Sci Rep. 2025 Aug 7;15(1):28886. doi: 10.1038/s41598-025-13786-7.

ABSTRACT

Vortioxetine is an antidepressant approved for the treatment of major depressive disorder (MDD). Given its widespread post-marketing clinical use, it is essential to explore its real-world safety. Reports were extracted from the FDA Adverse Event Reporting System (FAERS) from the third quarter of 2013 to the first quarter of 2025. Four disproportionality analysis methods, commonly used in pharmacovigilance to evaluate the relative reporting frequency of adverse events (AEs), were employed to identify AE signals associated with vortioxetine. These included the Reporting Odds Ratio (ROR), Proportional Reporting Ratio (PRR), Multi-item Gamma Poisson Shrink (MGPS), and Bayesian Confidence Propagation Neural Network (BCPNN). The median was used to describe the time to onset (TTO) of AEs, and Weibull distribution was employed to assess the trend of AE occurrence over time. In addition, sensitivity analyses were conducted to ensure the robustness of the findings. A total of 13,613 individual case safety reports (ICSRs) involving 34,156 AEs were analyzed. Females accounted for 60.9% of the reports, while males represented 26.5%. The median age of patients was 42 years (interquartile range: 26-59 years), with most cases (34.1%) in the 18-65 age group. The United States contributed the highest proportion of reports (77.4%). Common AEs included nausea (n = 2042, ROR = 5.11, PRR = 4.86, EBGM = 4.85, IC = 2.28), anxiety (n = 781, ROR = 5.3, PRR = 5.2, EBGM = 5.18, IC = 2.37 ), vomiting (n = 773, ROR = 3.23, PRR = 3.17, EBGM = 3.17, IC = 1.66), headache (n = 670, ROR = 1.96, PRR = 1.94, EBGM = 1.94, IC = 0.96), and somnolence (n = 212, ROR = 2, PRR = 1.99, EBGM = 1.99, IC = 0.99). Notably, several AEs not listed on the drug label, such as tinnitus (n = 79, ROR = 3.24, PRR = 3.24, EBGM = 3.23, IC = 1.69), urinary retention (n = 62, ROR = 3.57, PRR = 3.57, EBGM = 3.56, IC = 1.83), prolonged QT interval (n = 62, ROR = 3.14, PRR = 3.13, EBGM = 3.13, IC = 1.64), and restless legs syndrome (n = 48, ROR = 5.08, PRR = 5.08, EBGM = 5.06, IC = 2.34) were also identified. Most AEs occurred within the first month of treatment, with a median onset time of 15 days. Sensitivity analyses confirmed the consistency of these findings. This study provides new insights into the safety of vortioxetine and offers preliminary safety evidence. In addition, the findings may inform updates to prescribing information and guide post-marketing safety surveillance. However, the spontaneous nature of the FAERS database precludes establishing a causal relationship between vortioxetine and the reported AEs. Further prospective studies are needed to validate our findings.

PMID:40775011 | DOI:10.1038/s41598-025-13786-7

Sci Rep. 2025 Aug 7;15(1):28919. doi: 10.1038/s41598-025-14385-2.

ABSTRACT

Viloxazine and dextroamphetamine as newly approved drugs for the medical treatment of Attention Deficit Hyperactivity Disorder (ADHD) in recent years give new options for the treating of related disorders, including anxiety, and depression. In our research, we conducted an assessment of adverse drug reactions (ADRs) associated with the utilization of these two medications, as documented in the database. By analyzing the adverse drug reaction profiles and combining them with relevant reviews, we aim to help select the drug with the least risk to meet the specific needs of different patients. A retrospective descriptive analysis method was used in this study. The study classified two ADHD medications and extracted adverse drug reaction (ADR) reports for these medications from the World Health Organization-VigiAccess database. Data collected included patient demographic characteristics such as gender and age group, as well as geographic distribution based on global ADR reports. We compared the similarities and differences between the ADRs of the two drugs by calculating the proportion of ADRs reported for each drug. Finally, we also compared the most common general disorders and administration site conditions for various adverse effects. VigiAccess reported a total of 5394 adverse events (AEs) related to these two drugs. The most commonly reported age group was between 18 and 44 years and the three most common types of AEs were: general disease and site of administration conditions (2,548 cases, 20.5%), psychiatric disorders (2,012 cases, 16.1%) and neurologic disorders (1,822 cases, 14.6%). Dextroamphetamine had a significantly higher rate of reported adverse reactions in general disorders and administration site conditions compared to viloxazine. Beyond that there are other differences that exist. Using real-world data from WHO-VigiAccess and FAERS, we identified existing potential adverse reactions associated with viloxazine and dextroamphetamine, providing valuable insights for clinical reference. Although the study benefits from database utilization, its limitation lies in the spontaneous reporting system. Accurate drug safety evaluation requires future enhancements.

