Biochim Biophys Acta Mol Basis Dis. 2025 Aug 8:168014. doi: 10.1016/j.bbadis.2025.168014. Online ahead of print.

ABSTRACT

The metabolic reprogramming of tumor cells plays a critical role in cancer progression, contributing to drug resistance and tumor survival. Tyrosine kinase inhibitors (TKIs) have shown promising clinical results by targeting specific signaling pathways in cancer cell proliferation, survival, and metastasis and are now standard of care for NSCLC with actionable mutations. However, secondary resistance to TKIs remains a significant challenge. Here, we explored the rationale behind combining TKIs with an inhibitor of glucose metabolism (dichloroacetate, DCA), focusing on the synergistic effects from dual inhibition of oncogenic and metabolic reprogramming. We selected three NSCLC cell line models (H1975, H1993, A549) with EGFR/MET/KRAS mutations and determined the optimal DCA dose (500 μM) to reverse the Warburg effect. TKIs in combination with DCA (CI < 1, indicating synergy) altered cell metabolism, by improving oxidative phosphorylation via reduced glucose consumption (~50 %, p < 0.05) and increased ATP (~50 %, p < 0.0001), particularly mitoATP, confirmed by metabolite levels. The combination also reduced cell proliferation (S phase p < 0.001), increased cell death (~40 %, p < 0.0001 less MMP, ~1.6 fold more BIM, 2.5-fold more autophagy) and blocked invasion (~3 fold fewer protrusions). Our findings show DCA potentiates TKIs at lower doses, likely via Warburg effect reversal. These changes in tumor behaviour leads to a higher pro-apoptotic status responsible for an increased tumor response and, in parallel, the lower doses reduced alternative evasion pathways contributing to decrease of tumor invasion and resistance mechanism. This study shed light on a new potential combined therapeutic approach to improve clinical outcomes in targeted cancer therapy scenarios.

PMID:40784600 | DOI:10.1016/j.bbadis.2025.168014

J Am Pharm Assoc (2003). 2025 Aug 8:102898. doi: 10.1016/j.japh.2025.102898. Online ahead of print.

ABSTRACT

BACKGROUND: Despite clear benefits of statin therapy, utilization remains suboptimal. Concern for adverse effects are a top reason for declining or discontinuing a statin. Certain genetic variations can predispose a patient to statin intolerance.

OBJECTIVE: To offer Veterans pharmacogenomics testing to help guide statin therapy decision making and increase appropriate statin prescribing within a single Veterans Affairs Health Care System (VAHCS).

METHODS: A team of pharmacists designed a quality improvement (QI) initiative which included personalized phone calls offering pharmacogenomics testing and/or statin initiation. Patients initiated on a statin were assessed for adherence and tolerability at least four weeks after initiation.

RESULTS: A total of 107 patients were contacted for the statin initiative. About half [(n = 50 (47%)] initiated a statin, and of those, 45 (90%) completed pharmacogenomics testing for a genomics-guided statin prescription. Most patients initiated on a statin (72%) reported adherence and tolerance at least 4 weeks after starting statin therapy.

CONCLUSION: Pharmacogenomics testing can potentially be used as a tool in the statin initiation process to facilitate a patient-centered discussion and increase shared clinical decision making.

PMID:40784538 | DOI:10.1016/j.japh.2025.102898

Sci Rep. 2025 Aug 10;15(1):29279. doi: 10.1038/s41598-025-15275-3.

ABSTRACT

Weeds and crops contribute to a endless resistance for similar assets, which leads to potential declines in crop production and enlarged agricultural expenses. Conventional models of weed control like extensive pesticide use, appear with the hassle of environmental pollution and advancing weed battle. As the need for organic agricultural and pollutant-free products increases, there is a crucial need for revolutionary solutions. The rise of smart agricultural tools, containing satellite technology, unmanned aerial vehicles (UAV), and intelligent robots certifies to be paramount in dealing with weed-related challenges. Deep learning (DL) based object detection model has been carried out in numerous applications. As a result, need for instance-level analyses of the weed dataset places constraints on the significance of influential DL methods. Artificial intelligence (AI) led image analysis for weed recognition and mainly, machine learning (ML) and deep learning (DL) utilizing images from cultivated lands have commonly been employed in the literature for identifying numerous kinds of weeds that are cultivated beside crops. This method develops an Automated Weed Recognition and Classification using a Deep Learning Model with Lemrus Optimization (AWRC-DLMLO). The main purpose of the AWRC-DLMLO method is to effectively detect and classify weeds and crop. In the proposed AWRC-DLMLO technique, the main phase of Gaussian filtering (GF) utilizing image pre-processing is implemented to eliminate unwanted noise. The plant segmentation was also developed utilizing the Residual Attention U-Net (RA-UNet) for generating segments. The ShuffleNetV2 approach is exploited in the AWRC-DLMLO method to ascertain feature vector. Next, the lemurs optimization algorithm (LOA) is applied to increase the hyperparameter and fine-tune the DL technique, further enhancing its performance. Eventually, the cascading Q-network (CQN)model is employed for the classification process. To emphasize the improved weed detection performance of the projected AWRC-DLMLO method, a wide range of simulations were done. The extensive outcome highlighted the improvements of the developed AWRC-DLMLO technique with other existing models.

