Sci Rep. 2025 Aug 4;15(1):28371. doi: 10.1038/s41598-025-12464-y.

ABSTRACT

Cross-cultural sentiment analysis in restaurant reviews presents unique challenges due to linguistic and cultural differences across regions. The purpose of this study is to develop a culturally adaptive sentiment analysis model that improves sentiment detection across multilingual restaurant reviews. This paper proposes XLM-RSA, a novel multilingual model based on XLM-RoBERTa with Aspect-Focused Attention, tailored for enhanced sentiment analysis across diverse cultural contexts. We evaluated XLM-RSA on three benchmark datasets: 10,000 Restaurant Reviews, Restaurant Reviews, and European Restaurant Reviews, achieving state-of-the-art performance across all datasets. XLM-RSA attained an accuracy of 91.9% on the Restaurant Reviews dataset, surpassing traditional models such as BERT (87.8%) and RoBERTa (88.5%). In addition to sentiment classification, we introduce an aspect-based attention mechanism to capture sentiment variations specific to key aspects like food, service, and ambiance, yielding aspect-level accuracy improvements. Furthermore, XLM-RSA demonstrated strong performance in detecting cultural sentiment shifts, with an accuracy of 85.4% on the European Restaurant Reviews dataset, showcasing its robustness to diverse linguistic and cultural expressions. An ablation study highlighted the significance of the Aspect-Focused Attention, where XLM-RSA with this enhancement achieved an F1-score of 91.5%, compared to 89.1% with a simple attention mechanism. These results affirm XLM-RSA's capacity for effective cross-cultural sentiment analysis, paving the way for more accurate sentiment-driven insights in globally distributed customer feedback.

PMID:40759996 | DOI:10.1038/s41598-025-12464-y

Nat Methods. 2025 Aug 4. doi: 10.1038/s41592-025-02772-6. Online ahead of print.

ABSTRACT

Recent research in deep-learning-based foundation models promises to learn representations of single-cell data that enable prediction of the effects of genetic perturbations. Here we compared five foundation models and two other deep learning models against deliberately simple baselines for predicting transcriptome changes after single or double perturbations. None outperformed the baselines, which highlights the importance of critical benchmarking in directing and evaluating method development.

PMID:40759747 | DOI:10.1038/s41592-025-02772-6

Sci Rep. 2025 Aug 4;15(1):28405. doi: 10.1038/s41598-025-13949-6.

ABSTRACT

This study presents an Internet of Things (IoT)-enabled Deep Learning Monitoring (IoT-E-DLM) model for real-time Athletic Performance (AP) tracking and feedback in collegiate sports. The proposed work integrates advanced wearable sensor technologies with a hybrid neural network combining Temporal Convolutional Networks, Bidirectional Long Short-Term Memory (TCN + BiLSTM) + Attention mechanisms. It is designed to overcome key challenges in processing heterogeneous, high-frequency sensor data and delivering low-latency, sport-specific feedback. The system deployed edge computing for real-time local processing and cloud setup for high-complexity analytics, achieving a balance between responsiveness and accuracy. Extensive research was tested with 147 student-athletes across numerous sports, including track and field, basketball, soccer, and swimming, over 12 months at Shangqiu University. The proposed model achieved a prediction accuracy of 93.45% with an average processing latency of 12.34 ms, outperforming conventional and state-of-the-art approaches. The system also demonstrated efficient resource usage (CPU: 68.34%, GPU: 72.56%), high data capture reliability (98.37%), and precise temporal synchronization. These results confirm the model's effectiveness in enabling real-time performance monitoring and feedback delivery, establishing a robust groundwork for future developments in Artificial Intelligence (AI)-driven sports analytics.

PMID:40759726 | DOI:10.1038/s41598-025-13949-6

Sci Rep. 2025 Aug 4;15(1):28408. doi: 10.1038/s41598-025-14029-5.

