Overweight/obesity is a chronic disease that carries an increased risk of hypertension, diabetes mellitus, and premature death. Several epidemiological studies have demonstrated a clear relationship between salt intake and obesity, but the pathophysiologic mechanisms remain unknown. We hypothesized that ghrelin, which regulates appetite, food intake, and fat deposition, becomes elevated when one consumes a high-salt diet, contributing to the progression of obesity. We, therefore, investigated fasting ghrelin concentrations during a high-salt diet. Thirty-eight non-obese and normotensive subjects (aged 25 to 50 years) were selected from a rural community in Northern China. They were sequentially maintained on a normal diet for three days at baseline, a low-salt diet for seven days (3 g/day, NaCl), then a high-salt diet for seven days (18 g/day). The concentration of plasma ghrelin was measured using an immunoenzyme method (ELISA). High-salt intake significantly increased fasting ghrelin levels, which were higher during the high-salt diet (320.7 ± 30.6 pg/mL) than during the low-salt diet (172.9 ± 8.9 pg/mL). The comparison of ghrelin levels between the different salt diets was statistically-significantly different (p < 0.01). A positive correlation between 24-h urinary sodium excretion and fasting ghrelin levels was demonstrated. Our data indicate that a high-salt diet elevates fasting ghrelin in healthy human subjects, which may be a novel underlying mechanism of obesity.

Salt-sensitive individuals show earlier and more serious cardiac damage than nonsalt-sensitive ones. Some studies have suggested that microRNA-133a could reduce cardiac hypertrophy and myocardial fibrosis. The current study aims to investigate the different functions of high-salt intake on salt-sensitive (SS) rats and Sprague-Dawley (SD) rats and the involvement of microRNA-133a in these roles. After high-salt intervention, the left ventricular mass (LVW) and left ventricular mass index (LVMI) of the salt-sensitive high salt (SHS) group were obviously higher than those of the salt-sensitive low salt (SLS) group. However, the difference between the Sprague-Dawley high salt (DHS) group and the Sprague-Dawley low salt (DLS) group was not significant. Compared with SLS group, collagen I and connective tissue growth factor (CTGF) in the heart of SHS group were significantly higher, whereas no statistical difference was observed between the DHS group and the DLS group. Compared with low-salt diet, microRNA-133a in the heart of both strains were significantly decreased, but that in the SHS group decreased more significantly. These results suggest that high salt intervention could down-regulate the expression of myocardial microRNA-133a, which may be one of the mechanisms involved in myocardial fibrosis in salt-sensitive hypertension.

Cardiovascular diseases (CVDs) remain a major cause of mortality worldwide, and electrocardiograms (ECGs) are an important non-invasive tool for diagnosis. However, many ECG records are still stored as scanned paper images containing grid interference, noise, and variable image quality, which complicates automated analysis. This study presents a framework for four-class ECG image classification directly from paper-based records, covering Normal, Myocardial Infarction (MI), Abnormal Heartbeat (HB), and Post-Myocardial Infarction (PMI). The proposed method combines a targeted preprocessing pipeline with a CBAM-enhanced ResNet-18 architecture. The preprocessing stages are designed to suppress non-diagnostic background interference while preserving clinically relevant waveform morphology, and the attention mechanism refines feature learning in both channel and spatial dimensions. Under a unified training strategy, the proposed model achieved a test accuracy of 0.9674 and a macro-F1 score of 0.9702 on the independent test set. In direct comparison under the same evaluation protocol, the custom CNN reference model achieved 0.9022 test accuracy and 0.8950 macro-F1, indicating clear gains from the proposed architecture. These findings suggest that the combination of tailored preprocessing and attention-enhanced residual learning is effective for paper-based ECG image classification and may provide useful support for automated analysis of legacy ECG archives and resource-limited clinical settings.