IET Syst Biol. 2025 Jan-Dec;19(1):e70010. doi: 10.1049/syb2.70010.

ABSTRACT

Cancer is a serious and complex disease caused by uncontrolled cell growth and is becoming one of the leading causes of death worldwide. Anticancer peptides (ACPs), as a bioactive peptide with lower toxicity, emerge as a promising means of effectively treating cancer. Identifying ACPs is challenging due to the limitation of experimental conditions. To address this, we proposed a dual-channel-based deep learning method, termed ACP-DPE, for ACP prediction. The ACP-DPE consisted of two parallel channels: one was an embedding layer followed by the bi-directional gated recurrent unit (Bi-GRU) module, and the other was an adaptive embedding layer followed by the dilated convolution module. The Bi-GRU module captured the peptide sequence dependencies, whereas the dilated convolution module characterised the local relationship of amino acids. Experimental results show that ACP-DPE achieves an accuracy of 82.81% and a sensitivity of 86.63%, surpassing the state-of-the-art method by 3.86% and 5.1%, respectively. These findings demonstrate the effectiveness of ACP-DPE for ACP prediction and highlight its potential as a valuable tool in cancer treatment research.

PMID:40119615 | DOI:10.1049/syb2.70010

Pest Manag Sci. 2025 Mar 22. doi: 10.1002/ps.8790. Online ahead of print.

ABSTRACT

BACKGROUND: Rice diseases that are not detected in a timely manner may trigger large-scale yield reduction and bring significant economic losses to farmers.

AIMS: In order to solve the problems of insufficient rice disease detection accuracy and a model that is lightweight, this study proposes a lightweight rice disease detection method based on the improved YOLOv8. The method incorporates a full-dimensional dynamic convolution (ODConv) module to enhance the feature extraction capability and improve the robustness of the model, while a dynamic non-monotonic focusing mechanism, WIoU (weighted interpolation of sequential evidence for intersection over union), is employed to optimize the bounding box loss function for faster convergence and improved detection performance. In addition, the use of a high-resolution detector head improves the small target detection capability and reduces the network parameters by removing redundant layers.

RESULTS: Experimental results show a 66.6% reduction in parameters and a 61.9% reduction in model size compared to the YOLOv8n baseline. The model outperforms Faster R-CNN, YOLOv5s, YOLOv6n, YOLOv7-tiny, and YOLOv8n by 29.2%, 3.8%, 5.2%, 5.7%, and 5.2%, respectively, in terms of the mean average precision (mAP), which shows a significant improvement in the detection performance.

CONCLUSION: The YOLOv8-OW model provides a more effective solution, which is suitable for deployment on resource-limited mobile devices, to provide real-time and accurate disease detection support for farmers and further promotes the development of precision agriculture. © 2025 Society of Chemical Industry.

PMID:40119571 | DOI:10.1002/ps.8790

Genome Biol. 2025 Mar 21;26(1):64. doi: 10.1186/s13059-025-03519-4.

ABSTRACT

Discovering a lower-dimensional embedding of single-cell data can improve downstream analysis. The embedding should encapsulate both the high-level features and low-level variations. While existing generative models attempt to learn such low-dimensional representations, they have limitations. Here, we introduce scVAEDer, a scalable deep-learning model that combines the power of variational autoencoders and deep diffusion models to learn a meaningful representation that retains both global structure and local variations. Using the learned embeddings, scVAEDer can generate novel scRNA-seq data, predict perturbation response on various cell types, identify changes in gene expression during dedifferentiation, and detect master regulators in biological processes.

PMID:40119479 | DOI:10.1186/s13059-025-03519-4

J Cheminform. 2025 Mar 21;17(1):35. doi: 10.1186/s13321-025-00979-5.