ABSTRACT

In recent years, with the rise of digital twin technology in the field of artificial intelligence and the continuous advancement of hardware imaging equipment, significant progress has been made in the detection of structural damage in buildings and sculptures. Structural damage to cultural heritage buildings poses a major threat to their integrity, making accurate detection of such damage crucial for cultural heritage preservation. However, existing deep learning-based object detection technologies face limitations in achieving full coverage of architectural sculptures and enabling multi-angle, free observation, while also exhibiting substantial detection errors. To address these challenges, this paper proposes a detection method that integrates digital modeling with an improved YOLO algorithm. By scanning architectural scenes to generate digital twin models, this method enables full-angle and multi-seasonal scene transformations. Specifically, the Nanjing Sheli pagoda is selected as the research subject, where drone-based panoramic scanning is employed to create a digitalized full-scene model. The improved YOLO algorithm is then used to evaluate detection performance under varying weather and lighting conditions. Finally, evaluation metrics are utilized to automatically analyze detection accuracy and the extent of damage. Compared to traditional on-site manual measurement methods, the proposed YOLO-based automatic detection technology in digitalized scenarios significantly reduces labor costs while improving detection accuracy and efficiency. This approach provides a highly effective and reliable technical solution for assessing the extent of damage in historical buildings.

PMID:40759712 | DOI:10.1038/s41598-025-14029-5

Chemphyschem. 2025 Aug 4:e2500094. doi: 10.1002/cphc.202500094. Online ahead of print.

ABSTRACT

The generalized many-body expansion for building density matrices (GMBE-DM), truncated at the one-body level and combined with a purification scheme, is applied to rank protein-ligand binding affinities across two cyclin-dependent kinase 2 (CDK2) datasets and one Janus kinase 1 (JAK1) dataset, totaling 28 ligands. This quantum fragmentation-based method achieves strong correlation with experimental binding free energies (R2 = 0.84), while requiring less than 5 min per complex without extensive parallelization, making it highly efficient for rapid drug screening and lead prioritization. In addition, our physics-informed, machine learning-corrected dispersion potential (D3-ML) demonstrates even stronger ranking performance (R2 = 0.87), effectively capturing binding trends through favorable cancelation of non-dispersion, solvation, and entropic contributions, emphasizing the central role of dispersion interactions in protein-ligand binding. With sub-second runtime per complex, D3-ML offers exceptional speed and accuracy, making it ideally suited for high-throughput virtual screening. By comparison, the deep learning model Sfcnn shows lower transferability across datasets (R2 = 0.57), highlighting the limitations of broadly trained neural networks in chemically diverse systems. Together, these results establish GMBE-DM and D3-ML as robust and scalable tools for protein-ligand affinity ranking, with D3-ML emerging as a particularly promising candidate for large-scale applications in drug discovery.

PMID:40758915 | DOI:10.1002/cphc.202500094

Phytopathology. 2025 Aug 4. doi: 10.1094/PHYTO-05-25-0187-R. Online ahead of print.

ABSTRACT

Measuring individual components of pathogen reproduction is key to understanding mechanisms underlying rate-reducing quantitative resistance (QR). Simulation models predict that lesion expansion plays a key role in seasonal epidemics of foliar diseases, but measuring lesion growth with sufficient precision and scale to test these predictions under field conditions has remained impractical. We used deep learning-based image analysis to track 6889 individual lesions caused by Zymoseptoria tritici on 14 wheat cultivars across two field seasons, enabling 27,218 precise and objective measurements of lesion growth in the field. Lesion appearance traits reflecting specific interactions between particular host and pathogen genotypes were consistently associated with lesion growth, whereas overall effects of host genotype and environment were modest. Both host cultivar and cultivar-by-environment interaction effects on lesion growth were highly significant and moderately heritable (h2 ≥ 0.40). After excluding a single outlier cultivar, a strong and statistically significant association between lesion growth and overall QR was found. Lesion expansion appears to be an important component of QR to STB in most-but not all-wheat cultivars, underscoring its potential as a selection target. By facilitating the dissection of individual resistance components, our approach can support more targeted, knowledge-based breeding for durable QR.

