High-throughput detection allied with machine learning for precise monitoring of significant serum metabolic changes in Helicobacter pylori infection.

High-throughput detection of large-scale samples is the foundation for rapidly accessing massive metabolic data in precision medicine. Machine learning is a powerful tool for uncovering valuable information hidden within massive data. In this work, we achieved the extraction of a single fingerprinting of 1 µL serum within 5 s through a high-throughput detection platform based on functionalized nanoparticles. We quickly obtained over a thousand serum metabolic fingerprintings (SMFs) including those of individuals with Helicobacter pylori (HP) infection. Combining four classical machine learning models and enrichment analysis, we attempted to extract and confirm useful information behind these SMFs. Based on all fingerprint signals, all four models achieved area under the curve (AUC) values of 0.983-1. In particular, orthogonal partial least squares discriminant analysis (OPLS-DA) model obtained value of 1 in both the discovery and validation sets. Fortunately, we identified six significant metabolic features, all of which can greatly contribute to the monitoring of HP infection, with AUC values ranging from 0.906 to 0.985. The combination of these six significant metabolic features can enable the precise monitoring of HP infection in serum, with over 95 % of accuracy, specificity and sensitivity. The OPLS-DA model displayed optimal performance and the corresponding scatter plot visualized the clear distinction between HP and HC. Interestingly, they exhibit a consistent reduction trend compared to healthy controls, prompting us to explore the possible metabolic pathways and potential mechanism. This work demonstrates the potential alliance between high-throughput detection and machine learning, advancing their application in precision medicine.

Combined therapy with pioglitazone and FGF21 mRNA synergistically ameliorates metabolic disorders in NAFLD rats.

Background: Current study aims to investigate the ameliorative effect of pioglitazone (PIO) combined with mRNA encoding FGF21 (termed mFGF21) on the metabolic disorders in rats with nonalcoholic fatty liver disease (NAFLD) and its potential mechanism. Methods: In vitro functional activity of FGF21 protein expressed by mFGF21 was evaluated in human adipose-derived stem cells (hASCs). The pharmacokinetic profiles of FGF21 protein expressed by mFGF21 were investigated in normal SD rats and NAFLD rats, respectively. Results: As the results, it showed that the PIO could enhanced in vitro functional activity of FGF21 protein expressed from mFGF21 in hASCs. Not only that, mFGF21 turns the body into a processing plant for endogenous protein expression, which enhanced the pharmacokinetic profiles of FGF21 proteins. Combined treatment with PIO and mFGF21 significantly reduced body weight, fasting blood glucose levels, insulin levels and lipid metabolism in NAFLD rats compared with control or both two monotherapy groups. The results of H&E staining and Western blot revealed that combined treatment with PIO and mFGF21 significantly decreased hepatic fat accumulation in NAFLD rats by activating the SHP1/AMPK signaling pathway. Conclusions: Our finding collectively demonstrated that PIO and mFGF21 combination therapy could synergistically ameliorate metabolic disorders in NAFLD rats.

Evaluation of the association studies of single nucleotide polymorphisms and hepatocellular carcinoma: a systematic review.

PURPOSE: For decades of years, hundreds of candidate gene-based association studies explored the relationship between single nucleotide polymorphisms (SNPs) and hepatocellular carcinoma (HCC). There was no systematic review summarized the results of these association studies of candidate SNPs and HCC to date. In order to summarize the results of the association studies, we conducted a concise systematic review. METHODS: By searching Pubmed database before October 2010, we reviewed all the association studies about candidate SNPs and HCC. If the eligible study number on a given SNP was more than three, we conducted a meta-analysis. We reported here only the overall positive-association results with statistical significance and evaluated the reliability of the associations by using false-positive report probability (FPRP) analysis and the Venice guidelines on genetic epidemiology studies. RESULTS: Six SNPs of five genes (rs1800562 of HFE, rs17868323 and rs11692021 of UGT1A7, rs2279744 of MDM2, rs1143627 of IL-1B, and rs4880 of MnSOD) showed overall significant associations with HCC. The eligible number of the studies varied from three to nine. Two SNPs (rs1800562 of HFE and rs2279744 of MDM2) passed the FPRP threshold (FPRP < 0.20). According to the Venice guidelines, the associations between the two SNPs (rs1800562 and rs2279744) and HCC were of moderate evidence. CONCLUSIONS: Two SNPs (rs1800562 of HFE and rs2279744 of MDM2) were associated with HCC with moderate epidemiological evidence and deserve further study and additional biological and clinical assessment.