Mod Pathol. 2025 Mar 12:100747. doi: 10.1016/j.modpat.2025.100747. Online ahead of print.

ABSTRACT

Tumor differentiation represents an important driver of the biological behavior of various forms of cancer. Histologic features of tumor differentiation in hepatocellular carcinoma (HCC) include cyto-architecture, immunohistochemical profile, and reticulin framework. In this study, we evaluate the performance of an artificial intelligence (AI)-based model in quantifying features of HCC tumor differentiation and predicting cancer-related outcomes. We developed a supervised AI model using a cloud-based, deep-learning platform (Aiforia Technologies) to quantify histologic features of HCC differentiation, including various morphologic parameters (nuclear density, area, circularity, chromatin pattern, and pleomorphism), mitotic figures, immunohistochemical markers (hepar-1 and glypican-3), and reticulin expression. We applied this AI model to patients undergoing HCC curative resection and assessed whether AI-based features added value to standard clinical and pathologic data in predicting HCC-related outcomes. 99 HCC resection specimens were included. Three AI-based histologic variables were most relevant to HCC prognostic assessment: 1. percent of tumor occupied by neoplastic nuclei (nuclear area %), 2. quantitative reticulin expression in the tumor, and 3. Hepar-1 low (i.e. expressed in less than 50% of the tumor)/glypican-3 positive immunophenotype. Statistical models that included these AI-based variables outperformed models with combined clinical-pathologic features for overall survival (C-indexes of 0.81 vs 0.68), disease-free survival (C-indexes of 0.73 vs 0.68), metastasis (C-indexes of 0.78 vs 0.65), and local recurrence (C-indexes of 0.72 vs 0.68) for all cases, with similar results in the subgroup analysis of WHO grade 2 HCCs. Our AI model serves as proof-of-concept that HCC differentiation can be objectively quantified digitally by assessing a combination of biologically relevant histopathologic features. In addition, several AI-derived features were independently predictive of HCC-related outcomes in our study population, most notably nuclear area %, hepar-low/glypican 3-negative phenotype, and decreasing levels of reticulin expression, highlighting the relevance of quantitative analysis of tumor differentiation features in this context.

PMID:40086592 | DOI:10.1016/j.modpat.2025.100747

J Chem Inf Model. 2025 Mar 14. doi: 10.1021/acs.jcim.4c01749. Online ahead of print.

ABSTRACT

Antimicrobial peptides (AMPs) are a promising alternative for combating bacterial drug resistance. While current computer prediction models excel at binary classification of AMPs based on sequences, there is a lack of regression methods to accurately quantify AMP activity against specific bacteria, making the identification of highly potent AMPs a challenge. Here, we present a deep learning method, BERT-AmPEP60, based on the fine-tuned Bidirectional Encoder Representations from Transformers (BERT) architecture to extract embedding features from input sequences. Using the transfer learning strategy, we built regression models to predict the minimum inhibitory concentration (MIC) of peptides for Escherichia coli (EC) and Staphylococcus aureus (SA). In five independent experiments with 10% leave-out sequences as the test sets, the optimal EC and SA models outperformed the state-of-the-art regression method and traditional machine learning methods, achieving an average mean squared error of 0.2664 and 0.3032 (log μM), respectively. They also showed a Pearson correlation coefficient of 0.7955 and 0.7530, and a Kendall correlation coefficient of 0.5797 and 0.5222, respectively. Our models outperformed existing deep learning and machine learning methods that rely on conventional sequence features. This work underscores the effectiveness of utilizing BERT with transfer learning for training quantitative AMP prediction models specific for different bacterial species. The web server of BERT-AmPEP60 can be found at https://app.cbbio.online/ampep/home. To facilitate development, the program source codes are available at https://github.com/janecai0714/AMP_regression_EC_SA.

PMID:40086449 | DOI:10.1021/acs.jcim.4c01749

Comput Biol Chem. 2025 Mar 12;117:108423. doi: 10.1016/j.compbiolchem.2025.108423. Online ahead of print.

