Background: Malnutrition is common in patients with acute myocardial infarction (AMI) and is associated with a poor prognosis. The prognostic value of the prognostic nutritional index (PNI) in patients with AMI remains controversial. We aimed to explore the relationship between PNI and all-cause mortality in critically ill patients with AMI and evaluate the incremental prognostic value of PNI to commonly used prognostic assessment tools. Methods: The Medical Information Mart for Intensive Care-IV (MIMIC-IV) database was used to conduct a retrospective cohort analysis on 1180 critically ill patients with AMI. The primary endpoints were defined as 6-month and 1-year all-cause mortality. Cox regression analysis was used to investigate the relationship between admission PNI and all-cause mortality. The effect of adding PNI to sequential organ failure assessment (SOFA) score, or charlson comorbidity index (CCI) on its discriminative ability was assessed using C-statistic, net reclassification improvement (NRI), and integrated discrimination improvement (IDI). Results: Multivariate cox regression analysis demonstrated that the low PNI was regarded as an independent predictor of 1-year all-cause mortality in AMI patients admitted to ICU (adjusted Hazard Ratio: 95% CI = 1.75 (1.22–2.49)). The ROC test showed that admission PNI had a moderate predictive ability to predict all-cause mortality of critically ill patients with AMI. Furthermore, the net reclassification and integrated discrimination of the CCI alone model improved significantly with PNI. [C-statistic increased from 0.669 to 0.752, p < 0.001; NRI = 0.698, p < 0.001; IDI = 0.073, p < 0.001]. When PNI was added to the SOFA score, the C-statistic significantly improved from 0.770 to 0.805 (p < 0.001), and the NRI and IDI were estimated at 0.573 (p < 0.001) and 0.041 (p < 0.001), respectively. Conclusion: PNI could be a novel predictor for identifying patients at high risk of 1-year all-cause mortality in critically ill patients with AMI. The addition of PNI to the SOFA score or CCI may be useful for very early risk stratification.

Inflammation is the primary pathological process of myocardial ischemia/reperfusion injury (MI/RI). 7-Hydroxyflavone (HF), a natural flavonoid with a variety of bioactivities, plays a crucial role in various biological processes. However, its cardioprotective effects and the underlying mechanisms of MI/RI have not been investigated. This study aimed to explore whether pretreatment with HF could attenuate MI/RI-induced inflammation in rats and investigate its potential mechanisms. The results showed that pretreatment with HF could significantly improve the anatomic data and electrocardiograph parameters, reduce the myocardial infarct size, decrease markers of myocardial injury (aspartate transaminase, creatine kinase, lactate dehydrogenase, and cardiac troponin I), inhibit inflammatory cytokines (IL-1β, IL-6, and TNF-α), suppress oxidative stress, and recover the architecture of the cardiomyocytes. The cardioprotective effect of HF was connected with the regulation of the MAPK/NF-κB signaling pathway. What is more, molecular docking was carried out to prove that HF could be stably combined with p38, ERK1/2, JNK, and NF-κB. In summary, this is a novel study demonstrating the cardioprotective effects of HF against MI/RI in vivo. Consequently, these results demonstrate that HF can be considered a promising potential therapy for MI/RI.

Background: Prediabetes is associated with a high risk of colon cancer, and abdominal obesity, which can result in the secretion of several obesity-related adipocytokines, is an independent influencing factor for colonic polyps in prediabetes subjects. However, the correlation between adipocytokine levels and colonic polyps in prediabetes subjects is unclear. This research explores the relationship between plasma adiponectin, visfatin, leptin, and resistin levels and the development of colonic polyps in prediabetes subjects. Methods: A total of 468 prediabetes subjects who underwent electronic colonoscopy examinations were enrolled in this study; there were 248 cases of colonic polyps and 220 cases without colonic mucosal lesions. Then, colonic polyps patients with prediabetes were subdivided into a single-polyp group, multiple-polyps group, low-risk polyps group, or high-risk polyps group. In addition, 108 subjects with normal glucose tolerance who were frequency matched with prediabetes subjects by sex and age were selected as the control group; 46 control subjects had polyps, and 62 control subjects were polyp-free. Plasma adiponectin, visfatin, leptin, and resistin levels were measured in all the subjects, and the related risk factors of colonic polyps in prediabetes subjects were analysed. Results: Plasma adiponectin levels were significantly lower in the polyps group than in the polyp-free group [normal glucose tolerance (9.8 ± 4.8 vs 13.3 ± 3.9) mg/L, P = 0.013; prediabetes (5.6 ± 3.7 vs 9.2 ± 4.4) mg/L, P = 0.007]. In prediabetes subjects, plasma adiponectin levels were decreased significantly in the multiple polyps group [(4.3 ± 2.6 vs 6.7 ± 3.9) mg/L, P = 0.031] and the high-risk polyps group [(3.7 ± 2.9 vs 7.4 ± 3.5) mg/L, P < 0.001] compared to their control groups. Plasma visfatin levels were higher in the polyps group and the multiple-polyps group than those in their control groups (P = 0.041 and 0.042, respectively), and no significant difference in plasma leptin and resistin levels was observed between these three pairs of groups (all P > 0.05). The multivariate logistic regression analysis showed that lower levels of plasma adiponectin was a risk factor for colonic polyps, multiple colonic polyps, and high-risk colonic polyps in prediabetes subjects. Conclusions: Plasma adiponectin levels are inversely associated with colonic polyps, multiple colonic polyps, and high-risk colonic polyps in prediabetes subjects. And adiponectin may be involved in the development of colon tumours in prediabetes subjects.

