Nine compounds were isolated and purified from 90% ethanol extract of Alstonia mairei Lévl by using various chromatographic methods, including silica gel, SephadexLH-20, MCI Gel and ODS column chromatography, combined with semi-preparative liquid phase separation methods. Modern spectroscopic methods (1D and 2D NMR, UV, IR, MS, etc.) were used to identify the structures of the isolated compounds. They were identified as mairoside A (1), 3′,6-di-O-sinaloylsucrose (2), myristic acid (3), methyl myristate (4), ethyl myristate (5), 3,4,5-trimethoxycinnamic acid (6), 3,4,5-trimethoxybenzoic acid (7), vinoline (8), kaempferol-3-O-rutinoside (9), among which compound 1 is a new glycoside, compounds 4 and 5 are new natural products, and the nuclear magnetic data of compound 4 were reported for the first time.

Objective To investigate the effect of microRNA(miR)-4645-5p on the proliferation,invasion and epithelial-mesenchymal transition of esophageal cancer cells by targeting mucin 16(MUC16)and its mo-lecular mechanism.Methods The expression of miR-4645-5p in esophageal cancer tissues was analyzed online by TCGA database.The expression level of miR-4645-5p in esophageal cancer cell lines was analyzed by fluo-rescent real-time fluorescence quantitative polymerase chain reaction(qPCR).KYSE-30 cells were transfected with miR-4645-5p mimic and negative control mimic by lipofection technology,and were divided into miR-4645-5p group and control mimic group.The proliferation ability,migration ability and invasion ability of transfected KYSE-30 cells were analyzed by CCK-8 method,scratch test and Transwell test respectively.The target gene of miR-4645-5p was predicted by the bioinformatics website,and the binding of miR-4645-5p to the target gene was detected by the dual-luciferase reporter gene assay.The expression level of MUC16 mR-NA was detected by qPCR,and the protein expression levels of MUC16,transcription factor-1(ZEB-1),zonal atresia protein(ZO-1),tight junction protein-1(Claudin-1)and α-smooth muscle actin(α-SMA)were detected by Western blotting.Results The expression level of miR-4645-5p in esophageal cancer tissues was signifi-cantly lower than that in adjacent tissues(P＜0.01).Compared with HET-1 A,the expression of miR-4645-5p was lower in esophageal cancer cell lines(P＜0.05).After overexpression of miR-4645-5p,the proliferation a-bility of KYSE-30 cells was significantly reduced(P＜0.05),the migration ability was significantly reduced(P＜0.01)and the invasion ability was significantly reduced(P＜0.01).miR-4645-5p targeted and negatively regulated the expression of MUC16 mRNA(P＜0.01).After overexpression of miR-4645-5p,the protein ex-pression levels of MUC16,ZEB-1 and α-SMA were all down-regulated,and the protein expression levels of ZO-1 and Claudin-1 were up-regulated.Conclusion miR-4645-5p regulates the malignant biological behavior of esophageal cancer KYSE-30 cells by targeting MUC16.

Objective To explore the construction and visualization for knowledge graph of Ling Shu(Spiritual Pivot),with a view to providing ideas for the structured storage and display of the theoretical knowledge of the ancient Chinese medical books.Methods Using the professional idea of constructing knowledge graphs for reference,text mining technology was applied to construct the thesaurus,and then word division,entity recognition,and relationship extraction for the original text of Ling Shu were performed to get the elements of knowledge graph construction.The graph database Neo4j was used for the storage and query of the knowledge graph,and then the visual display of the knowledge graph was achieved.Results The 1 216 high-quality words consisting of the thesaurus of Ling Shu were obtained,and the construction of the knowledge graph of the theory of Ling Shu was realized.The constructed knowledge graph basically displayed the traditional Chinese medicine theories such as the correlation of visceral manifestations with essence qi,and the relationship between emotions and the five-zang organs described in Ling Shu,which made the retrieval and utilization of the related entities and relationships possible,and provided ideas for the structured storage and display of the theoretical knowledge of the ancient books of Chinese medicine.Conclusion The knowledge graph construction technology can be used to obtain the Chinese medicine theoretical knowledge graph of Ling Shu,and to display the knowledge connections of yin-yang and the five elements,and the internal organs and meridians expressed in the Ling Shu.The construction of the knowledge graph and its storage in the graph database enable the knowledge graph involved in the text of Ling Shu to be displayed in the form of visualized semantic network graph,and also make the embedding of other search systems such as the semantic search and semantic wiki possible,which will be helpful for the development of Chinese medicine intelligent medical services.