ABSTRACT

Protein-protein interactions (PPIs) are central to the mechanisms of signaling pathways and immune responses, which can help us understand disease etiology. Therefore, there is a significant need for efficient and rapid automated approaches to predict changes in PPIs. In recent years, there has been a significant increase in applying deep learning techniques to predict changes in binding affinity between the original protein complex and its mutant variants. Particularly, the adoption of graph neural networks (GNNs) has gained prominence for their ability to learn representations of protein-protein complexes. However, the conventional GNNs have mainly concentrated on capturing local features, often disregarding the interactions among distant elements that hold potential important information. In this study, we have developed a transformer-based graph neural network to extract features of the mutant segment from the three-dimensional structure of protein-protein complexes. By embracing both local and global features, the approach ensures a more comprehensive understanding of the intricate relationships, thus promising more accurate predictions of binding affinity changes. To enhance the representation capability of protein features, we incorporate a large-scale pre-trained protein language model into our approach and employ the global protein feature it provides. The proposed model is shown to be able to predict the mutation changes in binding affinity with a root mean square error of 1.10 and a Pearson correlation coefficient of near 0.71, as demonstrated by performance on test and validation cases. Our experiments on all five datasets, including both single mutant and multiple mutant cases, demonstrate that our model outperforms four state-of-the-art baseline methods, and the efficacy was subjected to comprehensive experimental evaluation. Our study introduces a transformer-based graph neural network approach to accurately predict changes in protein-protein interactions (PPIs). By integrating local and global features and leveraging pretrained protein language models, our model outperforms state-of-the-art methods across diverse datasets. The results of this study can provide new views for studying immune responses and disease etiology related to protein mutations. Furthermore, this approach may contribute to other biological or biochemical studies related to PPIs.Scientific contribution Our scientific contribution lies in the development of a novel transformer-based graph neural network tailored to predict changes in protein-protein interactions (PPIs) with excellent accuracy. By seamlessly integrating both local and global features extracted from the three-dimensional structure of protein-protein complexes, and leveraging the rich representations provided by pretrained protein language models, our approach surpasses existing methods across diverse datasets. Our findings may offer novel insights for the understanding of complex disease etiology associated with protein mutations. The novel tool can be applicable to various biological and biochemical investigations involving protein mutations.

PMID:40119464 | DOI:10.1186/s13321-025-00979-5

Cancer Cell Int. 2025 Mar 21;25(1):111. doi: 10.1186/s12935-025-03756-4.

ABSTRACT

Nowadays, gastric cancer has become a significant issue in the global cancer burden, and its impact cannot be ignored. The rapid development of artificial intelligence technology is attempting to address this situation, aiming to change the clinical management landscape of gastric cancer fundamentally. In this transformative change, machine learning and deep learning, as two core technologies, play a pivotal role, bringing unprecedented innovations and breakthroughs in the diagnosis, treatment, and prognosis evaluation of gastric cancer. This article comprehensively reviews the latest research status and application of artificial intelligence algorithms in gastric cancer, covering multiple dimensions such as image recognition, pathological analysis, personalized treatment, and prognosis risk assessment. These applications not only significantly improve the sensitivity of gastric cancer risk monitoring, the accuracy of diagnosis, and the precision of survival prognosis but also provide robust data support and a scientific basis for clinical decision-making. The integration of artificial intelligence, from optimizing the diagnosis process and enhancing diagnostic efficiency to promoting the practice of precision medicine, demonstrates its promising prospects for reshaping the treatment model of gastric cancer. Although most of the current AI-based models have not been widely used in clinical practice, with the continuous deepening and expansion of precision medicine, we have reason to believe that a new era of AI-driven gastric cancer care is approaching.

PMID:40119433 | DOI:10.1186/s12935-025-03756-4

BMC Med Imaging. 2025 Mar 21;25(1):95. doi: 10.1186/s12880-025-01636-x.

ABSTRACT

PURPOSE: To develop an automatic segmentation model for surgical marks, titanium clips, in target volume delineation of breast cancer radiotherapy after lumpectomy.

METHODS: A two-stage deep-learning model is used to segment the titanium clips from CT image. The first network, Location Net, is designed to search the region containing all clips from CT. Then the second network, Segmentation Net, is designed to search the locations of clips from the previously detected region. Ablation studies are performed to evaluate the impact of various inputs for both networks. The two-stage deep-learning model is also compared with the other existing deep-learning methods including U-Net, V-Net and UNETR. The segmentation accuracy of these models is evaluated by three metrics: Dice Similarity Coefficient (DSC), 95% Hausdorff Distance (HD95), and Average Surface Distance (ASD).

RESULTS: The DSC, HD95 and ASD of the two-stage model are 0.844, 2.008 mm and 0.333 mm, while their values are 0.681, 2.494 mm and 0.785 mm for U-Net, 0.767, 2.331 mm and 0.497 mm for V-Net, 0.714, 2.660 mm and 0.772 mm for UNETR. The proposed 2-stage model achieved the best performance among the four models.

