Int J Cardiovasc Imaging. 2025 Apr 26. doi: 10.1007/s10554-025-03408-8. Online ahead of print.

ABSTRACT

Non-contrast enhanced magnetic resonance coronary angiography (MRCA) is a promising coronary heart disease screening modality. However, its clinical application is hindered by inherent limitations, including low spatial resolution and insufficient contrast between coronary arteries and surrounding tissues. These technical challenges impede fast and automatic coronary artery segmentation. To tackle these issues, we propose a self-configuring deep learning-based approach for automating the segmentation of coronary arteries in MRCA images. The nnU-Net model was trained on MRCA data from 134 subjects and tested on data from 114 subjects. Two radiologists qualitatively evaluated all segmented arteries as good to excellent. Using coronary computed tomography angiography (CCTA) data from the 114 tested subjects as the gold standard. Specifically, we compared the number of branches, the total branch length, and the distance from the base of the coronary sinus to the origin of the corresponding main coronary artery obtained from manual and artificial intelligence measurements in MRCA images with those obtained from CCTA. Experiment results demonstrated that in validation nnU-Net can accurately segment from MRCA images with the Dice score of 0.903 and 0.962 for major coronary arteries and aorta, respectively.In Testing, nnU-Net achieved the Dice score of 0.726 and 0.890 for major coronary arteries and aorta, respectively. Integrating MRCA with nnU-Net to extract coronary arteries offers a non-invasive screening tool for the detection of coronary heart disease, potentially enhancing early detection and reducing reliance from CCTA.

PMID:40287548 | DOI:10.1007/s10554-025-03408-8

Sci Rep. 2025 Apr 26;15(1):14612. doi: 10.1038/s41598-025-99767-2.

ABSTRACT

Object detection methods based on deep learning have significantly reduced time-consuming tasks. Semantic segmentation has shown remarkable progress in the study of rocks, especially when applied to petrographic thin sections. Despite the development of various models for specific applications in this field with promising results, their widespread adoption remains limited. This hesitation is largely due to a lack of user confidence stemming from the absence of explainability in the outcomes provided by these models. This study explores the explainability of the state-of-the-art YOLOv11 model in detecting andalusite, biotite, and grains with oolitic textures. We trained three models using plane-polarized-light thin-section microphotographs of the selected targets. Subsequently, we applied color and singular value perturbations to the annotated images using color masks and analyzed the model's inference through connected region heatmaps. Our findings suggest that the trained models prioritize low-frequency attributes like shape, predominant colors, and contrast over the studied targets' characteristic tones. These insights contribute to the practical application of deep learning for detecting and segmenting grains and minerals in thin sections.

PMID:40287522 | DOI:10.1038/s41598-025-99767-2

Sci Rep. 2025 Apr 26;15(1):14696. doi: 10.1038/s41598-025-99535-2.

ABSTRACT

In existing breast cancer prediction research, most models rely solely on a single type of imaging data, which limits their performance. To overcome this limitation, the present study explores breast cancer prediction models based on multimodal medical images (mammography and ultrasound images) and compares them with single-modal models. We collected medical imaging data from 790 patients, including 2,235 mammography images and 1,348 ultrasound images, and conducted a comparison using six deep learning classification models to identify the best model for constructing the multimodal classification model. Performance was evaluated using metrics such as area under the receiver operating characteristic curve (AUC), sensitivity, specificity, precision, and accuracy to compare the multimodal and single-modal classification models. Experimental results demonstrate that the multimodal classification model outperforms single-modal models in terms of specificity (96.41% (95% CI:93.10%-99.72%)), accuracy (93.78% (95% CI:87.67%-99.89%)), precision (83.66% (95% CI:76.27%-91.05%)), and AUC (0.968 (95% CI:0.947-0.989)), while single-modal models excel in sensitivity. Additionally, heatmap visualization was used to further validate the classification performance of the multimodal model. In conclusion, our multimodal classification model shows strong potential in breast cancer screening tasks, effectively assisting physicians in improving screening accuracy.

