RESUMO
Yigongsan is derived from Xiaoer Yaozheng Zhijue written by QIAN Yi in the Northern Song dynasty， which is the No. 3 formula in the Catalogue of Ancient Famous Classical Formulas（The Second Batch of Pediatrics） released by the National Administration of Traditional Chinese Medicine（TCM） in September 2022， and it can be developed as a class 3.1 new TCM drug. By referring to ancient medical books and modern literature， this study conducted herbal textual research on Yigongsan from five aspects， including historical evolution， origin and processing， dosage conversion， usage and preparation methods， and functional application， then formed the key information table of this formula， in order to provide reference for the development of reference samples and preparations of Yigongsan. Based on the results of the study， it is recommended that Panax ginseng should be removed the basal part of stem（rhizoma）， Poria cocos should be removed the peel， Citrus reticulata should be cut into shreds and Glycyrrhiza uralensis should be used. According to 4.13 g/Qian（钱）， 1 g/slice for ginger， 3 g for each jujube and 300 mL/Zhan（盏）， the doses of Ginseng Radix， Poria， Atractylodis Macrocephalae Rhizoma， Glycyrrhizae Radix et Rhizoma， Citri Reticulatae Pericarpium， Zingiberis Rhizoma Recens， Jujubae Fructus were 1.652， 1.652， 1.652， 1.652， 1.652， 5， 6 g， and the total amount was 19.26 g. The decocting method was to crush the medicinal materials into fine powder with 50-80 mesh， add 300 mL of water and decoct to 210 mL for each dose， then remove the dregs and take it warmly. This formula was recorded in ancient books as the main treatment for the cold-deficiency of spleen and stomach， and Qi stagnation in children with vomiting and diarrhea and lack of appetite. It has been flexibly applied by later generations of physicians， and is often used to treat anorexia， inflammation of the digestive tract， diarrhea and other diseases in children.
RESUMO
Chinese herbal piece is an important component of the traditional Chinese medicine （TCM） system， and identifying their quality and grading can promote the development and utilization of Chinese herbal pieces. Utilizing deep learning for intelligent identification of Chinese herbal pieces can save time， effort， and cost， while also reasonably avoiding the constraints of human subjectivity， providing a guarantee for efficient identification of Chinese herbal pieces. In this study， a dataset containing 108 kinds of Chinese herbal pieces （14 058 images） was constructed，the basic YOLOv4 algorithm was employed to identify the 108 kinds of Chinese herbal pieces of our database The mean average precision （mAP） of the developed basic YOLOv4 model reached 85.3%. In addition， the receptive field block was introduced into the neck network of YOLOv4 algorithm， and the improved YOLOv4 algorithm was used to identify Chinese herbal pieces. The mAPof the improved YOLOv4 model achieved 88.7%， the average precision of 80 kinds of decoction pieces exceeded 80%， the average precision of 48 kinds of decoction pieces exceeded 90%. These results indicate that adding the receptive field module can help to some extent in the identification of Chinese herbal medicine pieces with different sizes and small volumes. Finally， the average precision of each kind of Chinese herbal medicine piece by the improved YOLOv4 model was further analyzed. Through in-depth analysis of the original images of Chinese herbal medicine pieces with low prediction average precision， it was clarified that the quantity and quality of original images of Chinese herbal medicine pieces are key to performing intelligent object detection. The improved YOLOv4 model constructed in this study can be used for the rapid identification of Chinese herbal pieces， and also provide reference guidance for the manual authentication of Chinese herbal medicine decoction pieces.
RESUMO
ObjectiveTo explore the pretreatment methods to promote the enzymatic digestion and extraction of active ingredients from Magnoliae Officinalis Cortex dregs（MOCD）， and to provide a reference basis for the utilization of resource components in MOCD. MethodLiquid chromatography-mass spectrometry（LC-MS） was used for qualitative analysis of resource components in MOCD with an Agilent C18 reversed-phase column（3.0 mm×100 mm， 2.7 µm） at the flow rate of 0.4 mL·min-1， the mobile phase was water（A）-acetonitrile（B） for gradient elution（0-3 min， 25%-48%B； 3-6 min， 48%-59%B； 6-10 min， 59%-80%B； 10-20 min， 80%-90%B； 20-25 min， 90%B）， electrospray ionization（ESI） was employed with negative ion mode scanning and scanning range of m/z 50-1 200. A high performance liquid chromatography（HPLC）， which refered to the determination in the 2020 edition of Chinese Pharmacopoeia， was used for quantitative analysis of resource components in MOCD. Four kinds of pretreatment agents were used to separate the resource components from MOCD， and the mechanism of different pretreatment agents was investigated by field emission scanning electron microscopy（FESEM）， X-ray powder diffraction（XRD） and Fourier transform infrared spectroscopy（FT-IR）. ResultMagnolol， honokiol and lignocellulose were identified as the main resource components of MOCD by qualitative and quantitative analysis. Under the conditions of 1% NaOH， reaction temperature at 80 ℃ and reaction time of 60 min， the concentration of reducing sugar produced by the enzymatic hydrolysis was 32.18 g·L-1， which was 79.8% higher than that of the untreated MOCD. After adding tween-80， the enzymatic hydrolysis time was reduced to 1/3 of the original time， the concentration of reducing sugar was increased by 102.0%. And the total recovery of magnolol and honokiol in the pretreatment solution was 69.23%. ConclusionMagnolol， honokiol and lignocellulosic components in MOCD are valuable for development and utilization， the combination of alkaline pretreatment and tween-80 can realize the recovery and utilization of these three resource components， which can provide a new idea for comprehensive utilization of resource components in MOCD.