CONCLUSION: With the two-stage searching strategy the accuracy to detect titanium clips can be improved comparing to those existing deep-learning models with one-stage searching strategy. The proposed segmentation model can facilitate the delineation of tumor bed and subsequent target volume for breast cancer radiotherapy after lumpectomy.

PMID:40119258 | DOI:10.1186/s12880-025-01636-x

Intensive Care Med Exp. 2025 Mar 21;13(1):39. doi: 10.1186/s40635-025-00746-8.

ABSTRACT

BACKGROUND: Patient-ventilator asynchrony (PVA) is a mismatch between the patient's respiratory drive/effort and the ventilator breath delivery. It occurs frequently in mechanically ventilated patients and has been associated with adverse events and increased duration of ventilation. Identifying PVA through visual inspection of ventilator waveforms is highly challenging and time-consuming. Automated PVA detection using Artificial Intelligence (AI) has been increasingly studied, potentially offering real-time monitoring at the bedside. In this review, we discuss advances in automatic detection of PVA, focusing on developments of the last 15 years.

RESULTS: Nineteen studies were identified. Multiple forms of AI have been used for the automated detection of PVA, including rule-based algorithms, machine learning and deep learning. Three licensed algorithms are currently reported. Results of algorithms are generally promising (average reported sensitivity, specificity and accuracy of 0.80, 0.93 and 0.92, respectively), but most algorithms are only available offline, can detect a small subset of PVAs (focusing mostly on ineffective effort and double trigger asynchronies), or remain in the development or validation stage (84% (16/19 of the reviewed studies)). Moreover, only in 58% (11/19) of the studies a reference method for monitoring patient's breathing effort was available. To move from bench to bedside implementation, data quality should be improved and algorithms that can detect multiple PVAs should be externally validated, incorporating measures for breathing effort as ground truth. Last, prospective integration and model testing/finetuning in different ICU settings is key.

CONCLUSIONS: AI-based techniques for automated PVA detection are increasingly studied and show potential. For widespread implementation to succeed, several steps, including external validation and (near) real-time employment, should be considered. Then, automated PVA detection could aid in monitoring and mitigating PVAs, to eventually optimize personalized mechanical ventilation, improve clinical outcomes and reduce clinician's workload.

PMID:40119215 | DOI:10.1186/s40635-025-00746-8

Sci Rep. 2025 Mar 21;15(1):9845. doi: 10.1038/s41598-025-94664-0.

ABSTRACT

Colorectal cancer (CRC) ranks as the third most common and second deadliest cancer worldwide. The immune system, particularly tertiary lymphoid structures (TLS), significantly influences CRC progression and prognosis. TLS maturation, especially in the presence of germinal centers, correlates with improved patient outcomes; however, consistent and objective TLS assessment is hindered by varying histological definitions and limitations of traditional staining methods. This study involved 656 patients with colorectal adenocarcinoma from CHU Brest, France. We employed dual immunohistochemistry staining for CD21 and CD23 to classify TLS maturation stages in whole-slide images and implemented a fivefold cross-validation. Using ResNet50 and Vision Transformer models, we compared various aggregation methods, architectures, and pretraining techniques. Our automated system, TLS-PAT, achieved high accuracy (0.845) and robustness (kappa = 0.761) in classifying TLS maturation, particularly with the Vision Transformer pretrained on ImageNet using Max Confidence aggregation. This AI-driven approach offers a standardized method for automated TLS classification, complementing existing detection techniques. Our open-source tools are designed for easy integration with current methods, paving the way for further research in external datasets and other cancer types.

PMID:40119179 | DOI:10.1038/s41598-025-94664-0

Sci Rep. 2025 Mar 21;15(1):9805. doi: 10.1038/s41598-025-94680-0.

ABSTRACT

Accurate embryo morphology assessment is essential in assisted reproductive technology for selecting the most viable embryo. Artificial intelligence has the potential to enhance this process. However, the limited availability of embryo data presents challenges for training deep learning models. To address this, we trained two generative models using two datasets-one we created and made publicly available, and one existing public dataset-to generate synthetic embryo images at various cell stages, including 2-cell, 4-cell, 8-cell, morula, and blastocyst. These were combined with real images to train classification models for embryo cell stage prediction. Our results demonstrate that incorporating synthetic images alongside real data improved classification performance, with the model achieving 97% accuracy compared to 94.5% when trained solely on real data. This trend remained consistent when tested on an external Blastocyst dataset from a different clinic. Notably, even when trained exclusively on synthetic data and tested on real data, the model achieved a high accuracy of 92%. Furthermore, combining synthetic data from both generative models yielded better classification results than using data from a single generative model. Four embryologists evaluated the fidelity of the synthetic images through a Turing test, during which they annotated inaccuracies and offered feedback. The analysis showed the diffusion model outperformed the generative adversarial network, deceiving embryologists 66.6% versus 25.3% and achieving lower Fréchet inception distance scores.

