J Phys Chem B. 2025 May 13. doi: 10.1021/acs.jpcb.5c03007. Online ahead of print.

NO ABSTRACT

PMID:40359295 | DOI:10.1021/acs.jpcb.5c03007

Clin Nucl Med. 2025 May 13. doi: 10.1097/RLU.0000000000005967. Online ahead of print.

ABSTRACT

PURPOSE: Sincalide-stimulated cholescintigraphy (SSC) calculates the gallbladder ejection fraction (GBEF) to diagnose functional gallbladder disorder. Currently, artificial intelligence (AI)-driven workflows that integrate real-time image processing and organ function calculation remain unexplored in nuclear medicine practice. This pilot study explored an AI-based application for gallbladder radioactivity tracking.

METHODS: We retrospectively analyzed 20 SSC exams, categorized into 10 easy and 10 challenging cases. Two human operators (H1 and H2) independently annotated the gallbladder regions of interest manually over the course of the 60-minute SSC. A U-Net-based deep learning model was developed to automatically segment gallbladder masks, and a 10-fold cross-validation was performed for both easy and challenging cases. The AI-generated masks were compared with human-annotated ones, with Dice similarity coefficients (DICE) used to assess agreement.

RESULTS: AI achieved an average DICE of 0.746 against H1 and 0.676 against H2, performing better in easy cases (0.781) than in challenging ones (0.641). Visual inspection showed AI was prone to errors with patient motion or low-count activity.

CONCLUSIONS: This study highlights AI's potential in real-time gallbladder tracking and GBEF calculation during SSC. AI-enabled real-time evaluation of nuclear imaging data holds promise for advancing clinical workflows by providing instantaneous organ function assessments and feedback to technologists. This AI-enabled workflow could enhance diagnostic efficiency, reduce scan duration, and improve patient comfort by alleviating symptoms associated with SSC, such as abdominal discomfort due to sincalide administration.

PMID:40359029 | DOI:10.1097/RLU.0000000000005967

Ann Hum Genet. 2025 May 13:e12601. doi: 10.1111/ahg.12601. Online ahead of print.

ABSTRACT

Pharmacogenomics, the use of germline genomic data to guide prescription to improve effective and safer medication, holds promise as a clinical intervention. To date in most health systems, there has been limited uptake of pharmacogenomic testing confined to a few single drug-gene associations. Here, we describe the current reactive model of single gene testing and the potential for this to change to a pre-emptive panel or genome-based approach. For this change to occur, three major challenges need to be addressed-the pharmacogenomic testing approach, the digital and data integration, and service delivery models. We explore some of the potential solutions and how pharmacogenomics can be integrated into routine care at scale for patient benefit.

PMID:40358420 | DOI:10.1111/ahg.12601

Cells. 2025 Apr 22;14(9):622. doi: 10.3390/cells14090622.

ABSTRACT

In the original publication [...].

PMID:40358202 | DOI:10.3390/cells14090622

J Physiol Biochem. 2025 May 13. doi: 10.1007/s13105-025-01082-x. Online ahead of print.

ABSTRACT

Neutrophil extracellular traps (NETs), web-like complex structures secreted by neutrophils, have emerged as key players in the modulation of immune responses and the immunopathogenesis of immune disorders. Initially described for their antimicrobial function, NETs now play a part in the fundamental processes of cancer biology, including cancer initiation, metastatic dissemination, and immune evasion strategies. NETs hijack anti-tumor immunity by entrapping circulating cancer cells, fostering the growth of tumors, and reorganizing the tumor microenvironment such that it is pro-malignancy. Emerging evidence emphasizes the role of NETosis coupled with non-coding RNAs-long non-coding RNAs (lncRNAs) and microRNAs (miRNAs)-as key regulators of gene expression and controllers of processes vital for cancer growth, such as immune response and programmed cell death processes like apoptosis, necroptosis, pyroptosis, and ferroptosis. Aberrantly expressed non-coding RNAs have been attributed to immune dysregulation and excessive NET production, promoting tumor growth. NETs are also associated with a myriad of pathological conditions, such as autoimmune disorders, cystic fibrosis, sepsis, and thrombotic disorders. New therapeutic approaches-such as DNase therapy and PAD4 inhibitors-target NET production and their degradation to modify immune function and the efficiency of immunotherapies. Further clarification of the intricate interactions of NETosis, lncRNAs, and miRNAs has the potential to establish new strategies for the suppression of the growth of tumors and preventing immune evasion. This review seeks to elucidate the interactions between NETosis and the regulatory networks involving non-coding RNAs that significantly contribute to the immunopathogenesis of cancer.