PMID:40775008 | DOI:10.1038/s41598-025-14385-2

J Clin Invest. 2025 Aug 7:e191824. doi: 10.1172/JCI191824. Online ahead of print.

NO ABSTRACT

PMID:40773289 | DOI:10.1172/JCI191824

Neuroimage Clin. 2025 Aug 6;48:103858. doi: 10.1016/j.nicl.2025.103858. Online ahead of print.

ABSTRACT

Accurate prediction of post-stroke language outcomes using machine learning offers the potential to enhance clinical treatment and rehabilitation for aphasic patients. This study of 758 English speaking stroke patients from the PLORAS project explores the impact of sample size on the performance of logistic regression and a deep learning (ResNet-18) model in predicting language outcomes from neuroimaging and impairment-relevant tabular data. We assessed the performance of both models on two key language tasks from the Comprehensive Aphasia Test: Spoken Picture Description and Naming, using a learning curve approach. Contrary to expectations, the simpler logistic regression model performed comparably or better than the deep learning model (with overlapping confidence intervals), with both models showing an accuracy plateau around 80% for sample sizes larger than 300 patients. Principal Component Analysis revealed that the dimensionality of the neuroimaging data could be reduced to as few as 20 (or even 2) dominant components without significant loss in accuracy, suggesting that classification may be driven by simple patterns such as lesion size. The study highlights both the potential limitations of current dataset size in achieving further accuracy gains and the need for larger datasets to capture more complex patterns, as some of our results indicate that we might not have reached an absolute classification performance ceiling. Overall, these findings provide insights into the practical use of machine learning for predicting aphasia outcomes and the potential benefits of much larger datasets in enhancing model performance.

PMID:40773787 | DOI:10.1016/j.nicl.2025.103858

Neural Netw. 2025 Aug 5;192:107928. doi: 10.1016/j.neunet.2025.107928. Online ahead of print.

ABSTRACT

We propose a novel Physics-Informed Neural Network (PINN) framework for solving Partial Differential Equations (PDEs) on manifolds represented by raw point clouds, without requiring any geometric priors such as level set functions or explicit surface parametrization. Unlike prior methods that assume the availability of normal vectors or a predefined representation of the surface, our approach reconstructs an implicit surface representation using normalizing flows, enabling accurate PDE solutions without requiring labeled training data. To the best of our knowledge, this is the first PINN framework that does not rely on predefined surface characteristics or supervision. Experimental results demonstrate that our method achieves high accuracy even with non-uniformly distributed and noisy point clouds, where traditional numerical approaches often fail. Furthermore, our approach exhibits significantly faster convergence compared to existing PINN-based methods that require explicit surface knowledge. This work highlights the potential of learning-based geometric methods in automating PDE simulations on arbitrary 3D surfaces.

PMID:40773782 | DOI:10.1016/j.neunet.2025.107928

Neural Netw. 2025 Aug 5;192:107903. doi: 10.1016/j.neunet.2025.107903. Online ahead of print.

ABSTRACT

Multivariate time series anomaly detection is crucial for various real-world applications to guarantee system reliability and safety. Many deep learning methods have been proposed for time series anomaly detection purposes. However, most existing approaches treat spatial and temporal features separately, limiting their capabilities to cope with complex interactions in real-world applications. In this study, we introduce MultiverseAD, a spatial-temporal synchronous attention network that incorporates causal knowledge to enhance anomaly detection performance in multivariate time series. MultiverseAD combines a dynamic spatial-temporal synchronous attention network with a static spatial-temporal causal graph. The network employs sliding graph attention to capture local and long-term dependencies, while the causal graph encodes static causal relationships to enhance the learning process. Experiments on eight public datasets have shown that MultiverseAD consistently surpasses twelve state-of-the-art models. Ablation experiment results further validate the effectiveness of both the causal graph and the synchronous attention network.

