Objective To determine the probability of identification of differential expression of biliary proteins induced by cholangiocarcinoma using 2D-DIGE. Methods Bile was obtained from 12patients with obstructive jaundice (including 6 cases of cholangiocarcinoma and 6 of cholelithiasis).Each sample was labeled with three different CyDyes (y3,Cy5,Cy2) including one internal standard,pooled from all the samples, and separated with 2-D DIGE in triplicate experiments. MALDI-TOF-MS and bioinformatics were adopted to identify and elucidate the significance of differentially expressed proteins in bile induced by cholangiocarcinoma. Results 55 matched protein spots differences in abundance were detected with statistical variance of two groups(Average Volum Ratio ≥1.5, t-test, P＜0. 05). Among these proteins, 13 PMF were obtained by MALDI-TOF-MS analysis. Eight proteins were identified by searching a protein database. Conclusion The differentially displayed proteomes between the pathological bile obtained from benign and malignant obstructive jaundice indicates the potential application of 2D-DIGE to identify the biomarker of cholangiocarcinoma.

Objective To investigate the expression and clinical significance of CD28 and CD160 in patients with chronic HIV infection.Methods 50 patients with HIV from January 2016 to January 2017 were selected as the observation group, and 50 healthy volunteers were recruited as control group.Observe and record general information of all participants, the expression of CD28, CD160 in CD4+and CD8+T cells, initial T cells (TN), the expression of CD160 in central memory T cells (TCM), effector memory T cells (TEM), end effector memory T cells (TEMRA), mean fluorescence intensity (MFI), viral load of two kinds of the cells, analyze the correlation between the expression level of CD28 and CD160 and CD4+T cell count and viral load.Results With the increase of CD160 expression of CD4+T cells, CD4+T cells showed a downward trend, there is a negative correlation between them (r=-0.561, P＜0.05), CD8+T cell number is on the rise, there is a positive correlation between them (r=0.619, P＜0.05), and HIV-RNA copy number increased with the increase of CD160 expression on CD4+T cells and CD8+T cells, both positive (r=0.684, P＜0.05, r=0.459, P＜0.05);with the increase of CD28 cells on the expression of CD4+T, CD4+, CD8+T cells showed a rising trend, there is a positive correlation between them (r=0.621, P＜0.05, r=0.527, P＜0.05, HIV-RNA) and the copy number decreased with the increase of the expression of CD28 and CD4+T on CD8+T cells, there is a negative correlation between them (r=-0.634, P＜0.05, r=-0.582, P＜0.05).There was no significant difference in the positive rate of expression in TEMRA subgroup and MFI of CD160 in CD8+T cell in two groups (P＞0.05).The positive rate and MFI of CD8+T cell CD160 in TN, TCM and TEM subgroups in observation group were significantly higher than those in control group (Tcm), with statistical significance.Conclusion The expression of CD28 in patients with chronic HIV infection is decreased, and the expression of CD160 is increased, which may be related to the decrease of HIV CD4+T and CD8+T cells, in which CD160 mainly affects the memory CD8+T.

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.