PMID:40358898 | DOI:10.1007/s13105-025-01082-x

Angew Chem Int Ed Engl. 2025 May 13:e202507835. doi: 10.1002/anie.202507835. Online ahead of print.

ABSTRACT

Approximately 40% of marketed drugs exhibit suboptimal pharmacokinetic profiles. Co-crystallization, where pairs of molecules form a multicomponent crystal, constitutes a promising strategy to enhance physicochemical properties without compromising pharmacological activity. However, finding promising co-crystal pairs is resource-intensive, due to the large and diverse range of possible molecular combinations. We present DeepCocrystal, a novel deep learning approach designed to predict co-crystal formation by processing the "chemical language" from a supramolecular vantage point. Rigorous validation of DeepCocrystal showed a balanced accuracy of 78% in realistic scenarios, outperforming existing models. Explainable AI approaches uncovered the decision-making process of DeepCocrystal, showing its capability to learn chemically relevant aspects of the "supramolecular language" that match experimental co-crystallization patterns. By leveraging properties of molecular string representations, DeepCocrystal can also estimate the uncertainty of its predictions. We harness this capability in a challenging prospective study and successfully discovered two novel co-crystals of diflunisal, an anti-inflammatory drug. This study underscores the potential of deep learning - and in particular of chemical language processing - to accelerate co-crystallization and ultimately drug development, in both academic and industrial contexts. DeepCocrystal is available as an easy-to-use web application at https://deepcocrystal.streamlit.app/.

PMID:40358977 | DOI:10.1002/anie.202507835

Interdiscip Sci. 2025 May 13. doi: 10.1007/s12539-025-00706-6. Online ahead of print.

ABSTRACT

The exploration of associations between circular RNAs (circRNAs) and diseases contributes to a deeper understanding of the pathogenesis of diseases. Many computational methods have been proposed for circRNA-disease associations identification. However, these methods still exhibit some limitations such as ignoring the effect of noise. In this paper, we proposed a new knowledge graph attribute mining attention network (KAATCDA) to predict circRNA-disease associations based on knowledge graph attribute network (KGA) and attribute mining attention network (AMA). Firstly, KGA is used to learn the feature representation of diseases. Then, the features of circRNAs are obtained using AMA, which are similar to disease feature representations. Finally, the scores of circRNA-disease associations are predicted based on circRNA feature representation and disease feature representation. Experiments of five-fold cross-validation on two datasets demonstrate that KAATCDA outperforms other state-of-the-art methods. In addition, the case study shows our method can effectively predict unknown circRNA-disease associations.

PMID:40358837 | DOI:10.1007/s12539-025-00706-6

Phys Eng Sci Med. 2025 May 13. doi: 10.1007/s13246-025-01542-0. Online ahead of print.

ABSTRACT

Benign Paroxysmal Positional Vertigo (BPPV) is a prevalent vestibular disorder encountered in clinical settings. Diagnosis of this condition primarily relies on the observation of nystagmus, which involves monitoring the eye movements of patients. However, existing medical equipment for collecting and analyzing nystagmus data has notable limitations and deficiencies. To address this challenge, a comprehensive BPPV nystagmus data collection and intelligent analysis framework has been developed. Our framework leverages a neural network model, Egeunet, in conjunction with mathematical statistical techniques like Fast Fourier Transform (FFT), enabling precise segmentation of eye structures and accurate analysis of eye movement data. Furthermore, an eye movement analysis method has been introduced, designed to enhance clinical decision-making, resulting in more intuitive and clear analysis outcomes. Benefiting from the high sensitivity of our eye movement capture and its robustness in the face of environmental conditions and noise, our BPPV nystagmus data collection and intelligent analysis framework has demonstrated outstanding performance in BPPV detection.