ABSTRACT

Molecules with potent anti-leishmanial activity play a crucial role in identifying treatments for leishmaniasis and aiding in the design of novel drugs to combat the disease, ultimately protecting individuals and populations. Various methods have been employed to represent molecular structures and predict effective anti-leishmanial molecules. However, each method faces challenges and limitations that must be addressed to optimize the drug discovery and design process. Recently, machine learning approaches have gained significant importance in overcoming the limitations of traditional methods across various fields. Therefore, there is an urgent need to build a computational pipeline using advanced machine learning and deep learning methods that help to predict anti-leishmanial activity of drug candidates. The proposed pipeline in this paper involves data collection, feature extraction, feature selection and prediction techniques. This review presents a comprehensive computational pipeline for anti-leishmanial drug discovery, highlighting its strengths, limitations, challenges, and future directions to improve treatment for this neglected tropical disease.

PMID:40086345 | DOI:10.1016/j.compbiolchem.2025.108423

Comput Biol Chem. 2025 Mar 8;117:108417. doi: 10.1016/j.compbiolchem.2025.108417. Online ahead of print.

ABSTRACT

The vast volumes of data are needed to train Deep Learning Models from scratch to identify illnesses in soybean leaves. However, there is still a lack of sufficient high-quality samples. To overcome this problem, we have developed the real-life SoyLeaf dataset and used the pre-trained Deep Learning Models to identify leaf diseases. In this paper, we have initially developed the real-life SoyLeaf dataset collected from the ICAR-Indian Institute of Soybean Research (IISR) Center, Indore field. This SoyLeaf dataset contains 9786 high-quality soybean leaf images, including healthy and diseased leaves. Following this, we have adapted data preprocessing techniques to enhance the quality of images. In addition, we have utilized several Deep Learning Models, i.e., fourteen Keras Transfer Learning Models, to determine which model best fits the dataset on SoyLeaf diseases. The accuracies of the proposed fine-tuned models using the Adam optimizer are as follows: ResNet50V2 achieves 99.79%, ResNet101V2 achieves 99.89%, ResNet152V2 achieves 99.59%, InceptionV3 achieves 99.83%, InceptionResNetV2 achieves 99.79%, MobileNet achieves 99.82%, MobileNetV2 achieves 99.89%, DenseNet121 achieves 99.87%, and DenseNet169 achieves 99.87%. Similarly, the accuracies of the proposed fine-tuned models using the RMSprop optimizer are as follows: ResNet50V2 achieves 99.49%, ResNet101V2 achieves 99.45%, ResNet152V2 achieves 99.45%, InceptionV3 achieves 99.58%, InceptionResNetV2 achieves 99.88%, MobileNet achieves 99.73%, MobileNetV2 achieves 99.83%, DenseNet121 achieves 99.89%, and DenseNet169 achieves 99.77%. The experimental results of the proposed fine-tuned models show that only ResNet50V2, ResNet101V2, InceptionV3, InceptionResNetV2, MobileNet, MobileNetV2, DenseNet121, and DenseNet169 have performed better in terms of training, validation, and testing accuracies than other state-of-the-art models.

PMID:40086344 | DOI:10.1016/j.compbiolchem.2025.108417

Int J Surg. 2025 Mar 14. doi: 10.1097/JS9.0000000000002322. Online ahead of print.

ABSTRACT

BACKGROUND: The majority of patients with hepatocellular carcinoma (HCC) miss the opportunity of radical resection, making ICIs-based conversion therapy a primary option. However, challenges persist in predicting response and identifying the optimal patient subset. The objective is to develop a CT-based clinical-radiomics model to predict durable clinical benefit (DCB) of ICIs-based treatment in potentially convertible HCC patients.

METHODS: The radiomics features were extracted by pyradiomics in training set, and machine learning models was generated based on the selected radiomics features. Deep learning models were created using two different protocols. Integrated models were constructed by incorporating radiomics scores, deep learning scores, and clinical variables selected through multivariate analysis. Furthermore, we analyzed the relationship between integrated model scores and clinical outcomes related to conversion therapy in the entire cohort. Finally, radiogenomic analysis was conducted on bulk RNA and DNA sequencing data.