PMID:40758903 | DOI:10.1094/PHYTO-05-25-0187-R

PLoS One. 2025 Aug 4;20(8):e0328507. doi: 10.1371/journal.pone.0328507. eCollection 2025.

ABSTRACT

Mural image recognition plays a critical role in the digital preservation of cultural heritage; however, it faces cross-cultural and multi-period style generalization challenges, compounded by limited sample sizes and intricate details, such as losses caused by natural weathering of mural surfaces and complex artistic patterns.This paper proposes a deep learning model based on DenseNet201-FPN, incorporating a Bidirectional Convolutional Block Attention Module (Bi-CBAM), dynamic focal distillation loss, and convex regularization. First, a lightweight Feature Pyramid Network (FPN) is embedded into DenseNet201 to fuse multi-scale texture features (28 × 28 × 256, 14 × 14 × 512, 7 × 7 × 1024). Second, a bidirectional LSTM-driven attention module iteratively optimizes channel and spatial weights, enhancing detail perception for low-frequency categories. Third, a dynamic temperature distillation strategy (T = 3 → 1) balances supervision from teacher models (ResNeXt101) and ground truth, improving the F1-score of rare classes by 6.1%. Experimental results on a self-constructed mural dataset (2,000 images,26 subcategories.) demonstrate 87.9% accuracy (+3.7% over DenseNet201) and real-time inference on edge devices (63ms/frame at 8.1W on Jetson TX2). This study provides a cost-effective solution for large-scale mural digitization in resource-constrained environments.

PMID:40758742 | DOI:10.1371/journal.pone.0328507

PLOS Digit Health. 2025 Aug 4;4(8):e0000939. doi: 10.1371/journal.pdig.0000939. eCollection 2025 Aug.

ABSTRACT

The potential of deep learning to predict renal obstruction using kidney ultrasound images has been demonstrated. However, these image-based classifiers have incorporated information using only single-visit ultrasounds. Here, we developed machine learning (ML) models incorporating ultrasounds from multiple clinic visits for hydronephrosis to generate a hydronephrosis severity index score to discriminate patients into high versus low risk for needing pyeloplasty and compare these against models trained with single clinic visit data. We included patients followed for hydronephrosis from three institutions. The outcome of interest was low risk versus high risk of obstructive hydronephrosis requiring pyeloplasty. The model was trained on data from Toronto, ON and validated on an internal holdout set, and tested on an internal prospective set and two external institutions. We developed models trained with single ultrasound (single-visit) and multi-visit models using average prediction, convolutional pooling, long-short term memory and temporal shift models. We compared model performance by area under the receiver-operator-characteristic (AUROC) and area under the precision-recall-curve (AUPRC). A total of 794 patients were included (603 SickKids, 102 Stanford, and 89 CHOP) with a pyeloplasty rate of 12%, 5%, and 67%, respectively. There was no significant difference in developing single-visit US models using the first ultrasound vs. the latest ultrasound. Comparing single-visit vs. multi-visit models, all multi-visit models fail to produce AUROC or AUPRC significantly greater than single-visit models. We developed ML models for hydronephrosis that incorporate multi-visit inference across multiple institutions but did not demonstrate superiority over single-visit inference. These results imply that the single-visit models would be sufficient in aiding accurate risk stratification from single, early ultrasound images.

PMID:40758672 | DOI:10.1371/journal.pdig.0000939

J Vis Exp. 2025 Jul 18;(221). doi: 10.3791/68664.