PMID:40358819 | DOI:10.1007/s13246-025-01542-0

Abdom Radiol (NY). 2025 May 13. doi: 10.1007/s00261-025-04984-y. Online ahead of print.

ABSTRACT

OBJECTIVE: To evaluate the possibility of performing renal vessel reconstruction on non-enhanced CT images using deep learning models.

MATERIALS AND METHODS: 177 patients' CT scans in the non-enhanced phase, arterial phase and venous phase were chosen. These data were randomly divided into the training set (n = 120), validation set (n = 20) and test set (n = 37). In training set and validation set, a radiologist marked out the right renal arteries and veins on non-enhanced CT phase images using contrast phases as references. Trained deep learning models were tested and evaluated on the test set. A radiologist performed renal vessel reconstruction on the test set without the contrast phase reference, and the results were used for comparison. Reconstruction using the arterial phase and venous phase was used as the gold standard.

RESULTS: Without the contrast phase reference, both radiologist and model could accurately identify artery and vein main trunk. The accuracy was 91.9% vs. 97.3% (model vs. radiologist) in artery and 91.9% vs. 100% in vein, the difference was insignificant. The model had difficulty identify accessory arteries, the accuracy was significantly lower than radiologist (44.4% vs. 77.8%, p = 0.044). The model also had lower accuracy in accessory veins, but the difference was insignificant (64.3% vs. 85.7%, p = 0.094).

CONCLUSION: Deep learning models could accurately recognize the right renal artery and vein main trunk, and accuracy was comparable to that of radiologists. Although the current model still had difficulty recognizing small accessory vessels, further training and model optimization would solve these problems.

PMID:40358703 | DOI:10.1007/s00261-025-04984-y

Eur J Nucl Med Mol Imaging. 2025 May 13. doi: 10.1007/s00259-025-07321-z. Online ahead of print.

ABSTRACT

PURPOSE: In this study, we investigated the utility of a novel developed anti-CEACAM5 VHH for cancer diagnosis and its potential of being a targeting-moiety of VHH-drug conjugates for cancer therapy.

METHODS: Anti-CEACAM5 VHH (6B11) affinity and specific cellular binding was confirmed by ELISA, FACS and immunofluorescence in cancer cell lines with varying CEACAM5 expression levels. Intracellular penetration ability within tumor spheroids was tested with Oregon Green 488 labeled 6B11 (OG488-6B11). Biodistribution and binding specificity of 99mTc-radiolabeled 6B11 was tested in A549 CEACAM5 overexpressing (A549-CEA5-OV) and knockout (A549-CEA5-KO) tumor-bearing mice upon SPECT/CT imaging, γ-counting and autoradiography. The therapeutic efficacy of 6B11 and 6F8 (anti-CEACAM5 VHH with lower binding affinity) was tested by viability, wound healing and adhesion assays. To verify the potential of VHHs as a warhead for VHH-drug conjugation, an internalization assay with OG488 labeled VHH was performed.

RESULT: 6B11 demonstrated high binding affinity (EC50 0.5nM) and cellular binding. OG488-6B11 penetrated tumor spheroids completely at 24 h, while a conventional antibody was only visible at the spheroid periphery. SPECT imaging indicated higher uptake (p < 0.05) in A549-CEA5-OV tumors, resulting in increased tumor-to-blood ratios especially at 4 (2.0016 ± 1.1893, p = 0.035) and 24 (2.9371 ± 2.0683, p = 0.003) hpi compared to A549-CEA5-KO tumors at 4 (0.5640 ± 0.3576) and 24 (0.8051 ± 0.4351) hpi. 99mTc-6B11 was predominantly renally cleared. Autoradiography and immunohistochemistry confirmed these uptake patterns. 6B11 nor 6F8 did exhibit significant anti-cancer therapeutic efficacy in vitro. OG488-6B11 was effectively internalized and accumulated in cells in a time-dependent manner, to end up in the lysosomes.

