Changes of Serum Ficolin-3 and C5b-9 in Patients with Heart Failure

Pharmacodynamics and Mechanism of Astragali Radix and Anemarrhenae Rhizoma in Treating Chronic Heart Failure by Inhibiting Complement Activation

Astragali radix (AR) and anemarrhenae rhizoma (AAR) are used clinically in Chinese medicine for the treatment of chronic heart failure (CHF), but the exact therapeutic mechanism is unclear. In this study, a total of 60 male C57BL/6 mice were divided into 5 groups, namely sham, model, AR, AAR, and AR-AAR. In the sham group, the chest was opened without ligation. In the other groups, the chest was opened and the transverse aorta was ligated to construct the transverse aortic constriction model. After 8 weeks of feeding, mice were given medicines by gavage for 4 weeks. Left ventricular ejection fraction (LVEF) and left ventricular fractional shortening (LVFS) were detected by echocardiography. Heart weight index (HWI) and wheat germ agglutinin staining were used to evaluate cardiac hypertrophy. Hematoxylin-eosin staining was used to observe the pathological morphology of myocardial tissue. Masson staining was used to evaluate myocardial fibrosis. The content of serum brain natriuretic peptide (BNP) was detected by enzyme-linked immunosorbent assay kit. The content of serum immunoglobulin G (IgG) was detected by immunoturbidimetry. The mechanism of AR-AAR in the treatment of CHF was explored by proteomics. Western blot was used to detect the protein expressions of complement component 1s (C1s), complement component 9 (C9), and terminal complement complex 5b-9 (C5b-9). The results show that AR-AAR inhibits the expression of complement proteins C1s, C9, and C5b-9 by inhibiting the production of IgG antibodies from B cell activation, which further inhibits the complement activation, attenuates myocardial fibrosis, reduces HWI and cardiomyocyte cross-sectional area, improves cardiomyocyte injury, reduces serum BNP release, elevates LVEF and LVFS, improves cardiac function, and exerts myocardial protection.

Proteomic Profiling of Dilated Cardiomyopathy Plasma Samples ─ Searching for Biomarkers with Potential to Predict the Outcome of Therapy

Determination of the prognosis and treatment outcomes of dilated cardiomyopathy is a serious problem due to the lack of valid specific protein markers. Using in-depth proteome discovery analysis, we compared 49 plasma samples from patients suffering from dilated cardiomyopathy with plasma samples from their healthy counterparts. In total, we identified 97 proteins exhibiting statistically significant dysregulation in diseased plasma samples. The functional enrichment analysis of differentially expressed proteins uncovered dysregulation in biological processes like inflammatory response, wound healing, complement cascade, blood coagulation, and lipid metabolism in dilated cardiomyopathy patients. The same proteome approach was employed in order to find protein markers whose expression differs between the patients well-responding to therapy and nonresponders. In this case, 45 plasma proteins revealed statistically significant different expression between these two groups. Of them, fructose-1,6-bisphosphate aldolase seems to be a promising biomarker candidate because it accumulates in plasma samples obtained from patients with insufficient treatment response and with worse or fatal outcome. Data are available via ProteomeXchange with the identifier PXD046288.

Efficacy of metoprolol combined with torasemide in elderly patients with degenerative valvular heart disease and heart failure and its influence on NT-proBNP levels

Objective

To investigate the efficacy of metoprolol combined with torasemide in elderly patients with degenerative valvular heart disease (DVHD) and heart failure and its influence on N-terminal pro-B-type natriuretic peptide (NT-proBNP) concentrations.

Methods

The records of 129 DVHD patients ≥60 years old with heart failure diagnosed and treated in our hospital from January 2020 to February 2022 were retrospectively selected. According to the treatment records, 62 patients received metoprolol treatment (Control-group), and 67 patients received metoprolol combined with torasemide treatment (Observation-group). The changes in cardiac function, NT-proBNP and inflammatory factor concentrations and clinical efficacy were analyzed and compared between the two groups before and after treatment.

