Objective To identify immune cell-related biomarkers in lung adenocarcinoma (LUAD) using weighted gene co-expression network analysis (WGCNA). Methods Based on data from The Cancer Genome Atlas (TCGA) database, a gene co-expression network was constructed for the TCGA-LUAD dataset using the "WGCNA" R package, and genes were clustered into different modules. Concurrently, the Estimation of STromal and Immune cells in MAlignant Tumours using Expression data (ESTIMATE) algorithm was applied to the tumor samples in the TCGA-LUAD dataset. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed to evaluate the biological functions of genes within the most significantly correlated module. Candidate hub genes from the key module were intersected with a protein-protein interaction (PPI) network to identify the final hub genes. The prognostic performance of these hub genes and their correlation with immune cell infiltration were validated using Kaplan-Meier curves and the Tumor IMmune Estimation Resource (TIMER) algorithm. Finally, a multivariate Cox regression analysis was conducted on the identified hub genes to construct a prognostic risk model. Results In the co-expression network, the brown module was found to be highly correlated with the ImmuneScore, StromalScore, and ESTIMATE Score. Five immune-related hub genes were identified: CD53, PLEK, SPI1, IL10RA, and C3AR1. Enrichment analysis of the brown module revealed that its genes were primarily enriched in GO terms such as "regulation of innate immune response" and KEGG pathways like the "NF-kappa B signaling pathway". Furthermore, the expression levels of these five hub genes were significantly and positively correlated with the infiltration abundance of various immune cells. The immune relevance of the model was validated by the Immunophenoscore (IPS) and the Tumor Immune Dysfunction and Exclusion (TIDE) score. Moreover, the established RiskScore demonstrated significant potential in predicting the response to immunotherapy. Conclusion These five immune-related key genes may serve as novel and effective potential therapeutic targets for LUAD immunotherapy, facilitating the development of personalized diagnosis and treatment strategies for patients with LUAD.

Objective:To compare the differences among four routine lipid testing systems in detecting high triglyceride (TG) serum samples and evaluate the accuracy and consistency of the four homogeneous low-density lipoprotein cholesterol (LDL-C) and high-density lipoprotein cholesterol (HDL-C) reagents using vertical auto profile (VAP) as the reference method.Methods:A retrospective study was conducted on 249 serum samples with elevated TG levels collected from the Department of Laboratory Medicine at the Second Xiangya Hospital of Central South University between January and October 2024. TG, total cholesterol (TC), LDL-C, and HDL-C were measured using four homogeneous detection systems: Beckman Coulter (USA), Wako Pure Chemical Industries (Japan), Mindray (China), and Roche Diagnostics (Germany). VAP was used to analyze lipoprotein subfractions, including very-low-density lipoprotein cholesterol (VLDL-C), intermediate-density lipoprotein cholesterol (IDL-C), LDL-C, lipoprotein(a) cholesterol [Lp(a)-C], and HDL-C. The mean coefficient of variation ( CV) across the four systems was calculated for each parameter. Pearson correlation and ordinal logistic regression (OLR) were used to assess correlations between the four HDL-C/LDL-C systems and VAP. Bland-Altman plots were generated to evaluate biases, and deviations were calculated. For parameters with significant deviations, multivariate linear regression and standardized coefficients were used to analyze correlations between biases and lipoprotein subfractions. Based on the Chinese Guidelines for Lipid Management (2023), LDL-C and non-HDL-C treatment goals were categorized into five risk levels (ultra-high, high, moderate, high-risk, and low-risk). VAP results defined LDL-C/non-HDL-C intervals, and the four systems′ concordance in risk classification was evaluated. Samples were grouped into A, B, C, D ( n=63, 62, 62, 62) by TG concentration, and ANOVA, chi-square, and Fisher exact tests assessed intergroup differences. Results:The mean CVs across systems for TG, TC, LDL-C, HDL-C, and non-HDL-C were 2.98%, 1.76%, 18.10%, 5.60%, 2.58%, respectively. Pearson correlations between LDL-C results (Beckman, Wako, Mindray, Roche) and VAP were 0.889, 0.854, 0.899, and 0.973; mean relative deviations were 54.8%, 41.0%, 49.3%, and 3.6%; classification accuracies were 6.0% (15/249), 21.3% (53/249), 9.2% (23/249), and 76.7% (191/249). HDL-C deviations were 18.7%, 15.1%, 11.1%, and 8.7%, with correlations ( r) of 0.883, 0.911, 0.959, and 0.950 (all P<0.001). LDL-C means showed no intergroup differences (A-D), but CV increased with TG levels ( P<0.001). HDL-C means and CVs showed no significant intergroup differences. Beckman, Wako, and Mindray LDL-C results exhibited significant positive biases correlated with TG and VLDL-C (multivariate regression; P<0.05); VLDL-C had the strongest influence (standardized coefficients: 0.820, 0.394, 0.813; P<0.001). Non-HDL-C classifications matched VAP in 92.4% (Beckman), 85.9% (Wako), 94.0% (Mindray), and 93.2% (Roche), with no intergroup differences. Conclusion:For high-TG sera, Beckman, Wako, and Mindray LDL-C exhibited significant positive biases correlated with TG and VLDL-C, while Roche LDL-C showed minimal deviation. TG, TC, HDL-C, and non-HDL-C results showed minimal variation across the four systems. All systems demonstrated comparable accuracy for non-HDL-C compared to VAP. The non-HDL-C measured by the four detection systems demonstrates high accuracy and consistency in atherosclerotic cardiovascular disease risk stratification and lipid-lowering goal assessment, and it is unaffected by TG levels.

