Sci Rep. 2025 Aug 5;15(1):28490. doi: 10.1038/s41598-025-10670-2.

ABSTRACT

Today, the problem of using social media to spread false information is not only widespread but also quite serious. The extensive dissemination of fake news, regardless of whether it is produced by human beings or computer programs, has a negative impact not only on society but also on individuals in terms of politics and society. Currently of social networks, the quick dissemination of news provides a challenge when it comes to establishing the reliability of the information in a satisfactory manner. Because of this, the requirement for automated technologies that can identify fake news has become of the utmost importance. Existing fake news detection methods often suffer from challenges such as limited labeled data, inability to fully capture complex linguistic nuances, and inadequate integration of different embedding techniques, which restrict their effectiveness and generalizability. In this work, we propose a novel multi-stage transfer learning framework that leverages the strengths of pre-trained large language models, particularly RoBERTa, tailored specifically for fake news detection in limited data scenarios. Unlike prior studies which primarily rely on standard fine-tuning, our approach introduces a systematic comparison of word embedding techniques such as Word2Vec and one-hot encoding, combined with a refined fine-tuning process to enhance model performance and interpretability. The experimental results on two real-world benchmark datasets demonstrate that our method achieves a significant accuracy improvement of at least 3.9% over existing state-of-the-art models, while also providing insights into the role of embedding techniques in fake news classification. To address these limitations, our approach fills the gap by combining multi-stage transfer learning with embedding comparisons and task-specific optimizations, enabling more robust and accurate detection on small datasets. Based on the findings of our experiments conducted on two datasets derived from the real world, we have determined that the transfer learning-based strategy that we have developed can outperform the most advanced approaches by a minimum of 3.9% in terms of accuracy and offering a rational explanation.

PMID:40764622 | DOI:10.1038/s41598-025-10670-2

BMC Public Health. 2025 Aug 5;25(1):2650. doi: 10.1186/s12889-025-24017-z.

ABSTRACT

The respiratory virus known as human metapneumovirus (hMPV) is linked to seasonal outbreaks and primarily affects elderly people and young children. Infodemiology, which uses digital data sources, including social media, online news, and search trends, is a useful substitute for monitoring public concerns and risk perceptions because surveillance gaps and underreporting impede public health interventions despite their clinical value. To assess public search interest, we analyzed global search behavior between June 1, 2024, and June 1, 2025, and examined over 1.3 million tweets collected during the peak outbreak period from January to March 2025. Our findings show a sharp rise in public interest following official reports of HMPV outbreak in China, with simultaneous search peaks across both hemispheres regardless of season. Search activity expanded to 177 countries and revealed sustained interest in Australia, Thailand, the United Kingdom, and the United States. Regional differences in terminology and platform usage were also observed, with non-English-speaking countries favoring the abbreviation "HMPV" and English-speaking regions more often using the full term. Additionally, discrepancies between search activity and social media engagement in some countries point to distinct patterns of public information-seeking behavior. These results underscore the importance of adapting health communication strategies to local language norms and preferred digital platforms. They also highlight the need for real-time monitoring and proactive responses to misinformation. Together, search and social media data offer a valuable lens for understanding public sentiment and improving the reach, accuracy, and impact of global outbreak communication.

PMID:40764541 | DOI:10.1186/s12889-025-24017-z

BMC Anesthesiol. 2025 Aug 5;25(1):394. doi: 10.1186/s12871-025-03239-z.

NO ABSTRACT

PMID:40764535 | DOI:10.1186/s12871-025-03239-z

Sci Rep. 2025 Aug 6;15(1):28669. doi: 10.1038/s41598-025-11574-x.