AIM To identify the endophytic fungus G-(JK)-2 from Orixa japonica Thunb.and to study its secondary metabolites and their α-glucosidase inhibitory activities.METHODS Through the ITS sequence,the evolutionary tree that identifies the endophytic fungus G-(JK)-2 was established.Then 45 days rice solid medium of endophytic fungus G-(JK)-2 was extracted by methanol,and then by ethyl acetate.The ethyl acetate extract was separated and purified by silica gel chromatography,Sephadex LH-20,and semi-preparative HPLC.The structures of obtained compounds were identified by physicochemical properties and spectral data.Their α-glucosidase inhibitory activities were evaluated by PNPG method.RESULTS The endophytic fungus G-(JK)-2 from O.japonica was identified as Fusarium nematophilum.Thirteen compounds were isolated and identified as p-hydroxybenzaldehyde(G1),4-hydroxyacetophenone(G2),anhydromevalonolactone(G3),flazine(G4),salicylic acid(G5),p-hydroxybenzoic acid(G6),di-(2-ethylhexyl)-phthalate(G7),terephthalic acid bis(2-ethyl-hexyl)ester(G8),thymine(G9),uridine(G10),adenosine(G11),2′-deoxyuridine(G12),nicotinic acid(G13).The inhibitory effect of each compound on α-glucosidase was in sequence of G4>G11>G10>G13>G12.CONCLUSION All compounds are first isolated from the endophytic fungi of the O.japonica,and G10,G11,G13 are first isolated from the endophytic fungi of Fusarium.G4 and G11 have mild inhibition to α-glucosidase.

AIM To identify the chemical components of Longmu Qingxin Mixture by UPLC-Q-TOF-MS and study its material basis for the treatment of attention deficit hyperactivity disorder.METHODS The sample was detected by mass spectrometry in positive and negative ion mode on a Waters CORTECS? UPLC? T3 chromatographic column.The data were analyzed with Peakview 1.2 software and matched with the Natural Products HR-MS/MS Spectral Library 1.0 database,and the components were identified in combination with literature reports.The material basis of Longmu Qingxin Mixture for the treatment of attention deficit hyperactivity disorder was analysed according to the identified components.RESULTS Forty chemical components were identified,including 11 flavonoids,6 monoterpene glycosides,4 triterpene saponins,3 phenolic acids,6 alkaloids etc.,which mainly derived from Radix Astragali,Radix Paeoniae Alba,Radix Scutellariae,licorice root,Ramulus Uncariae cum,etc.,baicalein,formononetin,astragaloside Ⅳ and rhynchophylline may be the material basis for the therapeutic effect of Longmu Qingxin Mixture.CONCLUSION UPLC-Q-TOF-MS can quickly identify the chemical components of Longmu Qingxin Mixture.Flavonoids,triterpene saponins and alkaloids may be the material basis for Longmu Qingxin Mixture for the treatment of attention deficit hyperactivity disorder,which can provide the basis for its material basis research,quality standard establishment and pharmacological study of the dismantled formula.

To explore the absorption mechanism of APS-Ⅱ in vivo by establishing M cell model. First, Astragalus polysaccharides (APS) was divided into two different molecular weight polysaccharides APS-I ( > 2 000 kDa) and APS-II (10 kDa) by ultrafiltration, and APS-II (10 kDa) was prepared and fluorescently labeled. Meanwhile, M cell model was constructed by Caco-2 cells and Raji cells. The M cell model was treated with transport inhibitors to explore the transport of APS-Ⅱ on M cells. The results show that FITC has been successfully labeled to the end of APS-Ⅱ, and the M cell model was successfully constructed, which found that APS-Ⅱ could be transported by M cells, and four transport inhibitors of 5-(N-ethyl-N-isopropyl) amiloride (EIPA), genistein, dynasore and nocodazole indicated that APS-II may enter cells through clathrin and caveolin-mediated endocytosis.

italic>Astragalus polysaccharides are the most immunoregulatory active and abundant substances in Astragalus, with anti-tumor, anti-viral, and immune-promoting biological activities. They have been widely used in clinical practice. Previous studies have found that Astragalus polysaccharides are mainly composed of two different polysaccharides, APS-I (>2 000 kDa) and APS-Ⅱ (10 kDa), with APS-Ⅱ (10 kDa) being the most active component of Astragalus polysaccharides. We used α-1,4-glucan endo-hydrolysis enzyme to degrade APS-Ⅱ into oligosaccharides, and screened the immune activity of oligosaccharides in vitro. We found that the overall immune activity of 2-9 oligosaccharides was low, while the immune activity of 10-14 oligosaccharides was strong, and the activity was better than that of untreated APS-Ⅱ. To investigate the key structural features of APS-Ⅱ oligosaccharides that play a role in immune activity, we used MALDI-TOF-MS biochemical mass spectrometry and high-resolution mass spectrometry instrument ESI-Q Exactive-MS to analyze the APS enzymatic oligosaccharides. By comparing, we found that 10-14 oligosaccharides contain 1→4 and 1→6 branched structures with coexisting linkages, suggesting that 1→4 and 1→6 linkages in branched structures are key structural features that play a role in the immune activity of APS-Ⅱ, laying a theoretical foundation for the structure-activity relationship of Astragalus polysaccharides and oligosaccharides.