PMID:40785014 | DOI:10.1038/s41598-025-15275-3

Sci Rep. 2025 Aug 10;15(1):29266. doi: 10.1038/s41598-025-15269-1.

ABSTRACT

Diabetic Retinopathy (DR) is a complication caused by diabetes that can destroy the retina, leading to blurred vision and even blindness. We propose a multi-attention residual refinement architecture that enhances conventional CNN performance through three strategic modifications: class-specific multi-attention for diagnostic feature weighting, space-to-depth preprocessing for improved spatial information preservation, and Squeeze-and-Excitation blocks for enhanced representational capacity. Our framework demonstrates universal applicability across different CNN architectures (ResNet, DenseNet, EfficientNet, MobileNet), consistently achieving 2-5% performance improvements on the EyePACS dataset while maintaining computational efficiency. The attention mechanism provides interpretable visualizations that align with clinical pathological patterns, validating the model's diagnostic reasoning.

PMID:40785010 | DOI:10.1038/s41598-025-15269-1

J Med Ultrason (2001). 2025 Aug 11. doi: 10.1007/s10396-025-01557-w. Online ahead of print.

ABSTRACT

PURPOSE: This study aimed to objectively and quantitatively analyze fetal motor behavior using DeepLabCut (DLC), a markerless posture estimation tool based on deep learning, applied to four-dimensional ultrasound (4DUS) data collected during the second trimester. We propose a novel clinical method for precise assessment of fetal neurodevelopment.

METHODS: Fifty 4DUS video recordings of normal singleton fetuses aged 12 to 22 gestational weeks were analyzed. Eight fetal joints were manually labeled in 2% of each video to train a customized DLC model. The model's accuracy was evaluated using likelihood scores. Intra- and inter-rater reliability of manual labeling were assessed using intraclass correlation coefficients (ICC). Angular velocity time series derived from joint coordinates were analyzed to quantify fetal movement patterns and developmental coordination.

RESULTS: Manual labeling demonstrated excellent reproducibility (inter-rater ICC = 0.990, intra-rater ICC = 0.961). The trained DLC model achieved a mean likelihood score of 0.960, confirming high tracking accuracy. Kinematic analysis revealed developmental trends: localized rapid limb movements were common at 12-13 weeks; movements became more coordinated and systemic by 18-20 weeks, reflecting advancing neuromuscular maturation. Although a modest increase in tracking accuracy was observed with gestational age, this trend did not reach statistical significance (p < 0.001).

CONCLUSION: DLC enables precise quantitative analysis of fetal motor behavior from 4DUS recordings. This AI-driven approach offers a promising, noninvasive alternative to conventional qualitative assessments, providing detailed insights into early fetal neurodevelopmental trajectories and potential early screening for neurodevelopmental disorders.

PMID:40785001 | DOI:10.1007/s10396-025-01557-w

NPJ Digit Med. 2025 Aug 10;8(1):513. doi: 10.1038/s41746-025-01885-8.