PMID:40119109 | DOI:10.1038/s41598-025-94680-0

Environ Sci Technol. 2025 Mar 22. doi: 10.1021/acs.est.4c11275. Online ahead of print.

ABSTRACT

Gaseous pollutants such as CO, NO2, O3, and SO2 are linked to adverse clinical outcomes in patients with fibrotic interstitial lung diseases (fILDs), particularly idiopathic pulmonary fibrosis. However, the effect of various exposure estimation methods on these findings remains unclear. This study aims to evaluate three spatial approaches─nearest neighbor (NN), inverse distance weighting (IDW), and Kriging─for estimating gaseous pollutant exposures and to assess how these methods affect health outcome estimates in fILD patients. A 10-fold cross-validation showed that Kriging had the lowest prediction error compared to NN and IDW, with RMSE for CO = 0.43 ppm (11%), O3 = 5.9 ppb (14%), SO2 = 2.7 ppb (12%), and NO2 = 7.6 ppb (9%), respectively. Kriging also excelled over other methods across wide spatial and temporal ranges, showing the highest spatial R2 for CO and O3 and the highest temporal R2 for SO2 and NO2. In a large cohort of patients with fILD, higher levels of CO, SO2, and NO2 exposure were associated with lower pulmonary function. The magnitude of association and its precision were higher in SO2 and CO estimated by the Kriging method. This study underscores Kriging as a robust method for estimating gaseous pollutant levels and offers valuable insights for future epidemiological studies.

PMID:40119855 | DOI:10.1021/acs.est.4c11275

Adv Sci (Weinh). 2025 Mar 22:e2405434. doi: 10.1002/advs.202405434. Online ahead of print.

ABSTRACT

Idiopathic pulmonary fibrosis (IPF) remains an incurable form of interstitial lung disease with sub-optimal treatments that merely address adverse symptoms or slow fibrotic progression. Here, inhalable hsa-miR-30a-3p-loaded liposomes (miR-30a) for the treatment of bleomycin-induced pulmonary fibrosis in mice are presented. It was previously found that exosomes (Exo) derived from lung spheroid cells are therapeutic in multiple animal models of pulmonary fibrosis and are highly enriched for hsa-miR-30a-3p. The present study investigates this miRNA as a singular factor to treat IPF. Liposomes containing miR-30a mimic can be delivered to rodents through dry powder inhalation. Inhaled miR-30a and Exo consistently lead to improved pulmonary function across six consecutive pulmonary function tests and promote de-differentiation of profibrotic myofibroblasts. The heterogenous composure of Exo also promotes reparative alveolar type I and II cell remodeling and vascular wound healing through broad transforming growth factor-beta signaling downregulation, while miR-30a targets myofibroblast de-differentiation through CNPY2/PERK/DDIT3 signaling. Overall, inhaled miR-30a represses the epithelial-mesenchymal transition of myofibroblasts, providing fibrotic attenuation and subsequent improvements in pulmonary function.

PMID:40119620 | DOI:10.1002/advs.202405434

Cell Death Dis. 2025 Mar 21;16(1):196. doi: 10.1038/s41419-025-07522-2.