Blood lipid testing serves as the foundation for clinical lipid management. Ensuring the accuracy of blood lipid test results, particularly the precision and stability of low low-density lipoprotein cholesterol (LDL-C) values, is crucial for evaluating therapeutic effects among individuals undergoing lipid management and developing subsequent effective lipid-modulatoring strategies. Clinical laboratories should not only focus on quality control measures during the pre-analytical, analytical, and post-analytical phases of testing but also pay attention to variations in laboratory indicators and cutoff values for high, moderate, and low-risk population stratification based on clinical guidelines. Additionally, it is essential to understand the impact of high triglyceride levels on LDL-C testing and provide relevant education to both doctors and patients. By revamping the traditional format of blood lipid test reports to align with the concepts and requirements of lipid management guidelines, laboratories can make a substantial valuable contribution to individual lipid management in the modern era of lipid detection and monitoring.

Objective:To discuss the effect of sericin on the phosphatidylinositol 3-kinase(PI3K)/protein kinase B(Akt)/nuclear factor-κB(NF-κB)signaling pathway and apoptosis in the streptozotocin(STZ)-damaged INS-1 cells,and to clarify its mechanism.Methods:The INS-1 cells were cultured with complete medium containing 0,0.1,0.3,1.0,3.0,and 10.0 μmol·L-1 Akt1 inhibitor A-674563,10 mmol·L-1 STZ,and 600 mg·L-1 sericin,and divided into 0,0.1,0.3,1.0,3.0,and 10.0 μmol·L-1 A-674563 groups,and the control group(complete medium without drugs)was set up.Cell counting kit-8(CCK-8)method was used to detect the survival rates of the INS-1 cells,and the half-maximal inhibitory concentration(IC50)value was calculated to determine the optimal inhibitory concentration of A-674563,which was further verified by Western blotting method.The INS-1 cells were divided into normal control group(complete medium),model group(10 mmol·L-1 STZ+complete medium),and low,medium,and high doses of sericin groups(10 mmol·L-1 STZ+150 mg·L-1 sericin+complete medium,10 mmol·L-1 STZ+300 mg·L-1 sericin+complete medium,and 10 mmol·L-1 STZ+600 mg·L-1 sericin+complete medium).CCK-8 method was used to detect the survival rates of the INS-1 cells in various groups to determine the optimal concentration of sericin.Additionally,the INS-1 cells were divided into normal control group(complete medium),model group(10 mmol·L-1 STZ+complete medium),sericin group(10 mmol·L-1 STZ+600 mg·L-1 sericin+complete medium),and A-674563 group(10 mmol·L-1 STZ+600 mg·L-1 sericin+0.3 μmol·L-1 A-674563+complete medium).Flow cytometry was used to detect the apoptotic rates of the INS-1 cells in various groups;real-time fluorescence quantitative PCR(RT-qPCR)method was used to detect the expression levels of Akt1,NF-κB,tumor necrosis factor-α(TNF-α),and interleukin-6(IL-6)mRNA in the INS-1 cells in various groups;Western blotting method was used to detect the expression levels of phosphorylated Akt1(p-Akt1)and NF-κB proteins in the INS-1 cells in various groups;enzyme linked immunosorbent assay(ELISA)method was used to detect the levels of TNF-α and IL-6 in the INS-1 cells in various groups.Results:The survival rates of the INS-1 cells in control group was 100.00%±0.00%;in 0,0.1,0.3,1.0,3.0,and 10.0 μmol·L-1 A-674563+10 mmol·L-1 STZ+600 mg·L-1 sericin+complete medium groups,which were 82.50%±2.28%,69.47%±1.94%,51.51%±1.74%,38.94%±1.57%,24.79%±1.14%,and 19.85%±1.03%,respectively.The IC?? value of A-674563 for INS-1 cells was 0.3 μmol·L-1,and 0.3 μmol·L-1 A-674563 was selected for subsequent experiments.Compared with 0 μmol·L-1 A-674563,the expression level of p-Akt1 protein in the INS-1 cells after treated with 0.3 μmol·L-1 A-674563+10 mmol·L-1 STZ+600 mg·L-1 sericin+complete