Background HIV testing is an essential gateway to HIV prevention and treatment thus controlling the HIV epidemic. More innovative interventions are needed to increase HIV testing among men who have sex with men (MSM) since their testing rate is still low. We proposed an online HIV test results exchange mechanism whereby the one without a certified online HIV report will be asked to test HIV for exchanging HIV report with others. The exchange mechanism is developed as an extension to the existing online HIV testing service system. Through the extended system, MSM can obtain certified online HIV reports and exchange their reports with friends via WeChat. This study aims to assess effectiveness of the exchange mechanism to increase the HIV testing rate among MSM. Methods This study will use a cluster randomized controlled trial (RCT) design. Participants are recruited based on the unit of individual social network, the sender and the receivers of the HIV report. An individual social network is composed of one sender (ego) and one or more receivers (alters). In this study, MSM in an HIV testing clinic are recruited as potential egos and forwarded online reports to their WeChat friends voluntarily. Friends are invited to participate by report links and become alters. Ego and alters serve as a cluster and are randomized to the group using the certified online HIV report with exchange mechanism (intervention group) or without exchange mechanism (control group). Alters are the intervention targeting participants. The primary outcome is HIV testing rate. Other outcomes are sexual transmitted infections, sexual behaviors, HIV testing norms, stigma, risk perception and HIV report delivery. The outcomes will be assessed at baseline and follow-up questionnaires. Analysis will be according to intention to treat approach and using mixed-effect models with networks and individuals as random effects. Discussion This is the first study to evaluate the effectiveness of an HIV test result exchange mechanism to increase the HIV testing among MSM. This assessment of the intervention will also provide scientific evidence on other potential effects. Findings from this study will yield insights for sustainability driven by communities' intrinsic motivation. Trail registration: ClinicalTrials.gov NCT03984136. Registered 12 June 2019.

Background: This study aimed to develop and validate nomograms for predicting overall survival (OS) and cancer-specific survival (CSS) in small intestinal gastrointestinal stromal tumours (SI GISTs). Methods: Patients diagnosed with SI GISTs were retrieved from the Surveillance, Epidemiology, and End Results (SEER) database and further randomly divided into the training and validating cohorts. Univariate and multivariate cox analyses were conducted in the training set to determine independent prognostic factors to build nomograms for predicting 3- and 5-year OS and CSS. The performance of the nomograms was assessed by concordance index (C-index), calibration plot and the area receiver operating characteristic (ROC) curve (AUC). Results: A total of 776 patients with SI GISTs were retrospectively collected from the SEER database. OS nomogram was constructed based on age, surgery, imatinib treatment and AJCC stage, while CSS nomogram incorporated age, surgery, tumor grade and AJCC stage. In the training set, C-index for the OS nomogram was 0.773 [95% confidence intervals (95% CI): 0.722–0.824], CSS nomogram 0.806 (95% CI: 0.757–0.855). In internal validation cohort, the C-index for the OS nomogram was 0.741, while for the CSS nomogram 0.819. Well-corresponded calibration plots both in OS and CSS nomogram models were noticed. The comparisons of AUC values showed that the established nomograms exhibited superior discrimination power than 7th TNM staging system. Conclusion: Our nomogram can effectively predict 3- and 5-year OS and CSS in patients with SI GISTs, and its use can help improve the accuracy of personalized survival prediction and facilitate to provide constructive therapeutic suggestions.

Background As yet, there is no unified method of treatment for the evaluation and management of gastric low-grade intraepithelial neoplasia (LGIN) worldwide. Methods Patients with gastric LGIN who had been treated with Helicobacter pylori eradication were gathered retrospectively. Based on several relevant characteristics described and analyzed by LASSO regression analysis and multivariable logistic regression, a prediction nomogram model was established. C-index, the area under the receiver operating characteristic curve (AUC), calibration plot, and decision curve analysis (DCA) were adopted to evaluate the accuracy and reliability of the model. Results A total of 309 patients with LGIN were randomly divided into the training groups and the validation groups. LASSO regression analysis and multivariable logistic regression identified that 6 variables including gender, size, location, borderline, number, and erosion were independent risk factors. The nomogram model displayed good discrimination with a C-index of .765 (95% confidence interval: .702-.828). The accuracy and reliability of the model were also verified by an AUC of .764 in the training group and .757 in the validation group. Meanwhile, the calibration curve and the DCA suggested that the predictive nomogram had promising accuracy and clinical utility. Conclusions A predictive nomogram model was constructed and proved to be clinically applicable to identify high-risk groups with possible pathologic upgrade in patients with gastric LGIN. Since it is regarded that strengthening follow-up or endoscopic treatment of high-risk patients may contribute to improving the detection rate or reducing the incidence of gastric cancer, the predictive nomogram model provides a reliable basis for the treatment of LGIN.

Background: At present, there is no unified treatment for the evaluation and management of gastric low-grade intraepithelial neoplasia (LGIN) all over the world.Methods: Patients who were Helicobacter pylori eradicated, with low-grade gastric intraepithelial neoplasia were gathered.Several demographic and clinicopathological characteristics were described and analyzed retrospectively by LASSO regression analysis and multivariable logistic regression. Then the predictive nomogram was established. C-index, the area under the receiver operating characteristic curve (AUC) , calibration plot and decision curve analysis (DCA) were used to evaluate the accuracy and reliability of the model. Results: A total of 309 patients with LGIN were included, divided into training groups and validation groups randomly. LASSO regression analysis and multivariable logistic regression showed that six variables, gender, size, location, border line, number and erosion were independent risk factors for progression of gastric LGIN. The nomogram model displayed good discrimination with a C-index of 0.765 (95% confidence interval: 0.702–0.828). High C-index value of 0.768 could still be reached in the internal validation. The accuracy and reliability of the model was also verified by the AUC of 0.764 in the training group and 0.757 in the validation group. The calibration curve showed the model was in good agreement with the actual results as well. Decision curve analysis suggested that the predictive nomogram had clinical utility. Conclusions: A predictive nomogram model was successfully established and proved to identify high-risk groups with possible pathologic upgrade in patients with gastric LGIN. It suggested that after identifying high-risk patients, strengthening follow-up or endoscopic treatment may benefit in improving the detection rate or reducing the incidence of gastric cancer, which providing a reliable basis for the treatment of LGIN.

Background Age is an independent prognostic factor for small cell lung cancer (SCLC). We aimed to construct a nomogram survival prediction for elderly SCLC patients based on the Surveillance, Epidemiology, and End Results (SEER) database. Methods A total of 2851 elderly SCLC patients from the SEER database were selected as a primary cohort, which were randomly divided into a training cohort and an internal validation cohort. Additionally, 512 patients from two institutions in China were identified as an external validation cohort. We used univariate and multivariate to determine the independent prognostic factors and establish a nomogram to predict survival. The value of the nomogram was evaluated by calibration plots, concordance index (C-index) and decision curve analysis (DCA). Results Ten independent prognostic factors were determined and integrated into the nomogram. Calibration plots showed an ideal agreement between the nomogram predicted and actual observed probability of survival. The C-indexes of the training and validation groups for cancer-specific survival (CSS) (0.757 and 0.756, respectively) based on the nomogram were higher than those of the TNM staging system (0.631 and 0.638, respectively). Improved AUC value and DCA were also obtained in comparison with the TNM model. The risk stratification system can significantly distinguish individuals with different survival risks. Conclusion We constructed and externally validated a nomogram to predict survival for elderly patients with SCLC. Our novel nomogram outperforms the traditional TNM staging system and provides more accurate prediction for the prognosis of elderly SCLC patients.