Expert Rev Neurother. 2025 Feb 19. doi: 10.1080/14737175.2025.2469041. Online ahead of print.

ABSTRACT

INTRODUCTION: Post-traumatic epilepsy (PTE) accounts for 10% to 20% of all symptomatic epilepsies and 5% of all forms of epilepsy, and drug resistance is reported in up to 45% of cases.

AREAS COVERED: This is a focused narrative review that discusses the available data on the current and new PTE treatments, giving particular attention to the last 10 years.

EXPERT OPINION: Despite the disappointing results of many antiseizure medications (ASMs) in preventing epileptogenicity, it is still unclear whether the early intervention could lead to different clinical phenotypes in terms, for example, of seizure severity, drug resistance and comorbidity patterns. The same applies to compounds targeting neuroinflammation, oxidative stress and neurotransmission modulation. The heterogeneity of etiologies leading to PTE has limited the investigation and implementation of specific interventions. New studies must focus on identifying common pathways and mechanisms shared by different etiological processes, identifying biomarkers, and validating animal models of epileptogenesis concerning PTE. Drug repurposing research will facilitate rapid translation into clinical research. Multitarget drug combinations will also receive increasing attention. In terms of non-pharmacological treatments, Vagus Nerve Stimulation seems to be a good option, while epilepsy surgery and Deep Brain Stimulation deserve further attention.

PMID:39968755 | DOI:10.1080/14737175.2025.2469041

MMW Fortschr Med. 2025 Feb;167(Suppl 1):44-45. doi: 10.1007/s15006-024-4578-8.

NO ABSTRACT

PMID:39969735 | DOI:10.1007/s15006-024-4578-8

J Med Internet Res. 2025 Feb 19;27:e66157. doi: 10.2196/66157.

ABSTRACT

BACKGROUND: In recent years, the use of digital technology in the education of health care professionals has surged, partly driven by the COVID-19 pandemic. However, there is still a need for focused research to establish evidence of its effectiveness.

OBJECTIVE: This study aimed to define the gaps in the evidence for the efficacy of digital education and to identify priority areas where future research has the potential to contribute to our understanding and use of digital education.

METHODS: We used a 2-stage approach to identify research priorities. First, an umbrella review of the recent literature (published between 2020 and 2023) was performed to identify and build on existing work. Second, expert consensus on the priority research questions was obtained using a modified Delphi method.

RESULTS: A total of 8857 potentially relevant papers were identified. Using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology, we included 217 papers for full review. All papers were either systematic reviews or meta-analyses. A total of 151 research recommendations were extracted from the 217 papers. These were analyzed, recategorized, and consolidated to create a final list of 63 questions. From these, a modified Delphi process with 42 experts was used to produce the top-five rated research priorities: (1) How do we measure the learning transfer from digital education into the clinical setting? (2) How can we optimize the use of artificial intelligence, machine learning, and deep learning to facilitate education and training? (3) What are the methodological requirements for high-quality rigorous studies assessing the outcomes of digital health education? (4) How does the design of digital education interventions (eg, format and modality) in health professionals' education and training curriculum affect learning outcomes? and (5) How should learning outcomes in the field of health professions' digital education be defined and standardized?

CONCLUSIONS: This review provides a prioritized list of research gaps in digital education in health care, which will be of use to researchers, educators, education providers, and funding agencies. Additional proposals are discussed regarding the next steps needed to advance this agenda, aiming to promote meaningful and practical research on the use of digital technologies and drive excellence in health care education.

PMID:39969988 | DOI:10.2196/66157

J Endocrinol Invest. 2025 Feb 19. doi: 10.1007/s40618-025-02548-x. Online ahead of print.

