Novel nomogram to predict the overall survival of postoperative patients with gastric signet.

BACKGROUND: The TNM staging system cannot accurately predict the prognosis of postoperative gastric signet ring cell carcinoma (GSRC) given its unique biological behavior, epidemiological features, and various prognostic factors. Therefore, a reliable postoperative prognostic evaluation system for GSRC is required. This study aimed to establish a nomogram to predict the overall survival (OS) rate of postoperative patients with GSRC and validate it in the real world. METHODS: Clinical data of postoperative patients with GSRC from 2002 to 2014 were collected from the Surveillance, Epidemiology, and End Results database and randomly assigned to training and internal validation sets at a 7:3 ratio. The external validation set used data from 124 postoperative patients with GSRC who were admitted to the Affiliated Tumor Hospital of Harbin Medical University between 2002 and 2014. The independent risk factors affecting OS were screened using univariate and multivariate analyses to construct a nomogram. The performance of the model was evaluated using the C-index, receiver operating characteristic curve (ROC), calibration curve, decision analysis (DCA) curve, and adjuvant chemotherapy decision analysis. RESULTS: Univariate/multivariate analysis indicated that age, stage, T, M, regional nodes optimized (RNE), and lymph node metastasis rate (LNMR) were independent risk factors affecting prognosis. The C-indices of the training, internal validation, and external validation sets are 0.741, 0.741, and 0.786, respectively. The ROC curves for the first, third, and fifth years in three sets had higher areas under the curves, (training set, 0.782, 0.864, 0.883; internal validation set, 0.781, 0.863, 0.877; external validation set, 0.819, 0.863, 0.835). The calibration curve showed high consistency between the nomogram-predicted 1-, 3-, and 5-year OS and the actual OS in the three queues. The DCA curve indicated that applying the nomogram enhanced the net clinical benefits. The nomogram effectively distinguished patients in each subgroup into high- and low-risk groups. Adjuvant chemotherapy can significantly improve OS in high-risk group (P = 0.034), while the presence or absence of adjuvant chemotherapy in low-risk group has no significant impact on OS (P = 0.192). CONCLUSIONS: The nomogram can effectively predict the OS of patients with GSRC and may help doctors make personalized prognostic judgments and clinical treatment decisions.

Does China's green power trading policy play a role? - Evidence from renewable energy generation enterprises.

Green power trading is an institutional innovation proposed by China to promote green and sustainable development. This policy aims to relieve the serious debt pressure of renewable energy generation enterprises, thus laying the foundation for achieving carbon reduction targets. This paper empirically examines the role of green power trading by constructing the difference-in-differences (DID) model. Our findings indicate that green power trading significantly reduces the debt levels of policy-covered generation enterprises. This effect is more pronounced for state-owned enterprises, companies with better ESG performance, companies in regions with higher degree of marketization and companies specializing in renewable energy power generation. Further research discusses the impact mechanism of green power trading in terms of external and internal environmental channels. We find that a strong green development atmosphere, especially the external atmosphere, will significantly reinforce the weakening effect of green power trading on corporate debt. Our research provides empirical evidence for the success of green power trading policy in its pilot phase.

SMYD Family Members Serve as Potential Prognostic Markers and Correlate with Immune Infiltrates in Gastric Cancer.

Background: The SMYD family comprises a group of genes encoding lysine methyltransferases, which are closely related to tumorigenesis. However, a systematic understanding of their role in gastric cancer (GC) is lacking. Methods: Using databases and tools such as the Cancer Genome Atlas, Human Protein Atlas, Kaplan-Meier Plotter, Gene Expression Profiling Interactive Analysis, and Metascape, we comprehensively analyzed differences in SMYD expression and its prognostic value as well as the association of SMYDs with immune cell infiltration, tumor mutational burden (TMB), and microsatellite instability (MSI). We conducted functional enrichment analysis and explored a competing endogenous RNA mechanism regulating SMYD mRNA and protein levels in patients with GC. Results: In GC, the expression of SMYD2/3/4/5 mRNA was significantly upregulated, as opposed to that of SMYD1 mRNA, which was significantly downregulated. The protein levels of SMYDs were consistent with mRNA levels. SMYD1/2/4/5 was negatively correlated with overall survival; SMYD1/2/3/5 was negatively correlated with progression-free survival. Our SMYD-based signature and nomogram model may be useful for inferring the prognosis of GC. All SMYDs were closely associated with the infiltration of six immune cell types: uncharacterized, CD8+ T, CD4+ T, macrophage, endothelial, and B cells. TMB was significantly negatively correlated with SMYD1 expression, while a significant positive correlation was observed with SMYD2/5. Furthermore, MSI was significantly positively correlated with SMYD2/5 expression. Long non-coding RNAs, such as chr22-38_28785274-29006793.1, XLOC_002309, and CTD-2008N3.1, were suggested to regulate SMYD expression by sponging multiple microRNAs. Conclusion: SMYDs are differentially expressed in GC and are thus potential prognostic markers. SMYD expression is closely related to immune infiltration, TMB, and MSI, all of which are closely related to the response to targeted immune therapy.