CONCLUSION: The anti-CEACAM5 VHH 6B11 is a good candidate for SPECT-based cancer diagnosis and can be potentially used as targeting moiety in the development of VHH-based drug conjugates for cancer treatments.

PMID:40358697 | DOI:10.1007/s00259-025-07321-z

Int J Surg. 2025 May 12. doi: 10.1097/JS9.0000000000002486. Online ahead of print.

ABSTRACT

BACKGROUND: Given the current limited accuracy of imaging screening for Hepatic Echinococcosis (HCE) in under-resourced areas, the authors developed and validated a Multimodal Imaging system (HEAC) based on plain Computed Tomography (CT) combined with ultrasound for HCE screening in those areas.

METHODS: In this study, we developed a multimodal deep learning diagnostic system by integrating ultrasound and plain CT imaging data to differentiate hepatic echinococcosis, liver cysts, liver abscesses, and healthy liver conditions. We collected a dataset of 8979 cases spanning 18 years from eight hospitals in Xinjiang China, including both retrospective and prospective data. To enhance the robustness and generalization of the diagnostic model, after modeling CT and ultrasound images using EfficientNet3D and EfficientNet-B0, external and prospective tests were conducted, and the model's performance was compared with diagnoses made by experienced physicians.

RESULTS: Across internal and external test sets, the fused model of CT and ultrasound consistently outperformed the individual modality models and physician diagnoses. In the prospective test set from the same center, the fusion model achieved an accuracy of 0.816, sensitivity of 0.849, specificity of 0.942, and an AUC of 0.963, significantly exceeding physician performance (accuracy 0.900, sensitivity 0.800, specificity 0.933). The external test sets across seven other centers demonstrated similar results, with the fusion model achieving an overall accuracy of 0.849, sensitivity of 0.859, specificity of 0.942, and AUC of 0.961.

CONCLUSION: The multimodal deep learning diagnostic system that integrates CT and ultrasound significantly increases the diagnosis accuracy of HCE, liver cysts, and liver abscesses. It beats standard single-modal approaches and physician diagnoses by lowering misdiagnosis rates and increasing diagnostic reliability. It emphasizes the promise of multimodal imaging systems in tackling diagnostic issues in low-resource areas, opening the path for improved medical care accessibility and outcomes.

PMID:40358633 | DOI:10.1097/JS9.0000000000002486

Bioinformatics. 2025 May 13:btaf302. doi: 10.1093/bioinformatics/btaf302. Online ahead of print.

ABSTRACT

MOTIVATION: Advances in high throughput technologies enabled large access to various types of omics. Each omics provides a partial view of the underlying biological process. Integrating multiple omics layers would help have a more accurate diagnosis. However, the complexity of omics data requires approaches that can capture complex relationships. One way to accomplish this is by exploiting the known regulatory links between the different omics, which could help in constructing a better multimodal representation.

RESULTS: In this article, we propose CrossAttOmics, a new deep-learning architecture based on the cross-attention mechanism for multi-omics integration. Each modality is projected in a lower dimensional space with its specific encoder. Interactions between modalities with known regulatory links are computed in the feature representation space with cross-attention. The results of different experiments carried out in this paper show that our model can accurately predict the types of cancer by exploiting the interactions between multiple modalities. CrossAttOmics outperforms other methods when there are few paired training examples. Our approach can be combined with attribution methods like LRP to identify which interactions are the most important.

AVAILABILITY: The code is available at https://github.com/Sanofi-Public/CrossAttOmics and https://doi.org/10.5281/zenodo.15065928. TCGA data can be downloaded from the Genomic Data Commons Data Portal. CCLE data can be downloaded from the depmap portal.

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PMID:40358524 | DOI:10.1093/bioinformatics/btaf302

Bioinformatics. 2025 May 13:btaf303. doi: 10.1093/bioinformatics/btaf303. Online ahead of print.