PMID:40287494 | DOI:10.1038/s41598-025-99535-2

Sci Rep. 2025 Apr 26;15(1):14652. doi: 10.1038/s41598-025-99451-5.

ABSTRACT

Neuroblastoma presents a wide variety of clinical phenotypes, demonstrating different levels of benignity and malignancy among its subtypes. Early diagnosis is essential for effective patient management. Computed tomography (CT) serves as a significant diagnostic tool for neuroblastoma, utilizing machine vision imaging, which offers advantages over traditional X-ray and ultrasound imaging modalities. However, the high degree of similarity among neuroblastoma subtypes complicates the diagnostic process. In response to these challenges, this study presents a modified version of the You Only Look Once (YOLO) algorithm, called YOLOv8-IE. This revised approach integrates feature fusion and inverse residual attention mechanisms. The aim of YOLO-IE is to improve the detection and classification of neuroblastoma tumors. In light of the image features, we have implemented the inverse residual-based attention structure (iRMB) within the detection network of YOLOv8, thereby enhancing the model's ability to focus on significant features present in the images. Additionally, we have incorporated the centered feature pyramid EVC module. Experimental results show that the proposed detection network, named YOLO-IE, attains a mean Average Precision (mAP) 7.9% higher than the baseline model, YOLO. The individual contributions of iRMB and EVC to the performance improvement are 0.8% and 3.6% above the baseline model, respectively. This study represents a significant advancement in the field, as it not only facilitates the detection and classification of neuroblastoma but also demonstrates the considerable potential of machine learning and artificial intelligence in the realm of medical diagnosis.

PMID:40287486 | DOI:10.1038/s41598-025-99451-5

Sci Data. 2025 Apr 26;12(1):703. doi: 10.1038/s41597-025-04965-2.

ABSTRACT

The performance and scope of computer vision methods applied to ophthalmic images is highly dependent on the availability of labelled training data. While there are a number of colour fundus photography datasets, FOVEA is to the best of our knowledge the first dataset that matches high-quality annotations in the intraoperative domain with those in the preoperative one. It comprises data from 40 patients collected at Moorfields Eye Hospital (London, UK) and includes preoperative and intraoperative retinal vessel and optic disc annotations performed by two independent clinical research fellows, as well as short video clips showing the retinal fundus though biomicroscopy imaging in the intraoperative setting. The annotations were validated and converted into binary segmentation masks, with the code used available on GitHub. We expect this data to be useful for deep learning applications aimed at supporting surgeons during vitreoretinal surgery procedures e.g. by localising points of interest or registering additional imaging modalities.

PMID:40287417 | DOI:10.1038/s41597-025-04965-2

Curr Probl Diagn Radiol. 2025 Apr 18:S0363-0188(25)00082-9. doi: 10.1067/j.cpradiol.2025.04.011. Online ahead of print.

ABSTRACT

PURPOSE: This review aims to provide a comprehensive overview of the transformative impact of FDA-approved artificial intelligence (AI) products in abdominal imaging. It explores the evolution of AI in radiology, its rigorous FDA clearance process, and its role in revolutionizing diagnostic and non-diagnostic tasks across various abdominal organs.

METHODS: Through a review of literature, this study categorizes AI products based on their applications in liver, prostate, bladder, kidney, and overall abdominal imaging. It analyzes the diagnostic and non-diagnostic functionalities of these AI solutions, elucidating their capabilities in enhancing disease detection, image quality, workflow efficiency, and longitudinal comparison standardization.

RESULTS: The review identifies numerous FDA-approved AI products tailored for abdominal imaging, showcasing their diverse applications, from lesion detection and characterization to volume estimation and quantification of organ health parameters. These AI solutions have demonstrated their efficacy in improving diagnostic accuracy, streamlining radiological workflows, and ultimately optimizing patient care across various abdominal pathologies.

