Protein-centric omics analysis reveals circulating complements linked to non-viral liver diseases as potential therapeutic targets

Background/Aims@#To evaluate the causal correlation between complement components and non-viral liver diseases and their potential use as druggable targets. @*Methods@#We conducted Mendelian randomization (MR) to assess the causal role of circulating complements in the risk of non-viral liver diseases. A complement-centric protein interaction network was constructed to explore biological functions and identify potential therapeutic options. @*Results@#In the MR analysis, genetically predicted levels of complement C1q C chain (C1QC) were positively associated with the risk of autoimmune hepatitis (odds ratio 1.125, 95% confidence interval 1.018–1.244), while complement factor H-related protein 5 (CFHR5) was positively associated with the risk of primary sclerosing cholangitis (PSC;1.193, 1.048– 1.357). On the other hand, CFHR1 (0.621, 0.497–0.776) and CFHR2 (0.824, 0.703–0.965) were inversely associated with the risk of alcohol-related cirrhosis. There were also significant inverse associations between C8 gamma chain (C8G) and PSC (0.832, 0.707–0.979), as well as the risk of metabolic dysfunction-associated steatotic liver disease (1.167, 1.036–1.314). Additionally, C1S (0.111, 0.018–0.672), C7 (1.631, 1.190–2.236), and CFHR2 (1.279, 1.059–1.546) were significantly associated with the risk of hepatocellular carcinoma. Proteins from the complement regulatory networks and various liver diseaserelated proteins share common biological processes. Furthermore, potential therapeutic drugs for various liver diseases were identified through drug repurposing based on the complement regulatory network. @*Conclusions@#Our study suggests that certain complement components, including C1S, C1QC, CFHR1, CFHR2, CFHR5, C7, and C8G, might play a role in non-viral liver diseases and could be potential targets for drug development.

Predictive value of proximal and distal ureteral diameter ratio for impacted stones in the middle and upper ureter / 中华泌尿外科杂志

Objective:To evaluate the predictive value of proximal ureteral diameter (D1)to distal ureteral diameter (D2)ratio (DDR) for impacted stones in the middle and upper ureter.Methods:The clinical data of 173 patients with middle and upper ureteral calculi admitted to the Third Hospital of Shanxi Medical University from January 2014 to November 2021 were retrospectively analyzed. There were 75 males and 98 females, with the median age of 56.0 (51.0, 62.0) years old and median body mass index of 26.1 (24.8, 27.2) kg/m 2. The imaging data of the patients were analyzed. The impacted stones were defined as the inability of the contrast agent to pass through the site of obstruction when intravenous urography or CT urography was performed, resulting in the inability of the ureter to visualize normally in parts below the site of obstruction. D1 was defined as the proximal ureteral diameter at the lower pole of the kidney on horizontal CT images. D2 was defined as the ureteral diameter 3 cm from the calculi. The stone diameter, stone CT value, D1, D2, and DDR were compared between impacted stone group and non-impacted stone group. Univariate logistic regression analysis was used to analyze the different indicators. Random number table was used to divide the training set and validation set according to the ratio of 7∶3. Through least absolute shrinkage and selection operator(LASSO) regression analysis, the independent influencing factors were obtained and the nomogram model was established (Model 1). Receiver operating characteristic (ROC) curve and area under the curve (AUC) were used to verify the predictive efficacy of the model, and the other three effective models (Model 2-4) were constructed by stepwise multivariate logistic regression. The deLong test was used to compare whether there was a significant difference in the AUC between Model 1 and the other three models, and the net benefit of patients was analyzed by clinical decision curve analysis(DCA). Results:In this study, 64 cases (37.0%) were impacted ureteral calculi and 109 cases (63.0%) were non-impacted ureteral calculi, and there were significant differences in diameter[7.8(6.2, 8.8)mm vs. 6.3(5.2, 8.1)mm] , CT value[878.5(763.8, 940.5)HU vs.764.0 (613.0, 854.0) HU], D1[11.1(8.9, 14.9) mm vs. 9.1(7.1, 10.8) mm], D2[4.1(3.1, 4.9) mm vs. 5.0(4.1, 5.9) mm] and DDR[3.1(2.3, 3.9) vs. 1.8(1.4, 2.4)] between the two groups( P < 0.05). The results of univariate logistic regression analysis showed that stone diameter ( OR = 1.333, P < 0.001), CT value ( OR = 1.002, P=0.002), D1 ( OR = 1.146, P<0.001), D2 ( OR = 0.652, P < 0.001) and DDR ( OR = 2.995, P<0.001) were the influencing factors of impacted stones. The training set and validation set included 122 cases and 51 cases, respectively, without significant differences in their image characteristics and outcomes ( P > 0.05). The results of LASSO regression analysis showed that λ corresponding to the simplest result in the optimal range was 0.0908, and three variables were included at this time, and the influencing factors of impacted stones were stone diameter (coefficient 0.0700, OR = 1.073), CT value (coefficient 0.0003, OR = 1.001) and DDR (coefficient 0.5960, OR = 1.815). Moreover, Model 1 was established. According to the model fitting results, ROC curves were plotted, and the AUC of Model 1 was 0.862, and the AUCs of Model 2-4 were 0.859, 0.762, and 0.793, respectively. After deLong test, there was no significant difference between Model 1 and Model 2 ( Z = 0.248, P = 0.804). The AUC of Model 1 was superior to that of Model 3 ( Z = 2.888, P = 0.004) and Model 4 ( Z = 2.321, P = 0.020). The DCA suggested that Model 1 could improve the net benefit rate by up to approximately 21% of patients. Conclusions:DDR is the influencing factor of impacted ureteral calculi, and the model constructed by DDR, stone CT value and stone diameter can effectively predict the probability of impacted ureteral calculi in the middle and upper ureter.