ABSTRACT

Brain tumors are a significant challenge to human health as they impair the proper functioning of the brain and the general quality of life, thus requiring clinical intervention through early and accurate diagnosis. Although current state-of-the-art deep learning methods have achieved remarkable progress, there is still a gap in the representation learning of tumor-specific spatial characteristics and the robustness of the classification model on heterogeneous data. In this paper, we introduce a novel Pyramidal Attention-Based bi-partitioned T Network (PABT-Net) that combines the hierarchical pyramidal attention mechanism and T-block based bi-partitioned feature extraction, and a self-convolutional dilated neural classifier as the final task. Such an architecture increases the discriminability of the space and decreases the false forecasting by adaptively focusing on informative areas in brain MRI images. The model was thoroughly tested on three benchmark datasets, Figshare Brain Tumor Dataset, Sartaj Brain MRI Dataset, and Br35H Brain Tumor Dataset, containing 7023 images labeled in four tumor classes: glioma, meningioma, no tumor, and pituitary tumor. It attained an overall classification accuracy of 99.12%, a mean cross-validation accuracy of 98.77%, a Jaccard similarity index of 0.986, and a Cohen's Kappa value of 0.987, indicating superb generalization and clinical stability. The model's effectiveness is also confirmed by tumor-wise classification accuracies: 96.75%, 98.46%, and 99.57% in glioma, meningioma, and pituitary tumors, respectively. Comparative experiments with the state-of-the-art models, including VGG19, MobileNet, and NASNet, were carried out, and ablation studies proved the effectiveness of NASNet incorporation. To capture more prominent spatial-temporal patterns, we investigated hybrid networks, including NASNet with ANN, CNN, LSTM, and CNN-LSTM variants. The framework implements a strict nine-fold cross-validation procedure. It integrates a broad range of measures in its evaluation, including precision, recall, specificity, F1-score, AUC, confusion matrices, and the ROC analysis, consistent across distributions. In general, the PABT-Net model has high potential to be a clinically deployable, interpretable, state-of-the-art automated brain tumor classification model.

PMID:40764518 | DOI:10.1038/s41598-025-11574-x

Nat Commun. 2025 Aug 5;16(1):7203. doi: 10.1038/s41467-025-62602-3.

ABSTRACT

Extracting weak signals from noise is a generic challenge in experimental science. In catalysis, it manifests itself as the need to quantify chemical reactions on nanoscopic surface areas, such as single nanoparticles or even single atoms. Here, we address this challenge by combining the ability of nanofluidic reactors to focus reaction product from tiny catalyst surfaces towards online mass spectrometric analysis with the high capacity of a constrained denoising auto-encoder to discern weak signals from noise. Using CO oxidation and C2H4 hydrogenation on Pd as model reactions, we demonstrate that the catalyst surface area required for online mass spectrometry can be reduced by ≈ 3 orders of magnitude compared to state of the art, down to a single nanoparticle with 0.0072 ± 0.00086 μm2 surface area. These results advocate deep learning to improve resolution in mass spectrometry in general and for online reaction analysis in single-particle catalysis in particular.

PMID:40764516 | DOI:10.1038/s41467-025-62602-3

Sci Rep. 2025 Aug 5;15(1):28585. doi: 10.1038/s41598-025-10712-9.

ABSTRACT

Kidney cancer is a leading cause of cancer-related mortality, with renal cell carcinoma (RCC) being the most prevalent form, accounting for 80-85% of all renal tumors. Traditional diagnosis of kidney cancer requires manual examination and analysis of histopathology images, which is time-consuming, error-prone, and depends on the pathologist's expertise. Recently, deep learning algorithms have gained significant attention in histopathology image analysis. In this study, we developed an efficient and robust deep learning architecture called RenalNet for the classification of subtypes of RCC from kidney histopathology images. The RenalNet is designed to capture cross-channel and inter-spatial features at three different scales simultaneously and combine them together. Cross-channel features refer to the relationships and dependencies between different data channels, while inter-spatial features refer to patterns within small spatial regions. The architecture contains a CNN module called multiple channel residual transformation (MCRT), to focus on the most relevant morphological features of RCC by fusing the information from multiple paths. Further, to improve the network's representation power, a CNN module called Group Convolutional Deep Localization (GCDL) has been introduced, which effectively integrates three different feature descriptors. As a part of this study, we also introduced a novel benchmark dataset for the classification of subtypes of RCC from kidney histopathology images. We obtained digital hematoxylin and eosin (H&E) stained WSIs from The Cancer Genome Atlas (TCGA) and acquired region of interest (ROIs) under the supervision of experienced pathologists resulted in the creation of patches. To demonstrate that the proposed model is generalized and independent of the dataset, it has experimented on three well-known datasets. Compared to the best-performing state-of-the-art model, RenalNet achieves accuracies of 91.67%, 97.14%, and 97.24% on three different datasets. Additionally, the proposed method significantly reduces the number of parameters and FLOPs, demonstrating computationally efficient with 2.71 × [Formula: see text] FLOPs & 0.2131 × [Formula: see text] parameters.