Results

After treatment, left ventricular ejection fraction (LVEF), left ventricular end-diastolic diameter (LVEDD) and the mitral ratio of peak early to late diastolic filling velocity (E/A) were lower, with a greater decrease in the Observation-group compared to the Control-group (P<0.05). After treatment, NT-proBNP and interleukin-1beta (IL-1β), tumor necrosis factor-alpha (TNF-α), interleukin 6 (IL-6) concentrations were lower in both groups, with a greater decrease in the Observation-group compared to the Control-group (P<0.05). The total clinical efficacy of the Observation-group was significantly higher than the Control-group (P<0.05). There was no significant difference in the total incidence of adverse drug reactions between the two groups (P>0.05).

Conclusion

Metoprolol combined with torasemide has a significant therapeutic effect on DVHD patients with heart failure. This drug combination improved cardiac function, reduced NT-proBNP concentrations and has good safety.

Multiple-model machine learning identifies potential functional genes in dilated cardiomyopathy

Introduction

Machine learning (ML) has gained intensive popularity in various fields, such as disease diagnosis in healthcare. However, it has limitation for single algorithm to explore the diagnosing value of dilated cardiomyopathy (DCM). We aim to develop a novel overall normalized sum weight of multiple-model MLs to assess the diagnosing value in DCM.

Methods

Gene expression data were selected from previously published databases (six sets of eligible microarrays, 386 samples) with eligible criteria. Two sets of microarrays were used as training; the others were studied in the testing sets (ratio 5:1). Totally, we identified 20 differently expressed genes (DEGs) between DCM and control individuals (7 upregulated and 13 down-regulated).

Results

We developed six classification ML methods to identify potential candidate genes based on their overall weights. Three genes, serine proteinase inhibitor A3 (SERPINA3), frizzled-related proteins (FRPs) 3 (FRZB), and ficolin 3 (FCN3) were finally identified as the receiver operating characteristic (ROC). Interestingly, we found all three genes correlated considerably with plasma cells. Importantly, not only in training sets but also testing sets, the areas under the curve (AUCs) for SERPINA3, FRZB, and FCN3 were greater than 0.88. The ROC of SERPINA3 was significantly high (0.940 in training and 0.918 in testing sets), indicating it is a potentially functional gene in DCM. Especially, the plasma levels in DCM patients of SERPINA3, FCN, and FRZB were significant compared with healthy control.

Discussion

SERPINA3, FRZB, and FCN3 might be potential diagnosis targets for DCM, Further verification work could be implemented.

Identification of potential biomarkers of inflammation-related genes for ischemic cardiomyopathy

Objective

Inflammation plays an important role in the pathophysiology of ischemic cardiomyopathy (ICM). We aimed to identify potential biomarkers of inflammation-related genes for ICM and build a model based on the potential biomarkers for the diagnosis of ICM.

Materials and methods

The microarray datasets and RNA-Sequencing datasets of human ICM were downloaded from the Gene Expression Omnibus database. We integrated 8 microarray datasets via the SVA package to screen the differentially expressed genes (DEGs) between ICM and non-failing control samples, then the differentially expressed inflammation-related genes (DEIRGs) were identified. The least absolute shrinkage and selection operator, support vector machine recursive feature elimination, and random forest were utilized to screen the potential diagnostic biomarkers from the DEIRGs. The potential biomarkers were validated in the RNA-Sequencing datasets and the functional experiment of the ICM rat, respectively. A nomogram was established based on the potential biomarkers and evaluated via the area under the receiver operating characteristic curve (AUC), calibration curve, decision curve analysis (DCA), and Clinical impact curve (CIC).

Results

64 DEGs and 19 DEIRGs were identified, respectively. 5 potential biomarkers (SERPINA3, FCN3, PTN, CD163, and SCUBE2) were ultimately selected. The validation results showed that each of these five potential biomarkers showed good discriminant power for ICM, and their expression trends were consistent with the bioinformatics results. The results of AUC, calibration curve, DCA, and CIC showed that the nomogram demonstrated good performance, calibration, and clinical utility.

Conclusion

SERPINA3, FCN3, PTN, CD163, and SCUBE2 were identified as potential biomarkers associated with the inflammatory response to ICM. The proposed nomogram could potentially provide clinicians with a helpful tool to the diagnosis and treatment of ICM from an inflammatory perspective.