ABSTRACT

Oral potentially malignant disorder poses a significant risk of malignant transformation, particularly in cases with epithelial dysplasia (OED). Current OED assessment methods are invasive and lack reliable decision-support tools for cancer risk evaluation and follow-up optimization. This study developed and validated OMMT-PredNet, a fully automated multimodal deep learning framework requiring no manual ROI annotation, for non-invasive OED identification and time-dependent cancer risk prediction. Utilizing data from 649 histopathologically confirmed leukoplakia cases across multiple institutions (2003-2024), including 598 cases in the primary cohort and 51 in the external validation set, the model integrated paired high-resolution clinical images and medical records. OMMT-PredNet achieved an AUC of 0.9592 (95% CI: 0.9491-0.9693) for cancer risk prediction and 0.9219 (95% CI: 0.9088-0.9349) for OED identification, with high specificity (MT: 0.9490; OED: 0.9182) and precision (MT: 0.9442; OED: 0.9303). Calibration and decision curve analyses confirmed clinical applicability, while external validation demonstrated robustness. This multidimensional model effectively predicts OED and cancer risk, highlighting its global applicability in enhancing oral cancer screening and improving patient outcomes.

PMID:40784991 | DOI:10.1038/s41746-025-01885-8

Sci Rep. 2025 Aug 10;15(1):29241. doi: 10.1038/s41598-025-15005-9.

ABSTRACT

The Internet of Things (IoT) plays a significant part in the healthcare field. The growth of smart devices, smart sensors, and advanced lightweight communication protocols has created an opportunity to connect medical devices for monitoring biomedical signals and identifying patients' illnesses without human involvement, known as the Internet of Medical Things (IoMT). The IoMT enables a medical method to connect various smart devices, such as hospital assets, wearable sensors, and medical examination instruments, to create an information platform. In recent times, the IoMT has been extensively utilized in various areas, including disease diagnosis, smart hospitals, infectious disease tracking, and remote health monitoring. Still, safety is one of the key requirements for the success of IoMT systems. Thus, at present, deep learning (DL) is considered a safe IoMT system, as it can enhance the system's performance. In this manuscript, the Ensemble of Deep Learning and Metaheuristic Optimisation algorithms for the Critical Health Monitoring (EDLMOA-CHM) technique is proposed. The EDLMOA-CHM technique aims to develop and evaluate effective methods for monitoring health conditions in the IoMT to enhance healthcare system security and patient safety. Initially, the Z-score normalization method is employed in the data pre-processing step to clean, transform, and organize raw data into an appropriate format. For the feature selection process, the binary grey wolf optimization (BGWO) model is employed to identify and retain the most significant features in the dataset. The classification process utilizes ensemble models, including the Temporal Convolutional Network (TCN), the Attention-based Bidirectional Gated Recurrent Unit (A-BiGRU), and the Hybrid Deep Belief Network (HDBN) techniques. To further optimize model performance, the pelican optimization algorithm (POA) is utilized for hyperparameter tuning to ensure that the optimum hyperparameters are chosen for enhanced accuracy. To demonstrate the improved performance of the EDLMOA-CHM model, a comprehensive experimental analysis is conducted using the healthcare IoT dataset. The comparison analysis of the EDLMOA-CHM model demonstrated a superior accuracy value of 99.56% over existing techniques.

PMID:40784985 | DOI:10.1038/s41598-025-15005-9

Sci Rep. 2025 Aug 10;15(1):29237. doi: 10.1038/s41598-025-10075-1.

ABSTRACT

Feature fusion is a widely adopted strategy in multi-biometrics to enhance reliability, performance and real-world applicability. While combining multiple biometric sources can improve recognition accuracy, practical performance depends heavily on feature dependencies, redundancies, and selection methods. This study provides a comprehensive analysis of multimodal hand biometric recognition systems. We aim to guide the design of efficient, high-accuracy biometric systems by evaluating trade-offs between classical and learning-based approaches. For feature extraction, we employ Zernike moments and log-Gabor filters, evaluating multiple selection techniques to optimize performance. While baseline palmprint and fingerprint systems exhibit varying classification rates. Our feature fusion method achieves a consistent 99.29% identification rate across diverse classifiers. Additionally, we explore EfficientNET as an end-to-end feature extractor and classifier, comparing its fusion performance with the traditional approach. Our findings emphasize feature selection as the key of building efficient and stable recognition systems. Using the minimal optimal feature set, we achieve an equal error rate (EER) of 0.71%, demonstrating superior efficiency and accuracy.

PMID:40784983 | DOI:10.1038/s41598-025-10075-1

Sci Rep. 2025 Aug 10;15(1):29260. doi: 10.1038/s41598-025-14476-0.

