Pharmacogenomics. 2025 May 28:1-8. doi: 10.1080/14622416.2025.2510186. Online ahead of print.

ABSTRACT

BACKGROUND: Green tea extract was tested for the secondary prevention of colorectal adenoma in the placebo-controlled MIRACLE trial. Genome-wide screening on adenoma recurrence was performed in n = 550 participants 3 years after randomization to green tea or placebo intake.

METHODS: Single Marker Analysis followed by regression analyses was calculated for all 700.078 markers assuming an additive genetic model and including all covariates from the main MIRACLE trial analysis. The outcome was an adenoma rate at 3-year follow-up colonoscopy comparing participants carrying a genetic variant versus wildtype.

RESULTS: The gene showing the strongest association with the outcome in both, SMA as well as regression analysis, was the organic anion transporter SLCO1A2. In the variant carriers, the adenoma frequency was 41.4% in the green tea group and 35.7% in the placebo group (RR 1.16 [0.81; 1.65] p = 0.61), whereas in the nonvariant carriers, the frequency of reoccurrence was 54.5% in the green tea group and 66.5% in the placebo group (RR 0.82 [0.69; 0.97], p = 0.03).

CONCLUSION: Individuals with genetic variants in the transporter SLCO1A2 may be protected against colon adenoma irrespective of the green tea intake. In nonvariant carriers of SLCO1A2, green tea was associated with a clear benefit in outcome (18% risk reduction).

PMID:40433816 | DOI:10.1080/14622416.2025.2510186

mSystems. 2025 May 28:e0019625. doi: 10.1128/msystems.00196-25. Online ahead of print.

ABSTRACT

Human microbiome plays a crucial role in host health and disease by mediating the impact of environmental factors on clinical outcomes. Mediation analysis is a valuable tool for dissecting these complex relationships. However, existing approaches are primarily designed for cross-sectional studies. Modern clinical research increasingly utilizes long follow-up periods, leading to complex data structures, particularly in metagenomic studies. To address this limitation, we introduce a novel mediation framework based on structural equation modeling that leverages linear mixed-effects models using penalized quasi-likelihood estimation with a debiased lasso. We applied this framework to a 16S rRNA sputum microbiome data set collected from patients with cystic fibrosis over 10 years to investigate the mediating role of the microbiome in the relationship between clinical states, disease aggressiveness phenotypes, and lung function. We identified richness as a key mediator of lung function. Specifically, Streptococcus was found to be significantly associated with mediating the decline in lung function on treatment compared to exacerbation, while Gemella was associated with the decline in lung function on recovery. This approach offers a powerful new tool for understanding the complex interplay between microbiome and clinical outcomes in longitudinal studies, facilitating targeted microbiome-based interventions.

IMPORTANCE: Understanding the mechanisms by which the microbiome influences clinical outcomes is paramount for realizing the full potential of microbiome-based medicine, including diagnostics and therapeutics. Identifying specific microbial mediators not only reveals potential targets for novel therapies and drug repurposing but also offers a more precise approach to patient stratification and personalized interventions. While traditional mediation analyses are ill-equipped to address the complexities of longitudinal metagenomic data, our framework directly addresses this gap, enabling robust investigation of these increasingly common study designs. By applying this framework to a decade-long cystic fibrosis study, we have begun to unravel the intricate relationships between the sputum microbiome and lung function decline across different clinical states, yielding insights that were previously unknown.

PMID:40434093 | DOI:10.1128/msystems.00196-25

Ther Adv Respir Dis. 2025 Jan-Dec;19:17534666251340334. doi: 10.1177/17534666251340334. Epub 2025 May 28.

ABSTRACT

BACKGROUND: People with cystic fibrosis (pwCF) are increasingly considering their reproductive options. Currently, there are many genetic testing options available for pwCF and their reproductive partners. Healthcare providers, including genetic counselors, can educate pwCF about these options and support them through the decision-making process.

OBJECTIVE: This study explored the role of genetics in the reproductive decisions of pwCF and their perspectives and experiences surrounding prenatal and preconception genetic counseling and testing.

DESIGN: We conducted a qualitative study of a national US sample of pwCF age ⩾18 years recruited from the CF Foundation Community Voice platform.

METHODS: We conducted and recorded semi-structured telephone interviews with participants. We utilized Dedoose software and applied inductive thematic analysis to code the interview transcripts and elicit themes.

