AIM　To elucidate the structure of the glycan of SPPA-1, a glycoconjugate isolated from Spirulina platensis. METHODS　Methylation analysis, GC/MS, and 1D, 2DNMR techniques were used to determine the structures of the glycoconjugate (SPPA-1). RESULTS　SPPA-1 was only composed of α-D-glucose and shown to be a (1→4) linked α-D-glucan to which a few glucosyl side chains are attached at O-6 of the glucosyl residues of the main chain. CONCLUSION　The glycan of SPPA-1 is a new glucan.

AIM　To determine the effects of glycoconjugates and their glycans from Lycium barbarum L. on inhibiting low density lipoprotein (LDL) peroxidation. METHODS　Using Cu2+-induced oxidation as a model, the oxidative production of thiobarbituric acid-reactive substances (TBARS) and the LDL electrophoresis migration on agarose gel were measured. RESULTS　The effects of glycoconjugates and their glycans from Lycium barbarum L. on inhibiting LDL peroxidation were different, among them, glycoconjugate LbGp5 showed the best effect on inhibiting LDLperoxidation. CONCLUSION　The glycoconjugates can inhibit LDL peroxidatin while their glycans showed no effects on inhibiting LDL peroxidation.

A polysaccharide SPPC 1 was isolated from spirulina platensis by acetone fractional precipation, free protein was removed by trichloroacetic acid(TCA). Purification of the polysaccharide by Sephadex G 75 colume and CM Sephadex C 50 colume.The homogeneity of this polysaccharide was proved by HPLC and CE. molecular weight was 1.62?10 6. GC showed that the glycoconjugate was composed of Rha,Xyl,Man and Glc. Molar ratio is Rha:Xyl:Man:Glc=6.24:0.37:0.13:0.71. IR, 1HNMR spectrum indicated the presence of ? linkage glycoside. ESR experiment showed that SPPC 1 could eliminate O . 2 radicals.

Object To study the isolation and purification of a polysaccharide, obtained from Paecilomyces tenuipes Samson, its molecular weight, sugar composition, and mode of linkage Methods Crude polysaccharide was extracted by water at ambient temperature and purified on Sephadex G 100 column Its monosaccharide composition was determined by ionic ion exchange column after complete hydrolysis with acid Their mode of linkage was determined by methylation and glycosidic linkage established by IR and NMR spectra Results HPLC spectrum showed that the polysaccharide was of homogeneous composition, which was also proved latter by GC MS and NMR Conclusion Polysaccharide obtained from P tenuipes Samson is ? (1→6) linked and composed of only D glucose The molecular weight was 2 05?10 4

Gastric cancer is one of the most common malignant gastrointestinal tumors.Docetaxel alone or combination with other drugs can attenuate the progress of disease,prolong the overall response rate and the median overall survival rate in advanced gastric cancer.However,the incidence of toxicities is high.Moreover,there is no uniform standard for dosage and course for docetaxel treatment.Currently,its efficacy is not definite.

As a computer science that seeks to simulate the problem of human intelligence, artificial intelligence ( AI ) is developing rapidly in many fields. The application of AI in the field of ophthalmology is increasing. With the development of medical informatization and internet medical care, medical data and machine learning algorithms continue to accumulate, and AI systems are continuously optimized and upgraded during the development of technology and applications. This paper summarized the application status of AI in ophthalmology from the aspects of data demand,source and format,application and optimization innovation of related algorithms,demand and improvement of hardware computing force,and analyzed the development status,challenges and future directions. Although there are some problems to be solved in the current development and application of AI,it is believed that AI will play an important role in clinical medicine in the near future.

