ABSTRACT
Background/Objectives: Gastroesophageal reflux disease (GORD) is caused by gastric contents refluxing back into the oesophagus and oral cavity. It can lead to injuries to the mucosa in the form of erosion and ulcers. Our past research have shown acid reflux severity and disease progression is associated with alternations in the microbiota of the distal oesophagus. The aim of this study was to explore whether changes in the oral microbiota occurred in GORD patients and establish any associations with reflux severity. Methods: Fresh mouthwash samples were collected from 58 patients experiencing reflux symptoms referred for 24 h pH monitoring. The participants were categorised into three groups based on their DeMeester scores: Normal (<14.72), Mild (14.2-50), and Moderate/severe (>51). Microorganism identity and diversity were generated using hypervariable tag sequencing and analysing the V1-V3 region of the 16S rRNA gene. Results: No differences in microbiota diversity were found in oral microbiota between groups using the Chiao1 diversity index and Shannon diversity index. Microbiota in the Mild group showed reductions in Rothia dentocariosa and Lautropia, while Moryella and Clostridiales_1 were increased compared with the Normal group. In the Moderate/severe group, the abundance of Rothia aeria was reduced compared with the Normal group, while Schwartzia, Rs_045, Paludibacter, S. satelles, Treponema, and T. socranskii all had increased abundance. The abundance of Prevotella pallens was higher in the Mild group compared with Moderate/severe, while S. satelles and Paludibacter abundances were lower. Conclusions: Our study shows the oral microbiome show significant differences between acid reflux severity groups, as categorised by DeMeester score.
ABSTRACT
Lycium (also known as Goji berry) is used in traditional Chinese medicine (TCM) with claimed benefits, including eye and liver protection, immune system fortification and blood glucose control. The commercially available product comes from either the L. barbarum or L. chinense species, with the former dominating the marketplace due to its better taste profile. The main objective of this study was to develop a validated LC-ESI-MS/MS method to quantify multiple key bio-active analytes in commercially available Lycium berries and to qualitatively assess these samples using a principal component analysis (PCA). A LC-ESI-MS/MS method for the quantitation of seven analytes selected using the Herbal Chemical Marker Ranking System (Herb MaRS) was developed. The Herb MaRS ranking system considered bioavailability, bioactivity and physiological action of each target analyte, its intended use and the commercial availability of an analytical standard. After method optimization combining high resolving power with selective detection, seven analytes were quantified and the Lycium samples were quantitatively profiled. Chromatographic spectra were also obtained using longer run-time LC-UV and GC-MS methods in order to qualitatively assess the samples using a principal component analysis (PCA). The result of the method validation procedure was a 15.5 min LC-ESI-MS/MS method developed for the quantification of seven analytes in commercial Lycium samples. Wide variation in analyte concentration was observed with the following results (analyte range in mg/g): rutin, 16.1-49.2; narcissin, 0.37-1.65; nictoflorin, 0.26-0.78; coumaric acid, 6.84-12.2; scopoletin, 0.33-2.61; caffeic acid, 0.08-0.32; chlorogenic acid, 1.1-9.12. The quantitative results for the L. barbarum and L. chinense species samples indicate that they cannot be differentiated based on the bio-actives tested. A qualitative assessment using PCA generated from un-targeted LC-UV and GC-MS phytochemical spectra led to the same conclusion. The un-targeted quantitative and qualitative phytochemical profiling indicates that commercial L. barbarum and L. chinense cannot be distinguished using chemical analytical methods. Genetic fingerprinting and pharmacological testing may be needed to ensure the efficacy of commercial Lycium in order to validate label claims.