models with a wide variety of datasets, developing effective implementation techniques, and assessing their affect on time and cost efficiency.

PMID:40308224 | PMC:PMC12040697 | DOI:10.3389/fneur.2025.1532398

Front Pharmacol. 2025 Apr 16;16:1552251. doi: 10.3389/fphar.2025.1552251. eCollection 2025.

ABSTRACT

BACKGROUND: Idiopathic pulmonary fibrosis (IPF) has a higher morbidity and poor prognosis. Gui-Zhi-Fu-Ling-Wan (GFW) is a traditional Chinese herbal formula which exerts anti-inflammatory and anti-oxidative effects. The goal was to determine the protective effect of GFW on bleomycin (BLM)-induced pulmonary fibrosis.

METHODS: One hundred and twenty-four mice were randomly divided into eight groups, and orally supplemented with GFW (1 g/kg) in 1 week ago and continuing to 1 week later of single BLM intratracheal injection (5.0 mg/kg). Lung tissues were collected in 7 days and 21 days after BLM injection. BEAS-2B cells were pretreated with GFW (100 μg/mL) for three consecutive days before BLM (10 μg/mL) exposure. Cells were harvested in 12 or 24 h after BLM co-culture.

RESULTS: GFW supplementation alleviated BLM-induced alveolar structure destruction and inflammatory cell infiltration in mice lungs. BLM-incurred collagen deposition was attenuated by GFW. In addition, GFW pretreatment repressed BLM-evoked downregulation of E-cadherin, and elevation of N-cadherin and Vimentin in mouse lungs. Besides, BLM-excited GPX4 reduction, ferritin increases, lipid peroxidation, and free iron overload were significantly relieved by GFW pretreatment in mouse lungs and BEAS-2B cells. Notably, BLM-provoked mitochondrial reactive oxygen species (mtROS) excessive production, elevation of mitochondrial stress markers, such as HSP70 and CLPP, and mitochondrial injury, were all abolished in mouse lungs and BEAS-2B cells by GFW pretreatment.

CONCLUSION: GFW supplementation attenuated BLM-evoked lung injury and pulmonary fibrosis partially through repressing EMT and mtROS-mediated ferroptosis in pulmonary epithelial cells.

PMID:40308766 | PMC:PMC12041222 | DOI:10.3389/fphar.2025.1552251

R Soc Open Sci. 2025 Apr 30;12(4):241025. doi: 10.1098/rsos.241025. eCollection 2025 Apr.

ABSTRACT

Different isogenic cells exhibit different responses to the same extracellular signals. Several authors assumed that this variation arose from stochastic signalling noise with the implication that single eukaryotic cells could not detect their surroundings accurately, but work by us and others has shown that the variation is dominated instead by persistent cell-to-cell differences. Here, we analysed previously published data to quantify the sources of variation in pheromone-induced gene expression in Saccharomyces cerevisiae. We found that 91% of response variation was due to stable cell-to-cell differences, 8% from experimental measurement error, and 1% from signalling noise and expression noise. Low noise enabled precise signalling; individual cells could transmit over 3 bits of information through the pheromone response system and so respond differently to eight different pheromone concentrations. Additionally, if individual cells could reference their responses against constitutively expressed proteins, then cells could determine absolute pheromone concentrations with 2 bits of accuracy. These results help explain how individual yeast cells can accurately sense and respond to different extracellular pheromone concentrations.

PMID:40309186 | PMC:PMC12040454 | DOI:10.1098/rsos.241025

R Soc Open Sci. 2025 Apr 30;12(4):241791. doi: 10.1098/rsos.241791. eCollection 2025 Apr.