RESULTS: We interviewed 21 participants (76.2% women, 95.2% White, 4.8% Hispanic, 57.1% parents, 23.8% considering parenthood). Key themes included: (1) pwCF appeared to understand the genetics of CF and were typically first introduced to CF genetics by CF providers, school, or their parents; (2) pwCF had diverse perspectives on having a child with CF; (3) carrier testing was an important consideration for some participants when making decisions about biological parenthood; (4) participants understood the role of genetic counselors and valued their knowledge, but only half previously met with a genetic counselor; (5) pwCF believed genetics information should be presented during childhood/adolescence and reinforced when interested in family planning.

CONCLUSION: pwCF have discrepant views on passing on CF to future offspring, and although there is recognition of the role of genetic counseling and a desire for knowledge from genetic testing, genetic considerations are but one factor involved in parenthood decisions. Future work should develop patient-, provider-, or system-based interventions to best integrate high-quality genetics and genetic counseling care into the CF team for those with CF considering parenthood.

PMID:40434014 | DOI:10.1177/17534666251340334

Med Oncol. 2025 May 28;42(7):222. doi: 10.1007/s12032-025-02759-5.

ABSTRACT

Pancreatic cancer is a highly aggressive malignancy with rising incidence and mortality rates, often diagnosed at advanced stages. Conventional imaging methods, such as computed tomography (CT) and magnetic resonance imaging (MRI), struggle to assess tumor characteristics and vascular involvement, which are crucial for treatment planning. This paper explores the potential of diffusion magnetic resonance imaging (dMRI) in enhancing pancreatic cancer diagnosis and treatment. Diffusion-based techniques, such as diffusion-weighted imaging (DWI), diffusion tensor imaging (DTI), intravoxel incoherent motion (IVIM), and diffusion kurtosis imaging (DKI), combined with emerging AI‑powered analysis, provide insights into tissue microstructure, allowing for earlier detection and improved evaluation of tumor cellularity. These methods may help assess prognosis and monitor therapy response by tracking diffusion and perfusion metrics. However, challenges remain, such as standardized protocols and robust data analysis pipelines. Ongoing research, including deep learning applications, aims to improve reliability, and dMRI shows promise in providing functional insights and improving patient outcomes. Further clinical validation is necessary to maximize its benefits.

PMID:40434720 | DOI:10.1007/s12032-025-02759-5

Trop Anim Health Prod. 2025 May 28;57(5):230. doi: 10.1007/s11250-025-04475-8.

ABSTRACT

Lumpy Skin Disease (LSD) presents significant risks and economic challenges to global cattle farming. Effective and accurate classification of LSD is essential for managing the disease and reducing its impacts. Manual diagnosis is time-consuming, labor-intensive, and requires experienced personnel. Automated classification methods provide advantages by reducing labor and improving accuracy. This study proposes an automated algorithm for LSD classification using machine learning. The method uses a carefully curated dataset of images from both LSD-infected cattle and healthy cattle. Inception V3 was employed to extract features from complex lesion patterns in infected cattle images, comparing them to healthy cattle images. Support Vector Machines (SVM) were used to classify the extracted features. The results show the model achieved an 84% accuracy rate, with precision at 80%, recall at 83%, and an F1 score of 82%. These results were compared with other machine learning models, including Logistic Regression, Random Forest, Decision Tree, and AdaBoost. SVM outperformed other models, demonstrating consistent evaluation precision at 0.84. For further enhancement, expanding the dataset with high-quality images and applying advanced machine learning algorithms like Vision Transformers (ViTs), MobileNetV2, and Visual Geometry Group (VGG) could refine automated LSD classification. The aim is to improve disease management practices in the livestock industry through better classification systems.

PMID:40434587 | DOI:10.1007/s11250-025-04475-8

Indian J Ophthalmol. 2025 Jun 1;73(6):797-806. doi: 10.4103/IJO.IJO_1810_24. Epub 2025 May 28.