PMID:40764501 | DOI:10.1038/s41598-025-10712-9

Sci Rep. 2025 Aug 5;15(1):28564. doi: 10.1038/s41598-025-05225-4.

ABSTRACT

In recent years, traffic encryption technology has been widely adopted for user information protection, leading to a substantial increase in encrypted traffic in communication networks. To address issues such as unclear local key features and low classification accuracy in traditional malicious traffic detection and normal application classification, this paper introduces an encrypted traffic classification encoder based on lightweight graph representation. By converting packet byte sequences into graphs to construct byte-level traffic graphs, we propose building a weighted output applied through a weight matrix to facilitate model lightweighting. The lightweight graph representation serves as the network input, and the design mainly includes an embedding layer, a traffic encoder layer based on graph neural networks, and a time information extraction layer, which can separately embed headers and payloads. We propose using GraphSAGE with sampling averaging to encode each byte-level traffic graph into an overall representation vector for each packet. For end-to-end training, an improved Transformer-based model is employed with relative position encoding of time series to generate final classification results for downstream tasks. To evaluate the reliability of the method, the proposed approach is tested on three application classification datasets: WWT, ISCX-2012, and ISCX-Tor, for classifying network encrypted traffic and conducting ablation experiments for comparison. Ultimately, comparison are made with more than 12 baseline models. The results show that the F1 scores reached 0.9938 and 0.9856 on ISCX-2012 and ISCX-Tor, respectively. Through lightweight experiments, it is found that the number of parameters is reduced by 18.2% compared to that of the original model TFE-GNN. Therefore, the results indicate that the proposed improved method can enhance the accuracy of detecting network traffic applications and abnormal behaviors while reducing the model's parameter count. Considering both the model parameters and accuracy dimensions, this paper introduces a lightweight graph representation-based encrypted traffic classification encoder that outperforms various existing models.

PMID:40764490 | DOI:10.1038/s41598-025-05225-4

Sci Rep. 2025 Aug 5;15(1):28506. doi: 10.1038/s41598-025-14461-7.

ABSTRACT

With urbanization accelerating and transportation demand growing, road damage has become an increasingly pressing issue. Traditional manual inspection methods are not only time-consuming but also costly, struggling to meet current demands. As a result, adopting deep learning-based road damage detection technologies has emerged as a leading-edge and efficient solution. This paper presents an enhanced object detection algorithm built upon YOLOv5. By integrating CA (Channel Attention) and SA (Spatial Attention) dual-branch attention mechanisms alongside the GIoU (Generalized Intersection over Union) loss, the model's detection accuracy and localization capabilities are strengthened. The dual-branch attention mechanisms enhance feature representation in channel and spatial dimensions, while the GIoU loss optimizes bounding box regression-yielding notable improvements, particularly in small object detection and bounding box localization accuracy. Public datasets are used for training and testing, with pavement distress indices derived from simulated detection calculations. Experimental results show that compared to existing methods, this algorithm boosts the retrieval rate by 2.3%, increases the average value by 0.3, and improves the harmonic mean F1 by 0.7 relative to other models. Additionally, expected pavement evaluation results are obtained through calculating PCI (Pavement Condition Index) values.

PMID:40764422 | DOI:10.1038/s41598-025-14461-7

Comput Biol Med. 2025 Aug 4;196(Pt B):110867. doi: 10.1016/j.compbiomed.2025.110867. Online ahead of print.

ABSTRACT

BACKGROUND: Idiopathic pulmonary fibrosis (IPF) is a degenerative pulmonary condition marked by a substantial accumulation of extracellular matrix and chronic inflammation. Matrix metalloproteinase-7 (MMP-7) is integral to fibrosis and likely a curative focus. This study investigates the therapeutic potential of Tylophora indica (T. indica) plant extract for treating IPF, utilizing in vivo and in silico approaches that target MMP-7.

METHODS AND MATERIALS: T. indica extract was administered to a bleomycin-induced IPF mouse model at 200 and 300 mg/kg/day doses. Efficacy was evaluated through histological analysis and quantitative RT-PCR to measure MMP-7 expression. In silico molecular dynamics simulation and molecular docking identified bioactive compounds from T. indica that could inhibit MMP-7. ADMET profiling was used to evaluate these substances' pharmacological potential and safety.

