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Lysosomal autophagy plays a critical role in ensuring the continuity of autophagic flux, however bioinformatics studies investigating biomarkers associated with lysosomal autophagy in cerebral ischemia-reperfusion injury(CIRI) remain scarce. Three datasets (GSE61616, GSE97537, and GSE82146) were retrieved from the GEO database. After batch correction, GSE61616 and GSE97537 were integrated into a combined dataset, while GSE82146 served as the validation set. Lysosomal autophagy-related genes (LRGs) obtained from the GeneCards database were intersected with differentially expressed genes (DEGs) identified between the CIRI group and the control group, resulting in the identification of 36 lysosomal autophagy-related differentially expressed genes (LRDEGs). Functional enrichment analysis using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) revealed that LRDEGs are primarily involved in biological processes such as membrane fusion and SNARE binding and are significantly enriched in inflammation-related pathways. We built a diagnostic model for CIRI and identified the biomarkers Vamp8 . The results were further validated by quantitative real-time PCR (qPCR) and Western blotting. Simultaneously, immune infiltration analysis was performed. Finally, the mRNA-miRNA and mRNA-RBP interaction regulatory networks of the key genes revealed that Vamp8 is regulated by miR-325-3p , Elavl1 , Zfp36 , and Pabpc1 .

Kidney transplantation is considered the most effective treatment for end-stage renal failure. Recent studies have shown that the significance of the immune microenvironment after kidney transplantation in determining prognosis of patients. Therefore, this study aimed to conduct a bibliometric analysis to provide an overview of the knowledge structure and research trends regarding the immune microenvironment and survival in kidney transplantation. Our search included relevant publications from 2013 to 2023 retrieved from the Web of Science core repository and finally included 865 articles. To perform the bibliometric analysis, we utilized tools such as VOSviewer, CiteSpace, and the R package \"bibliometrix\". The analysis focused on various aspects, including country, author, year, topic, reference, and keyword clustering. Based on the inclusion criteria, a total of 865 articles were found, with a trend of steady increase. China and the United States were the countries with the most publications. Nanjing Medical University was the most productive institution. High-frequency keywords were clustered into 6 areas, including kidney transplantation, transforming growth factor β, macrophage, antibody-mediated rejection, necrosis factor alpha, and dysfunction. Antibody mediated rejection (2019-2023) was the main area of research in recent years. This groundbreaking bibliometric study comprehensively summarizes the research trends and advances related to the immune microenvironment and survival after kidney transplantation. It identifies recent frontiers of research and highlights promising directions for future studies, potentially offering fresh perspectives to scholars in the field.

Objective To explore the perception of good death of patients with end-stage cancer by nurses in the oncology department. Method In the study we used a phenomenological approach and semi-structured interviews. A total of 11 nurses from the oncology department of a Grade A hospital in Taizhou were interviewed on the cognition of good death from July 1 to September 30, 2022. Colaizzi’s analysis method was used to analyse the interview data. This study followed the consolidated criteria for reporting qualitative research (COREQ). Result Four themes were identified: a strong sense of responsibility and mission; To sustain hope and faith; The important role of family members; Improve patients’ quality of life. Conclusion The nurses in the department of oncology have a low level of knowledge about the “good death”, and the correct understanding and view of the “good death” is the premise of the realization of \" good death”. The ability of nursing staff to improve the “good death”, attention, and meet the needs and wishes of individuals and families, is the guarantee of the realization of “good death”.

Background Cognitive impairment is one of the main complications after a stroke and seriously affects the quality of life and survival time of patients, thereby causing a heavy burden on the social economy and public health. Although exercise is an effective non‐pharmacological strategy for prevention and treatment of cognitive impairment, the mechanism(s) of this effect remain unclear. Methods The current study investigated the effects of irisin treatment on the behavioral characteristics of mice with post‐stroke cognitive impairment (PSCI). The expression levels of platelet endothelial cell adhesion molecule 1 (PECAM, PECAM‐1, CD31), glial fibrillary acidic protein (GFAP), vascular endothelial growth factor (VEGF), and molecules in the adenosine 5‐monophosphate (AMP)‐activated protein kinase (AMPK)–endothelial nitric oxide synthase (eNOS) signaling cascade in the hippocampus were then measured. Results Irisin significantly enhances learning and memory functions in cases of PSCI. This improvement correlates with a reduction in cerebral infarction size and decreased neuronal death. Additionally, irisin treatment resulted in a marked decrease in the levels of astrocytic scar formation in the cortex. Furthermore, irisin activates the AMPK‐eNOS signaling pathway, which promotes the expression of VEGF. The irisin compounds are involved in the process of brain angiogenesis and play a critical role in endothelial and reactive astrocytes function. Conclusion The study revealed a potential mechanism by which exercise‐induced irisin secretion may attenuate PSCI. Irisin improved endothelial dysfunction and neuroinflammation, suggesting it may be a promising target for PSCI therapy. Irisin/FNDC5 is a crucial regulator of the cognitive benefits of exercise in post‐stroke cognitive impairment. The following schematic diagram shows that increasing the irisin/FNDC5 level can activate the AMPK‐eNOS pathway, promoting lower infraction volume and learning and memory cognitive functions.