CONCLUSION: In conclusion, the integration of AI into abdominal imaging represents a paradigm shift in modern radiology. By empowering radiologists with advanced tools for timely diagnosis, precise treatment planning, and improved patient outcomes, FDA-approved AI products herald a new era of innovation in abdominal imaging. Collaboration between developers, regulatory bodies, and the medical community will be paramount in harnessing the full potential of AI to reshape the future of abdominal radiology.

PMID:40287285 | DOI:10.1067/j.cpradiol.2025.04.011

Neuroscience. 2025 Apr 24:S0306-4522(25)00328-8. doi: 10.1016/j.neuroscience.2025.04.035. Online ahead of print.

ABSTRACT

Alzheimer's Disease (AD) as one of the most prevalent neurodegenerative disorders worldwide, characterized by significant memory and cognitive decline in its later stages, severely impacting daily lives. Consequently, early diagnosis and accurate assessment are crucial for delaying disease progression. In recent years, multimodal imaging has gained widespread adoption in AD diagnosis and research, particularly the combined use of Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET). The complementarity of these modalities in structural and metabolic information offers a unique advantage for comprehensive disease understanding and precise diagnosis. With the rapid advancement of deep learning techniques, efficient fusion of MRI and PET multimodal data has emerged as a prominent research focus. This review systematically surveys the latest advancements in deep learning-based multimodal fusion of MRI and PET images for AD research, with a particular focus on studies published in the past five years (2021-2025). It first introduces the main sources of AD-related data, along with data preprocessing and feature extraction methods. Then it summarizes performance metrics and multimodal fusion techniques. Next, it explores the application of various deep learning models and their variants in multimodal fusion tasks. Finally, it analyzes the key challenges currently faced in the field, including data scarcity and imbalance, inter-institutional data heterogeneity, etc., and discusses potential solutions and future research directions. This review aims to provide systematic guidance for researchers in the field of MRI and PET multimodal fusion, with the ultimate goal of advancing the development of early AD diagnosis and intervention strategies.

PMID:40286904 | DOI:10.1016/j.neuroscience.2025.04.035

Sci Rep. 2025 Apr 26;15(1):14622. doi: 10.1038/s41598-025-98490-2.

ABSTRACT

Emerging evidence suggests that N6-methyladenosine (m6A) modification significantly influences lung injury, lung cancer, and immune responses. The current study explores the potential involvement of m6A modification in the development of IPF. This research analyzed the GSE93606 dataset of 20 non-IPF and 154 IPF patients, identifying 26 m6A regulators and developing predictive models with RF and SVM, assessed via ROC curves. A nomogram was created with selected m6A factors, including molecular subtyping, PCA for m6A features, immune cell analysis, DEG identification, and functional enrichment. In vitro experiments on MRC-5 cells used RT-qPCR and Western blotting, and virtual drug screening targeted the WTAP protein through molecular docking. Analysis revealed 26 differential m6A regulators in IPF patients, with 16 significant; IGFBP2 and YTHDF2 were overexpressed, while others decreased. RF and SVM models identified predictive m6A regulators, and a nomogram was developed using five factors to predict IPF incidence. Distinct m6A patterns showed changes in RNA levels of specific genes in the BLM-induced group, and five compounds targeting WTAP were identified. This research explored m6A factors' impact on IPF diagnosis and prognosis, identifying WTAP as a potential biomarker.

PMID:40287490 | DOI:10.1038/s41598-025-98490-2

BMC Genomics. 2025 Apr 26;26(1):408. doi: 10.1186/s12864-025-11590-1.

ABSTRACT

BACKGROUND: Enterobacter hormaechei is of increasing concern as both an opportunistic and nosocomial pathogen, exacerbated by its evolving multidrug resistance. However, its taxonomy remains contentious, and little is known about its pathogenesis and the broader context of its resistome. In this study, a comprehensive comparative genomic analysis was undertaken to address these issues.