Fertility Preferences and Contraceptive Change in Low- and Middle-Income Countries.

The past four decades have witnessed an enormous increase in modern contraception in most low- and middle-income countries. We examine the extent to which this change can be attributed to changes in fertility preferences versus fuller implementation of fertility preferences, a distinction at the heart of intense debates about the returns to investments in family planning services. We analyze national survey data from five major survey programs: World Fertility Surveys, Demographic Health Surveys, Reproductive Health Surveys, Pan-Arab Project for Child Development or Family Health, and Multiple Indicator Cluster Surveys. We perform regression decomposition of change between successive surveys in 59 countries (330 decompositions in total). Change in preferences accounts for little of the change: less than 10 percent in a basic decomposition and about 15 percent under a more elaborate specification. This is a powerful empirical refutation of the view that contraceptive change has been driven principally by reductions in demand for children. We show that this outcome is not surprising given that the distribution of women according to fertility preferences is surprisingly stable over time.

Machine Learning-Based Model for the Prognosis of Postoperative Gastric Cancer.

BACKGROUND: The use of machine learning (ML) in predicting disease prognosis has increased, and scientists have adopted different methods for cancer classification to optimize the early screening of cancer to determine its prognosis in advance. In this study, we aimed at improving the prediction accuracy of gastric cancer in postoperation patients by constructing a highly effective prognostic model. METHODS: The study used postoperative gastric cancer patient data from the SEER database. The LASSO regression method was used to construct a clinical prognostic model, and four machine learning methods (Boruta algorithm, neural network, support vector machine, and random forest) were used to screen and recombine the features to construct an ML prognostic model. Clinical information on 955 postoperative gastric cancer patients collected from the Affiliated Tumor Hospital of Harbin Medical University was used for external verification. RESULTS: Experimental results showed that the AUC values of 1, 3 and 5 years in the training set, validation set and external validation set of clinical prognosis model and ML prognosis model directly established by LASSO regression are all around 0.8. CONCLUSION: Both models can accurately evaluate the prognosis of postoperative patients with gastric cancer, which may be helpful for accurate and personalized treatment of postoperative patients with gastric cancer.

Pristine Graphic Carbon Nitride Quantum Dots for the Visualized Detection of Latent Fingerprints.

User-friendly fingerprint powders, namely efficient, low-cost and nontoxic ones, are always desirable for the development of latent fingerprints (LFPs). Here, we described the use of pristine graphic carbon nitride quantum dots (g-C3N4 QDs) as a new kind of user-friendly fingerprint powder. The g-C3N4 QDs can be easily prepared from urea and sodium citrate precursors through low temperature solid-phase reaction. Due to their good optical properties and selective interactions with secretion residuals, the g-C3N4 QDs powders were exploited to develop LFPs on different substrates by the powder dusting technique. The LFP images on a plastic bag exhibited a high ridge and furrow contrast ratio, allowing for easy identification of level 1 - 3 details of LFPs. This work indicates that the g-C3N4 QD powders provide good performance for LFP visualization and is likely to be adopted for some applications in forensic investigations.

Soluble expression of single-chain variable fragment (scFv) in Escherichia coli using superfolder green fluorescent protein as fusion partner.