ABSTRACT

MOTIVATION: Chronic kidney disease (CKD) and Acute Kidney Injury (AKI) are prominent public health concerns affecting more than 15% of the global population. The ongoing development of spatially resolved transcriptomics (SRT) technologies presents a promising approach for discovering the spatial distribution patterns of gene expression within diseased tissues. However, existing computational tools are predominantly calibrated and designed on the ribbon-like structure of the brain cortex, presenting considerable computational obstacles in discerning highly heterogeneous mosaic-like tissue architectures in the kidney. Consequently, timely and cost-effective acquisition of annotation and interpretation in the kidney remains a challenge in exploring the cellular and morphological changes within renal tubules and their interstitial niches.

RESULTS: We present an empowered graph deep learning framework, REGNN (Relation Equivariant Graph Neural Networks), designed for SRT data analyses on heterogeneous tissue structures. To increase expressive power in the SRT lattice using graph modeling, REGNN integrates equivariance to handle n-dimensional symmetries of the spatial area, while additionally leveraging Positional Encoding to strengthen relative spatial relations of the nodes uniformly distributed in the lattice. Given the limited availability of well-labeled spatial data, this framework implements both graph autoencoder and graph self-supervised learning strategies. On heterogeneous samples from different kidney conditions, REGNN outperforms existing computational tools in identifying tissue architectures within the 10X Visium platform. This framework offers a powerful graph deep learning tool for investigating tissues within highly heterogeneous expression patterns and paves the way to pinpoint underlying pathological mechanisms that contribute to the progression of complex diseases.

AVAILABILITY: REGNN is publicly available at https://github.com/Mraina99/REGNN.

SUPPLEMENTARY INFORMATION: Found in the attached supplementary file 'SupplementaryFile_ManuscriptBioinformatics'.

PMID:40358510 | DOI:10.1093/bioinformatics/btaf303

Comput Methods Biomech Biomed Engin. 2025 May 13:1-25. doi: 10.1080/10255842.2025.2501634. Online ahead of print.

ABSTRACT

This study aims to introduce a novel, computationally lightweight feature extraction technique called Sliding Euclidean Pattern Quantization (SEPQ), which encodes local morphological patterns of ECG signals using Euclidean distance-based binary representations within sliding windows. The proposed SEPQ method was evaluated using two ECG datasets. The first dataset contained three labeled classes (CHF, ARR, and NSR), while the second included four classes (ventricular beats (VB), supraventricular beats (SVB), fusion beats (FB), and NSR). Extracted features were classified using several machine learning models, with LightGBM achieving the highest performance-over 99% accuracy on the first dataset and above 93% on the second. A convolutional neural network (CNN) model was also employed for comparative analysis, both on raw data and in a hybrid configuration with SEPQ, yielding moderate yet noteworthy performance. Experimental results confirm that SEPQ offers a robust, interpretable, and highly accurate solution for ECG signal classification.

PMID:40358468 | DOI:10.1080/10255842.2025.2501634

Radiology. 2025 May;315(2):e243463. doi: 10.1148/radiol.243463.

ABSTRACT

Background Forced vital capacity (FVC) is a standard measure of respiratory function in patients with amyotrophic lateral sclerosis (ALS) but has limitations, particularly for patients with bulbar impairment. Purpose To determine the value of deep learning-based chest CT-derived respiratory parameters in predicting ALS progression and survival. Materials and Methods This retrospective study included patients with ALS diagnosed between January 2010 and July 2023 who underwent chest CT at a tertiary hospital. Deep learning-based software was used to measure lung and respiratory muscle volume, normalized for height as the lung volume index (LVI) and respiratory muscle index (RMI). Differences in these parameters across King clinical stages were assessed using ordinal logistic regression. Tracheostomy-free survival was evaluated using Cox regression and time-dependent receiver operating characteristic analysis. Subgroup analysis was conducted for patients with bulbar impairment. In addition, a Gaussian process regressor model was developed to estimate FVC based on lung volume, respiratory muscle volume, age, and sex. Results A total of 261 patients were included in the study (mean age, 65.2 years ± 11.9 [SD]; 156 male patients). LVI and RMI decreased with increasing King stage (both P < .001). The high LVI and high RMI groups had better survival (both P < .001). After adjustment, LVI (hazard ratio [HR] = 0.998 [95% CI: 0.996, 1.000]; P = .021) and RMI (HR = 0.992 [95% CI: 0.988, 0.996]; P < .001) remained independent prognostic factors. In patients with bulbar impairment, LVI (HR = 0.998 [95% CI: 0.996, 1.000]; P = .029) and RMI (HR = 0.991 [95% CI: 0.987, 0.996]; P < .001) were independent prognostic factors. Time-dependent receiver operating characteristic curve analysis revealed no significant differences in survival prediction performance among LVI, RMI, and FVC. The Gaussian process regressor model estimated FVC with approximately 8% error. Conclusion The deep learning-derived CT metrics LVI and RMI reflected ALS stage, enabled FVC prediction, and supported assessment in patients with limited respiratory function. © RSNA, 2025 Supplemental material is available for this article. See also the editorial by Rahsepar and Bedayat in this issue.