RESULTS: Phylogenomic analysis revealed that E. hormaechei represents a complex, comprising three predicted species, E. hormaechei, E. hoffmannii and E. xiangfangensis, with the latter putatively comprising three distinct subspecies, namely oharae, steigerwaltii and xiangfangensis. The species and subspecies all display open and distinct pan-genomes, with diversification driven by an array of mobile genetic elements including numerous plasmid replicons and prophages, integrative conjugative elements (ICE) and transposable elements. These elements have given rise to a broad, relatively conserved set of pathogenicity determinants, but also a variable set of secretion systems. The E. hormaechei complex displays a highly mutable resistome, with most taxa being multidrug resistant.

CONCLUSIONS: This study addressed key issues pertaining to the taxonomy of the E. hormaechei complex, which may contribute towards more accurate identification of strains belonging to this species complex in the clinical setting. The pathogenicity determinants identified in this study could serve as a basis for a deeper understanding of E. hormaechei complex pathogenesis and virulence. The extensive nature of multidrug resistance among E. hormaechei complex strains highlights the need for responsible antibiotic stewardship to ensure effective treatment of these emerging pathogens.

PMID:40287657 | DOI:10.1186/s12864-025-11590-1

Cell Death Dis. 2025 Apr 26;16(1):344. doi: 10.1038/s41419-025-07565-5.

ABSTRACT

Chaperone proteins constitute a molecular machinery that controls proteostasis. HSPA2 is a heat shock-non-inducible member of the human HSPA/HSP70 family, which includes several highly homologous chaperone proteins. HSPA2 exhibits a cell type-specific expression pattern in the testis, brain, and multilayered epithelia. It is a crucial male fertility-related factor, but its role in somatic cells is poorly understood. Previously, we found that HSPA2 deficiency can impair epidermal keratinocyte differentiation. In this study, we confirmed the crucial role of HSPA2 in keratinocyte differentiation by investigating immortalized keratinocytes cultured in a reconstructed human epidermis model. Moreover, we uncovered the influence of HSPA2 on immunomodulation. Transcriptomic analysis revealed that the total loss of HSPA2 affected the expression of genes related to keratinocyte differentiation and interleukin- and interferon-mediated signaling. The functional analysis confirmed bidirectional changes associated with the loss of HSPA2. The HSPA2 knockout in HaCaT and Ker-CT keratinocytes, but not HSPA2 overproduction, impaired granular layer development as evidenced by reduced levels of late keratinocyte differentiation markers, filaggrin and involucrin, along with structural abnormalities in the upper epidermal layer. Differentiation defects were accompanied by increased mRNA expression and extracellular secretion of keratinocyte-derived pro-inflammatory IL-6 cytokine and CCL2, CCL8, CXCL1, CXCL6, and CXCL10 chemokines. The loss of HSPA2 also led to increased expression of extracellular HSPA1 and interferon-stimulated genes and secretion of immune cell modulator SLAMF7. Knocking down HSPA1 expression in keratinocytes decreased the secretion of IL-6 and CCL5 release, suggesting extracellular HSPA1's role in the HSPA2-regulated molecular network. To summarize, we uncovered the complex homeostatic role of HSPA2 in epidermal keratinocytes. Our results suggest that dysfunction in HSPA2 activity could be an important pathogenicity factor and potential therapeutic target for inflammatory cutaneous diseases.

PMID:40287440 | DOI:10.1038/s41419-025-07565-5

Life Sci. 2025 Apr 24:123658. doi: 10.1016/j.lfs.2025.123658. Online ahead of print.

ABSTRACT

AIMS: Obesity cardiomyopathy (OCM) is associated with mitochondrial dysfunction caused by altered mitochondrial dynamics. Extracellular mitochondria (exMito) are released following tissue injury under various conditions. While the excessive mitochondrial fission-mediated release of exMito as a mechanism for mitochondrial quality control in several inflammatory disorders, its role in OCM remains unclear. The present work aimed to determine if excessive mitochondrial fission and associated exMito mediate the chronic inflammatory response and cardiac remodeling in OCM.

