Preparation, characterization, oral bioavailability, and pharmacodynamic study of eugenol-porous silica solidified powder.

Eugenol possesses anti-inflammatory and antioxidant properties, and may serve as a potential therapeutic agent for hepatic fibrosis. However, the development of solid eugenol formulations is challenging due to its volatility. To address this issue, this study employed porous silica to adsorb solidified eugenol. The solidified powder was characterized using Fourier transform infrared spectroscopy (FTIR), differential scanning calorimetry (DSC), and scanning electron microscopy (SEM). In addition, the differences in in vitro release and oral bioavailability between eugenol and solidified eugenol powder were investigated. The effectiveness of eugenol and eugenol powder in treating liver fibrosis was investigated using enzyme-linked immunosorbent assay (ELISA), polymerase chain reaction (PCR), and histopathological observations. Our results indicate that porous silica can effectively solidify eugenol into powder at a lower dosage. Furthermore, we observed that porous silica accelerates eugenol release in vitro and in vivo. The pharmacodynamic results indicated that eugenol has a positive therapeutic effect against hepatic fibrosis and that porous silica does not affect its efficacy. In conclusion, porous silica was able to solidify eugenol, which may facilitate the preparation and storage of solid formulations.

Fe(III)/peroxymonosulfate oxidation system for the degradation of rhein, a toxic component abundance in rhubarb residue.

Rhubarb is widely used in health care, but causing a great amount of rhein-containing herbal residue. Rhein with several toxicities might pollute environment, damage ecology and even hazard human health if left untreated. In this study, the degradation effects of bisulfite- (BS) and peroxymonosulfate- (PMS) based oxidation systems on rhein in rhubarb residue were compared and investigated. The effects of BS and PMS with two valence states of ferric ion (Fe) on the degradation of rhein in rhubarb residue were optimized for the selection of optimal oxidation system. The influences of reaction temperature, reaction time and initial pH on the removal of rhein under the optimal oxidation system were evaluated. The chemical profiles of rhubarb residue with and without oxidation process were compared by UPLC-QTOF-MS/MS, and the degradation effects were investigated by PLS-DA and S plot/OPLS-DA analysis. The results manifested that PMS showed relative higher efficiency than BS on the degradation of rhein. Moreover, Fe(III) promoted the degradation effect of PMS, demonstrated that Fe(III)/PMS is the optimal oxidation system to degrade rhein in rhubarb residue. Further studies indicated that the degradation of rhein by the Fe(III)/PMS oxidation system was accelerated with the prolong of reaction time and the elevation of reaction temperature, and also affected by the initial pH. More importantly, Fe(III)/PMS oxidation system could degrade rhein in rhubarb residue completely under the optimal conditions. In conclusion, Fe(III)/PMS oxidation system is a feasible method to treat rhein in rhubarb residue.

Pharmacodynamic study of chitosan in combination with salvianolic acid B in the treatment of CC14 -induced liver fibrosis in mice.

Liver fibrosis is a chronic liver lesion caused by excessive deposition of the extracellular matrix after liver damage, resulting in fibrous scarring of liver tissue. The progression of liver fibrosis is partially influenced by the gut microbiota. Chitosan can play a therapeutic role in liver fibrosis by regulating the gut microbiota based on the 'gut-liver axis' theory. Salvianolic acid B can inhibit the development of liver fibrosis by inhibiting the activation of hepatic stellate cells and reducing the production of extracellular matrix. In this study, the therapeutic effect of chitosan in combination with salvianolic acid B on liver fibrosis was investigated in a mouse liver fibrosis model. The results showed that the combination of chitosan and salvianolic acid B was better than the drug alone, improving AST/ALT levels and reducing the expression of α-SAM, COL I, IL-6 and other related genes. It improved the structure of gut microbiota and increased the abundance of beneficial bacteria such as Lactobacillus. The above results could provide new ideas for the clinical treatment of liver fibrosis.

Metagenomic-based pathogen surveillance for children with severe pneumonia in pediatric intensive care unit.

