The effects of Massa Medicata Fermentata on the digestive function and intestinal flora of mice with functional dyspepsia.

Introduction: The purpose of this study was to identify the chemical components of Massa Medicata Fermentata (MMF) in different fermentation methods, analyze its regulatory effects on gastrointestinal propulsion and intestinal flora in mice with food accumulation, and further explore its mechanism of action in the treatment of dyspepsia. Methods: The chemical compositions of three kinds of MMF were identified using the UPLC-Q- Exactive Orbitrap mass spectrometer. A model of spleen deficiency and food accumulation in mice was established. The gastric emptying rate and intestinal propulsion rate were calculated, serum gastrin concentration and cholinesterase activity were measured, and 16S rRNA microbial detection was performed in different groups of mouse feces. Results: The results showed that a total of 95 chemical components were identified from the three MMF extracts, 62 of which were the same, but there were differences in flavonoids and their glycosides, organic acids, and esters. MMF, PFMMF, and commercial MMF could all significantly improve the gastric emptying rate, intestinal propulsion rate, and GAS concentration in the serum of model mice; PFMMF has a better effect, while there was no significant difference in cholinesterase activity among the groups (p > 0.05). The 16S rRNA sequencing results showed that the MMF and PFMMF could increase the content of beneficial bacteria Bacteroidetes and decrease the pathogenic bacteria Verrucomicrobia in the intestines of model mice, while the commercial MMF could not. Discussion: Studies suggest that MMF has a variety of possible mechanisms for improving food accumulation and treating gastrointestinal dyspepsia, which provides reference value for the quality evaluation and clinical application of MMF.

NIR quantitative model trans-scale calibration from small scale to pilot scale via directed DOSC-SBC algorithm.

In order to solve the problem of inapplicability of NIR quantitative models due to the large difference between the modeling samples and the samples to be tested, Directed DOSC-SBC（DDOSC-SBC）algorithm is proposed in this paper based on Direct Orthogonal Signal Correction combined with Slope/Bias Correction (DOSC-SBC) algorithm. To obtain the suitable spectral matrix transfer parameters for the test set during DDOSC spectral preprocessing, several representative test samples in the test set were selected, then the spectral systematic errors between the modeling set and the test set were corrected with the SBC method in order to realize the trans-scale prediction of the NIR quantitative model. NIR data and the critical quality attributes（CQAs）were detected in the small scale and pilot scale pharmaceutical process of the fluidized bed granulation of dextrin and water extraction of honeysuckle. After the small scale model was calibrated via the directed DOSC-SBC algorithm which was guided by representative pilot scale samples, the small scale model was able to predict the pilot scale test samples more accurately. The NIR quantitative model trans-scale calibration from small scale to pilot scale was also successfully realized with a RPD value higher than 3.5 and RSEP value lower than 10%. DDOSC-SBC algorithm is a successful model trans-scale calibrated method that can be applied to NIR real-time monitoring of CQAs in the preparation process of Chinese herbal medicine.