PMID:40358443 | DOI:10.1148/radiol.243463

Radiology. 2025 May;315(2):e241252. doi: 10.1148/radiol.241252.

ABSTRACT

Although idiopathic pulmonary fibrosis (IPF) is a type of idiopathic interstitial pneumonia (IIP), it is different from other IIPs. IPF also differs from interstitial lung disease (ILD) with known causes, including connective tissue disease, exposure, cysts and/or airspace filling disease, and sarcoidosis. More than 90% of IPFs demonstrate progressive disease. Non-IPF ILD has been classified as progressive pulmonary fibrosis on the basis of disease behavior (progressive disease that gets worse over time) as opposed to classification based on cause and/or morphologic characteristics. Progressive fibrosis predictors in ILD include demographic characteristics, underlying connective tissue disease, more extensive disease at CT, honeycombing and usual interstitial pneumonia (UIP) pattern at CT, and greater impairment of lung function. Hypersensitivity pneumonitis (HP), a type of ILD, is separated into fibrotic and nonfibrotic types. Extensive peribronchiolar metaplasia supports the diagnosis of fibrotic HP over UIP, as does predominantly peribronchiolar disease with relative subpleural sparing at CT. Interstitial lung abnormality (ILA) is incidentally identified at CT; thus, ILA is under radiologist purview. Subpleural fibrotic ILA is a prognostic imaging biomarker, predictive of worse prognosis. Photon-counting CT can provide high spatial resolutions of up to 125 μm (in-plane) and 200 μm (through-plane) for improved evaluation of abnormalities.

PMID:40358445 | DOI:10.1148/radiol.241252

Brief Bioinform. 2025 May 1;26(3):bbaf211. doi: 10.1093/bib/bbaf211.

ABSTRACT

Traits in any organism are not independent, but show considerable integration, observed in a form of couplings and trade-offs. Therefore, improvement in one trait may affect other traits, often in undesired direction. To account for this problem, crop breeding increasingly relies on multi-trait genomic prediction (MT-GP) approaches that leverage the availability of genetic markers from different populations along with advances in high-throughput precision phenotyping. While significant progress has been made to jointly model multiple traits using a variety of statistical and machine learning approaches, there is no systematic comparison of advantages and shortcomings of the existing classes of MT-GP models. Here, we fill this knowledge gap by first classifying the existing MT-GP models and briefly summarizing their general principles, modeling assumptions, and potential limitations. We then perform an extensive comparative analysis with 10 traits measured in an Oryza sativa diversity panel using cross-validation scenarios relevant in breeding practice. Finally, we discuss directions that can enable the building of next generation MT-GP models in addressing pressing challenges in crop breeding.

PMID:40358423 | DOI:10.1093/bib/bbaf211

Front Immunol. 2025 Apr 28;16:1527103. doi: 10.3389/fimmu.2025.1527103. eCollection 2025.

