Background: Gastric cancer (GC) is a highly heterogeneous tumor with different responses to immunotherapy. Identifying immune subtypes and landscape of GC could improve immunotherapeutic strategies. Methods: Based on the abundance of tumor-infiltrating immune cells in GC patients from The Cancer Genome Atlas, we used unsupervised consensus clustering algorithm to identify robust clusters of patients, and assessed their reproducibility in an independent cohort from Gene Expression Omnibus. We further confirmed the feasibility of our immune subtypes in five independent pan-cancer cohorts. Finally, functional enrichment analyses were provided, and a deep learning model studying the pathological images was constructed to identify the immune subtypes. Results: We identified and validated three reproducible immune subtypes presented with diverse components of tumor-infiltrating immune cells, molecular features, and clinical characteristics. An immune-inflamed subtype 3, with better prognosis and the highest immune score, had the highest abundance of CD8+ T cells, CD4+ T–activated cells, follicular helper T cells, M1 macrophages, and NK cells among three subtypes. By contrast, an immune-excluded subtype 1, with the worst prognosis and the highest stromal score, demonstrated the highest infiltration of CD4+ T resting cells, regulatory T cells, B cells, and dendritic cells, while an immune-desert subtype 2, with an intermediate prognosis and the lowest immune score, demonstrated the highest infiltration of M2 macrophages and mast cells, and the lowest infiltration of M1 macrophages. Besides, higher proportion of EVB and MSI of TCGA molecular subtyping, over expression of CTLA4, PD1, PDL1, and TP53, and low expression of JAK1 were observed in immune subtype 3, which consisted with the results from Gene Set Enrichment Analysis. These subtypes may suggest different immunotherapy strategies. Finally, deep learning can predict the immune subtypes well. Conclusion: This study offers a conceptual frame to better understand the tumor immune microenvironment of GC. Future work is required to estimate its reference value for the design of immune-related studies and immunotherapy selection.

Owing to their potential adverse health effects, global contamination by microplastics (MPs) has attracted increased scientific and societal concerns. However, in vivo studies on MP toxicity, along with its effects and underlying mechanisms, remain limited. We recently found that non-coding RNA (ncRNAs) contribute to MP-mediated vascular toxicity. Moreover, previous studies have identified N6-methyladenosine (m6A) modifications in ncRNAs as influencing factors in cardiovascular disease. However, whether and how m6A modifications in ncRNAs are affected by MP-induced cardiotoxicity remain unknown. Herein, we profiled differentially expressed ncRNAs and their related m6A modification profiles in MP-exposed myocardial tissue using RNA sequencing (RNA-seq) and methylated RNA immunoprecipitation sequencing (MeRIP-seq). First, we observed that MPs accumulated in different organs and upregulated apoptosis in the heart, liver, spleen, and kidney cells. Furthermore, total m6A and METTL3 levels increased in the myocardium after exposure to MPs. RNA-seq results revealed that 392 lncRNAs and 302 circRNAs were differentially expressed in MP-treated mouse myocardium compared to the control group. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses showed that these altered lncRNAs and circRNAs were closely associated with endocytosis, cellular senescence, and cell cycle signaling pathways, which may cause cardiotoxicity. Furthermore, MeRIP-seq data showed different distributions and abundances of m6A modifications in lncRNAs and circRNAs. Additionally, we identified differentially m6A methylated lncRNAs and circRNAs through conjoint analysis of the two high-throughput sequencing datasets and found that both m6A modifications and the expression of circ-Arfgef2 and lncG3bp2 were upregulated after exposure to MPs. This suggests that MP-induced m6A modifications in ncRNAs are involved in cardiotoxicity. Our findings contribute to a better understanding of MP-induced cardiotoxicity and new molecular targets for treating cardiac injury.

Diabetes, especially type 2 diabetes (T2D), poses an unprecedented challenge to global public health. Hydration status also plays a fundamental role in human health, especially in people with T2D, which is often overlooked. This study aimed to explore the longitudinal associations between hydration status and the risk of T2D among the Chinese population. This study used data from the large community-based Kailuan cohort, which included adults who attended physical examinations from 2006 to 2007 and were followed until 2020. A total of 71,526 participants who eventually met the standards were divided into five hydration-status groups based on their levels of urine specific gravity (USG). Multivariable and time-dependent Cox proportional hazards models were employed to evaluate the associations of baseline and time-dependent hydration status with T2D incidence. Restricted cubic splines (RCS) analysis was used to examine the dose–response relationship between hydration status and the risk of T2D. Over a median 12.22-year follow-up time, 11,804 of the participants developed T2D. Compared with the optimal hydration-status group, participants with dehydration and severe dehydration had a significantly increased risk of diabetes, with adjusted hazard ratios (95% CI) of 1.30 (1.04–1.63) and 1.38 (1.10–1.74). Time-dependent analyses further confirmed the adverse effects of impending dehydration, dehydration, and severe dehydration on T2D incidence by 16%, 26%, and 33% compared with the reference group. Inadequate hydration is significantly associated with increased risks of T2D among Chinese adults. Our findings provided new epidemiological evidence and highlighted the potential role of adequate hydration status in the early prevention of T2D development.