ABSTRACT

The bond graph approach has been recognized as a useful conceptual basis for understanding the behaviour of living entities modelled as a system with hierarchical interacting parts exchanging energy. One such behaviour is oscillation, which underpins many essential biological functions. In this paper, energy-based modelling of biochemical systems using the bond graph approach is combined with classical feedback control theory to give a novel approach to the analysis, and potentially synthesis, of biochemical oscillators. It is shown that oscillation is dependent on the interplay between active and passive feedback and this interplay is formalized using classical frequency-response analysis of feedback systems. In particular, the phase margin is suggested as a simple scalar indicator of the presence or absence of oscillations; it is shown how this indicator can be used to investigate the effect of both the structure and parameters of biochemical system on oscillation. It follows that the combination of classical feedback control theory and the bond graph approach to systems biology gives a novel analysis and design methodology for biochemical oscillators. The approach is illustrated using an introductory example similar to the Goodwin oscillator, the Sel'kov model of glycolytic oscillations and the repressilator.

PMID:40309185 | PMC:PMC12040473 | DOI:10.1098/rsos.241791

Biophys Rep. 2025 Apr 30;11(2):129-142. doi: 10.52601/bpr.2024.240045.

ABSTRACT

In this study, we conducted a comprehensive proteomic analysis of B cells from the spleen, mesenteric lymph nodes (mLN), and peripheral blood mononuclear cells (PBMC) in a time-course model of systemic lupus erythematosus (SLE) using female MRL/lpr mice. By combining fluorescence-activated cell sorting (FACS) and 4D-Data-Independent Acquisition (4D-DIA) mass spectrometry, we quantified nearly 8000 proteins, identifying significant temporal and tissue-specific proteomic changes during SLE progression. PBMC-derived B cells exhibited early proteomic alterations by Week 9, while spleen-derived B cells showed similar changes by Week 12. We identified key regulatory proteins, including BAFF, BAFFR, and NFKB2, involved in B cell survival and activation, as well as novel markers such as CD11c and CD117, which have previously been associated with other immune cells. The study highlights the dynamic reprogramming of B cell proteomes across different tissues, with distinct contributions to SLE pathogenesis, providing valuable insights into the molecular mechanisms underlying B cell dysregulation in lupus. These findings offer potential therapeutic targets and biomarkers for SLE.

PMID:40308935 | PMC:PMC12035744 | DOI:10.52601/bpr.2024.240045

Virus Evol. 2025 Mar 11;11(1):veaf015. doi: 10.1093/ve/veaf015. eCollection 2025.

ABSTRACT

Mutations within the N-terminal domain (NTD) of the spike (S) protein are critical for the emergence of successful severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) viral lineages. The NTD has been repeatedly impacted by deletions, often exhibiting complex and dynamic patterns, such as the recurrent emergence and disappearance of deletions in dominant variants. This study investigates the influence of repair of NTD lineage-defining deletions found in the BA.1 lineage (Omicron variant) on viral success. We performed comparative genomic analyses of >10 million SARS-CoV-2 genomes from the Global Initiative on Sharing All Influenza Data (GISAID) EpiCov database to evaluate the detection of viruses lacking S:ΔH69/V70, S:ΔV143/Y145, or both. These findings were contrasted against a screening of publicly available raw sequencing data, revealing substantial discrepancies between data repositories, suggesting that spurious deletion repair observations in GISAID may result from systematic artifacts. Specifically, deletion repair events were approximately an order of magnitude less frequent in the read-run survey. Our results suggest that deletion repair events are rare, isolated events with limited direct influence on SARS-CoV-2 evolution or transmission. Nevertheless, such events could facilitate the emergence of fitness-enhancing mutations. To explore potential drivers of NTD deletion repair patterns, we characterized the viral phenotype of such markers in a surrogate in vitro system. Repair of the S:ΔH69/V70 deletion reduced viral infectivity, while simultaneous repair with S:ΔV143/Y145 led to lower fusogenicity. In contrast, individual S:ΔV143/Y145 repair enhanced both fusogenicity and susceptibility to neutralization by sera from vaccinated individuals. This work underscores the complex genotype-phenotype landscape of the spike NTD in SARS-CoV-2, which impacts viral biology, transmission efficiency, and immune escape potential, offering insights with direct relevance to public health, viral surveillance, and the adaptive mechanisms driving emerging variants.

PMID:40308784 | PMC:PMC12041916 | DOI:10.1093/ve/veaf015