Proton pump inhibitors may enhance the risk of digestive diseases by regulating intestinal microbiota

Proton pump inhibitors (PPIs) are the most used acid-inhibitory drugs, with a wide range of applications in the treatment of various digestive diseases. However, recently, there has been a growing number of digestive complications linked to PPIs, and several studies have indicated that the intestinal flora play an important role in these complications. Therefore, developing a greater understanding of the role of the gut microbiota in PPI-related digestive diseases is essential. Here, we summarize the current research on the correlation between PPI-related digestive disorders and intestinal flora and establish the altered strains and possible pathogenic mechanisms of the different diseases. We aimed to provide a theoretical basis and reference for the future treatment and prevention of PPI-related digestive complications based on the regulation of the intestinal microbiota

Identification of a novel bile marker clusterin and a public online prediction platform based on deep learning for cholangiocarcinoma

Abstract Background Cholangiocarcinoma (CCA) is a highly aggressive malignant tumor, and its diagnosis is still a challenge. This study aimed to identify a novel bile marker for CCA diagnosis based on proteomics and establish a diagnostic model with deep learning. Methods A total of 644 subjects (236 CCA and 408 non-CCA) from two independent centers were divided into discovery, cross-validation, and external validation sets for the study. Candidate bile markers were identified by three proteomics data and validated on 635 clinical humoral specimens and 121 tissue specimens. A diagnostic multi-analyte model containing bile and serum biomarkers was established in cross-validation set by deep learning and validated in an independent external cohort. Results The results of proteomics analysis and clinical specimen verification showed that bile clusterin (CLU) was significantly higher in CCA body fluids. Based on 376 subjects in the cross-validation set, ROC analysis indicated that bile CLU had a satisfactory diagnostic power (AUC: 0.852, sensitivity: 73.6%, specificity: 90.1%). Building on bile CLU and 63 serum markers, deep learning established a diagnostic model incorporating seven factors (CLU, CA19-9, IBIL, GGT, LDL-C, TG, and TBA), which showed a high diagnostic utility (AUC: 0.947, sensitivity: 90.3%, specificity: 84.9%). External validation in an independent cohort (n = 259) resulted in a similar accuracy for the detection of CCA. Finally, for the convenience of operation, a user-friendly prediction platform was built online for CCA. Conclusions This is the largest and most comprehensive study combining bile and serum biomarkers to differentiate CCA. This diagnostic model may potentially be used to detect CCA