PMID:40773781 | DOI:10.1016/j.neunet.2025.107903

JMIR AI. 2025 Aug 7;4:e74053. doi: 10.2196/74053.

ABSTRACT

BACKGROUND: Overcrowded emergency rooms might degrade the quality of care and overload the clinic staff. Assessing unscheduled return visits (URVs) to the emergency department (ED) is a quality assurance procedure to identify ED-discharged patients with a high likelihood of bounce-back, to ensure patient safety, and ultimately to reduce medical costs by decreasing the frequency of URVs. The field of machine learning (ML) has evolved considerably in the past decades, and many ML applications have been deployed in various contexts.

OBJECTIVE: This study aims to develop an ML-assisted framework that identifies high-risk patients who may revisit the ED within 72 hours after the initial visit. Furthermore, this study evaluates different ML models, feature sets, and feature encoding methods in order to build an effective prediction model.

METHODS: This study proposes an ML-assisted system that extracts the features from both structured and unstructured medical data to predict patients who are likely to revisit the ED, where the structured data includes patients' electronic health records, and the unstructured data is their medical notes (subjective, objective, assessment, and plan). A 5-year dataset consisting of 184,687 ED visits, along with 324,111 historical electronic health records and the associated medical notes, was obtained from Kaohsiung Veterans General Hospital, a tertiary medical center in Taiwan, to evaluate the proposed system.

RESULTS: The evaluation results indicate that incorporating convolutional neural network-based feature extraction from unstructured ED physician narrative notes, combined with structured vital signs and demographic data, significantly enhances predictive performance. The proposed approach achieves an area under the receiver operating characteristic curve of 0.705 and a recall of 0.718, demonstrating its effectiveness in predicting URVs. These findings highlight the potential of integrating structured and unstructured clinical data to improve predictive accuracy in this context.

CONCLUSIONS: The study demonstrates that an ML-assisted framework may be applied as a decision support tool to assist ED clinicians in identifying revisiting patients, although the model's performance may not be sufficient for clinic implementation. Given the improvement in the area under the receiver operating characteristic curve, the proposed framework should be further explored as a workable decision support tool to pinpoint ED patients with a high risk of revisit and provide them with appropriate and timely care.

PMID:40773678 | DOI:10.2196/74053

PLoS One. 2025 Aug 7;20(8):e0328302. doi: 10.1371/journal.pone.0328302. eCollection 2025.

ABSTRACT

Solar greenhouse is a primary agricultural facility in northern China during winter, providing a certain level of security for the demand for vegetables and melons in the northern regions. However, there remains a lack of uniformity between crop requirements and the light and thermal environment within the planting area of the greenhouse, resulting in non-uniform growth and development of crops. The present study set out with the objective of investigating the impact of the light environment on the internal conditions of a solar greenhouse. To this end, experimental measurements were employed in conjunction with deep learning models. The results showed that rates of change in air temperature and light intensity were significantly higher in the vertical than the horizontal direction, especially below 1,800 metres, where significant differenced in temperature and light distribution existIn the horizontal direction, the impact of light distribution on soil temperature was significant within a range of less than 4,500 mm from the southern base of the greenhouse. By contrast, the impact was less pronounced within a range of 4,500 to 9,000 mm, In the temporal dimension, light variation significantly affected soil temperatures within 150 mm of the surface, but had no significant effect on temperatures within the 300-600 mm range. Similarly, light variation significantly affected temperatures within 200 mm of the inner wall surface, but had no significant effect on temperatures within the 400-800 mm range.Furthermore, vertical differences in light intensity significantly affected temperatures within the 800 mm height range from the indoor ground level, whereas the impact at other heights was less pronounced. The LSTM prediction model was highly accurate, and this study provided the necessary data and theoretical basis for regulating the light and temperature environments in solar greenhouse.

PMID:40773526 | DOI:10.1371/journal.pone.0328302

PLoS Comput Biol. 2025 Aug 7;21(8):e1012272. doi: 10.1371/journal.pcbi.1012272. Online ahead of print.