ABSTRACT

Cardiovascular magnetic resonance (CMR) cine sequences serve as the cornerstone imaging technique for evaluating dynamic left atrial (LA) function in myocarditis patients. By capturing three-dimensional motion characteristics throughout the cardiac cycle with high temporal resolution, this modality provides critical data for analyzing myocardial contractile coordination and wall motion abnormalities. Key technological innovations, such as dynamic modeling and strain-encoded imaging, enable quantitative assessment of early-stage LA systolic-diastolic dysfunction in myocarditis. However, the primary challenges in cine sequence segmentation involve dynamic artifacts and spatiotemporal continuity modeling of thin-walled structures. Traditional threshold-based segmentation methods demonstrate limited consistency in dynamic sequences due to their inability to capture motion patterns. Deep learning approaches utilizing three-dimensional fully convolutional network (3D-FCN) achieved superior accuracy through three strategic enhancements: (1) Spatiotemporal feature fusion: This employed 3D convolutional kernels to simultaneously extract spatial structures and temporal dimensional features, thereby reducing motion blurring effects. (2) Dynamic skip connections: Incorporated within encoder-decoder architectures, these connections strengthened deformation correlation modeling across different cardiac phases through cross-temporal feature propagation. (3) Lightweight design: By utilizing patch-wise processing and depthwise separable convolutions, computational efficiency was optimized for real-time processing of large-scale four-dimensional datasets. The 3D-FCN achieved a Dice coefficient of 0.921 for LA segmentation, representing a 12.3% improvement over conventional methods. This design reduced the LA ejection fraction prediction error from 8.7% to 3.2%. The segmentation results directly facilitated the calculation of quantitative metrics, including LA volume-time curves and strain rates. These metrics supported the clinical diagnosis of myocarditis-associated atrial mechanical dysfunction.

PMID:40758568 | DOI:10.3791/68664

IEEE Trans Pattern Anal Mach Intell. 2025 Aug 4;PP. doi: 10.1109/TPAMI.2025.3590979. Online ahead of print.

ABSTRACT

Person re-identification (ReID) is to identify the same person across non-overlapping camera views. After a decade of development, the methods based on deep networks have achieved high performance on benchmarks and become mainstream. In applications, the features of gallery images extracted by deep learning-based methods are stored to speed up the query process and protect the sensitive information contained in the images. Unfortunately, it is demonstrated that turning the images into features cannot properly protect privacy, as these features could be reversed to the corresponding images, revealing the sensitive information they contain. Therefore, for preventing privacy leakage, recent methods learn their features against some feature reversal methods, and most conventional reversal methods focus on minimizing the difference between a reconstruction and its original image. However, there could be many reasonable reconstruction results from a single feature, and the conventional reversal methods will inevitably generate reconstruction results that lie in a different distribution from one of the original images, which cannot properly assess the private information for learning to protect and thus hamper the privacy-protected feature learning. To mitigate this problem, we enforce the reconstructions to follow the same distribution as the original images by the generative adversarial network (GAN). We operate this GAN-based feature reversal module accompanied by the conventional ReID feature extraction module and form a novel GAN-based feature privacy-protected person ReID model, which is expected to protect feature privacy so as against reversal attack and maintain ReID utility. We demonstrate that optimizing ReID model to accommodate privacy protection faces a double adversarial objective and is thus challenging. As a remedy, we design a novel two-step training and lazy update strategy that alternatively optimizes the feature extraction module and stabilizes the update process of the GAN-based feature reversal module. To evaluate the efficiency of the model in balancing its ReID utility and feature privacy protection, we introduce a novel metric called utility-reversibility ratio (URR). Compared with existing privacy-protected feature extraction models, the proposed method achieves a better balance between privacy protection and person ReID performance. Extensive experiments validate that our model can effectively protect feature privacy at a tiny accuracy cost, and validate the effectiveness of our model with the emerging diffusion model.

PMID:40758524 | DOI:10.1109/TPAMI.2025.3590979

IEEE Trans Pattern Anal Mach Intell. 2025 Aug 4;PP. doi: 10.1109/TPAMI.2025.3593987. Online ahead of print.

