Xinnaotongluo liquid protects H9c2 cells from H/R-induced damage by regulating MDM2/STEAP3

by Jiankun Cui, Qinwen Wang, Minghao Li

Xinnaotongluo liquid has been used to improve the clinical symptoms of patients with myocardial infarction. However, the molecular mechanism of Xinnaotongluo liquid is not completely understood. H9c2 cells exposed to hypoxia/reoxygenation (H/R) was used to simulate damage to cardiomyocytes in myocardial infarction in vitro. The biological indicators of H9c2 cells were measured by cell counting kit-8, enzyme linked immunoabsorbent assay, and western blot assay. In H/R-induced H9c2 cells, a markedly reduced murine double minute 2 (MDM2) was observed. However, the addition of Xinnaotongluo liquid increased MDM2 expression in H/R-induced H9c2 cells. And MDM2 overexpression strengthened the beneficial effects of Xinnaotongluo liquid on H9c2 cells from the perspective of alleviating oxidative damage, cellular inflammation, apoptosis and ferroptosis of H/R-induced H9c2 cells. Moreover, MDM2 overexpression reduced the protein expression of p53 and Six-Transmembrane Epithelial Antigen of Prostate 3 (STEAP3). Whereas, STEAP3 overexpression hindered the function of MDM2-overexpression in H/R-induced H9c2 cells. Our results insinuated that Xinnaotongluo liquid could protect H9c2 cells from H/R-induced damage by regulating MDM2/STEAP3, which provide a potential theoretical basis for further explaining the working mechanism of Xinnaotongluo liquid.

AE-GPT: Using Large Language Models to extract adverse events from surveillance reports-A use case with influenza vaccine adverse events

by Yiming Li, Jianfu Li, Jianping He, Cui Tao

Though Vaccines are instrumental in global health, mitigating infectious diseases and pandemic outbreaks, they can occasionally lead to adverse events (AEs). Recently, Large Language Models (LLMs) have shown promise in effectively identifying and cataloging AEs within clinical reports. Utilizing data from the Vaccine Adverse Event Reporting System (VAERS) from 1990 to 2016, this study particularly focuses on AEs to evaluate LLMs’ capability for AE extraction. A variety of prevalent LLMs, including GPT-2, GPT-3 variants, GPT-4, and Llama2, were evaluated using Influenza vaccine as a use case. The fine-tuned GPT 3.5 model (AE-GPT) stood out with a 0.704 averaged micro F1 score for strict match and 0.816 for relaxed match. The encouraging performance of the AE-GPT underscores LLMs’ potential in processing medical data, indicating a significant stride towards advanced AE detection, thus presumably generalizable to other AE extraction tasks.

The association between serum prolactin levels and live birth rates in non-PCOS patients: A retrospective cohort study

by Xiaoyuan Xu, Aimin Yang, Yan Han, Wei Wang, Guimin Hao, Na Cui

Background and objectives

This paper aimed to analyze the relationship between baseline prolactin (PRL) levels and live birth rates (LBRs) in patients undergoing embryo transfer who did not have polycystic ovarian syndrome (PCOS) using a retrospective design. Patient(s): A total of 20,877 patients who had undergone IVF/intracytoplasmic sperm injection (ICSI) between December 2014 and December 2019.

Materials and methods

We examined the association between PRL concentrations and LBRs using multivariate regression analysis. In addition, a model for nonlinear relationships based on a two-part linear regression was developed.

Results

Following adjustment for confounding factors, multivariate regression analysis confirmed a statistically significant correlation between serum PRL and LBR. Particularly, when blood PRL content was less than 14.8 ng/mL, there exists a positive relation between serum PRL and LBRs. In contrast, once PRL concentrations surpassed the inflection point at 14.8 ng/mL, a meaningful relationship could no longer be inferred between serum PRL and LBR.

Conclusions

Basal serum PRL levels were segmentally connected with LBRs.