ABSTRACT

Alzheimer's disease is a progressive neurodegenerative disorder marked by cognitive decline, memory loss, and behavioral changes. Early diagnosis, particularly identifying Early Mild Cognitive Impairment (EMCI), is vital for managing the disease and improving patient outcomes. Detecting EMCI is challenging due to the subtle structural changes in the brain, making precise slice selection from MRI scans essential for accurate diagnosis. In this context, the careful selection of specific MRI slices that provide distinct anatomical details significantly enhances the ability to identify these early changes. The chief novelty of the study is that instead of selecting all slices, an approach for identifying the important slices is developed. The ADNI-3 dataset was used as the dataset when running the models for early detection of Alzheimer's disease. Satisfactory results have been obtained by classifying with deep learning models, vision transformers (ViT) and by adding new structures to them, together with the model proposal. In the results obtained, while an accuracy of 99.45% was achieved with EfficientNetB2 + FPN in AD vs. LMCI classification from the slices selected with SSIM, an accuracy of 99.19% was achieved in AD vs. EMCI classification, in fact, the study significantly advances early detection by demonstrating improved diagnostic accuracy of the disease at the EMCI stage. The results obtained with these methods emphasize the importance of developing deep learning models with slice selection integrated with the Vision Transformers architecture. Focusing on accurate slice selection enables early detection of Alzheimer's at the EMCI stage, allowing for timely interventions and preventive measures before the disease progresses to more advanced stages. This approach not only facilitates early and accurate diagnosis, but also lays the groundwork for timely intervention and treatment, offering hope for better patient outcomes in Alzheimer's disease. The study is finally evaluated by a statistical significance test.

PMID:40784967 | DOI:10.1038/s41598-025-14476-0

BMC Biol. 2025 Aug 11;23(1):252. doi: 10.1186/s12915-025-02361-1.

ABSTRACT

BACKGROUND: Deep learning has emerged as a powerful tool in the analysis of biological data, including the analysis of large metagenome data. However, its application remains limited due to high computational costs, model complexity, and difficulty extracting biological insights from these artificial neural networks (ANNs). In this study, we applied a transfer learning approach using the ESM-2 protein structure prediction model and our own smaller ANN to classify proteins containing the domain of unknown function 3494 (DUF3494) by their source environments. DUF3494 is found in a diverse group of putative ice-binding and substrate-binding proteins across a range of environments in prokaryotic and eukaryotic microorganisms. They present a compelling test case for exploring the balance between prediction accuracy and interpretability in sequence classification.

RESULTS: Our ANN analysed 50,669 DUF3494 sequences from publicly available metagenomes, and successfully classified a large proportion of sequences by source environment (polar marine, glacier ice, frozen sediment, rock, subsurface). We identified environment-specific features that appear to drive classification. Our best-performing ANN was able to classify between 75.9 and 97.8% of sequences correctly. To enhance biological interpretability of these predictions, we compared this model with a genetic algorithm (GA), which, although it had lower predictive ability, provided transparent classification rules and predictors. Further in silico mutagenesis of key residues uncovered a vertically aligned column of amino acids on the b-face of the protein which was important for environmental differentiation, suggesting that both methods captured distinct evolutionary and ecological aspects of the sequences. Feature importance analysis identified that steric and electronic properties of the protein were associated with predictive ability.

CONCLUSIONS: Our findings highlight the utility of deep learning for classification of diverse biological sequences and provide a framework for combining methods to improve model interpretability and ecological insights.

PMID:40784938 | DOI:10.1186/s12915-025-02361-1

Sci Rep. 2025 Aug 10;15(1):29259. doi: 10.1038/s41598-025-13781-y.

ABSTRACT

Cervical cancer is a leading cause of death from malignant tumors in women, and accurate evaluation of occult lymph node metastasis (OLNM) is crucial for optimal treatment. This study aimed to develop several predictive models-including Clinical model, Radiomics models (RD), Deep Learning models (DL), Radiomics-Deep Learning fusion models (RD-DL), and a Clinical-RD-DL combined model-for assessing the risk of OLNM in cervical cancer patients.The study included 130 patients from Center 1 (training set) and 55 from Center 2 (test set). Clinical data and imaging sequences (T1, T2, and DWI) were used to extract features for model construction. Model performance was assessed using the DeLong test, and SHAP analysis was used to examine feature contributions. Results showed that both the RD-combined (AUC = 0.803) and DL-combined (AUC = 0.818) models outperformed single-sequence models as well as the standalone Clinical model (AUC = 0.702). The RD-DL model yielded the highest performance, achieving an AUC of 0.981 in the training set and 0.903 in the test set. Notably, integrating clinical variables did not further improve predictive performance; the Clinical-RD-DL model performed comparably to the RD-DL model. SHAP analysis showed that deep learning features had the greatest impact on model predictions. Both RD and DL models effectively predict OLNM, with the RD-DL model offering superior performance. These findings provide a rapid, non-invasive clinical prediction method.