RESULTS: T. indica extract at 300 mg/kg/day significantly reduced fibrosis and inflammation, improving histopathological scores and lowering MMP-7 expression. In silico analysis identified pergularinine, tylophorine, quercetin, kaempferol, and tylophorinidine as potent MMP-7 inhibitors with stronger binding affinities than pirfenidone, a standard anti-fibrotic drug. Molecular dynamics simulations confirmed the stability of these interactions, and the compounds showed favorable safety profiles in ADMET assessments.

CONCLUSION: T. indica extract demonstrated significant antifibrotic activity by downregulating MMP-7 expression and improving lung histopathology in the IPF mouse model. The identified phytochemicals show strong potential as natural MMP-7 inhibitors, suggesting T. indica as a prospective therapeutic agent for IPF. Additional clinical studies are required to validate these results.

PMID:40763678 | DOI:10.1016/j.compbiomed.2025.110867

Sci Rep. 2025 Aug 5;15(1):28599. doi: 10.1038/s41598-025-13419-z.

ABSTRACT

From cellular mechanotransduction to the formation of embryonic tissues and organs, mechanics has been shown to play an important role in the control of cell behavior and embryonic development. Most of our existing knowledge of how mechanics affects cell behavior comes from in vitro studies, mainly because measuring cell and tissue mechanics in 3D multicellular systems, and especially in vivo, remains challenging. Oil microdroplet sensors, and more recently gel microbeads, use surface deformations to directly quantify mechanical stresses within developing tissues, in vivo and in situ, as well as in 3D in vitro systems like organoids or multicellular spheroids. However, an automated analysis software able to quantify the spatiotemporal evolution of stresses and their characteristics from particle deformations is lacking. Here we develop STRESS (Surface Topography Reconstruction for Evaluation of Spatiotemporal Stresses), an analysis software to quantify the geometry of deformable particles of spherical topology, such as microdroplets or gel microbeads, that enables the automatic quantification of the temporal evolution of stresses in the system and the spatiotemporal features of stress inhomogeneities in the tissue. As a test case, we apply these new code to measure the temporal evolution of mechanical stresses using oil microdroplets in developing zebrafish tissues. Starting from a 3D timelapse of a droplet, the software automatically calculates the statistics of local anisotropic stresses, decouples the deformation modes associated with tissue- and cell-scale stresses, obtains their spatial features on the droplet surface and analyzes their spatiotemporal variations using spatial and temporal stress autocorrelations. We provide fully automated software in Matlab/Python and also in Napari (napari-STRESS), which allows the visualization of mechanical stresses on the droplet surface together with the microscopy images of the biological systems. The automated nature of the analysis will help users obtain quantitative information about mechanical stresses in a wide range of 3D multicellular systems, from developing embryos or tissue explants to organoids.

PMID:40764636 | DOI:10.1038/s41598-025-13419-z

Sci Rep. 2025 Aug 5;15(1):28596. doi: 10.1038/s41598-025-05085-y.

ABSTRACT

The Scorpaeniformes order encompasses a diverse array of teleost fish, including commercially important and venomous species. Fish venoms offer significant pharmacological potential, but incomplete phylogenetic understanding has hindered research. Resolving relationships among venomous fish families is crucial for studying venom evolution and discovering novel bioactive compounds. To address these phylogenetic uncertainties, we generated and assembled the complete mitochondrial genomes of Paracentropogon rubripinnis (Tetrarogidae) and Inimicus japonicus (Synanceiidae), two representative venomous species. The circular mitogenomes, 16,465 bp and 16,676 bp in length, respectively, contain the typical vertebrate mitochondrial gene complement. Comparative analyses revealed a highly conserved gene order and orientation across Scorpaeniformes, with slight variations in the Notothenioidei outgroups. We identified three novel conserved sequence blocks in the control regions and characterized structural features of protein-coding genes, tRNAs, and non-coding elements. Phylogenetic analyses using 13 mitochondrial protein-coding genes from 71 Scorpaeniformes and three outgroup species provided a higher-resolution phylogeny of the order, including 12 families and 31 genera. Our results support the monophyly of Tetrarogidae and Synanceiidae, placing them in an early-diverging position within the Scorpaeniformes phylogeny. This study provides insights into the phylogenetic positions of venomous fish families and lays a foundation for future research on fish venom evolution and applications.

PMID:40764620 | DOI:10.1038/s41598-025-05085-y

Nat Microbiol. 2025 Aug 5. doi: 10.1038/s41564-025-02076-7. Online ahead of print.