RESULTS: The top-performing integrated model demonstrated excellent predictive accuracy with an area under the curve (AUC) of 0.96 (95%CI: 0.94 ~ 0.99) in the training set and 0.88 (95%CI: 0.77 ~ 0.99) in the test set, effectively stratifying survival risk across the entire cohort and revealing significant disparity in overall survival (OS), as evidenced by Kaplan-Meier survival curves (p<0.0001). Moreover, integrated model scores exhibited associations with sequential resection among patients who achieved DCB and pathological complete response (pCR) among those who underwent sequential resection procedures. Notably, higher radiomics model was correlated with MHC I expression, angiogenesis-related processes, CD8 T cell-related gene sets, as well as a higher frequency of TP53 mutations along with increased levels of mutation burden and neoantigen.

CONCLUSION: The deep learning-based clinical-radiomics model exhibited satisfactory predictive capability in forecasting the DCB derived from ICIs-based conversion therapy in potentially convertible HCC, and was associated with a diverse range of immune-related mechanisms.

PMID:40085751 | DOI:10.1097/JS9.0000000000002322

Sci Adv. 2025 Mar 14;11(11):eadr9668. doi: 10.1126/sciadv.adr9668. Epub 2025 Mar 14.

ABSTRACT

Reconstructing the structure of thin films and multilayers from measurements of scattered x-rays or neutrons is key to progress in physics, chemistry, and biology. However, finding all structures compatible with reflectometry data is computationally prohibitive for standard algorithms, which typically results in unreliable analysis with only a single potential solution identified. We address this lack of reliability with a probabilistic deep learning method that identifies all realistic structures in seconds, redefining standards in reflectometry. Our method, prior-amortized neural posterior estimation (PANPE), combines simulation-based inference with adaptive priors that inform the inference network about known structural properties and controllable experimental conditions. PANPE networks support key scenarios such as high-throughput sample characterization, real-time monitoring of evolving structures, or the corefinement of several experimental datasets and can be adapted to provide fast, reliable, and flexible inference across many other inverse problems.

PMID:40085716 | DOI:10.1126/sciadv.adr9668

J Chem Inf Model. 2025 Mar 14. doi: 10.1021/acs.jcim.5c00008. Online ahead of print.

ABSTRACT

Understanding the tensile mechanical properties of Al-Er alloys at the atomic scale is essential, and molecular dynamics (MD) simulations offer valuable insights. However, these simulations are constrained by the unavailability of suitable interatomic potentials. In this study, the deep potential (DP) approach, aided by high-throughput first-principles calculations, was utilized to develop an Al-Er interatomic potential specifically for MD simulations. Systematic comparisons between the physical properties (e.g., energy-volume curves, melting point, elastic constants) predicted by the DP model and those obtained from density functional theory (DFT) demonstrated that the developed DP model for Al-Er alloys possesses reliable predictive capabilities while retaining DFT-level accuracy. Our findings confirm that Al3Er, Al2Er, and AlEr2 exhibit mechanical stability. The calculated melting point of Al3Er (1398 K) shows a 57 K deviation from the experimental value (1341 K). With the Er content increasing from 0.01% to 0.064 at.% in Al-Er alloys, the grain boundary (GB) concentration of Er atoms increases from 0.03 to 0.07% following Monte Carlo (MC) annealing optimization. The Al-0.05 at.%Er alloy exhibits the highest yield strength, with an increase of 0.128 GPa (6.1%) compared to pure Al. For Al-0.05 at.%Er alloys with varying average grain sizes, the GB concentration of Er atoms increases by about 1.4-1.6 times after MC annealing compared to the average Er content. Additionally, the Al-Er alloys reach the peak yield strength of 2.214 GPa when the average grain size is 11.72 nm. The GB segregation of Er atoms lowers the system energy and thus enhances stability. Notable changes in the segregation behavior of Er atoms were observed with increasing Er concentration and decreasing grain size. These results would facilitate the understanding of the mechanical characteristics of Al-Er alloys and offer a theoretical basis for developing advanced nanopolycrystalline Al-Er alloys.