PMID:40784909 | DOI:10.1038/s41598-025-13781-y

Sci Rep. 2025 Aug 10;15(1):29243. doi: 10.1038/s41598-025-13479-1.

ABSTRACT

Pulmonary disease can severely impair respiratory function and be life-threatening. Accurately recognizing pulmonary diseases in chest X-ray images is challenging due to overlapping body structures and the complex anatomy of the chest. We propose an adaptive multiscale feature fusion model for recognizing Chest X-ray images of pneumonia, tuberculosis, and COVID-19, which are common pulmonary diseases. We introduce an Adaptive Multiscale Fusion Network (AMFNet) for pulmonary disease classification in chest X-ray images. AMFNet consists of a lightweight Multiscale Fusion Network (MFNet) and ResNet50 as the secondary feature extraction network. MFNet employs Fusion Blocks with self-calibrated convolution (SCConv) and Attention Feature Fusion (AFF) to capture multiscale semantic features, and integrates a custom activation function, MFReLU, which is employed to reduce the model's memory access time. A fusion module adaptively combines features from both networks. Experimental results show that AMFNet achieves 97.48% accuracy and an F1 score of 0.9781 on public datasets, outperforming models like ResNet50, DenseNet121, ConvNeXt-Tiny, and Vision Transformer while using fewer parameters.

PMID:40784886 | DOI:10.1038/s41598-025-13479-1

Chest. 2025 Aug;168(2):e54-e55. doi: 10.1016/j.chest.2025.03.033.

NO ABSTRACT

PMID:40784717 | DOI:10.1016/j.chest.2025.03.033

Discov Oncol. 2025 Aug 11;16(1):1520. doi: 10.1007/s12672-025-03158-y.

ABSTRACT

BACKGROUND: Breast cancer is the most diagnosed cancer in women and the second leading cause of cancer-related deaths worldwide. Chemotherapy faces challenges like drug resistance, side effects, and recurrence, underscoring the need for innovative therapies. This study explores cryptolepine, a natural compound, for its therapeutic potential against heterogeneous BC by targeting specific molecular mechanisms.

METHODS: we conducted an ADMET analysis to assess cryptolepine's pharmacokinetic properties and drug-likeness. Target prediction was performed using SWISS-TARGET-PREDICTION and Integrative Pharmacology for BC. Identified targets were cross-referenced with BC-related genes from Gene Atlas, TCGA, and OMIM. Protein-protein interactions were analyzed using STRING, and pathway enrichment was assessed using KEGG and ShinyGO. Molecular docking and dynamics simulations evaluated cryptolepine's binding efficacy while in-vitro assays, including proliferation studies and mRNA expression analysis, validated these findings.

RESULTS: Cryptolepine demonstrated favorable drug-likeness and multi-target activity, interacting with key cancer pathways such as p53, STAT3, and PI3K-Akt. Network pharmacology revealed its potential to reduce drug resistance. Cryptolepine regulated important genes (PTGS2, STAT3, CCND1) across critical pathways (cAMP, PI3K/AKT, P53, IL6/JAK2/STAT3). Molecular docking confirmed strong binding (ΔG - 8.2 kcal/mol), and in-vitro assays showed IC50 values of 4.6 μM for MDA-MB-231 and 3.1 μM for Mcf-7. mRNA expression analysis indicated increased cytochrome C and BAX, while pro-caspase levels decreased.

CONCLUSION: Cryptolepine shows promise as a therapeutic candidate for BC. Future research should optimize its pharmacological profile for specificity and reduced toxicity.

PMID:40784974 | DOI:10.1007/s12672-025-03158-y

Trends Endocrinol Metab. 2025 Aug 9:S1043-2760(25)00151-1. doi: 10.1016/j.tem.2025.07.005. Online ahead of print.