ABSTRACT

This review explores the significant role of artificial intelligence (AI) in managing metabolic disorders like diabetes, obesity, metabolic dysfunction-associated fatty liver disease (MAFLD), and thyroid dysfunction. AI applications in this context encompass early diagnosis, personalized treatment plans, risk assessment, prevention, and biomarker discovery for early and accurate disease management. This review also delves into techniques involving machine learning (ML), deep learning (DL), natural language processing (NLP), computer vision, and reinforcement learning associated with AI and their application in metabolic disorders. The following study also enlightens the challenges and ethical considerations associated with AI implementation, such as data privacy, model interpretability, and bias mitigation. We have reviewed various AI-based tools utilized for the diagnosis and management of metabolic disorders, such as Idx, Guardian Connect system, and DreaMed for diabetes. Further, the paper emphasizes the potential of AI to revolutionize the management of metabolic disorders through collaborations among clinicians and AI experts, the integration of AI into clinical practice, and the necessity for long-term validation studies. The references provided in the paper cover a range of studies related to AI, ML, personalized medicine, metabolic disorders, and diagnostic tools in healthcare, including research on disease diagnostics, personalized therapy, chronic disease management, and the application of AI in diabetes care and nutrition.

PMID:39969797 | DOI:10.1007/s40618-025-02548-x

Forensic Sci Med Pathol. 2025 Feb 19. doi: 10.1007/s12024-025-00943-7. Online ahead of print.

ABSTRACT

Conducting sex estimation based on bones through morphometric methods increases the need for automatic image analyses, as doing so requires experienced staff and is a time-consuming process. In this study, sex estimation was performed with the EfficientNetB3, MobileNetV2, Visual Geometry Group 16 (VGG16), ResNet50, and DenseNet121 architectures on patellar magnetic resonance images via a developed model. Within the scope of the study, 6710 magnetic resonance sagittal patella image slices of 696 patients (293 males and 403 females) were obtained. The performance of artificial intelligence algorithms was examined through deep learning architectures and the developed classification model. Considering the performance evaluation criteria, the best accuracy result of 88.88% was obtained with the ResNet50 model. In addition, the proposed model was among the best-performing models with an accuracy of 85.70%. When all these results were examined, it was concluded that positive sex estimation results could be obtained from patella magnetic resonance image (MRI) slices without the use of the morphometric method.

PMID:39969760 | DOI:10.1007/s12024-025-00943-7

J Med Syst. 2025 Feb 19;49(1):29. doi: 10.1007/s10916-025-02153-8.

ABSTRACT

Chronic wounds, which take over four weeks to heal, are a major global health issue linked to conditions such as diabetes, venous insufficiency, arterial diseases, and pressure ulcers. These wounds cause pain, reduce quality of life, and impose significant economic burdens. This systematic review explores the impact of technological advancements on the diagnosis of chronic wounds, focusing on how computational methods in wound image and data analysis improve diagnostic precision and patient outcomes. A literature search was conducted in databases including ACM, IEEE, PubMed, Scopus, and Web of Science, covering studies from 2013 to 2023. The focus was on articles applying complex computational techniques to analyze chronic wound images and clinical data. Exclusion criteria were non-image samples, review articles, and non-English or non-Spanish texts. From 2,791 articles identified, 93 full-text studies were selected for final analysis. The review identified significant advancements in tissue classification, wound measurement, segmentation, prediction of wound aetiology, risk indicators, and healing potential. The use of image-based and data-driven methods has proven to enhance diagnostic accuracy and treatment efficiency in chronic wound care. The integration of technology into chronic wound diagnosis has shown a transformative effect, improving diagnostic capabilities, patient care, and reducing healthcare costs. Continued research and innovation in computational techniques are essential to unlock their full potential in managing chronic wounds effectively.

PMID:39969674 | DOI:10.1007/s10916-025-02153-8

Clin Oral Investig. 2025 Feb 19;29(2):143. doi: 10.1007/s00784-025-06216-5.

ABSTRACT

OBJECTIVES: Artificial Intelligence (AI), particularly deep learning, has significantly impacted healthcare, including dentistry, by improving diagnostics, treatment planning, and prognosis prediction. This systematic mapping review explores the current applications of deep learning in dentistry, offering a comprehensive overview of trends, models, and their clinical significance.

MATERIALS AND METHODS: Following a structured methodology, relevant studies published from January 2012 to September 2023 were identified through database searches in PubMed, Scopus, and Embase. Key data, including clinical purpose, deep learning tasks, model architectures, and data modalities, were extracted for qualitative synthesis.