PMID:40085549 | DOI:10.1021/acs.jcim.5c00008

Anal Chem. 2025 Mar 14. doi: 10.1021/acs.analchem.4c05707. Online ahead of print.

ABSTRACT

In this paper, we propose a new spectral data mechanism called LIBS pseudohyperspectral data cube. This mechanism allows for the utilization of multidimensional information from laser-induced plasma, transforming 1-D LIBS spectra into 3-D data cube. Specifically, two additional dimensions are introduced to capture spectral variations information, allowing more features to be learned during pretraining. Proposed mechanism can make the LIBS system more robust when handling unstable spectra acquired onsite, and can also allow LIBS take full advantage of deep learning algorithms. In the context of nuclear power plants, traditional LIBS classification faces significant challenges due to unstable spectra, which reduce the accuracy of classifying similar or tiny extreme condition materials. By combining deep learning algorithms with LIBS pseudohyperspectral data cube, we can capture spectral and other dimensional features to enhance classification accuracy. Experimental results show that, compared to traditional 1-D data processing, the new method significantly improves the classification accuracy of unstable spectra. Moreover, by incorporating an attention mechanism, the model can adaptively adjust the weights of different features, further improving classification accuracy to over 99%. Visualizing the attention mechanism's weight matrix allows us to identify the importance of different features in classification. Additionally, t-SNE visualizations demonstrate the clustering of different categories in the feature space, further validating the performance of the new method. We believe this data cube mechanism offers an effective new approach for applying deep learning algorithms and enhancing data dimensionality in the LIBS field.

PMID:40085530 | DOI:10.1021/acs.analchem.4c05707

IEEE Trans Med Imaging. 2025 Mar 14;PP. doi: 10.1109/TMI.2025.3551514. Online ahead of print.

ABSTRACT

Deep learning models have been widely investigated for computing and analyzing brain images across various downstream tasks such as disease diagnosis and age regression. Most existing models are tailored for specific tasks and diseases, posing a challenge in developing a foundation model for diverse tasks. This paper proposes a Dense Transformer Foundation Model with Mixture of Experts (DenseFormer-MoE), which integrates dense convolutional network, Vision Transformer and Mixture of Experts (MoE) to progressively learn and consolidate local and global features from T1-weighted magnetic resonance images (sMRI) for multiple tasks including diagnosing multiple brain diseases and predicting brain age. First, a foundation model is built by combining the vision Transformer with Densenet, which are pre-trained with Masked Autoencoder and self-supervised learning to enhance the generalization of feature representations. Then, to mitigate optimization conflicts in multi-task learning, MoE is designed to dynamically select the most appropriate experts for each task. Finally, our method is evaluated on multiple renowned brain imaging datasets including UK Biobank (UKB), Alzheimer's Disease Neuroimaging Initiative (ADNI), and Parkinson's Progression Markers Initiative (PPMI). Experimental results and comparison demonstrate that our method achieves promising performances for prediction of brain age and diagnosis of brain diseases.

PMID:40085471 | DOI:10.1109/TMI.2025.3551514

IEEE Trans Biomed Eng. 2025 Mar 14;PP. doi: 10.1109/TBME.2025.3551260. Online ahead of print.

ABSTRACT

Microwave-induced thermoacoustic tomography (TAT) is a hybrid imaging technique that combines microwave excitation with ultrasound detection to create detailed images of biological tissue. Most TAT systems require a costly amplification system (or a sophisticated high-power microwave source), which limits the wide adoption of this imaging modality. We have developed a rotating single-element thermoacoustic tomography (RTAT) system using a low-cost amplifier that has been optimized in terms of microwave signal pulse width and antenna placement. The optimized system, enhanced with signal averaging, advanced signal processing, and a deep learning computational core, successfully produced adequate-quality images. The system has been characterized in terms of spatial resolution, imaging depth, acquisition speed, and multispectral capabilities utilizing tissue-like phantoms, ex-vivo specimens and in-vivo imaging. We believe our low-cost, portable system expands accessibility for the research community, empowering more groups to explore thermoacoustic imaging. It supports the development of advanced signal processing algorithms to optimize both low-power and even high-power TAT systems, accelerating the clinical adoption of this promising imaging modality.