The growth of three plague phages from Qinghai Plateau in two Yersinia pestis strains(plague vaccine strains EV76 and 614F)and four non-Yersinia pestis strains(Yersinia pseudotuberculosis PTB3,PTB5,Escherichia coli V517,and Yersinia enterocolitica 52302-2)were detected through a micromethod based on the OmniLogTM microbial identification system and by the drop method,to provide a scientific basis for future ecological studies and classification based on the host range.For plague vaccine strains EV76 and 614F,successful phage infection and subsequent phage growth were observed in the host bacte-rium.Diminished bacterial growth and respiration and a concomitant decrease in color were observed with the OmniLogTM mi-crobial identification system at 33 ℃ for 48 h.Yersinia pseudotuberculosis PTB5 was sensitive to Yersinia pestis phage 476,but Yersinia pseudotuberculosis PST5 was insensitive to phage 087 and 072204.Three strains of non-Yersinia pestis(Yersinia pseudotuberculosis PTB3,Escherichia coli V517,and Yersinia enterocolitica 52302-2)were insensitive to Yersinia pestis pha-ges 087,072204,and 476 showed similar growth curves.The growth of phages 476 and 087,as determined with the drop method,in two Yersinia pestis strains(plague vaccine strains EV76 and 614F)and four non-Yersinia pestis strains(Yersinia pseudotuberculosis PTB3,Escherichia coli V517,and Yersin-ia enterocolitica 52302-2)showed the same results at 37 ℃,on the basis of comparisons with the OmniLogTM microbial i-dentification system;in contrast,phages 072204 did not show plaques on solid medium at 37 ℃ with plague vaccine strains EV76 and 614F.Determination based on the OmniLogTM detection system can be used as an alternative to the traditional determination of the host range,thus providing favorable application val-ue for determining the interaction between the phage and host bacteria.

Osteoporotic proximal humeral fracture (OPHF) is one of the common osteoporotic fractures in the aged, with an incidence only lower than vertebral compression fracture, hip fracture, and distal radius fracture. OPHF, secondary to osteoporosis and characterized by poor bone quality, comminuted fracture pattern, slow healing, and severely impaired shoulder joint function, poses a big challenge to the current clinical diagnosis and treatment. In the field of diagnosis, treatment, and rehabilitation of OPHF, traditional Chinese and Western medicine have accumulated rich experience and evidence from evidence-based medicine and achieved favorable outcomes. However, there is still a lack of guidance from a relevant consensus as to how to integrate the advantages of the two medical systems and achieve the integrated diagnosis and treatment. To promote the diagnosis and treatment of OPHF with integrated traditional Chinese and Western medicine, relevant experts from Orthopedic Expert Committee of Geriatric Branch of Chinese Association of Gerontology and Geriatrics, Youth Osteoporosis Group of Orthopedic Branch of Chinese Medical Association, Osteoporosis Group of Orthopedic Surgeon Branch of Chinese Medical Doctor Association, and Osteoporosis Committee of Shanghai Association of Integrated Traditional Chinese and Western Medicine have been organized to formulate Expert consensus on the diagnosis and treatment of osteoporotic proximal humeral fracture with integrated traditional Chinese and Western medicine ( version 2024) by searching related literatures and based on the evidences from evidence-based medicine. This consensus consists of 13 recommendations about the diagnosis, treatment and rehabilitation of OPHF with integrated traditional Chinese medicine and Western medicine, aimed at standardizing, systematizing, and personalizing the diagnosis and treatment of OPHF with integrated traditional Chinse and Western medicine to improve the patients ′ function.

Objective To achieve automatic segmentation of cell nuclei in gastrointestinal cancer pathological images by using a deep learning algorithm,so as to assist in the quantitative analysis of subsequent pathological images.Methods A total of 59 patients with gastrointestinal cancer treated in Ruijin Hospital,Shanghai Jiao Tong University School of Medicine from Jan 2022 to Feb 2022,were selected as the research objects.Python and LabelMe were used for data anonymization,image segmentation,and region of interest annotation of patients'pathological images.A total of 944 pathological images were included,and 9 703 nuclei were annotated.Then,a new semantic segmentation model based on deep learning was constructed.The model introduced deformable attention transformer(DAT)to realize automatic,accurate and efficient segmentation of pathological image nuclei.Finally,multiple segmentation evaluation criteria are used to evaluate the model's performance.Results The mean absolute error of the segmentation results of the model proposed in this paper was 0.112 6,and the dice coefficient(Dice)was 0.721 5.Its effect was significantly better than the U-net baseline model,and it was ahead of models such as ResU-net++,R2Unet and R2AttUnet.Moreover,the segmentation results were relatively stable with good generalization.Conclusion The segmentation model established in this study can accurately identify and segment the nuclei in the pathological images,with good robustness and generalization,which is helpful to play an auxiliary diagnostic role in practical applications.