Background: Oxidative stress and cell apoptosis play pivotal roles in the pathogenesis of doxorubicin (DOX)-induced myocardial injury. Heat shock protein-derived peptide (HSP-17) is a peptide which is low-expressed in DOX treated mouse heart tissue. It has high bioactivity and interspecies sequence consistency, and is predicted to have myocardial protective effect. Methods: Firstly, we added 1 µM DOX to H9c2 cell culture medium for 24 hours to construct the myocardial cytotoxicity model. Then we detected the effect of HSP-17 on DOX induced H9c2 cardiomyocyte injury by measuring cell viability and lactate dehydrogenase (LDH) level. In addition, reactive oxygen species (ROS) and tetraethylbenzimidazolylcarbocyanine iodide kits are used to evaluate the effect of the HSP-17 peptide on DOX-induced oxidative stress injury to cardiomyocytes, and the detection of apoptosis related proteins and flow cytometry were applied to detect the level of apoptosis. Furthermore, the protein expression levels [phosphorylated Akt (p-Akt) and phosphorylated PI3K (p-PI3K)] of the PI3K/Akt pathway were also detected by western blotting. Results: We found that the HSP-17 peptide can increase cell viability, protect mitochondrial potential, reduce LDH levels, and reduce ROS and cardiomyocyte apoptosis. In addition, we also observed that HSP-17 upregulated the expression level of p-Akt, and LY294002, a typical inhibitor of PI3K/Akt, was found to eliminate the protective roles of HSP-17. Conclusions: In conclusion, this study demonstrated that the HSP-17 peptide protected H9c2 cells against oxidative stress and apoptosis via PI3K/Akt pathway activation, which provides a new idea for the treatment of DOX-induced myocardial injury.

Background Lung cancer remains a major global health threat, with its biological complexity and patient heterogeneity posing significant challenges. Novel machine learning approaches now offer effective tools to interpret complex biological information hierarchies, showing promise to transform lung cancer treatment approaches. Methods We analyzed comprehensive biological datasets from TCGA and other databases, integrating DNA, RNA, miRNA, protein, and metabolite information. Multiple machine learning methods were employed to build diagnostic tools, treatment response predictors, and survival estimation models. Results Our machine learning approaches effectively distinguished cancer patients from healthy controls. Analysis identified unique molecular characteristics between lung cancer subtypes and discovered biomarkers that help predict treatment efficacy and patient prognosis. Adding clinical data to biological information significantly improved model accuracy and enhanced patient stratification. Conclusion This study marks significant progress toward precision cancer therapy by demonstrating how machine learning can help decode the complex biology of lung cancer.

To combat/control the COVID-19 pandemic, a complete lockdown was implemented in China for almost 6 months during 2020. To determine the impact of a long-term lockdown on the academic performance of first-year nursing students via mandatory online learning, and to determine the benefits of online teaching. The recruitment and academic performance of 1^st-year nursing students were assessed between 2019 [prior to COVID-19, n = 195, (146 women)] and 2020 [during COVID-19, n = 180 (142 women)]. The independent sample t test or Mann-Whitney test was applied for a comparison between these two groups. There was no significant difference in student recruitment between 2019 and 2020. The overall performance of the first-year students improved in the Biochemistry, Immunopathology, Traditional Chinese Medicine Nursing and Combined Nursing courses via mandatory online teaching in 2020 compared with traditional teaching in 2019. Suspension of in-class learning but continuing education virtually online has occurred without negatively impacting academic performance, thus academic goals are more than achievable in a complete lockdown situation. This study offers firm evidence to forge a path for developments in teaching methods to better incorporate virtual learning and technology in order to adapt to fast-changing environments. However, the psychological/psychiatric and physical impact of the COVID-19 lockdown and the lack of face-to-face interaction on these students remains to be explored.