PMID:40085469 | DOI:10.1109/TBME.2025.3551260

IEEE Trans Neural Netw Learn Syst. 2025 Mar 14;PP. doi: 10.1109/TNNLS.2025.3538335. Online ahead of print.

ABSTRACT

Motor imagery (MI), an important category in electroencephalogram (EEG) research, often intersects with scenarios demanding low energy consumption, such as portable medical devices and isolated environment operations. Traditional deep learning (DL) algorithms, despite their effectiveness, are characterized by significant computational demands accompanied by high energy usage. As an alternative, spiking neural networks (SNNs), inspired by the biological functions of the brain, emerge as a promising energy-efficient solution. However, SNNs typically exhibit lower accuracy than their counterpart convolutional neural networks (CNNs). Although attention mechanisms successfully increase network accuracy by focusing on relevant features, their integration in the SNN framework remains an open question. In this work, we combine the SNN and the attention mechanisms for the EEG classification, aiming to improve precision and reduce energy consumption. To this end, we first propose a noniterative leaky integrate-and-fire (NiLIF) neuron model, overcoming the gradient issues in traditional SNNs that use iterative LIF neurons for long time steps. Then, we introduce the sequence-based attention mechanisms to refine the feature map. We evaluated the proposed noniterative SNN with attention (NiSNN-A) model on two MI EEG datasets, OpenBMI and BCIC IV 2a. Experimental results demonstrate that: 1) our model outperforms other SNN models by achieving higher accuracy and 2) our model increases energy efficiency compared with the counterpart CNN models (i.e., by 2.13 times) while maintaining comparable accuracy.

PMID:40085464 | DOI:10.1109/TNNLS.2025.3538335

IEEE Trans Neural Netw Learn Syst. 2025 Mar 14;PP. doi: 10.1109/TNNLS.2025.3545967. Online ahead of print.

ABSTRACT

Many problems in science and engineering can be mathematically modeled using partial differential equations (PDEs), which are essential for fields like computational fluid dynamics (CFD), molecular dynamics, and dynamical systems. Although traditional numerical methods like the finite difference/element method are widely used, their computational inefficiency, due to the large number of iterations required, has long been a challenge. Recently, deep learning (DL) has emerged as a promising alternative for solving PDEs, offering new paradigms beyond conventional methods. Despite the growing interest in techniques like physics-informed neural networks (PINNs), a systematic review of the diverse neural network (NN) approaches for PDEs is still missing. This survey fills that gap by categorizing and reviewing the current progress of deep NNs (DNNs) for PDEs. Unlike previous reviews focused on specific methods like PINNs, we offer a broader taxonomy and analyze applications across scientific, engineering, and medical fields. We also provide a historical overview, key challenges, and future trends, aiming to serve both researchers and practitioners with insights into how DNNs can be effectively applied to solve PDEs.

PMID:40085460 | DOI:10.1109/TNNLS.2025.3545967

IEEE J Biomed Health Inform. 2025 Mar 14;PP. doi: 10.1109/JBHI.2025.3551365. Online ahead of print.

ABSTRACT

Immune thrombocytopenia (ITP) is a typically self-limiting and immune-mediated bleeding disorder in children. Approximately 20% of children with ITP experience chronicity, leading to reduced quality of life and increased treatment burden. The accurate prediction of chronicity would enable clinicians to make personalized treatment plans at an early stage. However, due to the self-limiting nature of ITP and the scarcity of available children patients, the data presents two prominent issues: small data and imbalanced class, which are unfavorable for effectively training a deep learning model. To handle these issues concurrently, we proposed a novel method that integrates contrastive learning with the Transformer. First, we adopt the FT-Transformer as our backbone, which allows our model to flexibly process heterogeneous tabular data. Second, we amplify and balance the original data via random masking and oversampling, respectively. Lastly, we build contrastive pairs according to the latent representations generated by the FT-Transformer encoder, such that the amplified and oversampled synthetic data can be utilized thoroughly. The experimental results on real-world ITP children data show that our proposal outperforms the state-of-the-art methods, and demonstrate the significant advantages of dealing with insufficient and imbalanced problems.