ABSTRACT

Microbiome engineering seeks to reshape microbial communities to improve ecosystem function. However, many efforts fail due to inadequate design principles, often resulting in a loss of key microorganisms and disruption of links between the engineered community and its intended function. In contrast, decades of research in macroecology have uncovered key principles governing the relationship between biodiversity and ecosystem function. Here we translate these ecological principles to microbiome engineering, focusing on three stages: microbiome design, colonization and maintenance. We propose new approaches that leverage underlying ecological dynamics-particularly niche dynamics-to optimize diversity and abundance to promote stability and functionality, especially in host-associated microbiomes. We also highlight key research priorities to apply macro-ecosystem insights to microbial systems. Improving microbiome engineering in this way holds promise for solving pressing challenges in medicine and agriculture, while providing understanding of ecological processes that maintain biodiversity across biological scales.

PMID:40764435 | DOI:10.1038/s41564-025-02076-7

Nat Commun. 2025 Aug 5;16(1):7189. doi: 10.1038/s41467-025-62318-4.

ABSTRACT

Accurate prediction of the functional impact of missense variants is important for disease gene discovery, clinical genetic diagnostics, therapeutic strategies, and protein engineering. Previous efforts have focused on predicting a binary pathogenicity classification, but the functional impact of missense variants is multi-dimensional. Pathogenic missense variants in the same gene may act through different modes of action (i.e., gain/loss-of-function) by affecting different aspects of protein function. They may result in distinct clinical conditions that require different treatments. We develop a new method, PreMode, to perform gene-specific mode-of-action predictions. PreMode models effects of coding sequence variants using SE(3)-equivariant graph neural networks on protein sequences and structures. Using the largest-to-date set of missense variants with known modes of action, we show that PreMode reaches state-of-the-art performance in multiple types of mode-of-action predictions by efficient transfer-learning. Additionally, PreMode's prediction of G/LoF variants in a kinase is consistent with inactive-active conformation transition energy changes. Finally, we show that PreMode enables efficient study design of deep mutational scans and can be expanded to fitness optimization of non-human proteins with active learning.

PMID:40764308 | DOI:10.1038/s41467-025-62318-4

Nat Commun. 2025 Aug 5;16(1):7178. doi: 10.1038/s41467-025-62593-1.

ABSTRACT

Spatial transcriptomics links gene expression with tissue morphology, however, current tools often prioritize genomic analysis, lacking integrated image interpretation. To address this, we present Thor, a comprehensive platform for cell-level analysis of spatial transcriptomics and histological images. Thor employs an anti-shrinking Markov diffusion method to infer single-cell spatial transcriptome from spot-level data, effectively combining gene expression and cell morphology. The platform includes 10 modular tools for genomic and image-based analysis, and is paired with Mjolnir, a web-based interface for interactive exploration of gigapixel images. Thor is validated on simulated data and multiple spatial platforms (ISH, MERFISH, Xenium, Stereo-seq). Thor characterizes regenerative signatures in heart failure, screens breast cancer hallmarks, resolves fine layers in mouse olfactory bulb, and annotates fibrotic heart tissue. In high-resolution Visium HD data, it enhances spatial gene patterns aligned with histology. By bridging transcriptomic and histological analysis, Thor enables holistic tissue interpretation in spatial biology.

PMID:40764306 | DOI:10.1038/s41467-025-62593-1

Gut Microbes. 2025 Dec;17(1):2541028. doi: 10.1080/19490976.2025.2541028. Epub 2025 Aug 5.