ABSTRACT

Physical exercise can play an important role both in primary and secondary cardiovascular disease prevention by virtue of its multisystem effects. These beneficial adaptations at the whole-body level include improvements in mitochondrial health, vascular function, and autonomic balance, together with attenuation of inflammation and the release of 'exerkines' with pleiotropic effects. Thus, several research groups have attempted to develop so-called 'exercise pills' or 'exercise mimetics': that is, substances that are theoretically capable of reproducing some of the cardiometabolic benefits associated with regular exercise. In this review we summarize pharmacological and phytochemical agents which, when used alone or in combination with exercise, may improve cardiometabolic health. We also discuss the current gaps and future steps needed to translate these findings into therapeutic applications.

PMID:40784868 | DOI:10.1016/j.tem.2025.07.005

Infect Chemother. 2025 Jul 18. doi: 10.3947/ic.2025.0048. Online ahead of print.

ABSTRACT

BACKGROUND: Albumin administration in patients with septic shock has shown potential benefits, but its association with the development of pulmonary complications remains unclear. We aimed to evaluate the impact of albumin administration on acute respiratory distress syndrome development in patients with septic shock.

MATERIALS AND METHODS: We analyzed clinical data from the Medical Information Mart for Intensive Care IV database and included adult patients with septic shock. Propensity score matching was used to balance the covariates between the albumin and non-albumin groups. The primary outcome was the development of moderate-to-severe acute respiratory distress syndrome within 7 days. Survival analysis using the log-rank test compared acute respiratory distress syndrome development rates between the groups. Subgroup analysis was used to evaluate the effect of albumin administration on the primary outcome in various subgroups.

RESULTS: Among the 2,132 eligible patients, 1,572 (73.7%) did not receive albumin, whereas 560 (26.3%) received albumin. After propensity score matching, the primary outcome was not significantly different between the two groups (17.5% in the albumin group vs. 16.3% in the non-albumin group; P=0.708). The Kaplan-Meier curve demonstrated no difference in the primary outcome between the groups. Subgroup analysis showed no significant association between albumin administration and increased acute respiratory distress syndrome development rate across various subgroups.

CONCLUSION: No significant difference in acute respiratory distress syndrome development was found between albumin and non-albumin groups of patients with septic shock. Albumin administration in patients with septic shock should be considered when clinically indicated, without undue concerns about acute respiratory distress syndrome development.

PMID:40784736 | DOI:10.3947/ic.2025.0048

J Infect Chemother. 2025 Aug 8:102789. doi: 10.1016/j.jiac.2025.102789. Online ahead of print.

ABSTRACT

PURPOSE: To determine the incidence of side effects associated with beta-lactam antibiotics commonly used in pneumonia treatment and the risk factors for each side effect.

METHODS: Patients with community-acquired or healthcare-associated pneumonia were prospectively enrolled between June 2002 and December 2012. Patients were administered with beta-lactam antibiotics in the treatment of pneumonia. All side effects observed during the antibiotic treatment were recorded. Multivariate analysis was performed to identify independent risk factors for each side effect.

RESULTS: A total of 1,162 patients were enrolled in this study. The antibiotics used for treatment were ampicillin/sulbactam in 362, piperacillin/tazobactam in 111, cefotiam in 89, ceftriaxone in 140, cefepime in 130, imipenem/cilastatin in 110, meropenem in 97, and others in 123 cases. Diarrhea, elevated liver enzyme levels, and skin rash were observed in 174 (15.0%), 52 (4.5%), and 28 (2.4%) patients, respectively. In multivariate analysis, female sex (p<0.05) and use of either piperacillin/tazobactam (p<0.05), cefepime (p<0.05), or imipenem/cilastatin (p<0.05) were significantly associated with diarrhea. Use of cefotiam (p<0.05) or meropenem (p<0.05) were significantly associated with elevated liver enzyme levels. No significant risk factors were found for skin rashes.

CONCLUSION: Broad-spectrum antibiotics tended to cause diarrhea more frequently. The use of cefotiam and meropenem was associated with increased liver enzyme levels.

PMID:40784408 | DOI:10.1016/j.jiac.2025.102789

J Chem Inf Model. 2025 Aug 10. doi: 10.1021/acs.jcim.4c02399. Online ahead of print.