ABSTRACT

The intracellular enzyme 11β-hydroxysteroid dehydrogenase type 1 (11βHSD1) catalyzes the interconversion of active glucocorticoid (cortisol) and its intrinsically inert form (cortisone) in metabolic tissues. Although 11βHSD1 is considered a promising therapeutic target in metabolic disorders such as type 2 diabetes, obesity, and nonalcoholic steatohepatitis because of its hepatic functions, its roles in other tissues have received less attention. In this study, we show that the 11βHSD1-specific inhibitor J2H-1702 facilitates the reversion of endothelial-to-mesenchymal transition in multicellular lung spheroid models encapsulating the complex crosstalk among lung cancer cells, vascular endothelial cells, and macrophages. In vascular endothelial cells, J2H-1702 not only suppressed interleukin-1α (IL-1α) expression but also attenuated reactive oxygen species-induced DNA damage by upregulating heme oxygenase-1. Additionally, in macrophages, which are key regulators of fibrogenesis, inhibition of 11βHSD1 markedly reduced IL-1β expression, thereby modulating the pro-inflammatory phenotype of activated macrophages. In mouse models of pulmonary fibrosis, including a bleomycin-induced idiopathic model and a radiation-induced model, J2H-1702 alleviated pulmonary fibrosis and markedly improved the efficacy of nintedanib. Collectively, our data suggest that J2H-1702 holds promise as a clinical candidate for the treatment of pulmonary fibrosis associated with reactive oxygen species-induced DNA damage, endothelial-to-mesenchymal transition, and inflammatory responses.

PMID:40118823 | DOI:10.1038/s41419-025-07522-2

New Phytol. 2025 Mar 21. doi: 10.1111/nph.70063. Online ahead of print.

ABSTRACT

Plant disease outbreaks, exacerbated by climate change, threaten food security and environmental sustainability world-wide. Plants interact with a wide range of microorganisms. The quest for resilient agriculture requires a deep insight into the molecular and ecological interplays between plants and their associated microbial communities. Omics methods, by profiling entire molecular sets, have shed light on these complex interactions. Nonetheless, deciphering the relationships among thousands of molecular components remains a formidable challenge, and studies that integrate these components into cohesive biological networks involving plants and associated microbes are still limited. Systems biology has the potential to predict the effects of biotic and abiotic perturbations on these networks. It is therefore a promising framework for addressing the full complexity of plant-microbiome interactions.

PMID:40119556 | DOI:10.1111/nph.70063

Rice (N Y). 2025 Mar 22;18(1):20. doi: 10.1186/s12284-025-00774-8.

ABSTRACT

Grain size is vital determinant for grain yield and quality, which specified by its three-dimensional structure of seeds (length, width and thickness). The ZINC FINGER-HOMEODOMAIN (ZHD) proteins play critical roles in plant growth and development. However, the information regarding the function in reproductive development of ZHD proteins is scarce. Here, we deeply characterized the phenotype of oszhd1, oszhd2, and oszhd1oszhd2. The single mutants of OsZHD1/2 were similar with wild type. Nevertheless, the double mutant displayed dwarfism and smaller reproductive organs, and shorter, narrower, and thinner grain size. oszhd1oszhd2 revealed a significant decrease in total cell length and number, and single cell width in outer parenchyma; reducing the average width of longitudinal epidermal cells, but the length were increased in outer and inner glumes of oszhd1oszhd2 compared with wild-type, oszhd1-1, oszhd2-1, respectively. OsZHD1 and OsZHD2 encoded the nucleus protein and were distributed predominately in stem and the developing spikelets, asserting their roles in grain size. Meanwhile, yeast two-hybrid, bimolecular fluorescence complementation, and Co-immunoprecipitation assay clarified that OsZHD1 could directly interacted with OsZHD2. The differential expression analysis showed that 839 DEGs, which were down-regulated in oszhd1oszhd2 than wild type and single mutants, were mainly enriched in secondary metabolite biosynthetic, integral component of membrane, and transporter activity pathway. Moreover, it is reliable that the altered expression of cell cycle and expansion-related and grain size-related genes were observed in RNA-seq data, highly consistent with the qRT-PCR results. Altogether, our results suggest that OsZHD1/2 are functional redundancy and involved in regulating grain size by influencing cell proliferation in rice.

PMID:40119214 | DOI:10.1186/s12284-025-00774-8

Cell Res. 2025 Mar 21. doi: 10.1038/s41422-025-01095-7. Online ahead of print.

