Inulin alters gut microbiota to alleviate post‐stroke depressive‐like behavior associated with the IGF‐1‐mediated MAPK signaling pathway

Abstract Introduction Gut microbiota dysbiosis is a key factor of the pathogenesis of post‐stroke depression (PSD). PSD is associated with increased hippocampal neuronal apoptosis and decreased synaptic connectivity. Inulin can be involved in hippocampal neuron protection through the microbiome–gut–brain axis. However, the neuroprotective effects of inulin in PSD are still to be further investigated. Methods By utilizing the GEO public database, we identify differentially expressed genes in the hippocampus following inulin intake. This can help us discover key signaling pathways through functional enrichment analysis. Furthermore, we validate the expression levels of signaling molecules in a rat model of PSD and examine the effects of inulin on behavioral changes and body weight. Additionally, conducting a microbiome analysis to identify significantly different microbial populations and perform correlation analysis. Results The intake of inulin significantly up‐regulated mitogen‐activated protein kinase signaling pathway in the hippocampus. Inulin changed in the gut microbiota structure, leading to an increase in the abundance of Lactobacillus and Clostridium_sensu_stricto_1 in the intestines of PSD rats, while decreasing the abundance of Ruminococcus UCG_005, Prevotella_9, Oscillospiraceae, and Clostridia UCG_014. Furthermore, the inulin diet elevated levels of insulin‐like growth factor 1 in the serum, which showed a positive correlation with the abundance of Lactobacillus. Notably, the consumption of inulin‐enriched diet increased activity levels and preference for sugar water in PSD rats, while also reducing body weight. Conclusion These findings highlight the potential therapeutic benefits of inulin in the management of depression and emphasize the importance of maintaining a healthy gut microbiota for PSD

Subtype-aware Unsupervised Domain Adaptation for Medical Diagnosis

Recent advances in unsupervised domain adaptation (UDA) show that transferable prototypical learning presents a powerful means for class conditional alignment, which encourages the closeness of cross-domain class centroids. However, the cross-domain inner-class compactness and the underlying fine-grained subtype structure remained largely underexplored. In this work, we propose to adaptively carry out the fine-grained subtype-aware alignment by explicitly enforcing the class-wise separation and subtype-wise compactness with intermediate pseudo labels. Our key insight is that the unlabeled subtypes of a class can be divergent to one another with different conditional and label shifts, while inheriting the local proximity within a subtype. The cases with or without the prior information on subtype numbers are investigated to discover the underlying subtype structure in an online fashion. The proposed subtype-aware dynamic UDA achieves promising results on a medical diagnosis task