MATERIALS AND METHODS: H9c2 cardiomyoblasts were treated with 200 μM palmitate (PA) to induce lipotoxicity. C57BL/6J mice were fed a high-fat diet (HFD) for 12 weeks to induce OCM. P110, a peptide inhibitor of Drp1/Fis1 interaction, was used to evaluate the impact of excessive mitochondrial fission on cardiac mitochondrial function, quality, and quantity of exMito, systemic inflammatory response, and cardiac contractile function in both models of OCM.

KEY FINDINGS: PA induced excessive mitochondrial fission, increased oxidative stress, decreased ATP level, and damaged exMito release in vitro. Exposure of naïve cardiomyoblasts to exMito isolated from PA treated cells resulted in mitochondrial dysfunction and a pro-inflammatory response. In vivo, HFD induced cardiac mitochondrial and contractile dysfunction, exMito release, and a pro-inflammatory response. Inhibition of Drp1/Fis1 interaction with P110 attenuated the observed effects both in vitro and in vivo.

SIGNIFICANCE: P110 limited lipid-induced mitochondrial dysfunction and decreased exMito release, subsequently improving the inflammatory state and contractile function in our OCM model. Drp1/Fis1 dependent fission and associated exMito release might serve as a therapeutic target for obesity induced cardiomyopathy.

PMID:40287058 | DOI:10.1016/j.lfs.2025.123658

Sci Total Environ. 2025 Apr 25;979:179484. doi: 10.1016/j.scitotenv.2025.179484. Online ahead of print.

ABSTRACT

Environmental stressors in the modern world can fundamentally affect human physiology and health. Exposure to stressors like air pollution, heat, and traffic noise has been linked to a pronounced increase in non-communicable diseases. Specifically, aircraft noise has been identified as a risk factor for cardiovascular and metabolic diseases, such as arteriosclerosis, heart failure, stroke, and diabetes. Noise stress leads to neuronal activation with subsequent stress hormone release that ultimately activates the renin-angiotensin-aldosterone system, increases inflammation and oxidative stress thus substantially affecting the cardiovascular system. However, despite the epidemiological evidence of a link between noise stress and metabolic dysfunction, the consequences of exposure at the molecular, metabolic level of the cardiovascular system are largely unknown. Here, we use a murine model system of short-term aircraft noise exposure to show that noise stress profoundly alters heart metabolism. Within 4 days of noise exposure, the heart proteome and metabolome bear the hallmarks of reduced potential for generating ATP from fatty-acid beta-oxidation, the tricarboxylic acid cycle, and the electron transport chain. This is accompanied by the increased expression of glycolytic metabolites, including the end-product, lactate, suggesting a compensatory shift of energy production towards anaerobic glycolysis. Intriguingly, the metabolic shift is reminiscent of what is observed in failing and ischaemic hearts. Mechanistically, we further show that the metabolic rewiring is likely driven by reactive oxygen species (ROS), as we can rescue the phenotype by knocking out NOX-2/gp91phox, a ROS inducer, in mice. Our results suggest that within a short exposure time, the cardiovascular system undergoes a fundamental metabolic shift that bears the hallmarks of cardiovascular disease. These findings underscore the urgent need to comprehend the molecular consequences of environmental stressors, paving the way for targeted interventions to mitigate health risks associated with chronic noise exposure in modern, environments heavily disturbed by noise pollution.

PMID:40286622 | DOI:10.1016/j.scitotenv.2025.179484

JACC Adv. 2025 Mar 26;4(5):101663. doi: 10.1016/j.jacadv.2025.101663. Online ahead of print.

ABSTRACT

BACKGROUND: Cardiac output (CO) is a quintessential property of the cardiovascular system, one whose estimation is vital to patient care in critical illness. The most common techniques for assessing CO, thermodilution (TD) and the estimated Fick (eFick) approximation, force tradeoffs that motivate a need for new methods.

OBJECTIVES: The purpose of this study was to novel CO estimators to fill key gaps in critical care medicine.

METHODS: Machine learning was used to estimate CO from physiology measurements made during routine clinical care in the intensive care unit (ICU) or cardiac catheterization lab. Models were trained and validated using a curated set of 13,172 ground-truth measurements of TD-CO from 4,825 patients. Model performance was evaluated using regression metrics, trajectory analysis, classification accuracy, and ΔCO tracking.

