Post-infarction cardiovascular remodeling and heart failure are the leading cause of myocardial infarction (MI)-driven death during the past decades. Experimental observations have involved intestinal microbiota in the susceptibility to MI in mice; however, in humans, identifying whether translocation of gut bacteria to systemic circulation contributes to cardiovascular events post-MI remains a major challenge. Here, we carried out a metagenomic analysis to characterize the systemic bacteria in a cohort of 49 healthy control individuals, 50 stable coronary heart disease (CHD) subjects, and 100 ST-segment elevation myocardial infarction (STEMI) patients. We report for the first time higher microbial richness and diversity in the systemic microbiome of STEMI patients. More than 12% of post-STEMI blood bacteria were dominated by intestinal microbiota (Lactobacillus, Bacteroides, and Streptococcus). The significantly increased product of gut bacterial translocation (LPS and d-lactate) was correlated with systemic inflammation and predicted adverse cardiovascular events. Following experimental MI, compromised left ventricle (LV) function and intestinal hypoperfusion drove gut permeability elevation through tight junction protein suppression and intestinal mucosal injury. Upon abrogation of gut bacterial translocation by antibiotic treatment, both systemic inflammation and cardiomyocyte injury in MI mice were alleviated. Our results provide the first evidence that cardiovascular outcomes post-MI are driven by intestinal microbiota translocation into systemic circulation. New therapeutic strategies targeting to protect the gut barrier and eliminate gut bacteria translocation may reduce or even prevent cardiovascular events post-MI.

The role of IL-33, particularly in tumor growth and tumor immunity, remains ill-defined. We show that exogenous IL-33 can induce robust antitumor effect through a CD8⁺ T cell–dependent mechanism. Systemic administration of rIL-33 alone was sufficient to inhibit growth of established tumors in transplant and de novo melanoma tumorigenesis models. Notably, in addition to a direct action on CD8⁺ T cell expansion and IFN-γ production, rIL-33 therapy activated myeloid dendritic cells (mDCs) in tumor-bearing mice, restored antitumor T cell activity, and increased Ag cross-presentation within the tumor microenvironment. Furthermore, combination therapy consisting of rIL-33 and agonistic anti-CD40 Abs demonstrated synergistic antitumor activity. Specifically, MyD88, an essential component of the IL-33 signaling pathway, was required for the IL-33–mediated increase in mDC number and upregulation in expression of costimulatory molecules. Importantly, we identified that the IL-33 receptor ST2, MyD88, and STAT1 cooperate to induce costimulatory molecule expression on mDCs in response to rIL-33. Thus, our study revealed a novel IL-33–ST2–MyD88–STAT1 axis that restores mDC activation and maturation in established cancer and, thereby, the magnitude of antitumor immune responses, suggesting a potential use of rIL-33 as a new immunotherapy option to treat established cancer.

Excessive gestational weight gain (GWG) is a potential risk factor for hypertensive disorders of pregnancy (HDP). We systematically reviewed three electronic databases for relevant articles published in English: PubMed, EMBASE and Web of Science. The Newcastle-Ottawa Scale was used to assess study quality. Random-effects meta-analyses were performed to supply a pooled estimation of the OR comparing the risk of HDP among healthy pregnant women with and without excessive GWG. The pooled estimation for the association between excessive GWG and the risk of HDPs yielded an odds ratio (OR) of 1.79 (95% CI: 1.61–1.99). A subgroup analysis showed that women who had excessive GWG were more likely to have an HDP (OR 1.82; 95% CI 1.53–2.17), preeclampsia (OR 1.92; 95% CI 1.36–2.72), or gestational hypertension (OR 1.67; 95% CI 1.43–1.95). The pooled estimation for the association between excessive GWG and the risk of HDPs among pregestational normal weight women yielded an OR of 1.57 (95% CI 1.26–1.96). A subgroup analysis showed that women who had excessive GWG were more likely to have HDP (OR 1.45; 95% CI 1.09–1.92) or gestational hypertension (OR 1.51; 95% CI 1.22–1.86). The summary ORs of pre-gestational underweight women and pre-gestational overweight and obese women were 2.17 (95% CI 1.56–3.02) and 1.32 (95% CI 1.08–1.63), respectively. The findings of this study suggest that excessive GWG in accordance with the IOM recommendations influences the rate of HDP.