Background: Pneumonia is a significant cause of morbidity and mortality in children. Metagenomic next-generation sequencing (mNGS) has the potential to assess the landscape of pathogens responsible for severe pulmonary infection. Methods: Bronchoalveolar lavage fluid (BALF) samples of 262 children with suspected pulmonary infections were collected from April 2019 to October 2021 in the Pediatric Intensive Care Unit (PICU) of Guangdong Women and Children Hospital. Both mNGS and conventional tests were utilized for pathogen detection. Results: A total of 80 underlying pathogens were identified using both mNGS and conventional tests. Respiratory syncytial virus (RSV), Staphylococcus aureus and rhinovirus were the most frequently detected pathogens in this cohort. The incidence rate of co-infection was high (58.96%, 148/251), with bacterial-viral agents most co-detected. RSV was the main pathogen in children younger than 6 months of age, and was also commonly found in older pediatric patients. Rhinovirus was prevalent in children older than 6 months. Adenovirus and Mycoplasma pneumoniae were more prevalent in children older than 3 years than in other age groups. Pneumocystis jirovecii was detected in nearly 15% of children younger than 6 months. Besides, influenza virus and adenovirus were rarely found in 2020 and 2021. Conclusions: Our study highlights the importance of using advanced diagnostic techniques like mNGS to improve our understanding of the microbial epidemiology of severe pneumonia in pediatric patients.

A longan yield estimation approach based on UAV images and deep learning.

Longan yield estimation is an important practice before longan harvests. Statistical longan yield data can provide an important reference for market pricing and improving harvest efficiency and can directly determine the economic benefits of longan orchards. At present, the statistical work concerning longan yields requires high labor costs. Aiming at the task of longan yield estimation, combined with deep learning and regression analysis technology, this study proposed a method to calculate longan yield in complex natural environment. First, a UAV was used to collect video images of a longan canopy at the mature stage. Second, the CF-YD model and SF-YD model were constructed to identify Cluster_Fruits and Single_Fruits, respectively, realizing the task of automatically identifying the number of targets directly from images. Finally, according to the sample data collected from real orchards, a regression analysis was carried out on the target quantity detected by the model and the real target quantity, and estimation models were constructed for determining the Cluster_Fruits on a single longan tree and the Single_Fruits on a single Cluster_Fruit. Then, an error analysis was conducted on the data obtained from the manual counting process and the estimation model, and the average error rate regarding the number of Cluster_Fruits was 2.66%, while the average error rate regarding the number of Single_Fruits was 2.99%. The results show that the method proposed in this paper is effective at estimating longan yields and can provide guidance for improving the efficiency of longan fruit harvests.

A novel approach for estimating the flowering rate of litchi based on deep learning and UAV images.

Litchi flowering management is an important link in litchi orchard management. Statistical litchi flowering rate data can provide an important reference for regulating the number of litchi flowers and directly determining the quality and yield of litchi fruit. At present, the statistical work regarding litchi flowering rates requires considerable labour costs. Therefore, this study aims at the statistical litchi flowering rate task, and a combination of unmanned aerial vehicle (UAV) images and computer vision technology is proposed to count the numbers of litchi flower clusters and flushes in a complex natural environment to improve the efficiency of litchi flowering rate estimation. First, RGB images of litchi canopies at the flowering stage are collected by a UAV. After performing image preprocessing, a dataset is established, and two types of objects in the images, namely, flower clusters and flushes, are manually labelled. Second, by comparing the pretraining and testing results obtained when setting different training parameters for the YOLOv4 model, the optimal parameter combination is determined. The YOLOv4 model trained with the optimal combination of parameters tests best on the test set, at which time the mean average precision (mAP) is 87.87%. The detection time required for a single image is 0.043 s. Finally, aiming at the two kinds of targets (flower clusters and flushes) on 8 litchi trees in a real orchard, a model for estimating the numbers of flower clusters and flushes on a single litchi tree is constructed by matching the identified number of targets with the actual number of targets via equation fitting. Then, the data obtained from the manual counting process and the estimation model for the other five litchi trees in the real orchard are statistically analysed. The average error rate for the number of flower clusters is 4.20%, the average error rate for the number of flushes is 2.85%, and the average error for the flowering rate is 1.135%. The experimental results show that the proposed method is effective for estimating the litchi flowering rate and can provide guidance regarding the management of the flowering periods of litchi orchards.

Design, Synthesis and Biological Evaluation of Potent Human Glyoxalase I Inhibitors.

Several glutathione derivatives bearing the S-(N-aryl-N-hydroxycarbamoyl) or S-(C-aryl-N-hydroxycarbamoyl) moieties (10, 10', 13-15) were synthesized, characterized, and their human glyoxalase I (hGLO1) inhibitory activity was evaluated. Compound 10 was proved to be the effective hGLO1 inhibitor with a Ki value of 1.0 nM and the inhibition effect of compound 10 on hGLO1 was nearly ten-fold higher than that of the strongest inhibitor 2 (Ki=10.0 nM) which has been reported in the field of glutathione-type hGLO1 inhibitors. Its diethyl ester prodrug 10' was able to penetrate cell membrane and had good inhibitory effect on the growth of NCI-H522 cell xenograft tumor model.