RESULTS: From 21,242 screened studies, 1,007 were included. Of these, 63.5% targeted diagnostic tasks, primarily with convolutional neural networks (CNNs). Classification (43.7%) and segmentation (22.9%) were the main methods, and imaging data-such as cone-beam computed tomography and orthopantomograms-were used in 84.4% of cases. Most studies (95.2%) applied fully supervised learning, emphasizing the need for annotated data. Pathology (21.5%), radiology (17.5%), and orthodontics (10.2%) were prominent fields, with 24.9% of studies relating to more than one specialty.

CONCLUSION: This review explores the advancements in deep learning in dentistry, particulary for diagnostics, and identifies areas for further improvement. While CNNs have been used successfully, it is essential to explore emerging model architectures, learning approaches, and ways to obtain diverse and reliable data. Furthermore, fostering trust among all stakeholders by advancing explainable AI and addressing ethical considerations is crucial for transitioning AI from research to clinical practice.

CLINICAL RELEVANCE: This review offers a comprehensive overview of a decade of deep learning in dentistry, showcasing its significant growth in recent years. By mapping its key applications and identifying research trends, it provides a valuable guide for future studies and highlights emerging opportunities for advancing AI-driven dental care.

PMID:39969623 | DOI:10.1007/s00784-025-06216-5

Curr Cardiol Rep. 2025 Feb 19;27(1):56. doi: 10.1007/s11886-025-02215-w.

ABSTRACT

PURPOSE OF REVIEW: This review explores the role of artificial intelligence (AI) in cardio-oncology, focusing on its latest application across problems in diagnosis, prognosis, risk stratification, and management of cardiovascular (CV) complications in cancer patients. It also highlights multi-omics analysis, explainable AI, and real-time decision-making, while addressing challenges like data heterogeneity and ethical concerns.

RECENT FINDINGS: AI can advance cardio-oncology by leveraging imaging, electronic health records (EHRs), electrocardiograms (ECG), and multi-omics data for early cardiotoxicity detection, stratification and long-term risk prediction. Novel AI-ECG models and imaging techniques improve diagnostic accuracy, while multi-omics analysis identifies biomarkers for personalized treatment. However, significant barriers, including data heterogeneity, lack of transparency, and regulatory challenges, hinder widespread adoption. AI significantly enhances early detection and intervention in cardio-oncology. Future efforts should address the impact of AI technologies on clinical outcomes, and ethical challenges, to enable broader clinical adoption and improve patient care.

PMID:39969610 | DOI:10.1007/s11886-025-02215-w

Int J Legal Med. 2025 Feb 19. doi: 10.1007/s00414-025-03452-y. Online ahead of print.

ABSTRACT

This study aimed to compare dental age estimates using Willems, Cameriere-Europe, London Atlas, and deep learning methods on panoramic radiographs of Turkish children. The dental ages of 1169 children (613 girls, 556 boys) who agreed to participate in the study were determined by 4 different methods. The Convolutional Neural Network models examined were implemented in the TensorFlow library. Simple correlations and intraclass correlations between children's chronological ages and dental age estimates were calculated. Goodness-of-fit criteria were calculated based on the errors in dental age estimates and the smallest possible values for the Akaike Information Criterion, the Bayesian-Schwarz Criterion, the Root Mean Squared Error, and the coefficient of determination value. Simple correlations were observed between dental age and chronological ages in all four methods (p < 0.001). However, there was a statistically significant difference between the average dental age estimates of methods other than the London Atlas for boys (p = 0.179) and the four methods for girls (p < 0.001). The intra-class correlation between chronological age and methods was examined, and almost perfect agreement was observed in all methods. Moreover, the predictions of all methods were similar to each other in each gender and overall (Intraclass correlation [ICCW] = 0.92, ICCCE=0.94, ICCLA=0.95, ICCDL=0.89 for all children). The London Atlas is only suitable for boys in predicting the age of Turkish children, Willems, Cameriere-Europe formulas, and deep learning methods need revision.

PMID:39969569 | DOI:10.1007/s00414-025-03452-y

Eur J Nucl Med Mol Imaging. 2025 Feb 19. doi: 10.1007/s00259-025-07156-8. Online ahead of print.

