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Abstract There is no treatment for acute aortic dissection (AAD) targeting inflammatory cells. We aimed to identify the new therapeutic targets associated with inflammatory cells. We characterized the specific distribution of myeloid cells of both human type A AAD samples and a murine AAD model generated using angiotensin II (ANGII) and β-aminopropionitrile (BAPN) by single-cell RNA sequencing (scRNA-seq). We also examined the effect of an anti-interleukin-1β (IL-1β) antibody in the murine AAD model. IL1B+ inflammatory macrophages and classical monocytes were increased in human AAD samples. Trajectory analysis demonstrated that IL1B+ inflammatory macrophages differentiated from S100A8/9/12+ classical monocytes uniquely observed in the aorta of AAD. We found increased infiltration of neutrophils and monocytes with the expression of inflammatory cytokines in the aorta and accumulation of inflammatory macrophages before the onset of macroscopic AAD in the murine AAD model. In blocking experiments using an anti-IL-1β antibody, it improved survival of murine AAD model by preventing elastin degradation. We observed the accumulation of inflammatory macrophages expressing IL-1β in both human AAD samples and in a murine AAD model. Anti-IL-1β antibody could improve the mortality rate in mice, suggesting that it may be a treatment option for AAD.

Abstract Numerous COVID-19 diagnostic imaging Artificial Intelligence (AI) studies exist. However, none of their models were of potential clinical use, primarily owing to methodological defects and the lack of implementation considerations for inference. In this study, all development processes of the deep-learning models are performed based on strict criteria of the “KAIZEN checklist”, which is proposed based on previous AI development guidelines to overcome the deficiencies mentioned above. We develop and evaluate two binary-classification deep-learning models to triage COVID-19: a slice model examining a Computed Tomography (CT) slice to find COVID-19 lesions; a series model examining a series of CT images to find an infected patient. We collected 2,400,200 CT slices from twelve emergency centers in Japan. Area Under Curve (AUC) and accuracy were calculated for classification performance. The inference time of the system that includes these two models were measured. For validation data, the slice and series models recognized COVID-19 with AUCs and accuracies of 0.989 and 0.982, 95.9% and 93.0% respectively. For test data, the models’ AUCs and accuracies were 0.958 and 0.953, 90.0% and 91.4% respectively. The average inference time per case was 2.83 s. Our deep-learning system realizes accuracy and inference speed high enough for practical use. The systems have already been implemented in four hospitals and eight are under progression. We released an application software and implementation code for free in a highly usable state to allow its use in Japan and globally.

Gut microbiota imbalance plays an important role in the pathogenesis of various diseases. Here, we determined microbe-microbe interactions and gut microbiome stability in a Japanese population with varying body mass indices (BMIs) and enterotypes. Using 16S ribosomal RNA gene sequencing, we analyzed gut microbial data from fecal samples obtained from 3,365 older Japanese individuals. The individuals were divided into lean, normal, and obese groups based on their BMIs. They were further categorized according to their gut microbiota enterotypes: Bacteroides (enterotype B), Prevotella (enterotype P), and Ruminococcus (enterotype R). We obtained data on different host factors, such as age, BMI, and disease status, using a survey questionnaire evaluated by the Mykinso gut microbiome testing service. Subsequently, we evaluated the co-occurrence network. Individual differences in BMI were associated with differences in co-occurrence networks. By exploring the network topology based on BMI status, we observed that the network density was lower in the lean group than that in the normal group. Furthermore, a simulation-based stability analysis revealed a lower resistance index in the lean group than those in the other two groups. Our results provide insights into various microbe-microbe interactions and gut microbial stability and could aid in developing appropriate therapeutic strategies targeting gut microbiota modulation to manage frailty. [ABSTRACT FROM AUTHOR]