ABSTRACT

Deep neural network (DNN) deployment has been confined to larger hardware devices due to their expensive computational requirements. This challenge has recently reached another scale with the emergence of large language models (LLMs). In order to reduce both their memory footprint and latency, a promising technique is quantization. It consists in converting floating point representations to low bit-width fixed point representations, usually by assuming a uniform mapping onto a regular grid. This process, referred to in the literature as uniform quantization, may however be ill-suited as most DNN weights and activations follow a bell-shaped distribution. This is even worse on LLMs whose weight distributions are known to exhibit large, high impact, outlier values. In this work, we propose an improvement over the most commonly adopted way to tackle this limitation in deep learning models quantization, namely, non-uniform quantization. NUPES leverages automorphisms to preserve the scalar multiplications. Such transformations are derived from power functions. However, the optimization of the exponent parameter and weight values remains a challenging and novel problem which could not be solved with previous post training optimization techniques which only learn to round up or down weight values in order to preserve the predictive function. We circumvent this limitation with a new paradigm: learning new quantized weights over the entire quantized space. Similarly, we enable the optimization of the power exponent, i.e. the optimization of the quantization operator itself during training by alleviating all the numerical instabilities. The resulting predictive function is compatible with integer-only low-bit inference. We show the ability of the method to achieve state-of-the-art compression rates in both, data-free and data-driven configurations. Our empirical benchmarks highlight the ability of NUPES to circumvent the limitations of previous post-training quantization techniques on transformers and large language models in particular.

PMID:40758517 | DOI:10.1109/TPAMI.2025.3593987

IEEE Trans Med Imaging. 2025 Aug 4;PP. doi: 10.1109/TMI.2025.3595111. Online ahead of print.

ABSTRACT

Classification of pathological images is the basis for automatic cancer diagnosis. Despite that deep learning methods have achieved remarkable performance, they heavily rely on labeled data, demanding extensive human annotation efforts. In this study, we present a novel human annotation-free method by leveraging pre-trained Vision-Language Models (VLMs). Without human annotation, pseudo-labels of the training set are obtained by utilizing the zero-shot inference capabilities of VLM, which may contain a lot of noise due to the domain gap between the pre-training and target datasets. To address this issue, we introduce VLM-CPL, a novel approach that contains two noisy label filtering techniques with a semi-supervised learning strategy. Specifically, we first obtain prompt-based pseudo-labels with uncertainty estimation by zero-shot inference with the VLM using multiple augmented views of an input. Then, by leveraging the feature representation ability of VLM, we obtain feature-based pseudo-labels via sample clustering in the feature space. Prompt-feature consensus is introduced to select reliable samples based on the consensus between the two types of pseudo-labels. We further propose High-confidence Cross Supervision by to learn from samples with reliable pseudo-labels and the remaining unlabeled samples. Additionally, we present an innovative open-set prompting strategy that filters irrelevant patches from whole slides to enhance the quality of selected patches. Experimental results on five public pathological image datasets for patch-level and slide-level classification showed that our method substantially outperformed zero-shot classification by VLMs, and was superior to existing noisy label learning methods. The code is publicly available at https://github.com/HiLab-git/VLM-CPL.

PMID:40758498 | DOI:10.1109/TMI.2025.3595111

IEEE Trans Med Imaging. 2025 Aug 4;PP. doi: 10.1109/TMI.2025.3594724. Online ahead of print.