medium was significantly decreased(P<0.05).The CCK-8 results showed that compared with normal control group,the survival rate of the INS-1 cells in model group was significantly decreased(P<0.05);compared with model group,the survival rates of the INS-1 cells in low,medium,and high doses of sericin groups were significantly increased(P<0.05);compared with low and medium doses of sericin groups,the survival rate of the INS-1 cells in high dose of sericin group was significantly increased(P<0.05).Thus,600 mg·L-1 sericin was selected for subsequent experiments.The CCK-8 results showed that compared with normal control group,the survival rate of the INS-1 cells in model group was significantly decreased(P<0.05);compared with model group,the survival rate of the INS-1 cells in sericin group was significantly increased(P<0.05);compared with sericin group,the survival rate of the INS-1 cells in A-674563 group was significantly decreased(P<0.05).The flow cytometry results showed that compared with normal control group,the apoptotic rate of the INS-1 cells in model group was significantly increased(P<0.05);compared with model group,the apoptotic rate of the INS-1 cells in sericin group was significantly decreased(P<0.05);compared with sericin group,the apoptotic rate of the INS-1 cells in A-674563 group was significantly increased(P<0.05).The RT-qPCR results showed that compared with normal control group,the expression level of Akt1 mRNA in the INS-1 cells in model group was significantly decreased(P<0.05);compared with model group,the expression levels of Akt1 mRNA in the INS-1 cells in low,medium,and high doses of sericin groups were significantly increased(P<0.05);compared with low and medium doses of sericin groups,the expression level of Akt1 mRNA in the INS-1 cells in high dose of sericin group was significantly increased(P<0.05).Compared with normal control group,the expression levels of NF-κB,TNF-α,and IL-6 mRNA in the INS-1 cells in model group were significantly increased(P<0.05);compared with model group,the expression levels of NF-κB,TNF-α,and IL-6 mRNA in the INS-1 cells in sericin group were significantly decreased(P<0.05);compared with sericin group,the expression level of NF-κB mRNA in the INS-1 cells in A-674563 group was significantly increased(P<0.05).The Western blotting results showed that compared with normal control group,the expression level of p-Akt1 protein in the INS-1 cells in model group was significantly decreased(P<0.05);compared with model group,the expression levels of p-Akt1 protein in the INS-1 cells in low,medium,and high doses of sericin groups were significantly increased(P<0.05);compared with low and medium doses of sericin groups,the expression level of p-Akt1 protein in the INS-1 cells in high dose of sericin group was significantly increased(P<0.05).Compared with normal control group,the expression level of NF-κB protein in the INS-1 cells in model group was significantly increased(P<0.05);compared with model group,the expression level of NF-κB protein in the INS-1 cells in sericin group was significantly decreased(P<0.05);compared with sericin group,the expression level of NF-κB protein in the INS-1 cells in A-674563 group was significantly increased(P<0.05).The ELISA results showed that compared with normal control group,the levels of TNF-α and IL-6 in the INS-1 cells in model group were significantly increased(P<0.05);compared with model group,the levels of TNF-α and IL-6 in the INS-1 cells in sericin group were significantly decreased(P<0.05);compared with sericin group,the levels of TNF-α and IL-6 in the INS-1 cells in A-674563 group were significantly increased(P<0.05).Conclusion:Sericin alleviates the PI3K/Akt/NF-κB signaling pathway-mediated inflammatory response and apoptosis by targeting Akt1,exerting a protective effect against STZ-induced damage in INS-1 cells.