ABSTRACT

Biased allosteric modulators (BAMs) of G protein-coupled receptors (GPCRs) have been at the forefront of drug discovery owing to their potential to selectively stimulate therapeutically relevant signaling and avoid on-target side effects. Although structures of GPCRs in complex with G protein or GRK in a BAM-bound state have recently been resolved, revealing that BAM can induce biased signaling by directly modulating interactions between GPCRs and these two transducers, no BAM-bound GPCR-arrestin complex structure has yet been determined, limiting our understanding of the full pharmacological profile of BAMs. Herein, we developed a chemical protein synthesis strategy to generate neurotensin receptor 1 (NTSR1) with defined hexa-phosphorylation at its C-terminus and resolved high-resolution cryo-EM structures (2.65-2.88 Å) of NTSR1 in complex with both β-arrestin1 and the BAM SBI-553. These structures revealed a unique "loop engagement" configuration of β-arrestin1 coupling to NTSR1 in the presence of SBI-553, markedly different from the typical "core engagement" configuration observed in the absence of BAMs. This configuration is characterized by the engagement of the intracellular loop 3 of NTSR1 with a cavity in the central crest of β-arrestin1, representing a previously unobserved, arrestin-selective conformation of GPCR. Our findings fill the critical knowledge gap regarding the regulation of GPCR-arrestin interactions and biased signaling by BAMs, which would advance the development of safer and more efficacious GPCR-targeted therapeutics.

PMID:40118988 | DOI:10.1038/s41422-025-01095-7

Sci Rep. 2025 Mar 21;15(1):9768. doi: 10.1038/s41598-025-94632-8.

ABSTRACT

Because the innate immunity might and fungi in the lungs might enhance the severity of lupus-induced diffuse alveolar hemorrhage (DAH), intraperitoneal pristane injection was performed in C57BL6 mice with intratracheal administration by Candida albicans or phosphate buffer solution (PBS). Despite the similar pristane-induced lupus (proteinuria, serum creatinine, and serum anti-dsDNA) at 5 weeks of the model, Candida administration worsened several characteristics, including mortality, body weight, serum cytokines (TNF-α and IL-6), and lung hemorrhage score, and cytokines in the lung tissue (TNF-α, IL-6, and IL-10), but not gut permeability (FITC-dextran assay), serum IL-10, immune cells in the spleens (flow cytometry analysis), and activities of peritoneal macrophages (polymerase-chain reaction). Although Candida administration reduced proteobacterial abundance and altered alpha and beta diversity compared with PBS control, lung microbiota was not different between Candida administration in pristane- and non-pristane-administered mice. Because of the prominent Gram-negative bacteria in lung microbiota and the role of neutrophils in DAH, lipopolysaccharide (LPS) with and without heat-killed Candida preparation was tested. Indeed, Candida preparation with LPS induced more severe pro-inflammatory neutrophils than LPS stimulation alone as indicated by the expression of several genes (TNF-α, IL-6, IL-1β, IL-10, Dectin-1, and NF-κB). In conclusion, the intratracheal Candida worsened pristane-induced lung hemorrhage partly through the enhanced neutrophil responses against bacteria and fungi. More studies on Candida colonization in sputum from patients with lupus-induced DAH are interesting.

PMID:40118938 | DOI:10.1038/s41598-025-94632-8

Nat Commun. 2025 Mar 21;16(1):2223. doi: 10.1038/s41467-025-57345-0.

ABSTRACT

The delay between the origin of animals in the Neoproterozoic and their Cambrian diversification remains perplexing. Animal diversification mirrors an expansion in marine shelf area under a greenhouse climate, though the extent to which these environmental conditions directly influenced physiology and early organismal ecology remains unclear. Here, we use a biogeochemical model to quantify oxygen dynamics at the sunlit sediment-water interface over day-night (diel) cycles at warm and cold conditions. We find that warm temperatures dictated physiologically stressful diel benthic oxic-anoxic shifts over a nutrient-rich shelf. Under these conditions, a population-and-phenotype model further show that the benefits of efficient cellular oxygen sensing that can offer adaptations to stress outweigh its cost. Since diurnal benthic redox variability would have expanded as continents were flooded in the end-Neoproterozoic and early Palaeozoic, we propose that a combination of physiological stress and ample resources in the benthic environment may have impacted the adaptive radiation of animals tolerant to oxygen fluctuations.

PMID:40118825 | DOI:10.1038/s41467-025-57345-0

Geriatr Gerontol Int. 2025 Mar 21. doi: 10.1111/ggi.70035. Online ahead of print.

ABSTRACT

AIM: The use of potentially inappropriate medications (PIMs) in older adults can increase the risk of drug-related adverse events. We aimed to examine the associations between PIMs, frailty, and each frailty component in community-dwelling older women.