ABSTRACT

Systematic identification of prebiotic-microbe interactions is essential for developing precision microbiome-targeted interventions to improve human health. In this study, we developed an in vivo systematic screening platform to evaluate microbiota-prebiotic crosstalk and applied it to identify a synbiotic combination effective against dextran sulfate sodium (DSS)-induced colitis in mice. Specifically, we first established a humanized gut microbiota mouse model by colonizing mice with 73 microbial strains, which showed highly abundant and prevalent in the human gut. Concurrently, we administered the mice with 28 different prebiotic or prebiotic candidates, including polyphenols, polysaccharides, vitamins, and minerals common in the market. Following the DSS-induced colitis, we evaluated the protective effects of each microbiota-prebiotic pairing. Fourteen prebiotic or prebiotic candidates, designated as the ESS group, significantly alleviated colitis, partly by enriching specific beneficial microbes such as Bacteroides thetaiotaomicron, Akkermansia muciniphila, and Erysipelatoclostridium ramosum prior to disease onset. Further experiments revealed two symbiotic combinations with the strongest anti-inflammatory effects: calcium-magnesium tablets (CMT) combined with either B. thetaiotaomicron or A. muciniphila. Mechanistically, CMT promoted the growth of B. thetaiotaomicron and alleviated inflammation by upregulating genes associated with probiotic activity. Finally, in an intervention trial involving healthy human volunteers, CMT selectively increased B. thetaiotaomicron abundance without altering the overall gut microbiota composition. Together, our study presents a systematic framework for elucidating microbe-prebiotic interactions, identifying synbiotic combinations with therapeutic potential, and advancing precision microbiome-based strategies for disease prevention and treatment.

PMID:40764272 | DOI:10.1080/19490976.2025.2541028

Genome Res. 2025 Aug 5:gr.280848.125. doi: 10.1101/gr.280848.125. Online ahead of print.

ABSTRACT

All animals live in tight association with complex microbial communities, yet studying the effects of individual bacteria remains challenging. Bacterial feeding nematodes are powerful systems to study host microbe interactions as worms can be grown on monoxenic cultures. Here, we present three different types of resources that may assist future research of cross-species interactions in the nematode Pristionchus pacificus, but also in other organisms. First, by sequencing the genomes of 84 Pristionchus-associated bacteria, we establish a genomic basis to study host microbe interactions and we demonstrate its utility to identify candidate pathways in the bacteria affecting chemotaxis behavior and survival in the nematodes. Second, we generated nematode transcriptomes of P. pacificus nematodes on 38 bacterial diets and characterized 60 coexpression modules with differential responses to environmental microbiota. Third, we link the microbial genome and host transcriptome data by predicting a global map of more than 2,800 metabolic interactions. These interactions represent statistical associations between variation in bacterial metabolic potential and differential transcriptomic responses of coexpression modules in the nematode. Analysis of the interactome identifies several intestinal modules as the primary response layer to diverse microbiota and reveals a number of broadly conserved metabolic interactions. In summary, our study establishes a multiomic framework for future mechanistic studies in P. pacificus and may also be conceptually transferred and reimplemented in other organisms in order to investigate the evolution of the host microbe interactome.

PMID:40764054 | DOI:10.1101/gr.280848.125

Genome Res. 2025 Aug 5. doi: 10.1101/gr.279906.124. Online ahead of print.

ABSTRACT

The spatial heterogeneity of gene expression has driven the development of diverse spatial transcriptomics technologies. Here, we present photocleavage and ligation sequencing (PCL-seq), a spatial indexing method utilizing a light-controlled DNA labeling strategy applied to tissue sections. PCL-seq employs photocleavable oligonucleotides and ligation adapters to construct transcriptional profiles of specific regions of interest (ROIs) designated via microscopically controlled photo-illumination. In frozen mouse embryos, PCL-seq generates spatially aligned gene expression matrices and produces high-quality data, detecting approximately 170,000 unique molecular identifiers (UMIs) and 8600 genes (illumination diameter = 100 µm). Moreover, PCL-seq is compatible with formalin-fixed paraffin-embedded (FFPE) tissues, successfully identifying thousands of differentially enriched transcripts in the digits and vertebrae of mouse embryo FFPE sections. Additionally, PCL-seq achieves subcellular resolution, as demonstrated by differential expression profiling between nuclear and cytoplasmic compartments. These characteristics establish PCL-seq as an accessible and versatile workflow for spatial transcriptomic analyses in both frozen and FFPE tissues with subcellular resolution.

PMID:40764053 | DOI:10.1101/gr.279906.124

Biomed Pharmacother. 2025 Aug 4;190:118390. doi: 10.1016/j.biopha.2025.118390. Online ahead of print.

