The oral-gut microbiome axis in inflammatory bowel disease: from inside to insight

Inflammatory bowel disease (IBD) is an idiopathic and persistent inflammatory illness of the bowels, leading to a substantial burden on both society and patients due to its high incidence and recurrence. The pathogenesis of IBD is multifaceted, partly attributed to the imbalance of immune responses toward the gut microbiota. There is a correlation between the severity of the disease and the imbalance in the oral microbiota, which has been discovered in recent research highlighting the role of oral microbes in the development of IBD. In addition, various oral conditions, such as angular cheilitis and periodontitis, are common extraintestinal manifestations (EIMs) of IBD and are associated with the severity of colonic inflammation. However, it is still unclear exactly how the oral microbiota contributes to the pathogenesis of IBD. This review sheds light on the probable causal involvement of oral microbiota in intestinal inflammation by providing an overview of the evidence, developments, and future directions regarding the relationship between oral microbiota and IBD. Changes in the oral microbiota can serve as markers for IBD, aiding in early diagnosis and predicting disease progression. Promising advances in probiotic-mediated oral microbiome modification and antibiotic-targeted eradication of specific oral pathogens hold potential to prevent IBD recurrence

Transient ischemia-reperfusion induces cortical hyperactivity and AMPAR trafficking in the somatosensory cortex.

Brain ischemia results from cardiac arrest, stroke or head trauma. The structural basis of rescuing the synaptic impairment and cortical dysfunctions induced in the stage of ischemic-reperfusion can occur if therapeutic interventions are applied in time, but the functional basis for this resilience remains elusive. Here, we explore the changes in cortical activity and a-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid receptor (AMPAR) GluA1 subunit in spine (sGluA1) after transient ischemia-reperfusion in vivo for 28 days. Using in vivo two-photon microscopy in the mouse somatosensory cortex, we found that the average frequency of Ca2+ transients in the spine (there was an unusual synchrony) was higher after 15 min of ischemia-reperfusion. In addition, the transient ischemia-reperfusion caused a reflective enhancement of AMPARs, which eventually restored to normal. The cortical hyperactivity (Ca2+ transients) and the increase in AMPARs were successfully blocked by an NMDA receptor antagonist. Thus, the increase of AMPARs, cortical hyperactivity and the unusual synchrony might be the reason for reperfusion injury after short-term transient ischemia

Post-marketing safety surveillance of pneumococcal vaccines: a real-world pharmacovigilance study using the U.S. vaccine adverse event reporting system (VAERS) database

BackgroundPneumococcal vaccines have been utilized in the United States for decades with extensive clinical safety records. However, comprehensive post-marketing pharmacovigilance evaluations for all available types remain lacking. This study aimed to assess adverse events following immunization (AEFI) using the VAERS database and analyze potential associations between adverse events (AEs) and vaccine administration based on VAERS data.MethodsWe retrieved all AEs associated with pneumococcal vaccines recorded in the VAERS database from 1990 through March 2025. Descriptive analyses were conducted to summarize the demographics, clinical characteristics, and vaccination profiles of reported cases. Disproportionality analysis was performed to detect potential safety signals between AEs and vaccine administration.ResultsThe VAERS database documented 157,244 individuals receiving pneumococcal vaccines, with 158,778 doses administered, capturing 632,481 AE reports following vaccination during the study period. Females showed higher AE reporting rates (54.29%) compared to males (36.88%), with the majority of cases (38.20%) occurring in individuals aged < 18 years. Complete recovery (44.20%) and hospitalization (14.94%) were the most common outcomes. Most AEs (77.11%) occurred within 0–30 days post-vaccination (median onset: 0 day). Pneumococcal polysaccharide vaccine (PPSV, 48.92%) and 13-valent pneumococcal conjugate vaccine (PCV13, 27.57%) constituted the predominant vaccine types. Disproportionality analysis identified 929 positive AE signals across 24 system organ classes (SOCs), with injection site erythema [reporting odds ratio (ROR) = 4.24], injection site swelling (ROR = 4.19), and injection site pain (ROR = 2.75) being the most frequent. Designated Medical Event (DME) screening revealed erythema multiforme (n = 398) and product contamination microbial (ROR = 11.25) as key safety signals. General disorders (ROR = 1.73) and skin conditions (ROR = 1.69) were the predominant SOC categories.ConclusionsThis post-marketing surveillance has revealed predominantly non-serious AEs, with most adverse events clustered within 30 days post-vaccination. These observations reinforce the established safety profile of pneumococcal vaccines while emphasizing temporal risk patterns to guide post-vaccination monitoring protocols and risk-benefit evaluations

Cross-Scene Hyperspectral Image Classification Based on Graph Alignment and Distribution Alignment

A domain alignment-based hyperspectral image (HSI) classification method was designed to address the heterogeneity in resolution and band between the source domain and target domain datasets of cross-scene hyperspectral images, as well as the resulting reduction in common features. Firstly, after preliminary feature extraction, perform two domain alignment operations: image alignment and distribution alignment. Image alignment aims to align hyperspectral images of different bands or time points, ensuring that they are within the same spatial reference framework. Distribution alignment adjusts the distribution of features of samples of different categories in the feature space to reduce the distribution differences of the same type of features between two domains. Secondly, adjust the consistency of the two alignment methods to ensure that the features obtained through different alignment methods exhibit consistency in the feature space, thereby improving the comparability and reliability of the features. In addition, this method considers multiple losses in the model from different perspectives and makes comprehensive adjustments through a unified optimization process to more comprehensively capture and utilize the correlation information between data. Experimental results on Houston 2013 and Houston 2018 datasets can improve the hyperspectral prediction performance between datasets with different resolutions and bands, effectively solving the problems of high cost and limited training samples in HSI labeling and significantly improving cross-scene HSI classification performance