ABSTRACT

PURPOSE: Positron Emission Tomography (PET) is a powerful molecular imaging tool that visualizes radiotracer distribution to reveal physiological processes. Recent advances in total-body PET have enabled low-dose, CT-free imaging; however, accurate organ segmentation using PET-only data remains challenging. This study develops and validates a deep learning model for multi-organ PET segmentation across varied imaging conditions and tracers, addressing critical needs for fully PET-based quantitative analysis.

MATERIALS AND METHODS: This retrospective study employed a 3D deep learning-based model for automated multi-organ segmentation on PET images acquired under diverse conditions, including low-dose and non-attenuation-corrected scans. Using a dataset of 798 patients from multiple centers with varied tracers, model robustness and generalizability were evaluated via multi-center and cross-tracer tests. Ground-truth labels for 23 organs were generated from CT images, and segmentation accuracy was assessed using the Dice similarity coefficient (DSC).

RESULTS: In the multi-center dataset from four different institutions, our model achieved average DSC values of 0.834, 0.825, 0.819, and 0.816 across varying dose reduction factors and correction conditions for FDG PET images. In the cross-tracer dataset, the model reached average DSC values of 0.737, 0.573, 0.830, 0.661, and 0.708 for DOTATATE, FAPI, FDG, Grazytracer, and PSMA, respectively.

CONCLUSION: The proposed model demonstrated effective, fully PET-based multi-organ segmentation across a range of imaging conditions, centers, and tracers, achieving high robustness and generalizability. These findings underscore the model's potential to enhance clinical diagnostic workflows by supporting ultra-low dose PET imaging.

CLINICAL TRIAL NUMBER: Not applicable. This is a retrospective study based on collected data, which has been approved by the Research Ethics Committee of Ruijin Hospital affiliated to Shanghai Jiao Tong University School of Medicine.

PMID:39969540 | DOI:10.1007/s00259-025-07156-8

Eur J Nucl Med Mol Imaging. 2025 Feb 19. doi: 10.1007/s00259-025-07134-0. Online ahead of print.

ABSTRACT

PURPOSE: Accurate prediction of adverse pathology (AP) in prostate cancer (PCa) patients is crucial for formulating effective treatment strategies. This study aims to develop and evaluate a multimodal deep learning model based on [18F]PSMA-1007 PET/CT and multiparametric MRI (mpMRI) to predict the presence of AP, and investigate whether the model that integrates [18F]PSMA-1007 PET/CT and mpMRI outperforms the individual PET/CT or mpMRI models in predicting AP.

METHODS: 341 PCa patients who underwent radical prostatectomy (RP) with mpMRI and PET/CT scans were retrospectively analyzed. We generated deep learning signature from mpMRI and PET/CT with a multimodal deep learning model (MPC) based on convolutional neural networks and transformer, which was subsequently incorporated with clinical characteristics to construct an integrated model (MPCC). These models were compared with clinical models and single mpMRI or PET/CT models.

RESULTS: The MPCC model showed the best performance in predicting AP (AUC, 0.955 [95% CI: 0.932-0.975]), which is higher than MPC model (AUC, 0.930 [95% CI: 0.901-0.955]). The performance of the MPC model is better than that of single PET/CT (AUC, 0.813 [95% CI: 0.780-0.845]) or mpMRI (AUC, 0.865 [95% CI: 0.829-0.901]). Additionally, MPCC model is also effective in predicting single adverse pathological features.

CONCLUSION: The deep learning model that integrates mpMRI and [18F]PSMA-1007 PET/CT enhances the predictive capabilities for the presence of AP in PCa patients. This improvement aids physicians in making informed preoperative decisions, ultimately enhancing patient prognosis.

PMID:39969539 | DOI:10.1007/s00259-025-07134-0

Radiol Artif Intell. 2025 Feb 19:e240115. doi: 10.1148/ryai.240115. Online ahead of print.