ABSTRACT

Recent advances in deep learning and machine learning have greatly increased the capabilities of extracting features for evaluating the response to anti VEGF treatment in patients with Diabetic Macular Edema (DME). In this review, we explore how these algorithms can be used for discriminating between responders and non-responders to anti vascular endothelial growth factor (VEGF) injections. Electronic databases, including PubMed, IEEE Xplore, BioMed, JAMA, and Google Scholar, were searched, and reference lists from relevant publications were also considered from inception till August 31, 2023, based on the inclusion and exclusion criteria. Data extraction was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The results focus on keywords such as DME, OCT, anti VEGF, and patient responses after anti VEGF injections. The article measures the effectiveness of different machine learning and deep learning algorithms, including linear discriminant analysis (LDA), ResNet-50, CNN with attention, quadratic discriminant analysis (QDA), random forest (RF), and support vector machines (SVM), in analyzing eyes that could tolerate extended interval dosing. According to a review of 50 relevant papers published between 2016 and 2023, the algorithms achieved an average automated sensitivity of 74% (95% CI: 0.55-0.92) in detecting treatment responses.

PMID:40434455 | DOI:10.4103/IJO.IJO_1810_24

Am J Physiol Lung Cell Mol Physiol. 2025 May 28. doi: 10.1152/ajplung.00058.2025. Online ahead of print.

ABSTRACT

Intracellular calcium (Ca2+) release via phospholipase C (PLC) following G-protein coupled receptor (GPCR) activation is typically linked to membrane depolarization and airway smooth muscle (ASM) contraction. However, recent findings show that bitter taste receptor agonists, such as chloroquine (CQ), induce a paradoxical and potent relaxation response despite activating the Ca2+ signaling pathway. This relaxation has been hypothesized to be driven by a distinct compartmentalization of calcium ions toward the cellular periphery, subsequently leading to membrane hyperpolarization, in contrast to the contractile effects of histamine. In this study, we further investigate the spatio-temporal dynamics of Ca2+ signaling in ASM cells using single-cell microscopy and deep learning-based segmentation, integrating the results into a comprehensive model of ASM ion channel dynamics to compare the effects of histamine, CQ, and flufenamic acid (FFA). Our results show that histamine induces a strong, synchronized calcium release, nearly twice as high as that of CQ, which produces a sustained but lower- magnitude response. Per-cell analysis reveals more variable and asynchronous Ca2+ signaling for CQ and FFA, with higher entropy compared to histamine. Integrating these findings into an ASM ion channel model, we observed that histamine-mediated Ca2+ release activates voltage-gated Ca2+ and Na+ channels (leading to depolarization). In contrast, CQ preferentially engages BKCa, SKCa, and chloride channels (promoting hyperpolarization). These findings provide insights into the unique mechanisms by which bitter taste receptor agonists can modulate ASM tone, offering potential therapeutic strategies for relaxing ASM and alleviating airway hyperresponsiveness in conditions such as asthma.

PMID:40434402 | DOI:10.1152/ajplung.00058.2025

Radiol Artif Intell. 2025 May 28:e240484. doi: 10.1148/ryai.240484. 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. Purpose To estimate the total lung volume (TLV) from real and synthetic frontal chest radiographs (CXR) on a pixel level using lung thickness maps generated by a U-Net deep learning model. Materials and Methods This retrospective study included 5,959 chest CT scans from two public datasets: the lung nodule analysis 2016 (n = 656) and the Radiological Society of North America (RSNA) pulmonary embolism detection challenge 2020 (n = 5,303). Additionally, 72 participants were selected from the Klinikum Rechts der Isar dataset (October 2018 to December 2019), each with a corresponding chest radiograph taken within seven days. Synthetic radiographs and lung thickness maps were generated using forward projection of CT scans and their lung segmentations. A U-Net model was trained on synthetic radiographs to predict lung thickness maps and estimate TLV. Model performance was assessed using mean squared error (MSE), Pearson correlation coefficient (r), and two-sided Student's t-distribution. Results The study included 72 participants (45 male, 27 female, 33 healthy: mean age 62 years [range 34-80]; 39 with chronic obstructive pulmonary disease: mean age 69 years [range 47-91]). TLV predictions showed low error rates (MSEPublic-Synthetic = 0.16 L2, MSEKRI-Synthetic = 0.20 L2, MSEKRI-Real = 0.35 L2) and strong correlations with CT-derived reference standard TLV (nPublic-Synthetic = 1,191, r = 0.99, P < .001; nKRI-Synthetic = 72, r = 0.97, P < .001; nKRI-Real = 72, r = 0.91, P < .001). When evaluated on different datasets, the U-Net model achieved the highest performance for TLV estimation on the Luna16 test dataset, with the lowest mean squared error (MSE = 0.09 L2) and strongest correlation (r = 0.99, P <.001) compared with CT-derived TLV. Conclusion The U-Net-generated pixel-level lung thickness maps successfully estimated TLV for both synthetic and real radiographs. ©RSNA, 2025.

