Biology

Doxorubicin (Dox)-induced cardiotoxicity (DIC) is a major clinical challenge in cancer therapy. Camel milk exosomes (CMEs) have been applied in anti-tumor treatments as they have a variety of effects, including on inflammation, oxidative stress, metastasis, and apoptosis. However, their role in DIC treatment remains incompletely understood. This research was designed to evaluate the protection provided by CMEs against DIC. The DIC mice were treated with Dox intraperitoneally and divided into a model group and groups treated with different doses of CMEs. Dox-induced H9c2 cell injury was also established and divided into a model group and groups treated with different concentrations of CMEs. The evaluation parameters in vitro included H9c2 cell viability, reactive oxygen species (ROS), mitochondria, and apoptotic cells. The apoptosis and autophagy markers, as well as the nuclear factor kappa B (NF-κB) and mitogen-activated protein kinase (MAPK) pathways, were assessed via Western blotting both in vivo and in vitro. In addition, transcriptome sequencing of cardiac tissue was also applied to investigate the related mechanisms. Our results indicate that CMEs significantly attenuated the cell viability reduction, apoptosis, and ROS production in H9c2 cells caused by Dox. CMEs also regulated autophagy, inhibited apoptosis, and inhibited the NF-κB and MAPK pathways. In conclusion, our findings demonstrate that CMEs exert a cardiac protective effect against DIC by inhibiting apoptosis and regulating autophagy via NF-κB and MAPK signaling pathways.

Microbial outcomes emerge from dynamic interactions among host defenses, microbial traits, and environmental context. This narrative review examines aquatic host–microbe systems, with emphasis on zebrafish and Vibrio species, as experimentally tractable aquatic models for understanding how interactions shift across a spectrum from mutualism to pathogenesis. We first define major aquatic invertebrate and vertebrate models, highlighting how each balances scalability, genetic accessibility, physiological complexity, and translational relevance. We then use Vibrio species as a framework for moving beyond the pathogen versus non-pathogen classification, emphasizing conditional pathogenicity, host-dependent outcomes, and shared molecular toolkits between symbionts and pathogens. Finally, we highlight host responses including barriers and immunity. Aquatic models reveal that disease results from context-dependent regulation of both host and microbial programs. By integrating the diversity of aquatic models, aquatic host–microbe research presents a unique opportunity to define the continuum of outcomes from mutualism to pathogenesis.

Background/Objectives: Tumor heterogeneity is reflected in cell composition, molecular states, spatial distribution, and microenvironment interactions. Spatial transcriptomics can map in situ expression and cell states but cannot fully explain upstream regulation, while spatial epigenomics provides complementary evidence such as chromatin accessibility, histone modifications, and deoxyribonucleic acid (DNA) methylation. This review summarizes the applications of spatial multi-omics and deep learning in prioritizing candidate drug targets in tumors. Methods: We reviewed spatial transcriptomics, spatial epigenomics, and combined sequencing technologies, with a focus on how deep learning supports the analysis and integration of spatial multi-omics data for candidate-target prioritization. Results: Deep learning facilitates the detection of abnormal regions, deciphering of cell origins, integration across samples, inference of cell–cell communication, and combination of imaging with omics data. Spatial multi-omics studies provide therapeutic insights into malignant cells, immunosuppression, stromal and vascular remodeling, and invasion and metastasis niches. Based on these applications, candidate targets can be evaluated in a layered manner according to spatial specificity, cell origin, regulatory consistency, reproducibility across patients, functional dependency, disease relevance, and druggability. Conclusions: Spatial multi-omics and deep learning can enhance the systematic and interpretable selection of candidate targets. However, computational associations cannot replace functional validation. Candidate targets still require verification through gene perturbation, drug sensitivity assays, organoids, animal models, and clinical cohorts to confirm their therapeutic potential.