ABSTRACT

"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. Fetal ventriculomegaly (VM) and its severity and associated central nervous system (CNS) abnormalities are important indicators of high risk for impaired neurodevelopmental outcomes. Recently, a novel fetal brain age prediction method using a 2D single-channel convolutional neural network (CNN) with multiplanar MRI slices showed the potential to detect fetuses with VM. The purpose of this study examines the diagnostic performance of deep learning-based fetal brain age prediction model to distinguish fetuses with VM (n = 317) from typically developing fetuses (n = 183), the severity of VM, and the presence of associated CNS abnormalities. The predicted age difference (PAD) was measured by subtracting predicted brain age from gestational age in fetuses with VM and typically development. PAD and absolute value of PAD (AAD) were compared between VM and typically developing fetuses. In addition, PAD and AAD were compared between subgroups by VM severity and the presence of associated CNS abnormalities in VM. Fetuses with VM showed significantly larger AAD than typically developing (P < .001), and fetuses with severe VM showed larger AAD than those with moderate VM (P = .004). Fetuses with VM and associated CNS abnormalities had significantly lower PAD than fetuses with isolated VM (P = .005). These findings suggest that fetal brain age prediction using the 2D single-channel CNN method has the clinical ability to assist in identifying not only the enlargement of the ventricles but also the presence of associated CNS abnormalities. ©RSNA, 2025.

PMID:39969279 | DOI:10.1148/ryai.240115

Cell Mol Life Sci. 2025 Feb 19;82(1):79. doi: 10.1007/s00018-025-05613-z.

ABSTRACT

Idiopathic pulmonary fibrosis (IPF) is a chronic and progressive interstitial pneumonia, with increasing incidence and prevalence. One of the cellular characteristics is the differentiation of fibroblasts to myofibroblasts. However, the metabolic-related signaling pathway regulated by circular RNAs (circRNAs) during this process remains unclear. Here, we demonstrated that circ0066187 promoted fibroblast-to-myofibroblast differentiation by metabolic-related signaling pathway. Mechanism analysis research identified that circ0066187 directly targeted signal transducer and activator of transcription 3 (STAT3)-mediated metabolism signal pathway to enhance fibroblast-to-myofibroblast differentiation by sponging miR-29b-2-5p, resulting in pulmonary fibrosis. Integrative multi-omics analysis of metabolomics and proteomics revealed three pathways co-enriched in proteomics and metabolomics, namely, Protein digestion and absorption, PI3K-Akt signaling pathway, and FoxO signaling pathway. In these three signaling pathways, seven differentially expressed metabolites such as L-glutamine, L-proline, adenosine monophosphate (AMP), L-arginine, L-phenylalanine, L-lysine and L-tryptophan, and six differentially expressed proteins containing dipeptidyl peptidase-4 (DPP4), cyclin D1 (CCND1), cyclin-dependent kinase 2 (CDK2), fibroblast growth factor 2 (FGF2), collagen type VI alpha 1 (COL6A1) and superoxide dismutase 2 (SOD2) were co-enriched. Gain-and loss-of-function studies and rescue experiments were performed to verify that circ0066187 promoted STAT3 expression by inhibiting miR-29b-2-5p expression to control the above metabolites and proteins. As a result, these metabolites and proteins provided the material basis and energy requirements for the progression of pulmonary fibrosis. In conclusion, circ0066187 can function as a profibrotic metabolism-related factor, and interference with circ0066187 can prevent pulmonary fibrosis. The finding supported that circ0066187 can be a metabolism-related therapeutic target for IPF treatment.

PMID:39969586 | DOI:10.1007/s00018-025-05613-z

Biomater Sci. 2025 Feb 19. doi: 10.1039/d4bm00906a. Online ahead of print.