PMID:40434310 | DOI:10.1148/ryai.240484

Radiol Artif Intell. 2025 May;7(3):e250260. doi: 10.1148/ryai.250260.

NO ABSTRACT

PMID:40434277 | DOI:10.1148/ryai.250260

Small Methods. 2025 May 28:e2500106. doi: 10.1002/smtd.202500106. Online ahead of print.

ABSTRACT

Advances in organoid models, as ex vivo mini-organs, and the development of screening imaging technologies have continuously driven each other forward. A complete understanding of organoids requires detailed insights into the intertwined intraorganoid and extraorganoid activities and how they change across time and space. This study introduces a multiplexed imaging platform that integrates label-free nanoplasmonic biosensing with fluorescence microscopy to offer simultaneous monitoring of dynamics occurring within and around arrays of single spheroids with spatiotemporal resolution. The label-free module employs nanoplasmonic biosensors with extraordinary optical transmission to track biomolecular secretions into the surroundings, while concurrent fluorescence imaging enables structural analysis and viability assessment. To perform multiparametric interrogation of the data from different channels over extended periods, a deep-learning-augmented image analysis is incorporated. The platform is applied to tumor spheroids to investigate vascular endothelial growth factor A secretion alongside morphometric changes and viability, showcasing its ability to capture variations in secretion and growth dynamics between untreated and drug-treated groups. This integrated approach advances comprehensive insights into organoid models and can complement existing technologies to accelerate discoveries in disease modeling and drug development.

PMID:40434268 | DOI:10.1002/smtd.202500106

Comput Methods Biomech Biomed Engin. 2025 May 28:1-14. doi: 10.1080/10255842.2025.2511222. Online ahead of print.

ABSTRACT

Major Depressive Disorder (MDD) is known as a widespread illness and needs a timely treatment. The treatment procedure is currently based on the trial and error between various treatments. An individualized treatment selection is crucial for saving time and financial resources and preventing possible side effects. Because of the complex nature of this problem, a Deep Learning (DL) approach, as a promising method for the precision medicine, was utilized to identify the responders to the treatment using pre-treatment EEG signals. Eighty-three patients with MDD participated in this study to receive treatment using Repetitive Transcranial Magnetic Stimulation (rTMS). A deep hybrid neural network was developed based on three pre-trained convolutional neural networks named DenseNet121, EfficientNetB0, and Xception. The training of each model was performed by feeding three types of EEG representations as the inputs into the models including the Wavelet Transform (WT) images, the connectivity matrix between electrode pairs, and the raw EEG signals. The performance of the proposed models were assessed for the three different input types and achieved the highest accuracy of 94.7% in classifying patients as responders or non-responders when utilizing a sequence of raw EEG images. For the WT and connectivity inputs the best accuracy of model was 94.38% and 94.25% respectively. Therefore, the proposed model can be a step forward towards the clinical implementation of an end-to-end treatment selection framework using raw EEG signals.

PMID:40434017 | DOI:10.1080/10255842.2025.2511222

Integr Org Biol. 2024 Sep 23;6(1):obae036. doi: 10.1093/iob/obae036. eCollection 2024.

ABSTRACT

Artificial intelligence (AI) is poised to revolutionize many aspects of science, including the study of evolutionary morphology. While classical AI methods such as principal component analysis and cluster analysis have been commonplace in the study of evolutionary morphology for decades, recent years have seen increasing application of deep learning to ecology and evolutionary biology. As digitized specimen databases become increasingly prevalent and openly available, AI is offering vast new potential to circumvent long-standing barriers to rapid, big data analysis of phenotypes. Here, we review the current state of AI methods available for the study of evolutionary morphology, which are most developed in the area of data acquisition and processing. We introduce the main available AI techniques, categorizing them into 3 stages based on their order of appearance: (1) machine learning, (2) deep learning, and (3) the most recent advancements in large-scale models and multimodal learning. Next, we present case studies of existing approaches using AI for evolutionary morphology, including image capture and segmentation, feature recognition, morphometrics, and phylogenetics. We then discuss the prospectus for near-term advances in specific areas of inquiry within this field, including the potential of new AI methods that have not yet been applied to the study of morphological evolution. In particular, we note key areas where AI remains underutilized and could be used to enhance studies of evolutionary morphology. This combination of current methods and potential developments has the capacity to transform the evolutionary analysis of the organismal phenotype into evolutionary phenomics, leading to an era of "big data" that aligns the study of phenotypes with genomics and other areas of bioinformatics.