Predicting the antigenic evolution of SARS-COV-2 with deep learning

The severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) antigenic profile evolves in response to the vaccine and natural infection-derived immune pressure, resulting in immune escape and threatening public health. Exploring the possible antigenic evolutionary potentials improves public health preparedness, but it is limited by the lack of experimental assays as the sequence space is exponentially large. Here we introduce the Machine Learning-guided Antigenic Evolution Prediction (MLAEP), which combines structure modeling, multi-task learning, and genetic algorithm to model the viral fitness landscape and explore the antigenic evolution via in silico directed evolution. As demonstrated by existing SARS-COV-2 variants, MLEAP can infer the order of variants along antigenic evolutionary trajectories, which is also strongly correlated with their sampling time. The novel mutations predicted by MLEAP are also found in immunocompromised covid patients and newly emerging variants, like BA. 4/5. In sum, our approach enables profiling existing variants and forecasting prospective antigenic variants, thus may help guide the development of vaccines and increase preparedness against future variants.

Effect of ASIC1 a on hepatic fibrosis under high glucose / 中国药理学通报

Aim To investigate the effect of ASIC1 a ( acid-sensing ion channel 1 a ) on the pathological change of diabetes complication liver fibrosis and the proliferation and activation of hepatic stellate cell ( HSC-T6 ) stimulated by PDGF-BB under hyperglyce-mia. Methods Diabetes rats model was established by streptozotocin ( STZ) , and liver fibrosis rats model was induced by carbon tetrachloride ( CCl4 ) . Then, the liver extent of damage and the expression of ASIC1 a were observed in the diabetic rats, liver fibrosis rats and diabetes complication liver fibrosis rats. In vitro, after pretreated with amiloride, HSC-T6 was treated with high glucose for 24 h and then stimulated with PDGF-BB for another 24 h. The proliferation and acti-vation of HSC-T6 were observed, and the expression of ASIC1a, α-SMA and collagen Ⅰ were detected by Western blot. Results Compared with the control group, rats from diabetic group induced by STZ, liver fibrosis group induced by CCl4 , and the diabetes com-plication liver fibrosis rats co-induced by STZ and CCl4 were all observed with liver damage at different levels, and tissue injury of complication group was most seri-ous. However, the expression of ASIC1a in the three model groups was significantly increased compared to the control group. ASIC1a level was most obvious in the diabetes complication liver fibrosis rats. Amiloride pretreatment significantly decreased ASIC1 a expression and inhibited PDGF-BB mediated proliferation and the expression ofα-SMA and collagenⅠin HSC-T6 under high glucose environment. Conclusion High ambient glucose aggravates HSC activation and hepatic fibrosis, and this may be related with the increasing expression of ASIC1a.

Computer assisted analysis of EUS images from stromal tumor and leiomyoma in esophagus and cardia / 中华消化内镜杂志

Objective To investigate the differences of EUS images between stromal tumors and leiomyoma in esophagus and cardia. Methods EUS image of 13 cases of stromal tumors and 11 cases of leiomyoma diagnozed immunohistochemically were collected. Information of gray scale including mean value and dissociation of lesions and submucosal layer were analyzed. Gray scale values of lesions were calibrated according to that of the submucosal layer. Results The mean value of corrected gray scale of stromal tumors was 0. 285, which was significantly different from that of leiomyoma (0. 185, P ＜ 0. 05). Dissociation of gray scale of stromal tumors was 8. 14, which was also significantly different from that of leiomyomia (4. 59,P ＜0. 05). Conclusion In esophagus and cardia, stromal tumors exhibits higher and more inhomogeneous ultrasound echo than leiomyoma.