Objective:To evaluate the value of applying an artificial intelligence (AI) system for diabetic retinopathy (DR) detection and referral in community.Methods:A diagnostic test study was conducted.Four hundred and twenty-one patients (812 eyes) diagnosed with diabetes in three Dongguan community healthcare centers from January 1, 2020 to December 31, 2021 were enrolled.There were 267 males, accounting for 63.42% and 154 females, accounting for 36.58%.The subjects were 18-82 years old, with an average age of (51.72±11.28) years.The disease course of the subjects was 0-30 years, with an average course of 3.00 (1.00, 7.00) years.At least one macula-centered 50-degree fundus image was taken for each eye to build a DR image database.All the images were independently analyzed by an AI-assisted diagnostic system for DR, trained and qualified community physicians and ophthalmologists to make diagnosis including with or without DR, referable diabetic retinopathy (RDR) and referral recommendation or not.With diagnoses from ophthalmologists as the standard, sensitivity and specificity of the AI system in detecting DR and RDR were evaluated.The consistency and effective referral rate of the AI system and community physicians in detecting DR, especially in detecting RDR were evaluzted.This study adhered to the Declaration of Helsinki.The study protocol was approved by the Ethics Committee of Dongguan Tungwah Hospital (No.2019DHLL046).Results:Of 812 eyes, 242 eyes were diagnosed with DR, including 23 with mild nonproliferative diabetic retinopathy (NPDR), 120 with moderate NPDR, 60 with severe NPDR and 39 with proliferative diabetic retinopathy (PDR). The other 570 eyes were diagnosed without DR.The sensitivity/specificity of AI system to detect DR and RDR was 87.60%/97.89% and 90.41%/96.29%, respectively.Compared with the ophthalmologists' diagnosis, the Cohen' s Kappa statistic of AI system to detect DR/RDR was 0.87/0.87, which was lower than 0.93/0.98 of community physicians.Among the referral-recommended cases by ophthalmologists, the effective referral rate of the AI system was 90.87% (199/219), which was higher than 89.50% (196/219) of community physicians, without statistically significant difference ( P=1.000). Conclusions:The AI system shows high sensitivity, specificity and consistency in DR detection, especially in RDR.The AI system is better in recognizing RDR than trained community physicians.

Objective To detect the expression of Beclin1 and Bcl2 in invasive pituitary adenomas and to explore the relationship of Beclin1 and Bci2 in invasive pituitary adenomas and the relativity between the 2 genes.Methods 61 specimens were classified into invasive group (32 cases) and non-invasive group (29 cases) according to the comprehensive evaluation of invasive pituitary adenomas.lmmunofluorescence analysis and RT-PCR were adopted respectively to detect the protein and mRNA expressions of Beclinl and Bcl2.The difference and relativity of Beclin1 and Bcl2 expression in invasive group and non-invasive group were analyzed.Results 32 specimens of pituitary adenoma were invasive and 29 were non-invasive.Beclin1 protein and mRNA expressions were lower in the invasive group than in the non-invasive group (P ＜0.01 ).Bcl2 protein and mRNA expressions were higher in the invasive group than in the non-invasive group (P ＜0.01 ).Pearson related analysis showed that Beclin1 mRNA expression was negtively correlated with Bcl2 mRNA expression in the invasive group ( r =-0.42,P =0.028 ).Conclusions Beclinl expression is decreased in invasive pituitary adenomas.The invasiveness of pituitary adenoma is closely related to the high expression of Bcl2 protein and mRNA,and the low expression of Beclin1 protein and mRNA.The inhibition of the autophagy may lead to the enhancement of the invasiveness of pituitary adenomas and that inhibition may come from the interaction of Beclin1 and Bcl2.

Developing new therapeutic agents for cancer immunotherapy is highly demanding due to the low response ratio of PD-1/PD-L1 blockade in cancer patients. Here, we discovered that the novel immune checkpoint VISTA is highly expressed on a variety of tumor-infiltrating immune cells, especially myeloid derived suppressor cells (MDSCs) and CD8⁺ T cells. Then, peptide C1 with binding affinity to VISTA was developed by phage displayed bio-panning technique, and its mutant peptide VS3 was obtained by molecular docking based mutation. Peptide VS3 could bind VISTA with high affinity and block its interaction with ligand PSGL-1 under acidic condition, and elicit anti-tumor activity in vivo. The peptide DVS3-Pal was further designed by d-amino acid substitution and fatty acid modification, which exhibited strong proteolytic stability and significant anti-tumor activity through enhancing CD8⁺ T cell function and decreasing MDSCs infiltration. This is the first study to develop peptides to block VISTA/PSGL-1 interaction, which could act as promising candidates for cancer immunotherapy.