Objective. Necrotizing enterocolitis (NEC) is a serious neonatal disease; this study aims to investigate the role of sulforaphane (SFN) in NEC-induced intestinal injury. Methods. An animal model of NEC was established in newborn mice and intragastrically administrated with SFN; then, the general status and survival of the mice were observed. H&E staining was used to observe the pathological changes of intestinal tissues. ELISA, immunohistochemical staining, and flow cytometry assays were used to detect the levels of inflammatory factors, including TNF-α, IL-6, and IL-17, the expression of Bax, Bcl-2, TLR4, and NF-κB, and the percentages of the Th17 and Treg cells, respectively. GSK-3β expression levels were measured by immunofluorescence. IEC-6 and FHC cells were induced with LPS to mimic NEC in vitro and coincubated with SFN; then, the inflammatory factor levels and cell apoptosis rate were detected. Finally, Western blot was used to assess the expression of PI3K/Akt/GSK-3β pathway-related proteins in vitro and in vivo. Results. SFN improved the survival rate of NEC mice during modeling, alleviated the severity of the intestinal injury, and reduced the proportion of Th17/Treg cells. SFN could inhibit TLR4 and NF-κB levels, decrease the release of inflammatory factors TNF-α and IL-6, suppress Bax expression, increase Bcl-2 expression, and inhibit apoptosis both in in vitro and in vivo models of NEC. Meanwhile, SFN regulated the expression of PI3K/Akt/GSK-3β pathway-related proteins in vitro and in vivo. Conclusion. SFN relieved the inflammatory response and apoptosis by regulating the PI3K/Akt/GSK-3β signaling pathway, thereby alleviating NEC in model mice and cells.

Coronary heart disease (CHD), the most common cause of mortality globally, poses a formidable challenge to modern healthcare systems. Artificial intelligence (AI) is playing an increasingly important role in multiple diagnostic applications of CHD, by facilitating early intervention and personalized treatment. This mini review describing the state of the art provides clinicians with updated insights into the transformative potential of AI to enhance CHD detection. AI can be used to increase diagnostic and prognostic accuracy. However, its reliance on homogeneous numerical data might potentially lead to misdiagnoses and unnecessary radiation exposure in diagnosing CHD. Multimodal data fusion brings new potential for accurate diagnosis and personalized medicine. Finally, unmet challenges and future research directions in ethical, regulatory, and technical aspects are discussed. This mini review is aimed at bridging the gap between AI advancements and practical applications in clinical settings, to achieve a future in which AI empowers CHD diagnosis in the context of a modern healthcare ecosystem.

Epicardial Adipose Tissue (EAT) volume is associated with the risk of cardiovascular events, which can be assessed by cardiac computed tomography. However, he optimal method and their prognostic utility in patients following transcatheter aortic valve replacement (TAVR) is unknown. We evaluated 258 participants, focusing on changes in EAT volume using cardiac CT enhancement. EAT volume was automatically computed as three-dimensional voxels between -190 to -30 HU on contrast-enhanced slices. Univariate and multivariable Cox regression analyses were conducted to assess the association of various clinical parameters and EAT volume indices with major adverse cardiovascular events (MACE). During a median follow-up of 2.0 years [IQR, 1.8-2.3 years], 34 participants (median age 73 [IQR: -13.1 to -8.3) years, 55.4 % male) experiencing MACE. The optimal cutoff values for EAT volume change fraction (EATVCF) was 15.2 %, determined by the Youden-index. Kaplan-Meier curve analysis revealed that patients with high EATVCF were at higher risk (p < .01). In Cox regression, EATVCF (hazard ratio [HR]: 0.92, 95 % CI: 0.87 to 0.97, p = .001) remained significantly associated with MACE after adjusting for clinical factors. The addition of EATVCF to the clinical model increased the net Reclassification Improvement (NRI) by 30.1 % (95 % CI: 0.07-1.16). EAT volume change fraction emerged as a significant predictor of MACE post-TAVR, highlighting the clinical value of EAT volume assessment in cardiovascular risk stratification.

The aim of this study was to evaluate the prognostic impact of computed tomography (CT)-based body composition parameters in metabolically unhealthy normal-weight patients (MUHNW) with aortic stenosis (AS) undergoing transcatheter aortic valve replacement (TAVR). This prospective cohort study involved adults with normal weight scheduled for TAVR. Patients were divided into two groups: metabolically healthy normal-weight (MHNW) and MUHNW. The primary outcome was major adverse cardiovascular events (MACEs), and the secondary outcomes were all-cause mortality and prolonged hospital stay. Logistic regression was used to explore the relationships between variables and outcomes, and Cox regression models were applied to examine the effects of different parameters on patient prognosis. Incremental discriminative improvement (IDI) and the C-index were used to determine the influence of hybrid parameters on the predictive efficiency of the model. The cohort study included 182 patients divided into two groups: MHNW metabolically healthy normal-weight (n = 97) and MUHNW (n = 85). Over a median follow-up of 0.8 years, multivariable logistic regression analysis revealed significant associations of the skeletal muscle index (SMind) (HR: 0.50, 95% CI: 0.29 – 0.84, p = 0.01), subcutaneous adipose tissue index (SATind) (HR: 0.63, 95% CI: 0.45 – 0.88, p = 0.01), and visceral adipose tissue index (VATind) (HR: 1.34, 95% CI: 1.10 – 1.63, p < 0.01) with the risk of experiencing MACEs, whereas epicardial adipose tissue (EAT) was not significantly associated with the risk of experiencing MACEs in the multivariable model (HR: 1.10, 95% CI: 0.97 – 1.24, p = 0.14). For the primary outcome, adjusted for significant covariates, the model had an IDI of 0.25 and a C-index of 0.8, with significant associations of SMind (HR: 0.73, 95% CI: 0.62 – 0.87, p < 0.01), SATind (HR: 0.87, 95% CI: 0.78 – 0.97, p = 0.01), and VATind (HR: 1.25, 95% CI: 1.12–1.40, p < 0.01) with the risk of experiencing MACEs. For the secondary outcome, the model had an IDI of 0.36 and a C-index of 0.93, with EAT showing a significant protective effect against all-cause mortality and prolonged hospital stay (HR: 0.97, 95% CI: 0.93 – 0.99; p = 0.04). Body composition parameters, including, VATind, SMind, and SATind, are significant predictors of MACEs in patients undergoing TAVR. Additionally, EAT shows a significant protective effect against all-cause mortality and prolonged hospital stay. These findings highlight the potential importance of comprehensive body composition assessments in the risk stratification and management of AS patients.