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Background and Aims Preoperative prediction of microvascular invasion (MVI) using a noninvasive method remain unresolved, especially in HBV-related in intrahepatic cholangiocarcinoma (ICC). This study aimed to build and validate a preoperative prediction model for MVI in HBV-related ICC.Methods Patients with HBV-associated ICC undergoing curative surgical resection were identified. Univariate and multivariate logistic regression analyses were performed to determine the independent risk factors of MVI in the training cohort. Then, a prediction model was built by enrolling the independent risk factors. The predictive performance was validated by receiver operator characteristic curve (ROC) and calibration in the validation cohort.Results Consecutive 626 patients were identified and randomly divided into the training (418, 67%) and validation (208, 33%) cohorts. Multivariate analysis showed that TBIL, CA19-9, tumor size, tumor number, and preoperative image lymph node metastasis were independently associated with MVI. Then, a model was built by enrolling former fiver risk factors. In the validation cohort, the performance of this model showed good calibration. The area under the curve was 0.874 (95% CI: 0.765–0.894) and 0.729 (95%CI: 0.706–0.751) in the training and validation cohort, respectively. Decision curve analysis showed an obvious net benefit from the model.Conclusion Based on clinical data, an easy model was built for the preoperative prediction of MVI, which can assist clinicians in surgical decision-making and adjuvant therapy.

Background: Human immunodeficiency virus type 1 (HIV-1) epidemic in China is featured by geographical diversity of epidemic patterns. Understanding the characteristics of regional HIV epidemic allows carrying out targeted prevention and control measures. However, in some regions of Northeast China, current HIV-1 epidemic feature is largely unknown.Methods: Information of 1006 newly diagnosed HIV-1 infected participants were collected before antiretroviral therapy during 2010-2016 in Harbin city of Northeast China. HIV-1 genotype was identified based on the viral gag and env genes. Comparison analyses were made among different participant groups and sampling time periods to understand HIV-1 epidemic trend. Multivariable logistic regression was used to evaluate the factors associated with immune status of participants.Results: Homosexual contact among men who have sex with men (MSM) was the main transmission route and CRF01_AE was the most dominant HIV-1 genotype. Newly diagnosed cases were getting younger, which was mainly due to the continuous increase in the proportion of young cases (aged < 30 years) among CRF01_AE-infected individuals, especially the CRF01_AE-infected MSM. The proportion of cases with good immune status (CD4 count > 500 cells/μl) continued to increase, and younger in age, HIV-1 infection via homosexual contact among MSM and infection by non-CRF01_AE genotype were positive factors for the good immune outcome.Conclusions: Young MSM have become a new vulnerable group for HIV-1 transmission in Northeast China. This group is changing local HIV-1 epidemic pattern. Measures for preventing and controlling HIV-1 infection among this population are urgently needed in the future.

Immune checkpoint inhibitors (ICIs), especially pembrolizumab, have improved outcomes in patients with advanced or metastatic gastric cancer (GC). However, there are no reliable markers for the evaluation of its efficacy. GC samples from the UCSC Xena Browser and Gene Expression Omnibus (GEO) databases were performed by bioinformatics analysis. The single sample gene set enrichment (ssGSEA) algorithm was used to calculate the prognostic genes expression differences score (ssGSEA score) for each sample. Weighted gene co-expression network analysis (WGCNA) was applied to identify the score-related key model. The hub gene was identified based on the intersection between tumor-related transcription factors (TFs) and the key module. The predictive ability for pembrolizumab response of the hub gene was evaluated by the area under the receiver operating characteristic (ROC) curve (AUC). The correlations between the hub gene and immune infiltration in GC were investigated by the CIBERSORT algorithm. According to the ssGSEA score, patients with GC were divided into the high- and low-score group. RUNX1T1 was the hub gene of the ICIs treatment in GC. Its high expression was related to the low-score group, which indicated low sensitivity to pembrolizumab and poor prognosis. RUNX1T1 showed good predictive power for pembrolizumab response (AUC = 0.742). The CIBERSORT analysis showed that a high expression for RUNX1T1 weakened the anti-tumor immune response by affecting the immune cell infiltration.Runx1T1 may be a potential predictor of pembrolizumab efficacy in GC and be a prognostic marker for GC.

The worst-hit area of coronavirus disease 2019 (COVID-19) in China was Wuhan City and its affiliated Hubei Province, where the outbreak has been well controlled. The case fatality rate (CFR) is the most direct indicator to evaluate the hazards of an infectious disease. However, most reported CFR on COVID-19 represent a large deviation from reality. We aimed to establish a more accurate way to estimate the CFR of COVID-19 in Wuhan and Hubei and compare it to the reality. The daily case notification data of COVID-19 from December 8, 2019, to May 1, 2020, in Wuhan and Hubei were collected from the bulletin of the Chinese authorities. The instant CFR of COVID-19 was calculated from the numbers of deaths and the number of cured cases, the two numbers occurred on the same estimated diagnosis dates. The instant CFR of COVID-19 was 1.3%-9.4% in Wuhan and 1.2%-7.4% in Hubei from January 1 to May 1, 2020. It has stabilized at 7.69% in Wuhan and 6.62% in Hubei since early April. The cure rate was between 90.1% and 98.8% and finally stabilized at 92.3% in Wuhan and stabilized at 93.5% in Hubei. The mortality rates were 34.5/100 000 in Wuhan and 7.61/100 000 in Hubei. In conclusion, this approach reveals a way to accurately calculate the CFR, which may provide a basis for the prevention and control of infectious diseases.