Single-chain variable fragment (scFv) has great prospect in medical therapies and diagnostic applications due to its binding affinity and low immunogenicity. However, the application of scFv is limited by its heterologous expression facing challenges of insoluble aggregation. sfGFP has been developed as fusion tag to facilitate the solubility of fusion partner in Escherichia coli. We designed fusion protein of anti-influenza PB2 scFv at C-terminus of sfGFP and successfully obtained soluble expression of sfGFP-scFv-His in Escherichia coli. The expression level of sfGFP-scFv-His reached at 20 mg/L of bacterial culture when the culture was induced with 0.1 mM IPTG at 18 °C for 16 h. And 6 mg scFv-His was obtained from the cleavage of 10 mg pure sfGFP-scFv-His with TEV protease. In addition, we found that sfGFP-scFv-His was more stable than scFv-His in chicken serum, suggesting that sfGFP not only facilitated the solubility of scFv in Escherichia coli, but also promoted the stability of scFv. The immunologic activity of sfGFP-scFv-His was confirmed by Western blot and ELISA; the results showed that anti-PB2 sfGFP-scFv-His exhibited specific binding to PB2. Hemagglutination and comparative real-time RT-PCR analysis indicated that sfGFP-scFv-His and scFv-His inhibited the replication of H1N1 influenza virus in the infected A549 cells. These results further develop the application of scFv as an agent, such as anti-influenza. Furthermore, soluble expression of scFv using sfGFP as fusion partner provide a cost-effective preparation model for manufacturing scFv against pandemic disease.

Linear DNA vaccine prepared by large-scale PCR provides protective immunity against H1N1 influenza virus infection in mice.

Linear DNA vaccines provide effective vaccination. However, their application is limited by high cost and small scale of the conventional polymerase chain reaction (PCR) generally used to obtain sufficient amounts of DNA effective against epidemic diseases. In this study, a two-step, large-scale PCR was established using a low-cost DNA polymerase, RKOD, expressed in Pichia pastoris. Two linear DNA vaccines encoding influenza H1N1 hemagglutinin (HA) 1, LEC-HA, and PTO-LEC-HA (with phosphorothioate-modified primers), were produced by the two-step PCR. Protective effects of the vaccines were evaluated in a mouse model. BALB/c mice were immunized three times with the vaccines or a control DNA fragment. All immunized animals were challenged by intranasal administration of a lethal dose of influenza H1N1 virus 2 weeks after the last immunization. Sera of the immunized animals were tested for the presence of HA-specific antibodies, and the total IFN-γ responses induced by linear DNA vaccines were measured. The results showed that the DNA vaccines but not the control DNA induced strong antibody and IFN-γ responses. Additionally, the PTO-LEC-HA vaccine effectively protected the mice against the lethal homologous mouse-adapted virus, with a survival rate of 100% versus 70% in the LEC-HA-vaccinated group, showing that the PTO-LEC-HA vaccine was more effective than LEC-HA. In conclusion, the results indicated that the linear H1N1 HA-coding DNA vaccines induced significant immune responses and protected mice against a lethal virus challenge. Thus, the low-cost, two-step, large-scale PCR can be considered a potential tool for rapid manufacturing of linear DNA vaccines against emerging infectious diseases.

Mathematical model of dynamic behavior of microbial desalination cells for simultaneous wastewater treatment and water desalination.

Microbial desalination cells (MDCs) are an emerging concept for simultaneous wastewater treatment and water desalination. This work presents a mathematical model to simulate dynamic behavior of MDCs for the first time through evaluating multiple factors such as organic supply, salt loading, and current generation. Ordinary differential equations were applied to describe the substrate as well as bacterial concentrations in the anode compartment. Local sensitivity analysis was employed to select model parameters that needed to be re-estimated from the previous studies. This model was validated by experimental data from both a bench- and a large-scale MDC system. It could fit current generation fairly well and simulate the change of salt concentration. It was able to predict the response of the MDC with time under various conditions, and also provide information for analyzing the effects of different operating conditions. Furthermore, optimal operating conditions for the MDC used in this study were estimated to have an acetate flow rate of 0.8 mL·min(-1), influent salt concentration of 15 g·L(-1) and salt solution flow rate of 0.04 mL·min(-1), and to be operated with an external resistor less than 30 Ω. The MDC model will be helpful with determining operational parameters to achieve optimal desalination in MDCs.