ABSTRACT

Colon cancer is currently the leading cause of cancer death in men and the second in women under 50. Standard therapy includes surgical resection and - in the case of non-resectable CRC -radiotherapy, chemotherapy and immunotherapy. One of the therapeutic approaches is also a combinational regimen. Numerous experimental, epidemiological and clinical studies suggest that non-steroidal anti-inflammatory drugs (NSAIDs) may exert anticancer effects against colon cancer. In our work, we studied the effect of pure indomethacin (IND) and two lipid hybrids containing IND on HT29 and HT29/Dx cells. We analyzed the cytotoxicity and anti-inflammatory potential of the compounds but also their ability to induce apoptosis and produce reactive oxygen species. Experimental investigations were complemented by theoretical studies on the based on Density Functional Theory (DFT), molecular docking and classical molecular dynamics. These studies enabled a detailed description of the ligands and host-guest complexes. Based on the molecular docking study a general picture of the binding affinity to ABCB1 and COX-2 proteins was obtained. Moreover, we were able to detect amino acids involved in the protein-ligand complex formation. Classical molecular dynamics provided information on the thermodynamic properties and stability of the investigated complexes. We found that lysophosphatidylcholine containing IND represented a promising candidate for adjuvant therapy of colon cancer.

PMID:40763485 | DOI:10.1016/j.biopha.2025.118390

Am J Health Promot. 2025 Aug 5:8901171251365229. doi: 10.1177/08901171251365229. Online ahead of print.

ABSTRACT

PurposeTo synthesize evidence on the incidence of COVID-19 vaccine-related adverse events across countries by income level.DesignSystematic review and meta-analysis of randomized trials.SettingStudies published 2020-2025, retrieved from EMBASE, PubMed/MEDLINE, and Scopus.SampleEleven trials with 7841 participants; seven from high-income and four from upper-middle-income countries.MeasuresIncidence per 100 vaccinated. Countries by income: low (≤$1145), lower-middle ($1146-4515), upper-middle ($4516-14,005), high (>$14,005). Inter-reviewer agreement assessed by kappa (0.684). Risk of bias evaluated with Cochrane RoB 2.AnalysisMantel-Haenszel random-effects models estimated relative risks (RR) with 95% confidence intervals. Heterogeneity assessed by I2. Subgroup analyses by income and dose.ResultsAEFI incidence was consistently higher in high-income vs upper-middle-income countries, especially after dose 2. Injection-site pain (68.1 vs 26.3 per 100), headache (45.7 vs 14.1), myalgia (42.5 vs 9.2), and fatigue (33.8 vs 11.4) were most common. Meta-analyses showed higher pooled RR in high-income settings: any AEFI after dose 1, RR = 1.83 (95% CI: 1.39-2.42); local, RR = 3.15; systemic, RR = 2.05. After dose 2, overall RR reached 2.94; local, 4.37; systemic, 2.48. All subgroup differences were significant.ConclusionHigher-income countries showed a greater incidence of mostly mild adverse events, particularly after the second dose. mRNA vaccines had the highest rates. Findings reveal income-based disparities and inform equitable post-vaccination monitoring.

PMID:40764277 | DOI:10.1177/08901171251365229

Dementia (London). 2025 Aug 5:14713012251366757. doi: 10.1177/14713012251366757. Online ahead of print.

ABSTRACT

Optimising antipsychotic prescribing in people living with dementia is important to manage symptoms and avoid adverse events. Clinical decision support tools that predict therapeutic response based on individual patient characteristics can help personalise prescribing and complement decision-making by prescribers. The aim of this study is to investigate the views of Australian prescribers on the development and use of a digital antipsychotic prescribing support tool in dementia. Thematic analysis was used to analyse the perspectives of Australian prescribers on using a digital prescribing support tool in dementia. Semi-structured, individual interviews were conducted with a sample of 14 clinicians. Themes were organised according to topic areas about the development and use of the tool. Clinicians expressed that the tool could assist in identifying risk, allowing prescribers to be more cautious with antipsychotic prescribing. The tool could promote informed decision-making by assisting prescribers to consider more factors prior to prescribing whilst serving as an educational tool to aid shared decision-making with patients and carers. Though there were benefits, clinicians raised that there are complexities of antipsychotic prescribing, as the tool may not account for situational need, where benefits may outweigh risks. Some clinicians expressed potential concerns with technology-based tools, where some prescribers may void their clinical judgement and over-rely on the tool. Some clinicians highlighted younger practitioners, general practitioners, nurses and pharmacists as potential users who could benefit from its use. Clinicians posed suggestions for development, including accessibility through an app, updating data as evidence and guidelines change, and prompts to aid decision-making. This study identified several considerations on the implementation of the tool in clinical practice. Perspectives raised by clinicians should be considered in the tool's future development.

PMID:40763920 | DOI:10.1177/14713012251366757