METHODS: This cross-sectional study included participants aged ≥65 years from a prospective cohort of older Japanese women. Frailty was classified using the Japanese version of Fried's Frailty Criteria, comprising five components. PIMs were identified using a screening tool for Japanese among regular prescription medications collected from participants' prescription notebooks. Multivariable logistic regression models adjusted for age and comorbidities were used to examine the association between PIMs (0, 1, 2, ≥3), frailty, and each component. The possible interactions between age groups (65-74 and ≥75 years) and PIMs were investigated. Age-stratified analyses were also performed.

RESULTS: We analyzed 530 older women (median age [interquartile range], 71 [68, 75] years) with a frailty prevalence of 5.5%. Three or more PIMs were associated with frailty and weight loss (adjusted odds ratio [95% confidence interval], 3.80 [1.23, 11.80], 2.53 [1.15, 5.39]). In age-stratified analyses, ≥3 PIMs were associated with weight loss (8.39 [1.79, 48.98]) in women aged ≥75 years, whereas 1 or 2 PIMs were associated with frailty (4.52 [1.17, 19.08]) or weakness (3.13 [1.22, 7.78]) in those aged 65-74 years.

CONCLUSIONS: Our results may suggest that the number of PIM prescriptions is associated with frailty and frailty components in older women. Longitudinal studies are required to clarify the causality between the number of PIMs and frailty. Geriatr Gerontol Int 2025; ••: ••-••.

PMID:40119543 | DOI:10.1111/ggi.70035

Neurotherapeutics. 2025 Mar 20:e00575. doi: 10.1016/j.neurot.2025.e00575. Online ahead of print.

ABSTRACT

Neuroblastoma is an aggressive childhood cancer that arises from the sympathetic nervous system. Despite advances in treatment, high-risk neuroblastoma remains difficult to manage due to its heterogeneous nature and frequent development of drug resistance. Drug repurposing guided by single-cell analysis presents a promising strategy for identifying new therapeutic options. Here, we aim to characterize high-risk neuroblastoma subpopulations and identify effective repurposed drugs for targeted treatment. We performed single-cell transcriptomic analysis of neuroblastoma samples, integrating bulk RNA-seq data deconvolution with clinical outcomes to define distinct malignant cell states. Using a systematic drug repurposing pipeline, we identified and validated potential therapeutic agents targeting specific high-risk neuroblastoma subpopulations. Single-cell analysis revealed 17 transcriptionally distinct neuroblastoma subpopulations. Survival analysis identified a highly aggressive subpopulation characterized by elevated UBE2C/PTTG1 expression and poor patient outcomes, distinct from a less aggressive subpopulation with favorable prognosis. Drug repurposing screening identified Adavosertib as particularly effective against the aggressive subpopulation, validated using SK-N-DZ cells as a representative model. Mechanistically, Adavosertib suppressed cell proliferation through AKT/mTOR pathway disruption, induced G2/M phase cell cycle arrest, and promoted apoptosis. Further analysis revealed UBE2C and PTTG1 as key molecular drivers of drug resistance, where their overexpression enhanced proliferation, Adavosertib resistance, and cell migration. This study establishes a single-cell-based drug repurposing strategy for high-risk neuroblastoma treatment. Our approach successfully identified Adavosertib as a promising repurposed therapeutic agent for targeting specific high-risk neuroblastoma subpopulations, providing a framework for developing more effective personalized treatment strategies.

PMID:40118716 | DOI:10.1016/j.neurot.2025.e00575

Eur J Med Chem. 2025 Mar 15;290:117516. doi: 10.1016/j.ejmech.2025.117516. Online ahead of print.

ABSTRACT

Ferroptosis has emerged as a promising therapeutic approach for a wide range of diseases. However, limited chemical diversity and poor drug-like profiles have hindered the development of effective ferroptosis inhibitors for clinical use. Herein, we identified drug-like imidazo[1,2-a]pyridine-3-amine derivatives as innovative ferroptosis inhibitors for injury-related diseases by drug scaffold repositioning strategy. Our findings established that the selected compounds exhibited high radical scavenging and effective membrane retention, thereby leading to significant suppression of lipid peroxidation and ferroptosis at nanomolar concentrations. Notably, compound C18, with low cytotoxicity and favorable pharmacokinetics properties, demonstrated remarkable in vivo neuroprotection against ischemic brain injury in mice. In conclusion, our investigations not only engender potent ferroptosis inhibitors with novel structural characteristics that warrant further development, but also serve as a valuable case study for drug repurposing in the discovery of additional ferroptosis inhibitors.

PMID:40117856 | DOI:10.1016/j.ejmech.2025.117516