PMID:40085458 | DOI:10.1109/JBHI.2025.3551365

Pain. 2025 Mar 12. doi: 10.1097/j.pain.0000000000003587. Online ahead of print.

ABSTRACT

Chronic headache is a common complication after mild traumatic brain injury (mTBI), which affects close to 70 million individuals annually worldwide. This study aims to test the utility of a unique, early predictive magnetic resonance imaging (MRI)-based classification model using structural brain MRI scans, a rarely used approach to identify high-risk individuals for post-mTBI chronic pain. We recruited 227 patients with mTBI after a vehicle collision, between March 30, 2016 and December 30, 2019. T1-weighted brain MRI scans from 128 patients within 72 hours postinjury were included and served as input for a pretrained 3D ResNet-18 deep learning model. All patients had initial assessments within the first 72 hours after the injury and performed follow-ups for 1 year. Chronic pain was reported in 43% at 12 months postinjury; remaining 57% were assigned to the recovery group. The best results were achieved for the axial plane with an average accuracy of 0.59 and an average area under the curve (AUC) of 0.56. Across the model's 8 folds. The highest performance across folds reached an AUC of 0.78, accuracy of 0.69, and recall of 0.83. Saliency maps highlighted the right insula, bilateral ventromedial prefrontal cortex, and periaqueductal gray matter as key regions. Our study provides insights at the intersection of neurology, neuroimaging, and predictive modeling, demonstrating that early T1-weighted MRI scans may offer useful information for predicting chronic head and neck pain. Saliency maps may help identify brain regions linked to chronic pain, representing an initial step toward targeted rehabilitation and early intervention for patients with mTBI to enhance clinical outcomes.

PMID:40084983 | DOI:10.1097/j.pain.0000000000003587

Radiol Imaging Cancer. 2025 Mar;7(2):e240213. doi: 10.1148/rycan.240213.

ABSTRACT

Purpose To develop deep learning (DL) radiopathomics models based on contrast-enhanced MRI and pathologic imaging to predict vessels encapsulating tumor clusters (VETC) and survival in hepatocellular carcinoma (HCC). Materials and Methods In this retrospective, multicenter study, 578 patients with HCC (mean age [±SD], 59 years ± 10; 442 male, 136 female) were divided into the training (n = 317), internal (n = 137), and external (n = 124) test sets. DL radiomics and pathomics models were developed to predict VETC using gadoxetic acid-enhanced MR and pathologic images. Deep radiomics score (DRS) and handcrafted and deep pathomics scores were compared between the group with VETC pattern in HCC (VETC+) and group without VETC pattern in HCC (VETC-). Multivariable Cox regression analyses were performed to identify independent prognostic factors, and the radiopathomics nomogram models were developed for early recurrence and progression-free survival (PFS). The prognostic power was evaluated using the concordance index (C index) and time-dependent receiver operating characteristic (ROC) curves. Results In the external test set, the Swin Transformer showed good performance for predicting VETC in both DL radiomics (area under the ROC curve [AUC], 0.77-0.79) and pathomics (AUC, 0.79) models. Patients with VETC+ HCC had significantly higher DRS and handcrafted and deep pathomics scores compared with patients with VETC- HCC in all datasets (all P < .001). The radiopathomics nomogram model incorporating DRS in the arterial phase and the handcrafted and deep pathomics scores achieved C indexes of 0.69, 0.60, and 0.67 for early recurrence and time-dependent AUCs of 0.83 (95% CI: 0.76, 0.91), 0.81 (95% CI: 0.68, 0.94), and 0.78 (95% CI: 0.67, 0.88) for 3-year PFS in the training, internal, and external test sets, respectively. Early recurrence and PFS rates statistically significantly differed between the high- and low-risk patients stratified by the radiopathomics nomogram model (all P < .05). Conclusion DL radiopathomics models effectively helped to predict VETC in HCC and assess the risk for early recurrence and PFS. Keywords: Hepatocellular Carcinoma, Deep Learning, MRI, Radiopathomics, Survival Supplemental material is available for this article. © RSNA, 2025.

