Searching for serum biomarkers linking coronary heart disease and Helicobacter pylori infection using infrared spectroscopy and artificial neural networks

The causal effect of Helicobacter pylori infection on coronary heart disease is mediated by the body mass index: a Mendelian randomization study

The association between Helicobacter pylori (H. pylori) infection and coronary heart disease (CHD) remains controversial, with an unclear causal link. This study employed bidirectional Mendelian randomization (MR) method, using H. pylori infection as the exposure, to investigate its causal relationship with CHD diagnosis, prognosis, and potential pathogenesis. H. pylori infection exhibited a causal association with body mass index (BMI) (β = 0.022; 95% CI 0.008-0.036; p = 0.001). Conversely, there was no discernible connection between H. pylori infection and the diagnosis of CHD (OR = 0.991; 95% CI 0.904-1.078; p = 0.842; IEU database; OR = 1.049; 95% CI 0.980-1.118; p = 0.178; FinnGen database) or CHD prognosis (OR = 0.999; 95% CI 0.997-1.001; p = 0.391; IEU database; OR = 1.022; 95% CI 0.922-1.123; p = 0.663; FinnGen database). Reverse MR analysis showed no causal effect of CHD on H. pylori infection. Our findings further support that H. pylori infection exerts a causal effect on CHD incidence, mediated by BMI. Consequently, eradicating or preventing H. pylori infection may provide an indirect clinical benefit for patients with CHD.

Rapid detection of serological biomarkers in gallbladder carcinoma using fourier transform infrared spectroscopy combined with machine learning

Gallbladder cancer (GBC) is the most common malignant tumour of the biliary tract. GBC is difficult to diagnose and treat at an early stage because of the lack of effective serum markers and typical symptoms, resulting in low survival rates. This study aimed to investigate the applicability of dried serum Fourier-transform infrared (FTIR) spectroscopy combined with machine learning algorithms to correctly differentiate patients with GBC from patients with gallbladder disease (GBD), cholangiocarcinoma (CCA), hepatocellular carcinoma (HCC) and healthy individuals. The differentiation between healthy individuals and GBC serum was better using principal component analysis (PCA) and linear discriminant analysis (LDA) for six spectral regions, especially in the protein (1710-1475 cm^-1) and combined (1710-1475 + 1354-980 cm^-1) region. However, the PCA-LDA model poorly differentiated GBC from GBD, CCA, and HCC in serum spectra. We evaluated the PCA- LDA, PCA-support vector machine (SVM), and radial basis kernel function support vector machine (RBF-SVM) models for GBC diagnosis and found that the RBF-SVM model performed the best, with 88.24-95% accuracy, 95.83% sensitivity, and 78.38-94.44% specificity in the 1710-1475 + 1354-980 cm^-1 region. This study demonstrated that serum FTIR spectroscopy combined with the RBF-SVM algorithm has great clinical potential for GBC screening.