Influencing factors and improvement paths of manufacturing innovation performance: Configuration analysis based on TOE framework

by Youcai Ma, Zhaobing Cui

Innovation is the first driving force to lead development, how to improve manufacturing innovation performance has become a hot topic. Based on 47 listed companies in the computer, communication and other electronic equipment manufacturing industry in the A-share market, this paper adopted the Fuzzy set qualitative comparative analysis (fsQCA) to explore the influencing factors of technology, organization and environment on the innovation performance of manufacturing industry and the improvement path. The findings are as follows: (1) A single condition is not a necessary condition for high innovation performance in manufacturing industry, but government support plays a key role in improving innovation performance in manufacturing industry. (2) There are two improvement paths for high innovation performance in manufacturing industry, which are specifically explained as “technology-environment dual improvement path” and “technology-organization-environment collaborative improvement path”. (3) The improvement of innovation performance in the manufacturing industry is the result of multiple factors, showing the characteristics of “all paths lead to the same destination”. Different manufacturing enterprises have different paths to improve innovation performance based on their actual conditions. Based on these findings, this study may provide some implications for the effective improvement of manufacturing innovation performance.

Emissions reduction strategy in a three-stage agrifood value chain: A dynamic differential game approach

by Huanhuan Wang, Xiaoli Fan, Qilan Zhao, Pengfei Cui

Agrifood systems account for 31% of global greenhouse gas emissions. Substantial emissions reduction in agrifood systems is critical to achieving the temperature goal set by the Paris Agreement. A key challenge in reducing GHG emissions in the agrifood value chain is the imbalanced allocation of benefits and costs associated with emissions reduction among agrifood value chain participants. However, only a few studies have examined agrifood emissions reduction from a value chain perspective, especially using dynamic methods to investigate participants’ long-term emissions reduction strategies. This paper helps fill this gap in the existing literature by examining the impact of collaborations among agrifood value chain participants on correcting those misallocations and reducing emissions in agrifood systems. We develop a dynamic differential game model to examine participants’ long-term emissions reduction strategies in a three-stage agrifood value chain. We use the Hamilton-Jacobi-Bellman equation to derive the Nash equilibrium emissions reduction strategies under non-cooperative, cost-sharing, and cooperative mechanisms. We then conduct numerical analysis and sensitivity analysis to validate our model. Our results show that collaboration among value chain participants leads to higher emissions reduction efforts and profits for the entire value chain. Specifically, based on our numerical results, the cooperative mechanism results in the greatest emissions reduction effort by the three participants, which leads to a total that is nearly three times higher than that of the non-cooperative mechanism and close to two times higher than the cost-sharing mechanism. The cooperative mechanism also recorded the highest profits for the entire value chain, surpassing the non-cooperative and cost-sharing mechanisms by around 37% and 16%, respectively. Our results provide valuable insights for policymakers and agrifood industry stakeholders to develop strategies and policies encouraging emissions reduction collaborations in the agrifood value chain and reduce emissions in the agrifood systems.

Promoting healthy cooking patterns in China: Analysis of consumer clusters and the evolution of cooking pattern trends

by Chuan Bo Liang, Bin Cui, Fu Rong Wang, Jing Peng, Jian Ying Ma, Mei Yin Xu, Jun Ke, Yi Tian, Zi Qi Cui

Cooking methods can change the composition of foods and have important effects on human health. The Chinese people have developed many distinct and unique cooking methods. However, the daily cooking patterns of Chinese people and the characteristics and evolution of trends in cooking patterns commonly used by Chinese consumers remain unclear. The objective of this study was to identify the major cooking patterns and discuss their effects on human health, as well as to identify the cooking pattern consumer clusters and the evolution of trends in Chinese consumer cooking patterns. From March to June 2021, this study interviewed 4,710 residents in Eastern China regarding the consumption frequency of each cooking method when food is prepared at home or when eating out. Exploratory factor analysis, K-Means cluster analysis, Chi-square test, pairwise comparisons of multiple sample rates, and multivariate linear regression were used to identify the cooking patterns and cooking pattern consumer clusters, to assess differences in consumption preferences between consumer clusters, and to examine the relationship between demographic characteristic variables and different cooking patterns. Results revealed three major cooking patterns, namely traditional Chinese (cooking methods with native Chinese characteristics), bland, and high-temperature cooking patterns, as well as seven cooking pattern consumer clusters and their demographic characteristics in the Eastern Chinese population. With increases in age, education level, and income, consumers tended to choose the healthy “Bland” cooking pattern. Further, there was a higher proportion of people aged 36–65 years in the C₃ cluster, which is characterized by the “Bland” cooking pattern. However, participants who were male and younger made fewer healthy choices in their cooking patterns. Specifically, a higher proportion of participants aged 21–35 years were found in the C₅ cluster, which is characterized by the unhealthy “High-temperature” cooking pattern. Therefore, culinary health education should focus on individuals who are male and young. Specifically, the shift in cooking patterns among people aged 21–35 years should receive special attention.