ABSTRACT

Fibrosis, a pathological hallmark of various chronic diseases, involves the excessive accumulation of extracellular matrix (ECM) components leading to tissue scarring and functional impairment. Understanding how cells interact with the ECM in fibrotic diseases such as idiopathic pulmonary fibrosis (IPF), is crucial for developing effective therapeutic strategies. This study explores the effects of decellularized extracellular matrix (dECM) coatings derived from non-IPF and IPF donor lung tissue samples on the behavior of primary human lung fibroblasts (HLFs). Utilizing a substrate coating method that preserves the diversity of in situ ECM, we studied both the concentration-dependent effects and the intrinsic biochemical cues of ECM on cell morphology, protein expression, mechanobiology biomarkers, and gene expression. Morphological analysis revealed that HLFs displayed altered spreading, shape, and nuclear characteristics in response to dECM coatings relative to control plastic, indicating a response to the physical and biochemical cues. Protein expression studies showed an upregulation of α-smooth muscle actin (α-SMA) in cells interacting with both non-IPF and IPF dECM coatings, that was more prominent at IPF dECM-coated surface. In addition, YAP localization, a marker of mechanotransduction, was also dysregulated on dECM coatings, reflecting changes in mechanical signaling pathways. Gene expression profiles were differentially regulated by the different dECM coatings. The developed dECM coating strategy in this work facilitates the integration of tissue-specific biochemical cues onto standard cell culture platforms, which is ideal for high-throughput screening. Importantly, it minimizes the requirement for human tissue samples, especially when compared to more sample-intensive 3D models like dECM-based hydrogels.

PMID:39968884 | DOI:10.1039/d4bm00906a

Protein Sci. 2025 Mar;34(3):e70061. doi: 10.1002/pro.70061.

ABSTRACT

Condensates formed by liquid-liquid phase separation are promising candidates for the development of synthetic cells and organelles. Here, we show that bacterial microcompartment shell proteins from Haliangium ochraceum (BMC-H) assemble into coatings on the surfaces of protein condensates formed by tandem RGG-RGG domains, an engineered construct derived from the intrinsically disordered region of the RNA helicase LAF-1. WT BMC-H proteins formed higher-order assemblies within RGG-RGG droplets; however, engineered BMC-H variants fused to RGG truncations formed coatings on droplet surfaces. These intrinsically disordered tags controlled the interaction with the condensed phase based on their length and sequence, and one of the designs, BMC-H-T2, assembled preferentially on the surface of the droplet and prevented droplet coalescence. The formation of the coatings is dependent on the pH and protein concentration; once formed, the coatings are stable and do not exchange with the dilute phase. Coated droplets could sequester and concentrate folded proteins, including TEV protease, with selectivity similar to uncoated droplets. Addition of TEV protease to coated droplets resulted in the digestion of RGG-RGG to RGG and a decrease in droplet diameter, but not in the dissolution of the coatings. BMC shell protein-coated protein condensates are entirely encodable and provide a way to control the properties of liquid-liquid phase-separated compartments in the context of synthetic biology.

PMID:39969154 | DOI:10.1002/pro.70061

Elife. 2025 Feb 19;13:RP102788. doi: 10.7554/eLife.102788.

ABSTRACT

Artificial intelligence (AI) models have been used to study the compositional regularities of proteins in nature, enabling it to assist in protein design to improve the efficiency of protein engineering and reduce manufacturing cost. However, in industrial settings, proteins are often required to work in extreme environments where they are relatively scarce or even non-existent in nature. Since such proteins are almost absent in the training datasets, it is uncertain whether AI model possesses the capability of evolving the protein to adapt extreme conditions. Antibodies are crucial components of affinity chromatography, and they are hoped to remain active at the extreme environments where most proteins cannot tolerate. In this study, we applied an advanced large language model (LLM), the Pro-PRIME model, to improve the alkali resistance of a representative antibody, a VHH antibody capable of binding to growth hormone. Through two rounds of design, we ensured that the selected mutant has enhanced functionality, including higher thermal stability, extreme pH resistance, and stronger affinity, thereby validating the generalized capability of the LLM in meeting specific demands. To the best of our knowledge, this is the first LLM-designed protein product, which is successfully applied in mass production.

PMID:39968946 | DOI:10.7554/eLife.102788

Aging Cell. 2025 Feb 19:e70015. doi: 10.1111/acel.70015. Online ahead of print.

ABSTRACT

Senescence is a cell fate associated with age-related pathologies; however, senescence markers are not well-defined. Using single cell multi-isotope imaging mass spectrometry (MIMS), we identified hypercondensed, transcriptionally silent DNA globules in a senescence model induced by dysfunctional telomeres. This architectural phenomenon was associated with geographically clustered transcriptional repression across somatic chromosomes with over-representation of cell cycle genes. Senescence-stimuli was associated with a higher frequency of cells that exhibited geographically concentrated transcriptional repression relative to control cells. This phenomenon was also observed in multiple other senescence models, including replicative senescence and irradiation. We further identified an enrichment of common pathways in all models of senescence, suggesting a common cellular response to this silencing phenomenon. Such large-scale clustered silencing of chromosomal segments rather than individual genes may explain senescence heterogeneity and a putative trajectory toward deep, irreversible senescence.