Due to the poor compatibility of amines under oxidative conditions and the low chemoselectivity between two different amides, the Hofmann rearrangement of amides involving amine or other amide nucleophiles poses significant challenges. Herein, we present a versatile electrochemical reaction that achieves highly chemoselective Hofmann rearrangement of two different amides with the addition of only sodium iodide, enabling the efficient synthesis of single N ‐acylureas. Additionally, under similar electrochemical conditions, both primary and secondary amines can also efficiently react with amides to produce unsymmetrical ureas. This electrosynthetic method exhibits good functional group tolerance and has been successfully employed to synthesize five herbicides, as well as to facilitate the late‐stage functionalization of over twenty amine‐containing drugs. Furthermore, mechanism studies indicated that the use of NaI guaranteed the feasibility and compatibility of the reaction.

Convolutional neural networks in the field of artificial intelligence show great potential in image recognition. It assisted endoscopy to improve the detection rate of early gastric cancer. The 5-year survival rate for advanced gastric cancer is less than 30%, while the 5-year survival rate for early gastric cancer is more than 90%. Therefore, earlier screening for gastric cancer can lead to a better prognosis. However, the detection rate of early gastric cancer in China has been extremely low due to many factors, such as the presence of gastric cancer without obvious symptoms, difficulty identifying lesions by the naked eye, and a lack of experience among endoscopists. The introduction of artificial intelligence can help mitigate these shortcomings and greatly improve the accuracy of screening. According to relevant reports, the sensitivity and accuracy of artificial intelligence trained on deep cirrocumulus neural networks are better than those of endoscopists, and evaluations also take less time, which can greatly reduce the burden on endoscopists. In addition, artificial intelligence can also perform real-time detection and feedback on the inspection process of the endoscopist to standardize the operation of the endoscopist. AI has also shown great potential in training novice endoscopists. With the maturity of AI technology, AI has the ability to improve the detection rate of early gastric cancer in China and reduce the death rate of gastric cancer related diseases in China.

Aim To optimize gastric cancer screening score and reduce screening costs using machine learning models. Methods This study included 228,634 patients from the Taizhou Gastric Cancer Screening Program. We used three machine learning models to optimize Li's gastric cancer screening score: Gradient Boosting Machine (GBM), Distributed Random Forest (DRF), and Deep Learning (DL). The performance of the binary classification models was evaluated using the area under the curve (AUC) and area under the precision–recall curve (AUCPR). Results In the binary classification model used to distinguish low-risk and moderate- to high-risk patients, the AUC in the GBM, DRF, and DL full models were 0.9994, 0.9982, and 0.9974, respectively, and the AUCPR was 0.9982, 0.9949, and 0.9918, respectively. Excluding Helicobacter pylori IgG antibody, pepsinogen I, and pepsinogen II, the AUC in the GBM, DRF, and DL models were 0.9932, 0.9879, and 0.9900, respectively, and the AUCPR was 0.9835, 0.9716, and 0.9752, respectively. Remodel after removing variables IgG, PGI, PGII, and G-17, the AUC in GBM, DRF, and DL was 0.8524, 0.8482, 0.8477, and AUCPR was 0.6068, 0.6008, and 0.5890, respectively. When constructing a tri-classification model, we discovered that none of the three machine learning models could effectively distinguish between patients at intermediate and high risk for gastric cancer (F1 scores in the GBM model for the low, medium and high risk: 0.9750, 0.9193, 0.5334, respectively; F1 scores in the DRF model for low, medium, and high risks: 0.9888, 0.9479, 0.6694, respectively; F1 scores in the DL model for low, medium, and high risks: 0.9812, 0.9216, 0.6394, respectively). Conclusion We concluded that gastric cancer screening indicators could be optimized when distinguishing low-risk and moderate to high-risk populations, and detecting gastrin-17 alone can achieve a good discriminative effect, thus saving huge expenditures.