Objective To study the efficiency and accuracy of artificial intelligence (AI) system based on fundus photograph in diabetic retinopathy(DR)screening,and evaluate the clinical application value of AI system. Methods A diagnostic trial was adopted. Total of 13683 color fundus photos were collected in Zhaoqing Gaoyao People's Hospital from March,2017 to November,2018. The AI system for DR (ZOC-DR-V1) was established,based on transfer learning + NASNet algorithm,by training 4465 precisely labeled fundus images (2510 normal,and 1955 with any stage of DR). One thousand confirmed fundus images (300 normal and 700 with any stage of DR),diagnosed by AI ( AI group ) and doctors ( 3 ophthalmologist doctors and 3 endocrinologist doctors ) ( doctor group ) , respectively. Ophthalmologist group and endocrinologist group were both composed of primary,intermediate and senior physicians. The mean reading time of each image and the total time of 1000 images were recorded. The accuracy and efficiency of AI system and doctor groups were compared. The reading process was divided into two stages. The diagnostic coincidence rate and the average reading time of each group between the two parts were calculated and compared. This study protocol was approved by Ethic Committee of Zhongshan Ophthalmic Center, Sun Yat-sen University (No. 2017KYPJ104). Results After training,the diagnostic coincidence rate of AI system (ZOC-DR-V1) in test set was 94. 7％,AUC was 0. 994. In this "man-machine to war",the diagnostic coincidence rate of primary,intermediate and senior endocrinologist was 94. 0％,91. 4％ and 93. 4％;the diagnostic coincidence rate of primary,intermediate and senior ophthalmologist was 92. 7％,94. 4％ and 95. 6％;the diagnostic coincidence rate of AI system was 95. 2％. There was no difference in the diagnostic coincidence rate between AI system and senior ophthalmologist ( P = 0. 749 ) . The mean reading time of each image of primary, intermediate and senior endocrinologists was (4. 63±1. 87),(3. 74±3. 47) and (5. 71±3. 47) seconds,and the total time of 1000 images of primary,intermediate and senior endocrinologists was 1. 29,1. 04 and 1. 58 hours;the mean reading time of each image of primary,intermediate and senior ophthalmologists was ( 7. 25 ± 6. 58 ) , ( 5. 18 ± 5. 01 ) and ( 5. 18 ± 3. 47 ) seconds,and the total time of 1000 images of primary,intermediate and senior endocrinologists was 2. 02,1. 44 and 1. 44 hours;the mean and total time of AI system was (1. 62±0. 67) seconds and 0. 45 hours,and the reading time of AI system was significantly shorter than that of the doctor groups (all at P=0. 000). The diagnostic coincidence rates between previous and posterior part of primary endocrinologist, primary and intermediate ophthalmologist were significantly different (χ2=11. 986,6. 517,10. 896;all at P<0. 05),and the mean reading time in the posterior part was significantly shorter than that in the previous part of intermediate and senior endocrinologist and primary ophthalmologist (t=4. 175,8. 189,5. 160;all at P<0. 01). While the reading time of AI system remained stable throughout the process(χ2=3. 151,P=0. 103;t=0. 038,P=0. 970). Conclusions The ophthalmic AI system based on fundus images has a good diagnostic efficiency,and its diagnostic coincidence rate can compare with senior ophthalmologist,providing a new method and platform for large-scale DR screening.

Objective:To establish an accurate retinal vascular network segmentation method for multiple fundus diseases, and to investigate the changing patterns of retinal vascular morphological parameters in these diseases.Methods:A retrospective study was conducted.Color fundus photography data of 829 patients with fundus diseases and 146 healthy adults were collected at Zhongshan Ophthalmic Center, Sun Yat-sen University from January 2020 to December 2023.The multi-path segmentation network was fine-tuned, and the color fundus photography data of diabetic retinopathy (DR), glaucoma and age-related macular degeneration (AMD) patients and healthy adults in the fundus image vessel segmentation public dataset were input for training until the loss value of the model stopped decreasing, and finally the trained multi-disease retinal vascular segmentation model was obtained.The retinal blood vessel morphological characteristics analysis method previously developed by our research group was used to analyze the subjects' color fundus images centered on the macula, the retinal blood vessel fractal dimension (D f), vascular area ratio (VAR), mean diameter (D m), tortuosity (τ) and other morphological characteristics parameters were extracted and compared among various disease groups.This study adhered to the Declaration of Helsinki.The study protocol was approved by the Ethics Committee of Zhongshan Ophthalmic Center, Sun Yat-sen University (No.2023KYPJ344).Written informed consent was obtained from each subject. Results:The accuracy of the multi-disease color fundus photography vessel segmentation model on the test set was 0.987, and the area under the receiver operating characteristic curve was 0.995.After adjustment for age and sex, there were statistically significant differences in adjusted D f, adjusted VAR, adjusted D m and τ among different groups ( F=27.87, 47.60, 26.48, 4.63; all at P<0.001).Adjusted D f in AMD group, DR group, diabetic macular edema (DME) group, retinitis pigmentosa (RP) group, branch retinal vein occlusion (BRVO) group and central retinal vein occlusion (CRVO) group was significantly decreased than in normal control group, and the differences were statistically significant (all at P<0.05).Adjusted VAR in all disease groups except optic neuritis group and central serous chorioretinopathy group was significantly decreased compared with normal control group, and the differences were statistically significant (all at P<0.05).The adjusted D m in DME, glaucoma, RP, BRVO and CRVO groups was significantly decreased than that in normal control group, and the differences were statistically significant (all at P<0.05).τ was not affected by age or sex and did not require adjustment.τ in DR group and DME group was significantly increased compared with normal control group, and the differences were statistically significant (both at P<0.05). Conclusions:An accurate retinal blood vessel segmentation method for various fundus diseases was successfully constructed.This method shows high accuracy in retinal blood vessel segmentation in color fundus photographs of various retinal diseases.There are significant differences in the morphological characteristics of retinal blood vessels among different retinal diseases.