RESULTS: Three established eFick models all performed poorly in the ICU because their static estimates of oxygen consumption could not track the dynamics of critical illness. In the postcardiac surgery intensive care unit, the best eFick model erred in its CO predictions by 30% (mean absolute percentage error [MAPE]) with a coefficient of determination (R2) of -1.5. The best model derived here, labeled CORE (Catheter Optimized caRdiac output Estimation), predicted CO with an MAPE of 14% (P < 0.001 vs eFick) and an R2 of 0.58. These estimates could be calculated from measurements obtained with either a pulmonary artery catheter or a central venous catheter. The CORE model was also robust to the presence of moderate or severe tricuspid regurgitation, achieving an MAPE of 16% and R2 of 0.65 relative to a ground-truth determined by the direct Fick technique with measured oxygen consumption.

CONCLUSIONS: CO models that account for dynamic physiology in ICU patients were more accurate than widely used eFick models and more versatile than TD. The performance of these models combined with their adaptation to vascular access, broad applicability, ease of use, and ease of deployment should enable them to benefit patients across diverse ICU settings.

PMID:40286350 | DOI:10.1016/j.jacadv.2025.101663

Viruses. 2025 Mar 28;17(4):491. doi: 10.3390/v17040491.

ABSTRACT

The SARS-CoV-2 proteases Mpro and PLpro are critical targets for antiviral drug development for the treatment of COVID-19. The 1,2,4-thiadiazole functional group is an inhibitor of cysteine proteases, such as papain and cathepsins. This chemical moiety is also present in ceftaroline fosamil (CF), an FDA-approved fifth-generation cephalosporin antibiotic. This study investigates the interactions between CF, its primary metabolites (M1 is dephosphorylated CF and M2 is an opened β-lactam ring) and derivatives (protonated M1H and M2H), and its open 1,2,4-thiadiazole rings derivatives (open-M1H and open-M2H) with SARS-CoV-2 proteases and evaluates CF's effects on in vitro viral replication. In silico analyses (molecular docking and molecular dynamics (MD) simulations) demonstrated that CF and its metabolites are potential inhibitors of PLpro and Mpro. Docking analysis indicated that the majority of the ligands were more stable with Mpro than PLpro; however, in vitro biochemical analysis indicated PLpro as the preferred target for CF. CF inhibited viral replication in the human Calu-3 cell model at submicromolar concentrations when added to cell culture medium at 12 h. Our results suggest that CF should be evaluated as a potential repurposing agent for COVID-19, considering not only viral proteases but also other viral targets and relevant cellular pathways. Additionally, the reactivity of sulfur in the 1,2,4-thiadiazole moiety warrants further exploration for the development of viral protease inhibitors.

PMID:40284934 | DOI:10.3390/v17040491

Pharmaceutics. 2025 Mar 26;17(4):419. doi: 10.3390/pharmaceutics17040419.

ABSTRACT

In the original publication [...].

PMID:40284534 | DOI:10.3390/pharmaceutics17040419

Pharmaceutics. 2025 Apr 19;17(4):536. doi: 10.3390/pharmaceutics17040536.

