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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 K i 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 (K i =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.

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.

BackgroundPneumonia 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.MethodsBronchoalveolar 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.ResultsA 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.ConclusionsOur 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.