ABSTRACT

Immune checkpoint inhibitors (ICIs) have revolutionized cancer therapy by enhancing T-cell-mediated immune responses against tumors. However, their use can lead to immune-related adverse events (irAEs) impacting patient outcomes. This single-center, observational study investigates the relationship between immune-related adverse events (irAEs) and survival outcomes and, to our knowledge, is the first of this kind in Polish population. Data of the 151 patients treated with ICIs, with or without chemotherapy, at the Department of Clinical Oncology and Chemotherapy in the Independent Public Hospital No. 4 in Lublin were collected from electronic medical records. Statistical analyses were performed using the Kaplan-Meier estimator, log-rank test, and multivariable Cox proportional hazard model (p < 0.05). IrAEs were observed in 38% of the patients, with the most common being thyroid dysfunction (11.9%) and dermal toxicity (6.6%). The median OS for patients with irAEs was 18.7 months, compared to 13.6 months for those without irAEs, though the difference was not statistically significant (p = 0.284). Patients with moderate toxicity had the highest median OS (26 months), while those with severe toxicity had a median OS of 6.41 months. Late-onset irAEs were associated with improved OS and PFS. Pack-years of smoking significantly impacted both OS (HR = 1.01, p = 0.014) and PFS (HR = 1.01, p = 0.011). Despite results not reaching statistical significance, the findings emphasize the clinical relevance of irAEs in treatment optimization and warrant further research to better understand their role in patient outcomes.

PMID:40356892 | PMC:PMC12066656 | DOI:10.3389/fimmu.2025.1527103

Front Pharmacol. 2025 Apr 28;16:1565628. doi: 10.3389/fphar.2025.1565628. eCollection 2025.

ABSTRACT

INTRODUCTION: The escalating prevalence of diabetes and non-alcoholic fatty liver disease (NAFLD) has intensified the search for effective therapeutic interventions. The current study investigates the potential of ondansetron, a Food and Drug Administration (FDA)-approved drug for conditions like nausea and vomiting, as a novel treatment option for these metabolic disorders.

METHODS: A multifaceted approach, encompassing computational analyses, in vitro enzyme inhibition assays, and in vivo experiments in a high-fat diet (HFD)-induced disease model in rats were employed.

RESULTS: Computational studies, including pharmacophore modeling, molecular docking, and molecular dynamics (MD) simulations, revealed the strong binding affinity of ondansetron to the allosteric site of protein tyrosine phosphatase 1B (PTP1B), a key regulator of insulin and lipid homeostasis. The in vitro enzyme inhibition assay further confirmed ondansetron's ability to directly inhibit PTP1B activity. Animal experiments demonstrated ondansetron's antihyperglycemic effects, reducing blood glucose levels and improving insulin sensitivity in HFD-fed rats. The drug also exhibited hepatoprotective properties, mitigating liver damage and improving tissue architecture. Additionally, ondansetron's anti-inflammatory and antioxidant activities were evident in its ability to reduce pro-inflammatory markers and oxidative stress in the liver.

DISCUSSION: These therapeutic effects position ondansetron as a promising candidate for further investigation in clinical settings for the treatment of diabetes and NAFLD and, hence, support the use of the drug repurposing approach for addressing the growing burden of metabolic diseases.

PMID:40356976 | PMC:PMC12066537 | DOI:10.3389/fphar.2025.1565628

Clin Exp Pharmacol Physiol. 2025 Jul;52(7):e70031. doi: 10.1111/1440-1681.70031.

ABSTRACT

Sepsis is a life-threatening disease caused by the dysregulation of the immune response. It is important to identify influential genes modulating the immune response in sepsis. In this study, we used P-NET, a biologically informed explainable artificial intelligence model, to evaluate the gene importance for sepsis. About 688 important genes were identified, and these genes were enriched in pathways involved in inflammation and immune regulation, such as the PI3K-Akt signalling pathway, necroptosis and the NF-κB signalling pathway. We further selected differentially expressed genes both at bulk and single-cell levels and found TIMP1, GSTO1 and MYL6 exhibited significant different expressions in multiple cell types. Moreover, the expression levels of these 3 genes were correlated with the abundance of important immune cells, such as M-MDSC cells. Further analysis demonstrated that these three genes were highly expressed in sepsis patients with worse outcomes, such as severe, non-survived and shock sepsis patients. Using a drug repositioning strategy, we found navitoclax, curcumin and rotenone could down-regulate and bind to these genes. In conclusion, TIMP1, GSTO1 and MYL6 may serve as promising biomarkers and targets for sepsis treatment.

PMID:40356040 | DOI:10.1111/1440-1681.70031