Dietary patterns and risk for gastric cancer: A case-control study in residents of the Huaihe River Basin, China

AimEvidence linking dietary patterns and the risk of gastric cancer was limited, especially in Chinese populations. This study aimed to explore the association between dietary patterns and the risk of gastric cancer in residents of the Huaihe River Basin, China.MethodsThe association between dietary patterns and the risk of gastric cancer was investigated through a case-control study. Dietary patterns were identified with factor analysis based on responses to a food frequency questionnaire (FFQ). Gastric cancer was diagnosed according to the International Classification of Diseases, 10th Revision (ICD 10). Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated across the tertiles of dietary pattern scores using unconditional logistic regression models.ResultsA total of 2,468 participants were included in this study. Six main dietary patterns were extracted, and those patterns explained 57.09% of the total variation in food intake. After adjusting for demographic characteristics, lifestyle factors, individual disease history, family history of cancer and Helicobacter. Pylori (H. pylori) infection, comparing the highest with the lowest tertiles of dietary pattern scores, the multivariable ORs (95% CIs) were 0.786 (0.488, 1.265; Ptrend < 0.001) for the flavors, garlic and protein pattern, 2.133 (1.299, 3.502; Ptrend < 0.001) for the fast food pattern, 1.050 (0.682, 1.617; Ptrend < 0.001) for the vegetable and fruit pattern, 0.919 (0.659, 1.282; Ptrend < 0.001) for the pickled food, processed meat products and soy products pattern, 1.149 (0.804, 1.642; Ptrend < 0.001) for the non-staple food pattern and 0.690 (0.481, 0.989; Ptrend < 0.001) for the coffee and dairy pattern.ConclusionsThe specific dietary patterns were associated with the risk of gastric cancer. This study has implications for the prevention of gastric cancer

Correction: Low magnitude high frequency vibration promotes adipogenic differentiation of bone marrow stem cells via P38 MAPK signal.

[This corrects the article DOI: 10.1371/journal.pone.0172954.]

Construction and validation of prognostic risk score model based on autophagy-related genes for head and neck squamous cell carcinoma

Objective To construct and validate a prognostic risk-scoring model based on autophagy-related genes for head and neck squamous cell carcinoma (HNSCC). Methods All HNSCC transcriptome expression data (RNA sequencing, RNA-seq) and clinical information downloaded from the Cancer Genome Atlas (TCGA) database, and differentially expressed genes were screened. These differentially expressed genes of HNSCC were intersected with autophagy related genes (ARGs) retrieved from the GeneCards database to obtain differentially expressed ARGs. After integrating clinical information, prognostic ARGs were obtained by prognostic analysis, and then enrichment analysis was performed. The least absolute shrinkage and selection operator (LASSO) regression and Cox regression model were used to construct a risk scoring model for predicting the prognosis and survival of HNSCC. The receiver operating characteristic (ROC) curve was drawn, the area under the curve (AUC) and the best cut-off value were calculated, and the patients were divided into the high- and low-risk score groups with the best cut-off value. Kaplan-Meier survival curve was drawn to assess the predictive performance of the model. The clinical information was integrated with the risk score, and the independent prognostic value of the risk score was evaluated by Cox regression analysis. Results The prognostic risk score model of HNSCC was constructed based on the 9 ARGs significantly related to prognosis were obtained by LASSO regression and Cox regression analysis through the prognostic analysis for differentially expressed ARGs which screened 20 ARGs related to prognosis. The survival time of the low-risk score group was better than that of the high risk score group, and the survival time of the two groups was significantly different (P < 0.001), according to ROC curve and Kaplan-Meier survival curve. The model showed good prediction performance in both the training set (the maximum AUC, 0.69) and the external validation set (the maximum AUC, 0.822). Cox regression analysis showed that the risk score was significantly correlated with the prognosis of HNSCC patients (P < 0.001), indicating that the risk score had independent prognostic value for HNSCC. Conclusion The HNSCC risk scoring model composed of 9 ARGs can effectively predict the prognosis of patients with HNSCC

Research on Multimodal Transport of Electronic Documents Based on Blockchain

Multimodal transport document collaboration is the foundation of multimodal transport operations. Blockchain technology can effectively address issues such as a lack of trust and difficulties in information sharing in current multimodal transport document collaboration. However, in current research on blockchain-based electronic documents, the bottleneck lies in the collaboration aspect of multimodal transport among multiple entities, known as the “one-bill coverage system” collaborative problem. The collaboration problem studied in this paper involves selecting suitable transport routes according to the shipper’s transport needs, and selecting the most suitable specific carrier from numerous carriers. To address the collaboration problem among multiple parties in the multimodal transport “one-bill coverage system”, a multiparty collaboration mechanism is designed. This mechanism includes two aspects: firstly, designing the architecture of the multimodal transport blockchain transport platform, which reengineers the operation process of the “one-bill coverage system” for container multimodal transport; secondly, constructing a multiparty collaboration decision-making model for the “one-bill coverage system” in multimodal transport. The model is solved and analyzed, and the collaboration strategy obtained is embedded in the application layer of the platform. Smart contracts related to the “one-bill coverage system” for multimodal transport are written in the Solidity language and deployed and executed on the Remix platform. The design of this mechanism can effectively improve the collaboration efficiency of participants in the “one-bill coverage system” for multimodal transport