Patients with Helicobacter pylori-positive gastric dysplasia are at a higher risk of progressing to gastric cancer (GC). Systemic inflammatory markers have been identified as predictors of poor prognosis in patients with GC. However, their role in gastric dysplasia remains elusive. The aim of this study is to evaluate the utility of systemic inflammatory markers as predictors in patients with -positive gastric dysplasia. This study included a total of 664 normal individuals and patients diagnosed with chronic gastritis from the Yangzhou screening cohort, as well as 135 patients with gastric dysplasia from the Affiliated Hospital of Yangzhou University, spanning the period from January 2017 to May 2024. The participants were randomly assigned to either a training group or a validation group in a ratio of 7:3. SPSS software was utilized to determine the optimal critical values for PLR and SIRI. Subsequently, univariate and multivariate regression analyses were conducted to assess the diagnostic utility of PLR and SIRI. A nomogram was developed to estimate the risk associated with gastric dysplasia. To evaluate model performance, receiver operating characteristic curves (ROC), calibration curves, decision curve analysis (DCA) and the Clinical Impact Curve (CIC) were generated. Eight independent risk factors, including the PLR and SIRI, were identified with a significance level of < 0.1. The area under the ROC curve (AUC) was found to be 0.859 for the training set (95% CI: 0.808-0.891) and 0.821 for the validation set (95% CI: 0.735-0.906). The results from ROC analysis calibration curves, DCA and CIC demonstrated that the nomogram possessed significant predictive value. PLR and SIRI are independent variables that influence the diagnosis of patients with gastric dysplasia. The nomogram model created using PLR and SIRI demonstrates a high predictive value for diagnosing individuals with gastric dysplasia.

A large-scale landslide occurred at the waste dump of Dagushan open-pit iron mine in Anshan, China. The landslide caused the collapse of 8.45 × 106 m3 of waste rock with a maximum runout distance of 150 m, resulting in the burial of 7.2 × 104 m2 of the site and destruction of numerous buildings and roads. Based on field investigations, laboratory tests, and numerical simulations, the failure characteristics of the landslide were investigated and the inducing factors as well as the failure mechanism were analyzed. According to field surveys, the landslide area was divided into the traction failure area, the main sliding area, and the accumulation area. A series of in situ and laboratory tests were performed to obtain the engineering properties of the waste material and the foundation silty clay of the waste dump. Then, a conceptual model of the landslide mechanism was constructed and the failure process was divided into four stages: foundation weakening stage, shearing and dislocation stage, sliding and shearing out stage, and tractive sliding stage. Two- and three-dimensional numerical simulations were performed to clarify the landslide evolution and related triggering mechanism combining the strength reduction method with the finite difference program FLAC3D. The results show that the saturation and weakening of foundation silty clay under the action of groundwater were the trigger factors of the landslide, which resulted in the shear failure of the weak foundation, and further triggered the mass collapse and formed the large-scale landslide. The sliding surface passed through the overlying waste materials and progressed along the weak foundation of the slope. The failure pattern obtained by numerical simulations matched closely with the observations in the field. The understanding of the landslide characteristics and mechanism may offer a reference for disaster identification and stability analysis of large-scale waste dumps around the world.

Selenium (Se) is a vital trace element in the regulation of inflammation and antioxidant reactions in both animals and humans. Se deficiency is rapidly affecting lung function. The present study investigated the molecular mechanism of Se deficiency aggravates reactive oxygen species (ROS)-induced inflammation, leading to fibrosis in lung. Mice fed with different concentrations of Se to establish the model. In the Se-deficient group, the ROS and malondialdehyde (MDA) was increased, and the activities of glutathione peroxidase (GSH-Px), superoxide dismutase (SOD), total antioxidant capacity (T-AOC), and catalase (CAT) reduced. The histopathological observation showed that Se deficiency lead to lung texture damage with varying degrees of degeneration, necrosis, shedding of some alveolar epithelial cells, and inflammatory cell infiltration. Immunohistochemistry showed that the expression of α-smooth muscle actin (α-SMA) increased. The fibrosis index was verified with Sirius red staining. The ELISA and qPCR results showed that the inflammatory cytokines (TNF-α and IL-1β) and ECM (collagen I, collagen IV, fibronectin, and laminin) were increased with ROS increasing, which was induced by Se deficiency. The results displayed that oxidative stress with Se deficiency led to an increase in tissue inhibitors of metalloproteinase (TIMPs), but a decrease in matrix metalloproteinases (MMPs). All the results indicated that Se deficiency induced excessive ROS accumulation to generate inflammation, which disrupted ECM homeostasis and aggravated fibrosis in the lung.