PMID:40084948 | DOI:10.1148/rycan.240213

J Chem Inf Model. 2025 Mar 14. doi: 10.1021/acs.jcim.5c00135. Online ahead of print.

ABSTRACT

Ischemic stroke's complex pathophysiology demands therapeutic approaches targeting multiple pathways simultaneously, yet current treatments remain limited. We developed an innovative drug discovery pipeline combining a deep learning approach with experimental validation to identify natural compounds with comprehensive neuroprotective properties. Our computational framework integrated SELFormer, a transformer-based deep learning model, and multiple deep learning algorithms to predict NC bioactivity against seven crucial stroke-related targets (ACE, GLA, MMP9, NPFFR2, PDE4D, and eNOS). The pipeline encompassed IC50 predictions, clustering analysis, quantitative structure-activity relationship (QSAR) modeling, and uniform manifold approximation and projection (UMAP)-based bioactivity profiling followed by molecular docking studies and experimental validation. Analysis revealed six distinct NC clusters with unique molecular signatures. UMAP projection identified 11 medium-activity (6 < pIC50 ≤ 7) and 57 high-activity (pIC50 > 7) compounds, with molecular docking confirming strong correlations between binding energies and predicted pIC50 values. In vitro studies using NGF-differentiated PC12 cells under oxygen-glucose deprivation demonstrated significant neuroprotective effects of four high-activity compounds: feruloyl glucose, l-hydroxy-l-tryptophan, mulberrin, and ellagic acid. These compounds enhanced cell viability, reduced acetylcholinesterase activity and lipid peroxidation, suppressed TNF-α expression, and upregulated BDNF mRNA levels. Notably, mulberrin and ellagic acid showed superior efficacy in modulating oxidative stress, inflammation, and neurotrophic signaling. This study establishes a robust deep learning-driven framework for identifying multitarget natural therapeutics for ischemic stroke. The validated compounds, particularly mulberrin and ellagic acid, are promising for stroke treatment development. Our findings demonstrate the effectiveness of integrating computational prediction with experimental validation in accelerating drug discovery for complex neurological disorders.

PMID:40084909 | DOI:10.1021/acs.jcim.5c00135

Recenti Prog Med. 2025 Mar;116(3):156-161. doi: 10.1701/4460.44555.

ABSTRACT

Pain is a complex, multidimensional experience involving significant challenges in both diagnosis and management. While acute pain serves as a critical warning mechanism, chronic pain encompasses intricate biological, psychological, and social components, complicating its assessment and treatment. Artificial intelligence (AI) technologies are revolutionizing medicine and healthcare. Here we present an overview of the recent advances in AI for pain medicine. For example, the emergence of automatic pain assessment (APA) methodologies offers promising avenues for more objective pain evaluation. For APA aims, AI technologies, including machine learning algorithms and deep learning architectures such as natural language processing systems, have shown potential in analyzing biosignals, facial expressions, and speech patterns related to pain. However, the integration of these objective measures with traditional self-reporting remains essential for a comprehensive approach to pain diagnosis. Notably, APA models can be implemented for pain diagnosis in newborn and non-communicative patients. Additionally, the application of AI extends beyond pain diagnosis to personalized treatment strategies, predict opioid use disorders, education and training, clinical trajectory definition, and telehealth and real-time. Despite the potential of these innovations, challenges such as validation, parameter selection, and ethical aspects of technical implementation must be addressed.

PMID:40084580 | DOI:10.1701/4460.44555

J Biophotonics. 2025 Mar 14:e70006. doi: 10.1002/jbio.70006. Online ahead of print.