Objective We propose a method for multi-label classification of traditional Chinese medicine(TCM)literature using graph neural networks to enhance sentence embeddings.This approach can effectively capture the relationships between similar articles.By integrating with the semantic information of the text,it improves classification performance.Methods Sentence embedding data of papers are obtained,and a heterogeneous network of traditional Chinese medicine literature is established.The representation information of papers on the heterogeneous network and their own sentence embedding information are learned through the GraphSAGE model of graph neural networks.The feature vectors obtained are then input into the model for multi-label classification.Results In a TCM literature dataset,the multi-label classification model based on graph neural networks achieved precision and F1 scores of 0.83 and 0.72,respectively,outperforming mainstream baseline models.Conclusion The effectiveness of the proposed method in the multi-label classification task for TCM journals.

As the longest and most widely distributed pair of nerves in the brain,the vagus nerve is involved in the regulation of many systems and organs.Recent studies have found that the vagus nerve may be involved in the occurrence of a variety of neuropsychiatric diseases by regulating the release of neurotransmitters(such as norepinephrine,5-hydroxytryptamine,gamma-aminobutyric acid and acetylcholine)and regulating the immune system and gut-brain axis.This article focuses on the regulatory mechanisms of the vagus nerve on neurotransmitters,immune system function,and the gut-brain axis,as well the therapeutic advances in vagus nerve stimulation for neurological and psychi-atric diseases such as epilepsy,depression and anxiety disorders.

As the longest and most widely distributed pair of nerves in the brain,the vagus nerve is involved in the regulation of many systems and organs.Recent studies have found that the vagus nerve may be involved in the occurrence of a variety of neuropsychiatric diseases by regulating the release of neurotransmitters(such as norepinephrine,5-hydroxytryptamine,gamma-aminobutyric acid and acetylcholine)and regulating the immune system and gut-brain axis.This article focuses on the regulatory mechanisms of the vagus nerve on neurotransmitters,immune system function,and the gut-brain axis,as well the therapeutic advances in vagus nerve stimulation for neurological and psychi-atric diseases such as epilepsy,depression and anxiety disorders.

Objective We propose a method for multi-label classification of traditional Chinese medicine(TCM)literature using graph neural networks to enhance sentence embeddings.This approach can effectively capture the relationships between similar articles.By integrating with the semantic information of the text,it improves classification performance.Methods Sentence embedding data of papers are obtained,and a heterogeneous network of traditional Chinese medicine literature is established.The representation information of papers on the heterogeneous network and their own sentence embedding information are learned through the GraphSAGE model of graph neural networks.The feature vectors obtained are then input into the model for multi-label classification.Results In a TCM literature dataset,the multi-label classification model based on graph neural networks achieved precision and F1 scores of 0.83 and 0.72,respectively,outperforming mainstream baseline models.Conclusion The effectiveness of the proposed method in the multi-label classification task for TCM journals.

