Vibrio parahaemolyticus is a major pathogen frequently found in seafood. Rapid and accurate detection of this pathogen is important for the control of bacterial foodborne diseases and to ensure food safety. In this study, we established a one-pot system that combines uracil-DNA glycosylase (UDG), loop-mediated isothermal amplification (LAMP), and clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein 12b (Cas12b) for detecting V. parahaemolyticus in seafood. This detection system can effectively perform identification using a single tube and avoid the risk of carry-over contamination.
Prostate cancer (PCa) is a pernicious tumor with high heterogeneity, which creates a conundrum for making a precise diagnosis and choosing an optimal treatment approach. Multiparametric magnetic resonance imaging (mp-MRI) with anatomical and functional sequences has evolved as a routine and significant paradigm for the detection and characterization of PCa. Moreover, using radiomics to extract quantitative data has emerged as a promising field due to the rapid growth of artificial intelligence (AI) and image data processing. Radiomics acquires novel imaging biomarkers by extracting imaging signatures and establishes models for precise evaluation. Radiomics models provide a reliable and noninvasive alternative to aid in precision medicine, demonstrating advantages over traditional models based on clinicopathological parameters. The purpose of this review is to provide an overview of related studies of radiomics in PCa, specifically around the development and validation of radiomics models using MRI-derived image features. The current landscape of the literature, focusing mainly on PCa detection, aggressiveness, and prognosis evaluation, is reviewed and summarized. Rather than studies that exclusively focus on image biomarker identification and method optimization, models with high potential for universal clinical implementation are identified. Furthermore, we delve deeper into the critical concerns that can be addressed by different models and the obstacles that may arise in a clinical scenario. This review will encourage researchers to design models based on actual clinical needs, as well as assist urologists in gaining a better understanding of the promising results yielded by radiomics.
A growing body of evidence has linked the gut microbiota to liver metabolism. The manipulation of intestinal microflora has been considered as a promising avenue to promote liver health. However, the effects of Lactobacillus gasseri LA39, a potential probiotic, on liver metabolism remain unclear. Accumulating studies have investigated the proteomic profile for mining the host biological events affected by microbes, and used the germ-free (GF) mouse model to evaluate host-microbe interaction. Here, we explored the effects of L. gasseri LA39 gavage on the protein expression profiles of the liver of GF mice. Our results showed that a total of 128 proteins were upregulated, whereas a total of 123 proteins were downregulated by treatment with L. gasseri LA39. Further bioinformatics analyses suggested that the primary bile acid (BA) biosynthesis pathway in the liver was activated by L. gasseri LA39. Three differentially expressed proteins (cytochrome P450 family 27 subfamily A member 1 (CYP27A1), cytochrome P450 family 7 subfamily B member 1 (CYP7B1), and cytochrome P450 family 8 subfamily B member 1 (CYP8B1)) involved in the primary BA biosynthesis pathway were further validated by western blot assay. In addition, targeted metabolomic analyses demonstrated that serum and fecal β-muricholic acid (a primary BA), dehydrolithocholic acid (a secondary BA), and glycolithocholic acid-3-sulfate (a secondary BA) were significantly increased by L. gasseri LA39. Thus, our data revealed that L. gasseri LA39 activates the hepatic primary BA biosynthesis and promotes the intestinal secondary BA biotransformation. Based on these findings, we suggest that L. gasseri LA39 confers an important function in the gut‒liver axis through regulating BA metabolism.