ABSTRACT

Melasma is a common pigmentary disorder accompanied by tissue changes in composition and structure through the epidermis and dermis. In this study, we propose to employ optical coherence tomography (OCT) combined with deep learning techniques for melasma diagnostics. Specifically, a portable spectral domain OCT system with a handheld probe was developed for clinical skin imaging. Then, a diagnostic model was built based on the VGG16 neural network by adding a spatial attention mechanism. The results show that a good differentiation with an accuracy of 94.2% can be achieved among health datasets from healthy volunteers, and melasma and tissue-around-melasma datasets from melasma patients. Moreover, the same trained model was applied to treatment evaluation, showing a good capability to assess antivascular medicine treatment. Thus, it can be concluded that OCT combined with deep learning techniques has a good potential to aid in clinical diagnosis and treatment evaluation of melasma.

PMID:40084480 | DOI:10.1002/jbio.70006

Am J Cancer Res. 2025 Feb 15;15(2):754-768. doi: 10.62347/XKFN1793. eCollection 2025.

ABSTRACT

Breast cancer is a disorder affecting women globally, and hence an early and precise classification is the best possible treatment to increase the survival rate. However, the breast cancer classification faced difficulties in scalability, fixed-size input images, and overfitting on limited datasets. To tackle these issues, this work proposes a Patho-Net model for breast cancer classification that overcomes the problems of scalability in color normalization, integrates the Gated Recurrent Unit (GRU) network with the U-Net architecture to process images without the need for resizing and computational efficiency, and addresses the overfitting problems. The proposed model collects and normalizes histopathology images using automated reference image selection with the Reinhard method for color standardization. Also, the Enhanced Adaptive Non-Local Means (EANLM) filtering is utilized for noise removal to preserve image features. These preprocessed images undergo semantic segmentation to isolate specific parts of an image, followed by feature extraction using an Improved Gray Level Co-occurrence Matrix (I-GLCM) to reveal fine patterns and textures in images. These features serve as input into the classification U-Net model integrated with GRU networks to improve the model performance. Finally, the classification result is expanded, and XAI is used for clear visual explanations of the model's predictions. The proposed Patho-Net model, which uses the 100X BreakHis dataset, achieves an accuracy of 98.90% in the classification of breast cancer.

PMID:40084355 | PMC:PMC11897615 | DOI:10.62347/XKFN1793

Int J Med Sci. 2025 Feb 18;22(6):1375-1392. doi: 10.7150/ijms.107694. eCollection 2025.

ABSTRACT

Background: Pyrimidine metabolism is a hallmark of tumor metabolic reprogramming, while its significance in the prognostic and therapeutic implications of patients with lung adenocarcinoma (LUAD) still remains unclear. Methods: In this study, an integrated framework of various machine learning and deep learning algorithms was used to develop the pyrimidine metabolism-related signature (PMRS). Its efficacy in genomic stability, chemotherapy and immunotherapy resistance was evaluated through comprehensive multi-omics analysis. The single-cell landscape of patients between PMRS subgroups was also elucidated. Subsequently, the biological functions of LYPD3, the most important coefficient factor in the PMRS model, were experimentally validated in LUAD cell lines. Results: The PMRS model with "random survival forest" algorithm exhibited the best performance and was utilized for further analysis. It displayed excellent accuracy and stability in various model evaluation assays. Compared to the PMRS-high subgroup, patients with lower PMRS scores had better survival outcomes, more stable genomic characteristics and higher sensitivity to immunotherapy. Single-cell analysis indicated that as PMRS increased, epithelial cells gradually exhibited malignant phenotypes with enhanced pyrimidine metabolism, while PMRS-high patients showed an inhibitory status of tumor immune microenvironment. Further experiments indicated that LYPD3 promoted the malignant progression in LUAD cell lines. Conclusion: Our study constructed the PMRS model, highlighting its potential value in the treatment and prognosis of LUAD patients and providing new insights into the individualized precision treatment for LUAD patients.

PMID:40084259 | PMC:PMC11898844 | DOI:10.7150/ijms.107694