ABSTRACT

Epigenetic mechanisms play a crucial role in driving transcript expression and shaping the phenotypic plasticity of glioblastoma stem cells (GSCs), contributing to tumor heterogeneity and therapeutic resistance. These mechanisms dynamically regulate the expression of key oncogenic and stemness-associated genes, enabling GSCs to adapt to environmental cues and evade targeted therapies. Importantly, epigenetic reprogramming allows GSCs to transition between cellular states, including therapy-resistant mesenchymal-like phenotypes, underscoring the need for epigenetic-targeting strategies to disrupt these adaptive processes. Understanding these epigenetic drivers of gene expression provides a foundation for novel therapeutic interventions aimed at eradicating GSCs and improving glioblastoma outcomes. Using machine learning (ML), we employ cross-patient prediction of transcript expression in GSCs by combining epigenetic features from various sources, including ATAC-seq, CTCF ChIP-seq, RNAPII ChIP-seq, H3K27Ac ChIP-seq, and RNA-seq. We investigate different ML and deep learning (DL) models for this task and ultimately build our final pipeline using XGBoost. The model trained on one patient generalizes to other 11 patients with high performance. Notably, H3K27Ac alone from a single patient is sufficient to predict gene expression in all 11 patients. Furthermore, the distribution of H3K27Ac peaks across the genomes of all patients is remarkably similar. These findings suggest that GSCs share a common distributional pattern of enhancer activity characterized by H3K27Ac, which can be utilized to predict gene expression in GSCs across patients. In summary, while GSCs are known for their transcriptomic and phenotypic heterogeneity, we propose that they share a common epigenetic pattern of enhancer activation that defines their underlying transcriptomic expression pattern. This pattern can predict gene expression across patient samples, providing valuable insights into the biology of GSCs.

PMID:40773517 | DOI:10.1371/journal.pcbi.1012272

PLoS One. 2025 Aug 7;20(8):e0328969. doi: 10.1371/journal.pone.0328969. eCollection 2025.

ABSTRACT

Accurate and reliable skeletal motion tracking is essential for rehabilitation monitoring, enabling objective assessment of patient progress and facilitating telerehabilitation applications. Traditional marker-based motion capture systems, while highly accurate, are costly and impractical for home rehabilitation, whereas marker-less methods often suffer from depth estimation errors and occlusions. Recent studies have explored various computer vision and deep learning approaches for human pose estimation, yet challenges remain in ensuring robust depth accuracy and tracking under occlusion conditions. This study proposes a three-dimensional human skeleton tracking system for upper limb activities that integrates temporal and spatial synchronization to improve depth estimation accuracy for rehabilitation exercises. The proposed system combines a 90° secondary camera to compensate for the depth prediction inaccuracies inherent in single-camera systems, reducing error margins by up to 0.4 m. In addition, a linear regression-based depth error correction model is implemented to refine depth coordinates, further improving tracking precision. The Kalman filtering framework is employed to enhance temporal consistency, allowing real-time interpolation of missing joint positions. Experimental results demonstrate that the proposed method significantly reduces depth estimation errors of the elbow and wrist joint (p < 0.001) compared to single camera setups, particularly in scenarios involving occlusions and non-frontal perspectives. This study provides a cost-effective and scalable solution for remote patient monitoring and motor function evaluation.

PMID:40773500 | DOI:10.1371/journal.pone.0328969

PLoS One. 2025 Aug 7;20(8):e0329555. doi: 10.1371/journal.pone.0329555. eCollection 2025.

ABSTRACT

The rapid development of Internet of Things (IoT) technology and deep learning has propelled the deployment of vision-based fire detection algorithms on edge devices, significantly exacerbating the trade-off between accuracy and inference speed under hardware resource constraints. To address this issue, this paper proposes FCMI-YOLO, a real-time fire detection algorithm optimized for edge devices. Firstly, the FasterNext module is proposed to reduce computational cost and enhance detection precision through lightweight design. Secondly, the Cross-Scale Feature Fusion Module (CCFM) and the Mixed Local Channel Attention (MLCA) mechanism are incorporated into the neck network to improve detection performance for small fire targets and reduce resource consumption. Finally, the Inner-DIoU loss function is proposed to optimize bounding box regression. Experimental results on a custom fire dataset demonstrate that FCMI-YOLO increases mAP@50 by 1.5%, reduces parameters by 40%, and lowers GFLOPs to 28.9% of YOLOv5s, demonstrating its practical value for real-time fire detection in edge scenarios with limited computational resources. The core code and dataset are available at https://github.com/ JunJieLu20230823/code.git.

PMID:40773480 | DOI:10.1371/journal.pone.0329555

PLoS One. 2025 Aug 7;20(8):e0328899. doi: 10.1371/journal.pone.0328899. eCollection 2025.