Objective:To compare the differences among four routine lipid testing systems in detecting high triglyceride (TG) serum samples and evaluate the accuracy and consistency of the four homogeneous low-density lipoprotein cholesterol (LDL-C) and high-density lipoprotein cholesterol (HDL-C) reagents using vertical auto profile (VAP) as the reference method.Methods:A retrospective study was conducted on 249 serum samples with elevated TG levels collected from the Department of Laboratory Medicine at the Second Xiangya Hospital of Central South University between January and October 2024. TG, total cholesterol (TC), LDL-C, and HDL-C were measured using four homogeneous detection systems: Beckman Coulter (USA), Wako Pure Chemical Industries (Japan), Mindray (China), and Roche Diagnostics (Germany). VAP was used to analyze lipoprotein subfractions, including very-low-density lipoprotein cholesterol (VLDL-C), intermediate-density lipoprotein cholesterol (IDL-C), LDL-C, lipoprotein(a) cholesterol [Lp(a)-C], and HDL-C. The mean coefficient of variation ( CV) across the four systems was calculated for each parameter. Pearson correlation and ordinal logistic regression (OLR) were used to assess correlations between the four HDL-C/LDL-C systems and VAP. Bland-Altman plots were generated to evaluate biases, and deviations were calculated. For parameters with significant deviations, multivariate linear regression and standardized coefficients were used to analyze correlations between biases and lipoprotein subfractions. Based on the Chinese Guidelines for Lipid Management (2023), LDL-C and non-HDL-C treatment goals were categorized into five risk levels (ultra-high, high, moderate, high-risk, and low-risk). VAP results defined LDL-C/non-HDL-C intervals, and the four systems′ concordance in risk classification was evaluated. Samples were grouped into A, B, C, D ( n=63, 62, 62, 62) by TG concentration, and ANOVA, chi-square, and Fisher exact tests assessed intergroup differences. Results:The mean CVs across systems for TG, TC, LDL-C, HDL-C, and non-HDL-C were 2.98%, 1.76%, 18.10%, 5.60%, 2.58%, respectively. Pearson correlations between LDL-C results (Beckman, Wako, Mindray, Roche) and VAP were 0.889, 0.854, 0.899, and 0.973; mean relative deviations were 54.8%, 41.0%, 49.3%, and 3.6%; classification accuracies were 6.0% (15/249), 21.3% (53/249), 9.2% (23/249), and 76.7% (191/249). HDL-C deviations were 18.7%, 15.1%, 11.1%, and 8.7%, with correlations ( r) of 0.883, 0.911, 0.959, and 0.950 (all P<0.001). LDL-C means showed no intergroup differences (A-D), but CV increased with TG levels ( P<0.001). HDL-C means and CVs showed no significant intergroup differences. Beckman, Wako, and Mindray LDL-C results exhibited significant positive biases correlated with TG and VLDL-C (multivariate regression; P<0.05); VLDL-C had the strongest influence (standardized coefficients: 0.820, 0.394, 0.813; P<0.001). Non-HDL-C classifications matched VAP in 92.4% (Beckman), 85.9% (Wako), 94.0% (Mindray), and 93.2% (Roche), with no intergroup differences. Conclusion:For high-TG sera, Beckman, Wako, and Mindray LDL-C exhibited significant positive biases correlated with TG and VLDL-C, while Roche LDL-C showed minimal deviation. TG, TC, HDL-C, and non-HDL-C results showed minimal variation across the four systems. All systems demonstrated comparable accuracy for non-HDL-C compared to VAP. The non-HDL-C measured by the four detection systems demonstrates high accuracy and consistency in atherosclerotic cardiovascular disease risk stratification and lipid-lowering goal assessment, and it is unaffected by TG levels.

Blood lipid testing serves as the foundation for clinical lipid management. Ensuring the accuracy of blood lipid test results, particularly the precision and stability of low low-density lipoprotein cholesterol (LDL-C) values, is crucial for evaluating therapeutic effects among individuals undergoing lipid management and developing subsequent effective lipid-modulatoring strategies. Clinical laboratories should not only focus on quality control measures during the pre-analytical, analytical, and post-analytical phases of testing but also pay attention to variations in laboratory indicators and cutoff values for high, moderate, and low-risk population stratification based on clinical guidelines. Additionally, it is essential to understand the impact of high triglyceride levels on LDL-C testing and provide relevant education to both doctors and patients. By revamping the traditional format of blood lipid test reports to align with the concepts and requirements of lipid management guidelines, laboratories can make a substantial valuable contribution to individual lipid management in the modern era of lipid detection and monitoring.