A 5-Year Survival Prediction Model for Chronic Heart Failure Patients Induced by Coronary Heart Disease with Traditional Chinese Medicine Intervention.

OBJECTIVE: This study aimed to construct a 5-year survival prediction model of coronary heart disease (CHD) induced chronic heart failure (CHF), which is supported by the traditional Chinese medicine (TCM) factor, and to verify the model. METHODS: Inpatients from January 1, 2012, to December 31, 2017, in seven hospitals in Shandong Province were studied. The random number table was used to randomly divide the seven hospitals into two groups (training set and verification set). In the training set, the least absolute shrinkage selection operator regression was first used to screen the independent variables. Logistic regression was then applied to construct a survival prediction model. The following nomogram visualizes the prediction model results. Finally, C-indices, calibration curves, and decision curves were used to discriminate and calibrate the established model and evaluate its practicability in the clinic. Bootstrap resampling and the verification set were used for internal and external verification, respectively. RESULTS: A total of 424 eligible patients were included in the model construction and verification. In this 5-year survival prediction model of patients with CHF induced by CHD, eight independent predictors were included. The series of C-indices for the training set, bootstrap resamples, and verification set was 0.885, 0.867, and 0.835, respectively, demonstrating the credibility of our model. Additionally, the receiver operating characteristic curve, calibration curve, and clinical decision curve analysis of the training and verification sets showed that this 5-year survival prediction model was good in discrimination, calibration, and clinical practicability. CONCLUSION: This work highlights eight independent factors affecting 5-year mortality in patients with CHF induced by CHD after discharge and further helps reallocate medical resources rationally by precisely identifying high-risk groups. The constructed prediction model not only plays a credible role in prediction but also demonstrates TCM intervention as a protective factor for the 5-year death of patients with CHF induced by CHD, thereby advancing the use of TCM in CHF.

On (Non-)Monotonicity and Phase Diagram of Finitary Random Interlacement.

In this paper, we study the evolution of a Finitary Random Interlacement (FRI) with respect to the expected length of each fiber. In contrast to the previously proved phase transition between sufficiently large and small fiber length, for all d≥3, FRI is NOT stochastically monotone as fiber length increases. At the same time, numerical evidence still strongly supports the existence and uniqueness of a critical fiber length, which is estimated theoretically and numerically to be an inversely proportional function with respect to system intensity.

Prediction of the COVID-19 outbreak in China based on a new stochastic dynamic model.

The current outbreak of coronavirus disease 2019 (COVID-19) has become a global crisis due to its quick and wide spread over the world. A good understanding of the dynamic of the disease would greatly enhance the control and prevention of COVID19. However, to the best of our knowledge, the unique features of the outbreak have limited the applications of all existing dynamic models. In this paper, a novel stochastic model was proposed aiming to account for the unique transmission dynamics of COVID-19 and capture the effects of intervention measures implemented in Mainland China. We found that: (1) instead of aberration, there was a remarkable amount of asymptomatic virus carriers, (2) a virus carrier with symptoms was approximately twice more likely to pass the disease to others than that of an asymptomatic virus carrier, (3) the transmission rate reduced significantly since the implementation of control measures in Mainland China, and (4) it was expected that the epidemic outbreak would be contained by early March in the selected provinces and cities in China.

Effect and safety of oral Chinese patent medicine for heart failure: A protocol for systematic review and network meta-analysis.

BACKGROUND: Heart failure (HF) is the terminal stage of various common cardiovascular diseases with quite a frequent readmission and a high mortality rate, and brings heavy financial burdens to families and society. Oral Chinese patent medicine (CPM) has been widely applied in the treatment of HF in China because of its simplicity, cheapness, convenience, and high efficiency. However, due to the large number and broad clinical selectivity of oral CPMs, there is a lack of uniformity and clinical application standardization. To choose more effective and safe medicine among so many oral CPMs is particularly essential for further improving the therapeutic effect. In this study, the efficacy and safety of different oral CPMs will be compared by a network meta-analysis (NMA), and the best CPM will be selected for the treatment of HF. METHODS: According to the search strategy, 4 English and 4 Chinese databases will be searched from the construction of the library to July 31, 2020. The NMA will include clinical randomized controlled trials (RCTs) of different oral CPMs in HF treatment. The methodological quality is assessed according to the bias risk assessment tool of Cochrane. The Bayesian NMA is performed by Aggregate Data Drug Information System (ADDIS), and the results are visualized using Stata 15.0 software. The GRADE approach is used to assess the quality of evidence and recommendation intensity. RESULTS: The NMA will identify the best oral CPM in the complementary treatment of HF. A peer-reviewed journal will publish the results of the study. CONCLUSION: This study can provide reliable evidence for the efficacy and safety of oral CPMs in the treatment of HF, and help decision-makers and patients to select more effective and safer oral CPM. PROTOCOL REGISTRATION NUMBER: INPLASY202090053.

Prediction of key genes and pathways involved in trastuzumab-resistant gastric cancer.

BACKGROUND: Trastuzumab has been prevailingly accepted as a beneficial treatment for gastric cancer (GC) by targeting human epidermal growth factor receptor 2 (HER2)-positive. However, the therapeutic resistance of trastuzumab remains a major obstacle, restricting the therapeutic efficacy. Therefore, identifying potential key genes and pathways is crucial to maximize the overall clinical benefits. METHODS: The gene expression profile GSE77346 was retrieved to identify the differentially expressed genes (DEGs) associated with the trastuzumab resistance in GC. Next, the DEGs were annotated by the gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. The DEGs-coded protein-protein interaction (PPI) networks and the prognostic values of the 20 hub genes were determined. Correlation of the hub genes were analyzed in The Cancer Genome Atlas. The prognostic values of hub genes were further validated by Kaplan-Meier (KM) plotter. RESULTS: A total of 849 DEGs were identified, with 374 in upregulation and 475 in downregulation. Epithelium development was the most significantly enriched term in biological processes while membrane-bounded vesicle was in cellular compartments and cell adhesion molecular binding was in molecular functions. Pathways in cancer and ECM-receptor interaction were the most significantly enriched for all DEGs. Among the PPI networks, 20 hub genes were defined, including CD44 molecule (CD44), HER-2, and cadherin 1 (CDH1). Six hub genes were associated with favorable OS while eight were associated with poor OS. Mechanistically, 2'-5'-oligoadenylate synthetase 1, 3 (OAS1, OAS3) and CDH1 featured high degrees and strong correlations with other hub genes. CONCLUSIONS: This bioinformatics analysis identified key genes and pathways for potential targets and survival predictors for trastuzumab treatment in GC.