ABSTRACT

Reconstructing brain sources is a fundamental challenge in neuroscience, crucial for understanding brain function and dysfunction. Electroencephalography (EEG) signals have a high temporal resolution. However, identifying the correct spatial location of brain sources from these signals remains difficult due to the ill-posed structure of the problem. Traditional methods predominantly rely on manually crafted priors, missing the flexibility of data-driven learning, while recent deep learning approaches focus on end-to-end learning, typically using the physical information of the forward model only for generating training data. We propose the novel hybrid method 3D-PIUNet for EEG source localization that effectively integrates the strengths of traditional and deep learning techniques. 3D-PIUNet starts from an initial physics-informed estimate by using the pseudo inverse to map from measurements to source space. Secondly, by viewing the brain as a 3D volume, we use a 3D convolutional U-Net to capture spatial dependencies and refine the solution according to the learned data prior. Training the model relies on simulated pseudo-realistic brain source data, covering different source distributions. Trained on this data, our model significantly improves spatial accuracy, demonstrating superior performance over both traditional and end-to-end data-driven methods. Additionally, we validate our findings with real EEG data from a visual task, where 3D-PIUNet successfully identifies the visual cortex and reconstructs the expected temporal behavior, thereby showcasing its practical applicability.

PMID:40758497 | DOI:10.1109/TMI.2025.3594724

IEEE J Biomed Health Inform. 2025 Aug 4;PP. doi: 10.1109/JBHI.2025.3595101. Online ahead of print.

ABSTRACT

Spatial transcriptomics (ST), known as an expensive medical examination, plays an important role in analyzing the spatial heterogeneity of tumors. When considering the correlation between tissue morphological patterns and gene profiles, predicting corresponding gene expression from pathology image obtained from affordable biopsies is regarded as an instantaneous and cost-effective alternative. However, accurately modeling the complex and nonlinear relationship between histological features and gene expression remains challenging. Existing deep learning models often struggle to generalize on limited ST datasets due to their large and overparameterized architectures. The primary advantage of kernel adaptive filtering (KAF) lies in its ability to transform a challenging nonlinear problem arising in the original space into a linear regression problem in the higher-dimensional feature space via kernel methods. Therefore, this paper proposes a framework called KAFSTExp, which utilizes the state-ofthe-art pathology foundation model UNI to encode image feature vectors, and then introduces the kernel least mean square algorithm with Nystrom approximation to predict the ¨ normalized transcript counts of specific genes. Extensive experiments show that KAFSTExp significantly improves prediction accuracy while reducing computational cost and training time. KAFSTExp demonstrates consistent performance gains across multiple ST datasets, achieving relative improvements in PCC ranging from 1.24% to 94.23%, with an average increase of 19.80% over the best-performing non-KAF methods. External validation and further clinical analysis confirm the generalization performance and clinical application value of the proposed KAFSTExp.

PMID:40758493 | DOI:10.1109/JBHI.2025.3595101

J Imaging Inform Med. 2025 Aug 4. doi: 10.1007/s10278-025-01623-2. Online ahead of print.

ABSTRACT

Hand-wrist radiographs are used in bone age prediction. Computer-assisted clinical decision support systems offer solutions to the limitations of the radiographic bone age assessment methods. In this study, a multi-output prediction model was designed to predict bone age and gender using digital hand-wrist radiographs. The InceptionV3 architecture was used as the backbone, and the model was trained and tested using the open-access dataset of 2017 RSNA Pediatric Bone Age Challenge. A total of 14,048 samples were divided to training, validation, and testing subsets with the ratio of 7:2:1, and additional specialized convolutional neural network layers were implemented for robust feature management, such as Squeeze-and-Excitation block. The proposed model achieved a mean squared error of approximately 25 and a mean absolute error of 3.1 for predicting bone age. In gender classification, an accuracy of 95% and an area under the curve of 97% were achieved. The intra-class correlation coefficient for the continuous bone age predictions was found to be 0.997, while the Cohen's κ coefficient for the gender predictions was found to be 0.898 ( p < 0.001). The proposed model aims to increase model efficiency by identifying common and discrete features. Based on the results, the proposed algorithm is promising; however, the mid-high-end hardware requirement may be a limitation for its use on local machines in the clinic. The future studies may consider increasing the dataset and simplification of the algorithms.

PMID:40758204 | DOI:10.1007/s10278-025-01623-2

J Chem Inf Model. 2025 Aug 4. doi: 10.1021/acs.jcim.5c01171. Online ahead of print.