ABSTRACT

Background/Objectives: Ovarian cancer remains one of the most commonly diagnosed malignancies among women worldwide. The heterogeneity among tumor subtypes and the emergence of treatment resistance have raised significant concerns regarding the long-term efficacy of chemotherapy, radiotherapy, and immunotherapy. In response to these challenges, drug repurposing strategies-utilizing existing drugs in novel therapeutic contexts-have gained increasing attention. This study aimed to investigate the cytotoxic and apoptotic effects of the combined application of doxorubicin (DX) and thymoquinone (TQ) on ovarian adenocarcinoma cells (OVCAR3). Methods: OVCAR3 cells were cultured in RPMI medium supplemented with 10% fetal bovine serum (FBS) and 1% penicillin/streptomycin. Cell viability and proliferation were assessed using the MTT assay following treatment with various concentrations of DX and TQ. NucBlue immunofluorescence staining was employed to examine nuclear morphology and to identify apoptosis-associated changes. Additionally, quantitative real-time polymerase chain reaction (qRT-PCR) was per-formed to evaluate the expression levels of apoptosis-related and oncogenic pathway genes, including RAF, RAS, Bcl-2, and Bax. Results: The results demonstrated that the combination of DX and TQ significantly reduced OVCAR3 cell viability and induced apoptosis in a dose-dependent manner. qRT-PCR analysis revealed a downregulation of RAS, RAF, and Bcl-2 expression, along with an upregulation of Bax, indicating activation of the intrinsic apoptotic pathway. These findings suggest that thymoquinone exerts an-ti-proliferative and pro-apoptotic effects by modulating the RAS/RAF signaling cascade. Furthermore, the co-administration of thymoquinone with doxorubicin potentiated these effects, suggesting a synergistic interaction between the two agents. Conclusions: Histopathological and molecular evaluations further confirmed the activation of apoptosis and the suppression of key oncogenic pathways. Collectively, these results underscore the therapeutic potential of thymoquinone as both a monotherapy and an adjuvant to conventional chemotherapy, warranting further validation in preclinical and clinical studies.

PMID:40284530 | DOI:10.3390/pharmaceutics17040536

Int J Environ Res Public Health. 2025 Mar 21;22(4):464. doi: 10.3390/ijerph22040464.

ABSTRACT

Data-driven approaches to clinical research are necessary for understanding and effectively treating infectious diseases. However, challenges such as issues with data validity, lack of collaboration, and difficult-to-treat infectious diseases (e.g., those that are rare or newly emerging) hinder research. Prioritizing innovative methods to facilitate the continued use of data generated during routine clinical care for research, but in an organized, accelerated, and shared manner, is crucial. This study investigates the potential of CURE ID, an open-source platform to accelerate drug-repurposing research for difficult-to-treat diseases, with COVID-19 as a use case. Data from eight US health systems were analyzed using least absolute shrinkage and selection operator (LASSO) regression to identify key predictors of 28-day all-cause mortality in COVID-19 patients, including demographics, comorbidities, treatments, and laboratory measurements captured during the first two days of hospitalization. Key findings indicate that age, laboratory measures, severity of illness indicators, oxygen support administration, and comorbidities significantly influenced all-cause 28-day mortality, aligning with previous studies. This work underscores the value of collaborative repositories like CURE ID in providing robust datasets for prognostic research and the importance of factor selection in identifying key variables, helping to streamline future research and drug-repurposing efforts.

PMID:40283693 | DOI:10.3390/ijerph22040464

J Clin Med. 2025 Apr 14;14(8):2673. doi: 10.3390/jcm14082673.

ABSTRACT

Epilepsy is a neurological disorder characterized by repeated convulsions. Antiepileptic drugs (AEDs) are the main course of therapy for epilepsy. These medications are given according to each patient's personal medical history and the types of seizures they suffer. They have been employed for decades to manage epilepsy, thus delivering relief from seizures through numerous mechanisms of action. Aside from their anticonvulsant attributes, current evidence suggests that certain AEDs may display potential inhibitory effects against cancer invasion and metastasis. This review explored the complicated interactions between the modes of action of AEDs and the pathways causing cancer, and the potential impact of AEDs on the invasion and metastasis of various forms of cancer, while addressing their associated side effects. For example, valproic acid inhibits histone deacetylase, causing hyperacetylation of genes, especially those regulating cell cycle, culminating in cell cycle arrest. Topiramate inhibits carbonic anhydrase, thus disrupting the acidic microenvironment needed for cancer cells to thrive. Lacosamide increases the slow inactivation of the voltage gated Na+ channel, thus inhibiting the growth, proliferation, and metastasis of many cancers. Although drug development is a complex task due to regulatory, intellectual property, and economic challenges, researchers are exploring drug repurposing tactics to overcome these challenges and to find new therapeutic alternatives for diseases like cancer. Thus, drug repurposing is considered among the most effective ways to develop drug candidates using novel properties and therapeutic characteristics, and this review also discusses these issues.