PMID:40433986 | PMC:PMC12082097 | DOI:10.1093/iob/obae036

Adv Sci (Weinh). 2025 May 28:e14732. doi: 10.1002/advs.202414732. Online ahead of print.

ABSTRACT

Accurate decoding of peripheral nerve signals is essential for advancing neuroscience research, developing therapeutics for neurological disorders, and creating reliable human-machine interfaces. However, the poor mechanical compliance of conventional metal electrodes and limited generalization of existing decoding models have significantly hindered progress in understanding peripheral nerve function. This study introduces low-impedance, soft-conducting polymer electrodes capable of forming stable, intimate contacts with peripheral nerve tissues, allowing for continuous and reliable recording of neural activity in awake animals. Using this unique dataset of neurophysiological signals, a neural network model that integrates both handcrafted and deep learning-derived features, while incorporating parameter-sharing and adaptation training strategies, is developed. This approach significantly improves the generalizability of the decoding model across subjects, reducing the reliance on extensive training data. The findings not only deepen the understanding of peripheral nerve function but also open avenues for developing advanced interventions to augment and restore neurological functions.

PMID:40433949 | DOI:10.1002/advs.202414732

J Ultrasound Med. 2025 May 28. doi: 10.1002/jum.16721. Online ahead of print.

ABSTRACT

OBJECTIVE: Congenital heart disease (CHD) is the most common birth defect and the leading cause of infant death from congenital anomalies. Limitations in standard-of-care fetal echocardiography lack hemodynamic insight. Cardiovascular computational modeling methods have been developed to simulate patient-specific morphology and hemodynamics, but are limited in applications for fetal diagnosis, as existing pipelines depend upon 3D CMR imaging data. There is no existing workflow for converting 2D echocardiograms into models of the fetal aorta. We aim to develop a methodology to create pulsatile 3D-aortic models from standard-of-care 2D echocardiograms to supplement fetal imaging with noninvasive predictions of hemodynamics in CHD diagnosis.

METHODS: Utilizing 2D fetal echocardiograms, edge detection algorithms are applied to delineate vessel boundaries. Cross-sectional diameters along the aortic arch and branch centerlines were segmented, integrated into 3D geometric models, and reconstructed using SimVascular. Patient-specific simulations were developed for three false-positive coarctation of the aorta (CoA) fetuses and 3 true positive CoA fetuses (postnatally confirmed), using echocardiogram and Doppler source data.

RESULTS: We propose a modeling methodology and set of boundary conditions that generate physiologically reasonable and cross-validated quantifications of fetal hemodynamics. Noninvasive predictions of fetal aortic pressures, flow streamlines, and vessel displacement offer insight into real-time hemodynamics and the stress of abnormal morphology on flow directions in the prenatal aorta.

CONCLUSIONS: We present a clinically useful pipeline for generating simulations of flow in the fetal aorta that capture fluid-structure interactions and generate noninvasive predictions of diagnostic hemodynamic indicators that could not previously be captured prenatally. This pipeline integrates into clinical diagnosis and offers insight into patient-specific physiology beyond a visualization of cardiac morphology alone, offering the potential to enhance the diagnostic precision of CHDs.

PMID:40434286 | DOI:10.1002/jum.16721

Am J Physiol Cell Physiol. 2025 May 28. doi: 10.1152/ajpcell.00913.2024. Online ahead of print.

ABSTRACT

Cells interact dynamically with the extracellular matrix (ECM), which provides both structural support and biochemical signals that regulate various cellular processes. Among these, the mechanical properties of the ECM, particularly stiffness, play a crucial role in governing cell differentiation, migration, and survival. Recent studies have highlighted the intricate relationship between ECM stiffness and cellular metabolism, influencing key pathways such as glucose, lipid and amino acid metabolism. This review explores how ECM stiffness modulates these metabolic processes, emphasizing the underlying mechano-transduction mechanisms. Additionally, we discuss emerging techniques that enable the investigation of ECM-mediated force sensing and response, providing new insights to the mechanoregulation of metabolism and its implications in disease and therapy.