ABSTRACT

Logistics networks are becoming increasingly complex and rely more heavily on real-time vehicle data, necessitating intelligent systems to monitor driver behavior and identify route anomalies. Traditional techniques struggle to capture the dynamic spatiotemporal relationships that define driver actions, route deviations, and operational inefficiencies in big fleets. This paper introduces GNN-RMNet, a hybrid deep learning system that combines GNN, ResNet, and MobileNet for interpretable, scalable, and efficient driver behavior profiling and route anomaly detection. GNN-RMNet utilizes spatiotemporal GPS trajectories and vehicle sensor streams to learn contextual and relational patterns from structured driving data in real time, thereby identifying dangerous driving and route violations. On a real-world GPS-vehicle sensor dataset, the proposed model achieves 98% accuracy, 97% recall, an F1-score of 97.5%, and domain-specific measures like Anomaly Detection Precision (96%) and Route Deviation Sensitivity (95%). Modular design offloads ResNet-GNN analytics to edge nodes while preserving MobileNet components for on-vehicle inference, resulting in reduced inference latency (32 ms). Comparing GNN-RMNet against baseline, ensemble, and hybrid models shows its accuracy, efficiency, and generalization advantages. Computational feasibility, anomaly scoring interpretability, and future deployment concerns, including cybersecurity, data privacy, and multimodal sensor integration, are all covered. For real-time fleet safety management and secure, intelligent, and context-aware logistics, GNN-RMNet seems promising. The framework incorporates multimodal, privacy-aware, and scalable driver analytics, enabling its use in intelligent transportation systems and urban logistics infrastructures.

PMID:40773479 | DOI:10.1371/journal.pone.0328899

PLoS One. 2025 Aug 7;20(8):e0329492. doi: 10.1371/journal.pone.0329492. eCollection 2025.

ABSTRACT

BACKGROUND: The rapid development of urban systems and rising requirements for sustainable development lift resource management issues in smart communities. A fundamental problem for contemporary communities involves effectively using energy and water resources and waste management systems under environmental limitations. Artificial intelligence (AI) techniques at an advanced level deliver new methods that optimize resource management systems.

OBJECTIVE: The research builds and examines a deep-learning framework that optimizes the management of smart community resources. The framework leverages long short-term memory (LSTM) networks for temporal data, convolutional neural networks (CNNs) for spatial analysis, and autoencoders for anomaly detection. The system focuses on two main objectives, which include better forecasting precision, optimum resource distribution, and efficient detection of operational problems.

METHODS: Research validation employed data from the Amsterdam Open Data Platform and Singapore Government Open Data Portal joined by crowdsourced platforms FixMyStreet and OneService. The preprocessing phase involved three stages, i.e., cleaning and normalization and feature engineering steps, before model training and testing phases. Predictive models received assessment based on Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R². A comparison with traditional methods revealed the proposed approach delivered superior performance results.

RESULTS: The deep learning framework demonstrated superior performance, achieving an average reduction of 18.7% in resource consumption and a 16.2% reduction in operational costs. The models outperformed baseline methods, with LSTMs achieving an MAE of 1.8 for water demand prediction and autoencoders detecting anomalies with an F1-score of 95.5%.

CONCLUSION: Due to its effective capabilities, the proposed framework solves challenges in resource management for smart communities while showing the potential of AI-driven solutions for sustainable urban development. Research results demonstrate that integrating sophisticated deep-learning methods yields more significant potential for optimizing resource utilization while improving operational effectiveness.

PMID:40773471 | DOI:10.1371/journal.pone.0329492

Science. 2025 Aug 7;389(6760):594-599. doi: 10.1126/science.adx4380. Epub 2025 Aug 7.

ABSTRACT

The immune system's central function is to maintain homeostasis by guarding the organism against dangerous external and internal stressors. Immunity's operational toolbox contains diverse processes, such as phagocytosis, antigen recognition, cell killing, and secretion of cytokines and antibodies. Although immune cells interact with each other, they also communicate with cells typically associated with other organ systems, including the nervous, circulatory, metabolic, musculoskeletal, endocrine, and hematopoietic. This abundant cross-talk shows that immunity transcends defense and homeostasis: It is a network that participates in many physiological processes necessary for life. By accessing the circulation and inhabiting every tissue, leukocytes sense, interpret, and regulate biological processes. In this Review, we highlight recent studies that illustrate the often bidirectional and symbiotic relationships through which the immune system regulates physiology.