PMID:40283503 | DOI:10.3390/jcm14082673

Cancers (Basel). 2025 Apr 18;17(8):1357. doi: 10.3390/cancers17081357.

ABSTRACT

BACKGROUND/OBJECTIVES: Beta adrenergic signaling has been implicated in cancer progression, leading to interest in repurposing beta blockers (BBs) as adjunctive anti-cancer agents. However, clinical findings are inconsistent. This systematic review and meta-analysis evaluates the association between BB use and survival outcomes in cancer patients.

METHODS: A systematic search of OVID Medline, EMBASE, and CENTRAL was conducted through 13 September 2023, for studies comparing survival outcomes in solid tumor patients using BBs versus non-users. Eligible studies reported hazard ratios (HRs) for overall survival (OS), progression-free survival (PFS), or cancer-specific survival (CSS). Perioperative studies and those without BB-specific HRs were excluded. Data extraction and quality assessment were performed in duplicate using ROBINS-I. A random-effects model was used, with heterogeneity assessed by the I2 statistic.

RESULTS: Seventy-nine studies (492,381 patients) met the inclusion criteria; 2.5% were prospective. The most frequently studied cancers were breast (n = 33), ovarian (n = 30), and colorectal (n = 28). BB use was associated with improved PFS (HR 0.78, 95% CI: 0.66-0.92, I2 = 79.8%), with significance maintained after excluding high-bias studies (HR 0.74, 95% CI: 0.61-0.91, I2 = 36.6%). No significant associations were observed for OS (HR 0.99, 95% CI: 0.94-1.04, I2 = 84.9%) or CSS (HR 0.95, 95% CI: 0.91-1.00, I2 = 77.4%).

CONCLUSIONS: BB use may be associated with longer PFS in cancer patients, but findings are limited by study design and heterogeneity; high-quality prospective studies are needed.

PMID:40282534 | DOI:10.3390/cancers17081357

Pharmaceutics. 2025 Mar 31;17(4):450. doi: 10.3390/pharmaceutics17040450.

ABSTRACT

The mRNA- and DNA-based "genetic" COVID-19 vaccines can induce a broad range of adverse events (AEs), with statistics showing significant variation depending on the timing and data analysis methods used. Focusing only on lipid nanoparticle-enclosed mRNA (mRNA-LNP) vaccines, this review traces the evolution of statistical conclusions on the prevalence of AEs and incidents associated with these vaccines, from initial underestimation of atypical, severe toxicities to recent claims suggesting the possible contribution of COVID-19 vaccinations to the excess deaths observed in many countries over the past few years. Among hundreds of different AEs listed in Pfizer's pharmacovigilance survey, the present analysis categorizes the main symptoms according to organ systems, with nearly all of them being affected. Using data from the US Vaccine Adverse Event Reporting System and a global vaccination dataset, a comparison of the prevalence and incidence rates of AEs induced by genetic versus flu vaccines revealed an average 26-fold increase in AEs with the use of genetic vaccines. The difference is especially pronounced in the case of severe 'Brighton-listed' AEs, which are also observed in COVID-19 and post-COVID conditions. Among these, the increases in incidence rates relative to flu vaccines, given as x-fold rises, were 1152x, 455x, 226x, 218x, 162x, 152x, and 131x for myocarditis, thrombosis, death, myocardial infarction, tachycardia, dyspnea, and hypertension, respectively. The review delineates the concept that genetic vaccines can be regarded as prophylactic immuno-gene therapies and that the observed chronic disabling AEs might be categorized as iatrogenic orphan diseases. It also examines the unique vaccine characteristics that could be causally related to abnormal immune responses which potentially lead to adverse events and complications. These new insights may contribute to improving the safety of this platform technology and assessing the risk/benefit balance of various products.

PMID:40284445 | DOI:10.3390/pharmaceutics17040450