PMID:40434254 | DOI:10.1152/ajpcell.00913.2024

J Virol. 2025 May 28:e0006725. doi: 10.1128/jvi.00067-25. Online ahead of print.

ABSTRACT

The eight-segmented RNA genome of influenza A virus (IAV) is transcribed and spliced into 10 major viral mRNAs in the nucleus of infected cells. Both transcription and splicing are facilitated by the host RNA polymerase II (Pol II) machinery via interactions between the viral ribonucleoprotein (vRNP) complex and various host factors. In this study, we demonstrate that IAV vRNPs recruit species-specific heterogeneous nuclear ribonucleoprotein M (hnRNPM) to support their replication in human and avian cells through distinct mechanisms. In A549 cells, human hnRNPM specifically facilitates the efficient transcription of HA, NA, M, and NS segments of WSN virus in a gene coding sequence-dependent manner. In contrast, in DF-1 cells, chicken hnRNPM restricts excessive splicing of M segment mRNA to ensure proper M2 protein production. Notably, human hnRNPM, with 34 additional amino acids compared with its chicken counterpart, fails to inhibit the M2 expression in DF-1 cells, whereas both human and chicken hnRNPM regulate WSN virus replication similarly in A549 cells. These findings highlight the host-specific roles of M2 levels in IAV replication and reveal how IAV co-opts host factors through virus genome sequence-dependent and host species-specific mechanisms, underscoring its high flexibility and adaptability during cross-species transmission.IMPORTANCEThe transcription and splicing of IAV genome in the nucleus of infected cells are precisely regulated to produce optimal amounts of viral proteins, ensuring efficient virus replication. In this study, we discovered that human hnRNPM regulates the IAV segment-specific differential transcription in a coding sequence-dependent manner in human cells. In contrast, chicken hnRNPM specifically inhibits M2 mRNA splicing to maintain proper M2 protein levels in avian cells. These species-specific regulatory mechanisms highlight the distinct replication strategies employed by IAV in human versus avian cells and underscore the complexity of cross-species transmission.

PMID:40434105 | DOI:10.1128/jvi.00067-25

Adv Sci (Weinh). 2025 May 28:e2413052. doi: 10.1002/advs.202413052. Online ahead of print.

ABSTRACT

Previous studies have suggested that pathological α-synuclein (α-Syn) mainly transmits along the neuronal network, but several key questions remain unanswered: 1) How many and which connections in the connectome are necessary for predicting the progression of pathological α-Syn? 2) How to identify risk genes that affect pathology spreading functioning at presynaptic or postsynaptic regions, and are these genes enriched in different cell types? Here, these questions are addressed with novel mathematical models. Strikingly, the spreading of pathological α-Syn is predominantly determined by the key subnetworks composed of only 2% of the strongest connections in the connectome. Genes associated with the selective vulnerability of brain regions to pathological α-Syn transmission are further analyzed to distinguish those functioning at presynaptic versus postsynaptic regions. Those risk genes are significantly enriched in microglial cells of presynaptic regions and neurons of postsynaptic regions. Gene regulatory network analyses are then conducted to identify "key drivers" of genes responsible for selective vulnerability and overlapping with Parkinson's disease risk genes. By identifying and discriminating between key gene mediators of transmission operating at presynaptic and postsynaptic regions, this study has demonstrated for the first time that these are functionally distinct processes.

PMID:40433888 | DOI:10.1002/advs.202413052

J Neurol. 2025 May 28;272(6):429. doi: 10.1007/s00415-025-13172-3.

ABSTRACT

BACKGROUND: Many women are exposed to antiseizure medications (ASMs) during pregnancy, raising concerns about pregnancy and offspring health risks. The current safety data remain insufficient, necessitating further investigation.

METHODS: Using data from the FDA Adverse Event Reporting System (2010-2023), this study employed both the Reporting Odds Ratio (ROR) and Bayesian Confidence Propagation Neural Network (BCPNN) for disproportionality analysis of pregnancy and offspring toxicity related to maternal ASM exposure. In addition, we performed signal adjustment by excluding polytherapy cases, and drug-drug interaction (DDI) signals of two ASMs were identified using Ω Shrinkage measures and Chi-square tests.