PMID:40773571 | DOI:10.1126/science.adx4380

Mol Plant Pathol. 2025 Aug;26(8):e70135. doi: 10.1111/mpp.70135.

ABSTRACT

Asian soybean rust (ASR), caused by the obligate biotrophic fungus Phakopsora pachyrhizi, was first reported in the continental United States of America (USA) in 2004 and over the years has been of concern to soybean production in the United States. The prevailing hypothesis is that P. pachyrhizi spores were introduced into the United States via hurricanes originating from South America, particularly hurricane Ivan. To investigate the genetic diversity and global population structure of P. pachyrhizi, we employed exome-capture based sequencing on 84 field isolates collected from different geographic regions worldwide. We compared the gene-encoding regions from all these field isolates and found that four major mitochondrial haplotypes are prevalent worldwide. Here, we provide genetic evidence supporting multiple incursions that have led to the currently established P. pachyrhizi population of the United States. Phylogenetic analysis of mitochondrial genes further supports this hypothesis. We observed limited genetic diversity in P. pachyrhizi populations across different geographic regions, suggesting a clonal population structure. Additionally, this study is the first to report the F129L mutation in the Cytb gene outside South America, which is associated with strobilurin tolerance. This study provides the first comprehensive characterisation of P. pachyrhizi population structures defined by genetic evidence from populations across major soybean-growing regions.

PMID:40773485 | DOI:10.1111/mpp.70135

PLoS One. 2025 Aug 7;20(8):e0329088. doi: 10.1371/journal.pone.0329088. eCollection 2025.

ABSTRACT

BACKGROUND: Osilodrostat is a medication recently approved for the treatment of Cushing's syndrome. However, there is a current dearth of large-scale studies on the adverse events associated with Osilodrostat. Consequently, this study aims to comprehensively evaluate these adverse events using data from the FDA Adverse Event Reporting System (FAERS).

METHODS: A disproportionality analysis was utilized to identify signals of adverse events linked to Osilodrostat. Furthermore, a Weibull distribution analysis was conducted to evaluate the temporal evolution of adverse events, and subgroup analyses were performed. The Wilcoxon test was applied to investigate differences in the temporal patterns of adverse events across different genders.

RESULTS: A total of 1,078 cases related to Osilodrostat were identified, including 3,744 adverse events. The most frequent and severe signals of adverse events were investigations, off-label use, fatigue, nausea, and adrenal insufficiency. The median time to onset of adverse events related to Osilodrostat was 52 days after starting the medication. There was a gender difference in the median time to onset of adverse events, with a median of 15 days for males and 34 days for females.

CONCLUSION: This study provides a comprehensive evaluation of adverse events related to Osilodrostat, confirming some known side effects and revealing other potential risks. This information offers valuable insights for the clinical application of Osilodrostat.

PMID:40773444 | DOI:10.1371/journal.pone.0329088

Stat Med. 2025 Aug;44(18-19):e70206. doi: 10.1002/sim.70206.

ABSTRACT

Many rare disease clinical trials are underpowered to detect a moderate treatment effect of an investigational product due to the limited number of participants available for the trials. In addition, given the complex, multisystemic nature of many rare diseases, it is challenging to confidently prespecify a single primary efficacy endpoint that is applicable to all trial participants with a heterogeneous clinical manifestation of their disease. Traditional trial designs and analysis methods often used in more common diseases to analyze the same endpoint(s) for all patients may be inefficient or impractical for a rare disease with heterogeneous clinical manifestations. To address these issues, we propose a novel trial design and analytic approach that allows for an evaluation of stratum-specific efficacy endpoints in a broader population of participants. We develop several nonparametric global test methods that can accommodate the novel design and provide global evaluation of treatment effects. Using a case example in patients with Fabry disease, our simulation studies illustrate that the novel design evaluated using the global test methods may be more sensitive to detect a treatment effect compared to the traditional design that uses the same endpoint(s) for all patients.

PMID:40772797 | DOI:10.1002/sim.70206

Cancer Chemother Pharmacol. 2025 Aug 7;95(1):79. doi: 10.1007/s00280-025-04801-9.