Pancreatic ductal adenocarcinoma (PDAC) is associated with highly immunosuppressive tumor microenvironment (TME) that can limit the efficacy of dendritic cell (DC) vaccine immunotherapy. Irreversible electroporation (IRE) is a local ablation approach. Herein, we test the hypothesis that IRE ablation can overcome TME immunosuppression to improve the efficacy of DC vaccination using Kras^LSL-G12D-p53^LSL-R172H-Pdx-1-Cre (KPC) orthotopic mouse model of PDAC. The median survival for mice treated with the combined IRE and DC vaccination was 77 days compared with sham control (35 days), DC vaccination (49 days), and IRE (44 days) groups (P = .006). Thirty-six percent of the mice treated with combination IRE and DC vaccination were still survival at the end of the study period (90 days) without visible tumor. The changes of tumor apparent diffusion coefficient (ΔADC) were higher in mice treated with combination IRE and DC vaccination than that of other groups (all P < .001); tumor ΔADC value positively correlated with tumor fibrosis fraction (R = 0.707, P < .001). IRE induced immunogenic cell death and alleviation of immunosuppressive components in PDAC TME when combined with DC vaccination, including increased tumor infiltration of CD8⁺ T cells and Granzyme B⁺ cells (P = .001, and P = .007, respectively). Our data show that IRE ablation can overcome TME immunosuppression to improve the efficacy of DC vaccination in PDAC. Combination IRE ablation and DC vaccination may enhance therapeutic efficacy for PDAC.

Background: Preoperative detection of lymph node (LN) metastasis is critical for planning treatments in colon cancer (CC). The clinical diagnostic criteria based on the size of the LNs are not sensitive to determine metastasis using CT images. In this retrospective study, we investigated the potential value of CT texture features to diagnose LN metastasis using preoperative CT data and patient characteristics by developing quantitative prediction models.Methods: A total of 390 CC patients, undergone surgical resection, were enrolled in this monocentric study. 390 histologically validated LNs were collected from patients and randomly separated into training (312 patients, 155 metastatic and 157 normal LNs) and test cohorts (78 patients, 39 metastatic and 39 normal LNs). Six patient characteristics and 146 quantitative CT imaging features were analyzed and key variables were determined using either exhaustive search or least absolute shrinkage algorithm. Two kernel-based support vector machine classifiers (patient-characteristic model and radiomic-derived model), generated with 10-fold cross-validation, were compared with the clinical model that utilizes long-axis diameter for diagnosis of metastatic LN. The performance of the models was evaluated on the test cohort by computing accuracy, sensitivity, specificity, and area under the receiver operating curve (AUC).Results: The clinical model had an overall diagnostic accuracy of 64.87%; specifically, accuracy of 65.38% and 62.82%, sensitivity of 83.87% and 84.62%, and specificity of 47.13% and 41.03% for training and test cohorts, respectively. The patient-demographic model obtained accuracy of 67.31% and 73.08%, the sensitivity of 62.58% and 69.23%, and specificity of 71.97% and 76.23% for training and test cohorts, respectively. Besides, the radiomic-derived model resulted in an accuracy of 81.09% and 79.49%, sensitivity of 83.87% and 74.36%, and specificity of 78.34% and 84.62% for training and test cohorts, respectively. Furthermore, the diagnostic performance of the radiomic-derived model was significantly higher than clinical and patient-demographic models (p < 0.02) according to the DeLong method.Conclusions: The texture of the LNs provided characteristic information about the histological status of the LNs. The radiomic-derived model leveraging LN texture provides better preoperative diagnostic accuracy for the detection of metastatic LNs compared to the clinically accepted diagnostic criteria and patient-demographic model.