RESULTS: 3,459 mothers were exposed to 23 ASMs, resulting in 10,910 adverse events. 59 malformation signals, 27 adverse perinatal outcome signals, and 35 dysplasia signals were identified. Among traditional ASMs, valproic acid (VPA) and carbamazepine (CBZ) exhibited the highest number of signals, while levetiracetam (LEV), lamotrigine (LTG), lacosamide, gabapentin, and topiramate (TPM) predominated among newer ASMs. Signals for cardiac malformations, adverse neurodevelopment, and adverse offspring growth outcomes were widespread, with the strongest signals for specific outcomes observed for zonisamide [ROR = 14.82, 95% CI: 5.43-40.41], gabapentin [ROR = 52.52, 95% CI: 15.68-175.95], and brivaracetam [ROR = 22.96, 95% CI: 8.42-62.61], respectively. Six DDI signals displayed ≥ 3, including LTG + LEV/VPA associated with malformation, CBZ + lacosamide/LTG, and VPA + clonazepam associated with fetal loss.

CONCLUSIONS: The potential risks associated with LEV and LTG surpass expectations, warranting further evaluation, particularly in combination therapy. In addition, ASMs with widespread signals, such as VPA, CBZ, TPM, and lacosamide, warrant heightened attention.

PMID:40434447 | DOI:10.1007/s00415-025-13172-3

Br J Clin Pharmacol. 2025 May 28. doi: 10.1002/bcp.70113. Online ahead of print.

ABSTRACT

This review highlights the role of reverse transcriptase (RT) inhibition in cellular regulation associated with non-terminal repeat retrotransposons and endogenous retroviruses. Based on their pleiotropic characteristics, RT inhibitors (RTIs) are discussed as potential anticancer agents. Both the nucleoside/nucleotide reverse transcriptase inhibitors (NRTIs) and non-nucleoside reverse transcriptase inhibitors (NNRTIs) display cytotoxicity in cancer cells which are likely mediated by endogenous RT inhibition and not necessarily by differing molecular structures. Three features of RTIs are evident in inducing cytotoxicity in cancer cells. Firstly, NRTIs and NNRTIs induce cell cycle arrest. Secondly, they suppress transposable elements, inhibit long interspersed nuclear elements (LINE)-1, with RTI key in cytotoxicity in cancer cells. Thirdly, the cyclic GMP-AMP-synthase-stimulator of interferon genes (cGAS-STING) pathway can be activated by LINE-1-derived cytoplasmic DNA with promotion of p21-dependent cell cycle arrest and cell-mediated immune response. This suggests that RTIs induce DNA strand breaks with incomplete retrotransposition, initiate cell cycle arrest and an immune response. Additionally, poly (ADP-ribose) polymerase 1 and 2 (PARP1, PARP2) and its relationship with DNA methylation is highlighted in the context of LINE-1 retrotransposition. There is a need to examine the relationship between PARP1, PARP2 and mutated BRCA proteins in normal and abnormal LINE-1 retrotransposition. This review explores how efavirenz and related RT inhibitors suppress endogenous reverse transcriptase, providing a basis for preclinical evaluation of RT inhibitors as potential repurposed drugs for cancer treatment.

PMID:40432477 | DOI:10.1002/bcp.70113

Viruses. 2025 May 18;17(5):722. doi: 10.3390/v17050722.

ABSTRACT

The COVID-19 pandemic has had a significant impact and continues to alarm the entire world due to the rapid emergence of new variants, even after mass vaccinations. There is still an urgent need for new antivirals or strategies to combat the SARS-CoV-2 infections; however, we have success stories with nirmatrelvir. Drug repurposing and drug discovery may lead to a successful SARS-CoV-2 antiviral; however, rapid drug use may cause unexpected mutations and antiviral drug resistance. Conversely, novel variants of the SARS-CoV-2 can diminish the neutralizing efficacy of vaccines, thereby enhancing viral fitness and increasing the likelihood of drug resistance emergence. Additionally, the disposal of antivirals in wastewater also contributes to drug resistance. Overall, the present review summarizes the strategies and mechanisms involved in the development of drug resistance in SARS-CoV-2. Understanding the mechanism of antiviral resistance is crucial to mitigate the significant healthcare threat and to develop effective therapeutics against drug resistance.

PMID:40431733 | DOI:10.3390/v17050722