Background: Myocardial infarction (MI) is one of the first cardiovascular diseases endangering human health. Inflammatory response plays a significant role in the pathophysiological process of MI. Messenger RNA (mRNA) has been proven to play a key role in cardiovascular diseases. Single-cell sequencing (SCS) technology is a new technology for high-throughput sequencing analysis of genome, transcriptome, and epigenome at the single-cell level, and it also plays an important role in the diagnosis and treatment of cardiovascular diseases. Machine learning algorithms have a wide scope of utilization in biomedicine and have demonstrated superior efficiency in clinical trials. However, few studies integrate these three methods to investigate the role of mRNA in MI. The aim of this study was to screen the expression of mRNA, investigate the function of mRNA, and provide an underlying scientific basis for the diagnosis of MI.Methods: In total, four RNA microarray datasets of MI, namely, GSE66360, GSE97320, GSE60993, and GSE48060, were downloaded from the Gene Expression Omnibus database. The function analysis was carried out by Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Disease Ontology (DO) enrichment analysis. At the same time, inflammation-related genes (IRGs) were acquired from the GeneCards database. Then, 52 co-DEGs were acquired from differentially expressed genes (DEGs) in differential analysis, IRGs, and genes from SCS, and they were used to construct a protein-protein interaction (PPI) network. Two machine learning algorithms, namely, (1) least absolute shrinkage and selection operator and (2) support vector machine recursive feature elimination, were used to filter the co-DEGs. Gene set enrichment analysis (GSEA) was performed to screen the hub-modulating signaling pathways associated with the hub genes. The results were validated in GSE97320, GSE60993, and GSE48060 datasets. The CIBERSORT algorithm was used to analyze 22 infiltrating immune cells in the MI and healthy control (CON) groups and to analyze the correlation between these immune cells. The Pymol software was used for molecular docking of hub DEGs and for potential treatment of MI drugs acquired from the COREMINE.Results: A total of 126 DEGs were in the MI and CON groups. After screening two machine learning algorithms and key co-DEGs from a PPI network, two hub DEGs (i.e., IL1B and TLR2) were obtained. The diagnostic efficiency of IL1B, TLR2, and IL1B + TLR2 showed good discrimination in the four cohorts. GSEA showed that KEGG enriched by DEGs were mainly related to inflammation-mediated signaling pathways, and GO biological processes enriched by DEGs were linked to biological effects of various inflammatory cells. Immune analysis indicated that IL1B and TLR2 were correlated with various immune cells. Dan shen, san qi, feng mi, yuan can e, can sha, san qi ye, san qi hua, and cha shu gen were identified as the potential traditional Chinese medicine (TCM) for the treatment of MI. 7-hydroxyflavone (HF) had stable combinations with IL1B and TLR2, respectively.Conclusion: This study identified two hub DEGs (IL1B and TLR2) and illustrated its potential role in the diagnosis of MI to enhance our knowledge of the underlying molecular mechanism. Infiltrating immune cells played an important role in MI. TCM, especially HF, was a potential drug for the treatment of MI.

Purpose: The objective of this study was to identify the potential regulatory mechanisms, diagnostic biomarkers, and therapeutic drugs for heart failure (HF). Methods: Differentially expressed genes (DEGs) between HF and non-failing donors were screened from the GSE57345, GSE5406, and GSE3586 datasets. Database for Annotation Visualization and Integrated Discovery and Metascape were used for Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses respectively. The GSE57345 dataset was used for weighted gene co-expression network analysis (WGCNA). The intersecting hub genes from the DEGs and WGCNA were identified and verified with the GSE5406 and GSE3586 datasets. The diagnostic value of the hub genes was calculated through receiver operating characteristic analysis and net reclassification index (NRI). Gene set enrichment analysis (GSEA) was used to filter out the signaling pathways associated with the hub genes. SYBYL 2.1 was used for molecular docking of hub targets and potential HF drugs obtained from the connection map. Results: Functional annotation of the DEGs showed enrichment of negative regulation of angiogenesis, endoplasmic reticulum stress response, and heart development. PTN, LUM, ISLR, and ASPN were identified as the hub genes of HF. GSEA showed that the key genes were related to the transforming growth factor-β (TGF-β) and Wnt signaling pathways. Sirolimus, LY-294002, and wortmannin have been confirmed as potential drugs for HF. Conclusion: We identified new hub genes and candidate therapeutic drugs for HF, which are potential diagnostic, therapeutic and prognostic targets and warrant further investigation.

Three-dimensional speckle tracking echocardiography (3D STE) is an emerging noninvasive method for predicting left ventricular remodeling (LVR) after acute myocardial infarction (AMI). Previous studies analyzed the predictive value of 3D STE with traditional models. However, no models that contain comprehensive risk factors were assessed, and there are limited data on the comparison of different 3D STE parameters. In this study, we sought to build a machine learning model for predicting LVR in AMI patients after effective percutaneous coronary intervention (PCI) that contains the majority of the clinical risk factors and compare 3D STE parameters values for LVR prediction. We enrolled 135 first-onset AMI patients (120 males, mean age 54 ± 9 years). All patients went through a 3D STE and a traditional transthoracic echocardiography 24 hours after reperfusion. A second echocardiography was repeated at the three-month follow-up to detect LVR (defined as a 20 percent increase in left ventricular end-diastolic volume). Six models were constructed using 15 risk factors. A receiver operator characteristic curve and four performance measurements were used as evaluation methods. Feature importance was used to compare 3D STE parameters. 26 patients (19.3%) had LVR. Our evaluation showed that RF can best predict LVR with the best AUC of 0.96. 3D GLS was the most valuable 3D STE parameters, followed by GCS, global area strain, and global radial strain (feature importance 0.146, 0.089, 0.087, and 0.069, respectively). To sum up, RF models can accurately predict the LVR after AMI, and 3D GLS was the best 3D STE parameters in predicting the LVR.