ABSTRACT

Aqueous solubility is one key property of a chemical compound that determines its possible use in different applications, from drug development to materials sciences. In this work, we present a model for the prediction of aqueous solubility that leverages a curated data set merged from four distinct sources. This data set encompasses a diverse range of organic compounds, providing a robust foundation for our investigation of solubility prediction. Our approach involves employing a variety of machine learning and deep learning models that combine an extensive array of chemical descriptors, fingerprints, and functional groups. This methodology is designed to address the complexities of solubility prediction and is tailored to achieve high accuracy and generalization. We tested the finalized model on a diverse data set of 1282 unique organic compounds from the Huuskonen data set. The results of our analysis demonstrate the success of our model, which, given an R2 value of 0.92 and an MAE value of 0.40, outperforms existing prediction methods for aqueous solubility on one of the most diverse data sets in the field.

PMID:40783839 | DOI:10.1021/acs.jcim.4c02399

HGG Adv. 2025 Aug 8:100490. doi: 10.1016/j.xhgg.2025.100490. Online ahead of print.

ABSTRACT

Genetic prediction of complex traits, enabled by large-scale genomic studies, has created new measures to understand individual genetic predisposition. Polygenic Risk Scores (PRS) offer a way to aggregate information across the genome, enabling personalized risk prediction for complex traits and diseases. However, conventional PRS calculation methods that rely on linear models are limited in their ability to capture complex patterns and interaction effects in high-dimensional genomic data. In this study, we seek to improve the predictive power of PRS through applying advanced deep learning techniques. We show that the Variational AutoEncoder-based model for PRS construction (VAE-PRS) outperforms currently state-of-the-art methods for biobank-level data in 14 out of 16 blood cell traits, while being computationally efficient. Through comprehensive experiments, we found that the VAE-PRS model offers the ability to capture interaction effects in high-dimensional data and shows robust performance across different pre-screened variant sets. Furthermore, VAE-PRS is easily interpretable via assessing the contribution of each individual marker to the final prediction score through the SHapley Additive exPlanations (SHAP) method, providing potential new insights in identifying trait-associated genetic variants. In summary, VAE-PRS presents a measure to genetic risk prediction for blood cell traits by harnessing the power of deep learning methods given appropriate training sample size, which could further facilitate the development of personalized medicine and genetic research.

PMID:40783786 | DOI:10.1016/j.xhgg.2025.100490

Biol Proced Online. 2025 Aug 9;27(1):29. doi: 10.1186/s12575-025-00286-1.

ABSTRACT

BACKGROUND: Hepatocellular carcinoma (HCC) is the most prevalent primary liver malignancy, contributing significantly to global mortality due to limited therapeutic options. Programmed cell death (PCD) and metabolism are key cancer hallmarks, influencing tumor progression and treatment response. However, their association in HCC remains insufficiently characterized.

METHODS: We utilized single-cell and bulk transcriptomic datasets to identify differentially expressed genes (DEGs) strongly associated with PCD and metabolism in HCC. Based on prognosis-related DEGs, patients and cells were stratified into high- and low-expression groups using corresponding computational algorithms. The intersecting DEGs from both datasets were analyzed using univariate Cox regression, and a prognostic risk score model was constructed through machine learning algorithms. The model was subsequently evaluated in the context of the immune microenvironment and its relevance to immunotherapeutic responses. Drug repurposing was pursued by integrating machine learning, deep learning, and molecular docking strategies to uncover potential therapeutic options. In parallel, consensus clustering analysis was performed to assess the grouping efficiency of the model-associated genes. Lastly, the expression of the model genes was evaluated in HCC mouse models and cell lines, and the biological function of a representative gene was further investigated through in vitro assays.

RESULTS: We developed an 18-gene signature based on PCD and metabolism with strong predictive value for overall survival (OS) in HCC patients. Malignant cells with high PCD-Metabolism scores may promote HCC progression by influencing immune infiltration, fibroblast differentiation, and cancer-related pathways. The model also correlated with immunotherapy sensitivity. Leveraging a drug repurposing strategy guided by the PCD-Metabolism model, we identified triazolothiadiazine and fluvastatin as promising compounds targeting RCN2 and CDK4, respectively. Clustering analysis identified two HCC subtypes (C1 and C2), and the subtype enriched with high-risk patients was associated with inferior OS. Notably, CCT3, a key gene in the model, was enriched in tumor regions, and its silencing was found to inhibit the proliferation and migration of HCC cells while regulating ferroptosis- and autophagy-related markers.

CONCLUSION: Our study established a PCD-Metabolism-based prognostic model for HCC, offering insights into disease biology and potential avenues for personalized therapy.

SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12575-025-00286-1.

PMID:40783519 | PMC:PMC12335101 | DOI:10.1186/s12575-025-00286-1