PMID:39968744 | DOI:10.1111/acel.70015

Front Cell Neurosci. 2025 Feb 4;19:1559821. doi: 10.3389/fncel.2025.1559821. eCollection 2025.

NO ABSTRACT

PMID:39968391 | PMC:PMC11832465 | DOI:10.3389/fncel.2025.1559821

Heliyon. 2025 Jan 10;11(3):e41894. doi: 10.1016/j.heliyon.2025.e41894. eCollection 2025 Feb 15.

ABSTRACT

Despite the vast vaccination campaigns against SARS-CoV-2, vaccine-resistant variants have emerged, and COVID-19 is continuing to spread with the fear of emergence of new variants that are resistant to the currently available anti-viral drugs. Hence, there is an urgent need to discover potential host-directed - rather than virus-directed - therapies against COVID-19. SARS-CoV-2 enters host cells through binding of the viral spike (S)-protein to the host angiotensin-converting enzyme 2 (ACE2) receptor, rendering the viral port of entry an attractive therapeutic target. Accordingly, this study aimed to investigate FDA-approved drugs for their potential repurposing to inhibit the entry point of SARS-CoV-2. Accordingly, the FDA-approved drugs library was enrolled in docking simulations to identify drugs that bind to the Spike-ACE2 interface. The drugs list retrieved by the docking simulations was shortlisted to 19 drugs based on docking scores and safety profiles. These drugs were screened for their ability to prevent binding between ACE2 and S-protein using an ELISA-based Spike-ACE2 binding assay. Five drugs showed statistically significant inhibition of binding between ACE2 and S-protein, ranging from 4 % to 37 %. Of those five, argatroban, glimepiride and ranolazine showed potential antiviral activity at IC50 concentrations well below their CC50 assessed by the plaque assay. Their mode of antiviral action was then determined using the plaque assay with some modifications, which revealed that argatroban acted mainly through a direct virucidal mechanism, while glimepiride largely inhibited viral replication, and ranolazine exerted its antiviral impact primarily through inhibiting viral adsorption. In conclusion, this study has identified three FDA-approved drugs - argatroban, glimepiride and ranolazine - which could potentially be repurposed and used for the management of COVID-19.

PMID:39968139 | PMC:PMC11834051 | DOI:10.1016/j.heliyon.2025.e41894

Small. 2025 Feb 19:e2409126. doi: 10.1002/smll.202409126. Online ahead of print.

ABSTRACT

This study advances sustainable pharmaceutical research for endometriosis by developing in vitro 3D cell culture models of endometriotic pathophysiology that allow antifibrotic drug candidates to be tested. Fibrosis is a key aspect of endometriosis, yet current cell models to study it remain limited. This work aims to bridge the translational gap between in vitro fibrosis research and preclinical testing of non-hormonal drug candidates. When grown in a 3D matrix of sustainably produced silk protein functionalized with a fibronectin-derived cell adhesion motif (FN-silk), endometrial stromal and epithelial cells respond to transforming growth factor beta-1 (TGF-β1) in a physiological manner as probed at the messenger RNA (mRNA) level. For stromal cells, this response to TGF-β1 is not observed in spheroids, while epithelial cell spheroids behave similarly to epithelial cell FN-silk networks. Pirfenidone, an antifibrotic drug approved for the treatment of idiopathic pulmonary fibrosis, reverses TGF-β1-induced upregulation of mRNA transcripts involved in fibroblast-to-myofibroblast transdifferentiation of endometrial stromal cells in FN-silk networks, supporting pirfenidone's potential as a repurposed non-hormonal endometriosis therapy. Overall, endometrial stromal cells cultured in FN-silk networks-which are composed of a sustainably produced, fully defined FN-silk protein-recapitulate fibrotic cellular behavior with high fidelity and enable antifibrotic drug testing.

PMID:39967482 | DOI:10.1002/smll.202409126