ABSTRACT

Drug-drug interactions (DDIs) present significant challenges within clinical pharmacology, as they can impact therapeutic outcomes, especially given the growing prevalence of polypharmacy. Traditional methods for the clinical validation of DDIs typically exhibit inefficiency and high cost, underscoring the necessity for more advanced computational methodologies. Although deep learning-based methods have improved DDI prediction performance, current approaches often face challenges in extracting and integrating multidimensional molecular features and capturing molecular reaction patterns. To overcome these limitations, we propose a Multidimensional Joint and Cross-learning (MDJCL) model that effectively integrates 1D, 2D, and 3D molecular features of drugs. A cross-attention fusion module aggregates multidimensional features while minimizing information loss, and a molecular-pair reaction module pinpoints potential interaction sites. Experimental results on benchmark data sets demonstrate that MDJCL consistently outperforms state-of-the-art models. Ablation studies reveal that each module contributes distinctively to the overall enhancement of evaluation metrics. These results validate the effectiveness of multidimensional feature integration and cross learning mechanisms in enhancing DDI prediction, offering a reliable tool for clinical decision-making and precision medicine.

PMID:40758117 | DOI:10.1021/acs.jcim.5c01171

Hum Brain Mapp. 2025 Aug 1;46(11):e70310. doi: 10.1002/hbm.70310.

ABSTRACT

Alzheimer's disease (AD) is accompanied by alterations in retinal vascular density (VD), but the mechanisms remain unclear. This study investigated the relationship among cerebral amyloid-β (Aβ) deposition, VD, and cognitive decline. We enrolled 92 participants, including 47 AD patients and 45 healthy control (HC) participants. VD across retinal subregions was quantified using deep learning-based fundus photography, and cerebral Aβ deposition was measured with 18F-florbetapir (18F-AV45) PET/MRI. Using the minimum bounding circle of the optic disc as the diameter (papilla-diameter, PD), VD (total, 0.5-1.0 PD, 1.0-1.5 PD, 1.5-2.0 PD, 2.0-2.5 PD) was calculated. Standardized uptake value ratio (SUVR) for Aβ deposition was computed for global and regional cortical areas, using the cerebellar cortex as the reference region. Cognitive performance was assessed with the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA). Pearson correlation, multiple linear regression, and mediation analyses were used to explore Aβ deposition, VD, and cognition. AD patients exhibited significantly lower VD in all subregions compared to HC (p < 0.05). Reduced VD correlated with higher SUVR in the global cortex and a decline in cognitive abilities (p < 0.05). Mediation analysis indicated that VD influenced MMSE and MoCA through SUVR in the global cortex, with the most pronounced effects observed in the 1.0-1.5 PD range. Retinal VD is associated with cognitive decline, a relationship primarily mediated by cerebral Aβ deposition measured via 18F-AV45 PET. These findings highlight the potential of retinal VD as a biomarker for early detection in AD.

PMID:40757876 | DOI:10.1002/hbm.70310

Mutagenesis. 2025 Aug 4:geaf014. doi: 10.1093/mutage/geaf014. Online ahead of print.