ABSTRACT

PURPOSE: Oxaliplatin (L-OHP) is a platinum-based anticancer agent that induces peripheral neuropathy (OIPN), a dose-limiting toxicity caused by platinum accumulation in the dorsal root ganglion (DRG) and neuronal damage. Proton pump inhibitors (PPIs) have recently been proposed as preventive agents for OIPN; however, they have not been clinically implemented. This study aimed to evaluate the ameliorative effects of PPIs on OIPN using real-world data and a pharmacometrics approach based on animal data.

METHODS: Real-world database analysis was conducted using the Japanese Adverse Drug Event Report (JADER) database. We calculated the reporting odds ratios to evaluate the effects of the candidate drugs. Rats were intravenously administered L-OHP (5 mg/kg) once a week. Omeprazole (2-20 mg/kg) or esomeprazole (1-10 mg/kg) was orally administered on the five times a week. Blood and DRG samples were collected after L-OHP administration. The OIPN was assessed using the von Frey test. A pharmacokinetic-toxicodynamic (PK-TD) model analysis was performed using the obtained data.

RESULTS: The JADER analysis suggested that omeprazole may have a suppressive effect on OIPN. In animal study, co-administration of omeprazole or esomeprazole significantly decreased the platinum concentration in the DRG compared with L-OHP monotherapy and suppressed the development of OIPN in a dose-dependent manner. The PK-TD model of platinum composed of the DRG compartment quantitatively described the preventive effects of omeprazole and esomeprazole on OIPN.

CONCLUSION: Omeprazole and esomeprazole may be valuable agents for suppressing OIPN by inhibiting platinum influx into the DRG and exerting a potential neuroprotective effect.

PMID:40773063 | DOI:10.1007/s00280-025-04801-9

Discov Oncol. 2025 Aug 7;16(1):1491. doi: 10.1007/s12672-025-03377-3.

ABSTRACT

BACKGROUND: High abundance of eosinophils has been proved to associated with favorable disease progression in non-small cell lung cancer (NSCLC) in the previous observational studies, but the causal relationship remains unclear. It is also unclear whether white blood cell (WBC) counts are essential for the risk of NSCLC.

METHODS: Using multiple methods of Mendelian randomization (MR), we assessed the causality of WBC count, particularly basophil, eosinophil, monocyte, lymphocyte, and neutrophil counts on the risk of NSCLC, which includes squamous carcinoma and adenocarcinoma. Single cell RNA-sequencing and RNA-sequencing analysis illustrate the underline mechanism of the causality and its biological effects.

RESULTS: Univariable MR analysis indicated the protective effect of elevated eosinophil counts on NSCLC and adenocarcinoma subtype. The protective effect of eosinophils persisted even after adjusting. The protective functions mainly effected by immune activating, and it contribute to better survival and favorable response to immune therapy. Univariate MR analysis also states the risk role of neutrophil. Sequencing based analysis proved the immune inhibit functions of neutrophil, which lead to worse survival and immune treatment response.

CONCLUSION: Our study indicated a correlation between circulating eosinophil counts, neutrophil counts, and the development of NSCLC. And sequencing analysis confirm this relationship and illustrated the underline mechanism.

PMID:40773084 | DOI:10.1007/s12672-025-03377-3

Pharmacogenomics. 2025 Aug 7:1-16. doi: 10.1080/14622416.2025.2541404. Online ahead of print.

ABSTRACT

Graft-versus-host disease (GvHD) remains a significant complication of allogeneic hematopoietic stem cell transplantation (HSCT), contributing to increased morbidity and mortality. Thus, continuous development of novel prophylactic and therapeutic approaches is crucial for GvHD prevention and management. With the current development of personalized medicine and a more patient-oriented approach, pharmacogenetics has the potential to become a critical factor in optimizing the prophylaxis and treatment of GvHD. This review explores the role of pharmacogenetic variants in prophylaxis and management of GvHD, including drugs such as calcineurin inhibitors, methotrexate, mycophenolate mofetil, cyclophosphamide, and corticosteroids. A deeper understanding of these genetic factors could help in developing a more personalized approach to GvHD management, improving clinical outcomes and minimizing adverse effects. This review underscores the need for more pharmacogenetic association studies, as well as for incorporating pharmacogenetic testing into clinical practice to refine drug selection and dosing strategies in GvHD treatment.

PMID:40772575 | DOI:10.1080/14622416.2025.2541404