ABSTRACT

The mutagenicity of chemical compounds is a key consideration in toxicology, drug development, and environmental safety. Traditional methods such as the Ames test, while reliable, are time-intensive and costly. With advances in imaging and machine learning, high-content assays like Cell Painting offer new opportunities for predictive toxicology. Cell Painting captures extensive morphological features of cells, which can correlate with chemical bioactivity. In this study, we leveraged Cell Painting data to develop machine learning models for predicting mutagenicity and compared their performance with structure-based models. We used two datasets: a Broad Institute dataset containing profiles of over 30,000 molecules and a US-EPA dataset with images of 1,200 chemicals tested at multiple concentrations. By integrating these datasets, we aimed to improve the robustness of our models. Among three algorithms tested - Random Forest, Support Vector Machine, and Extreme Gradient Boosting - the third showed the best performance for both datasets. Notably, selecting the most relevant concentration per compound, the Phenotypic Altering Concentration, significantly improved prediction accuracy. Our models outperformed traditional QSAR tools such as VEGA and the CompTox Dashboard for the majority of compounds, demonstrating the utility of Cell Painting features. The Cell Painting-based models revealed morphological changes related to DNA/RNA and ER perturbation, especially in mitochondria and nuclei, aligning with mutagenicity mechanisms. Despite this, certain compounds remained challenging to predict due to inherent dataset limitations and inter-laboratory variability in Cell Painting technology. The findings highlight the potential of Cell Painting in mutagenicity prediction, offering a complementary perspective to chemical structure-based models. Future work could involve harmonizing Cell Painting methodologies across datasets and exploring deep learning techniques to enhance predictive accuracy. Ultimately, integrating Cell Painting data with QSAR descriptors in hybrid models may unlock novel insights into chemical mutagenicity.

PMID:40757573 | DOI:10.1093/mutage/geaf014

J Chem Inf Model. 2025 Aug 4. doi: 10.1021/acs.jcim.5c01148. Online ahead of print.

ABSTRACT

Carbon capture through physical solvents reduces energy consumption and lowers environmental impact compared with conventional chemical absorption methods. Typical properties for solvent screening are solubility and selectivity. However, they require accurate prediction of vapor-liquid equilibrium (VLE), which remains a critical challenge due to the lack of enough available experimental data. This could be supplemented by in silico data prediction, provided that current prediction models are improved as this paper intends. When modeling physical solvents, a challenge arises due to the dominant role of nonbonding interactions and molecular geometry. For this purpose, a machine learning pipeline is developed using VLE results obtained from the quantum chemical-based thermodynamic model COnductor-like Screening MOdel for Real Solvents (COSMO-RS) and experimental data. A directed message passing neural network (D-MPNN) architecture is employed, leveraging molecular representations, additional features, and transfer learning to refine predictions. Two models, solubility and selectivity, are pretrained over 30,000 COSMO-RS simulated data points and fine-tuned with experimental VLE data sets for CO2 and common gas impurities (H2S, CH4, N2, and H2), respectively. The models' accuracy is significantly improved over that of COSMO alone by correcting bias in total pressure predictions. Experimental trends are successfully reproduced in the test data, confirming the physical consistency of the models. Sensitivity analysis confirms that molecular features have the highest impact on estimations, while the scaling effect of additional features is essential for accuracy. These results demonstrate the potential of the proposed methodology to systematically screen and optimize an extensive range of physical solvents on the basis of their chemical structure for carbon capture applications, reducing the reliance on costly and time-consuming experimental measurements.

PMID:40757514 | DOI:10.1021/acs.jcim.5c01148

Front Med (Lausanne). 2025 Jul 18;12:1628311. doi: 10.3389/fmed.2025.1628311. eCollection 2025.

ABSTRACT

Dry eye disease (DED) is a multifactorial ocular surface disorder characterized by ocular discomfort, visual disturbances, and potential structural damage. The heterogeneous etiology and symptomatology of DED pose significant challenges for accurate diagnosis and effective treatment. In recent years, artificial intelligence (AI), particularly deep learning (DL), has shown substantial promise in improving the objectivity and efficiency of DED assessment. This review provides a comprehensive synthesis of AI-assisted techniques for the quantification of key DED biomarkers, including tear film stability [e.g., tear meniscus height (TMH) and tear film break-up time (TBUT)], meibomian gland morphology, and corneal epithelial damage. We discuss how these technologies enhance diagnostic accuracy, standardize evaluation, and support personalized treatment. Collectively, these advancements underscore the transformative potential of AI in reshaping DED diagnostics and management.

PMID:40757197 | PMC:PMC12313631 | DOI:10.3389/fmed.2025.1628311