A Clinical Study on Chinese Five-Element Music Therapy Combined with Auricular-Plaster Therapy in Treating Perimenopausal Insomnia and Anxiety.

Objective: To evaluate the clinical effects of five-element music therapy combined with auricular-plaster therapy for perimenopausal insomnia with anxiety. This study aimed to leverage the complementary effects of both approaches to provide patients with a more comprehensive and personalized therapeutic experience. Methods: In this experiment, 90 cases of perimenopausal insomnia patients were selected and randomly assigned to the treatment group (45 cases) and the control group (45 cases) using the random number table method. In the treatment group, except for 2 cases who withdrew, the remaining 43 cases were observed and treated with Five Elements Music combined with auricular acupressure therapy. In the control group, except 3 cases withdrew, 42 cases were observed and given alprazolam oral treatment. The treatment course of both groups was 4 weeks. Data such as Pittsburgh Sleep Quality Index (PSQI) and Hamilton Anxiety Scale (HAMA) scores were also recorded for all patients before and after treatment. Results: After treatment, the total effective rate was 93.02% in the treatment group and 88.10% in the control group. The PSQI and HAMA scores of the two groups after treatment improved compared to before treatment(P < .01). The curative effect in the treatment group was superior to that in the control group in terms of sleep quality and anxiety. Conclusion: The five-element music therapy combined with auricular-plaster therapy is effective in the treatment of perimenopausal insomnia with anxiety. Compared with traditional therapy, Chinese medicine non-drug therapy has the characteristics of green safety, simple effect, and low cost. It can avoid adverse reactions caused by long-term use of drugs, so It is a safe and reliable method, worthy of recommendation for clinical use.

Identification and immunological characterization of cuproptosis-related molecular clusters in ischemic stroke.

The present study elucidated cuproptosis-related molecular clusters involved in ischemic stroke and developed predictive models. Transcriptomic and immunological profiles of ischemic stroke-related datasets were extracted from the Gene Expression Omnibus database. Next, we conducted weighted gene co-expression network analysis to determine cluster-specific differentially expressed genes (DEGs). Models such as random forest and eXtreme gradient boosting (XGB) were evaluated to select the best prediction performance model. Subsequently, we validated the model's predictive efficiency by using nomograms, decision curve analysis, calibration curves, and receiver operating characteristic curve analysis with an external dataset. We identified two cuproptosis-related clusters involved in ischemic stroke. The DEGs in Cluster 2 were closely associated with amino acid metabolism, various immune responses, and cell proliferation pathways. The XGB model showed lower residuals, a smaller root mean square error, and a greater area under the curve value (AUC = 0.923), thus exhibiting the best discriminative performance. The AUC value for the external validation dataset was 0.921, thus confirming the high performance of the model. NFE2L2, NLRP3, GLS, LIPT1, and MTF1 were identified as potential cuproptosis predictors, thus shedding new light on ischemic stroke pathogenesis and heterogeneity.

The interaction between estimated glomerular filtration rate and dietary magnesium intake and its effect on stroke prevalence: a cross-sectional study spanning 2003-2018.

Background: Despite the known associations of dietary magnesium intake and estimated glomerular filtration rate (eGFR) with cardiovascular diseases, their combined effects on stroke risk remain unclear. Therefore, this study aims to explore the associations of dietary magnesium intake and eGFR with stroke risk. Methods: The National Health and Nutrition Examination Survey (NHANES) data of 37,637 adult participants (≥18 years) from 2003 to 2018 was analyzed. Dietary magnesium intake was categorized as low (≤ 254 mg/day) and normal (> 254 mg/day) based on experimental data. Multiple logistic regression analyses and interaction tests were conducted to assess the associations of dietary magnesium intake and eGFR with stroke risk, with a focus on the interaction between different chronic kidney disease (CKD) stages based on eGFR levels and dietary magnesium intake. Additional analyses included multiplicative interaction analysis, restricted cubic spline analysis, and subgroup evaluations by age, sex, and ethnicity. Results: Dietary magnesium intake and eGFR were inversely correlated with the risk of stroke. Participants with low dietary magnesium intake had a higher stroke risk than those with normal magnesium intake (odds ratio [OR] 1.09, 95% confidence interval [CI]: 1.03-1.42). Likewise, low eGFR was associated with an elevated stroke risk compared with normal eGFR (OR 1.56, 95% CI: 1.15-2.13). Furthermore, the two factors showed a multiplicative interaction effect on stroke risk (OR 1.05, 95% CI: 1.01-1.09). We observed a significant interaction between stage G3 CKD and low dietary magnesium intake (OR 1.05, 95% CI: 1.01-1.09), suggesting a potential association with stroke risk. However, similar associations were not observed for stages G4 and G5, possibly due to the smaller number of participants with G4 and G5 CKD. The restricted cubic spline analysis revealed a non-linear relationship between dietary magnesium intake, eGFR, and stroke risk. The interaction between magnesium deficiency and low eGFR persisted in participants aged >60 years, as well as in females, non-Hispanic Black people, and people of other races. Conclusion: Dietary magnesium intake and eGFR correlate negatively with stroke prevalence. Moreover, there was an interaction between dietary magnesium intake and stroke prevalence across different CKD stages. Further large-scale prospective studies are needed to analyze the potential relationship between dietary magnesium intake, eGFR, and stroke.

The association between circulating Hcy and Lp-PLA2 levels and poststroke depression: A meta-analysis.

OBJECTIVE: To analyze the correlation between circulating homocysteine (Hcy) and lipoprotein-associated phospholipase A2 (Lp-PLA2) levels and poststroke depression (PSD). MATERIALS AND METHODS: Chinese (Chinese National Knowledge Infrastructure, Wanfang, and VIP) and English (PubMed, EMBASE, MEDLINE, and Cochrane Library) databases on the correlation between circulating Hcy and Lp-PLA2 and PSD were collected. Meta-analysis was performed to compare the distinctions in circulating Hcy and Lp-PLA2 levels between PSD and non-PSD groups. Meta-analysis was conducted by using STATA 15.0 software. RESULTS: A total of 20 literatures were included in this study. The level of circulating Lp-PLA2 in the PSD group was obviously higher than that in the non-PSD group (weighted mean differences: 2.75, 95%CI: 0.10-5.39, P = .002), which was an independent predictor of PSD (effect size = 0.05, 95%CI: 0.03, 0.07, P < .001). The level of circulating Hcy in the PSD group was obviously higher than that in the non-PSD group (weighted mean differences = 1.41, 95%CI: 1.01, 1.81, P < .001), which was an independent influencing factor for the occurrence of PSD (effect size = 0.07, 95%CI: 0.04, 0.09, P = .011). CONCLUSION: Circulating Hcy and Lp-PLA2 levels are linked to the development of PSD, and can be applied as predictive or diagnostic indicators.

OEDL: an optimized ensemble deep learning method for the prediction of acute ischemic stroke prognoses using union features.

Background: Early stroke prognosis assessments are critical for decision-making regarding therapeutic intervention. We introduced the concepts of data combination, method integration, and algorithm parallelization, aiming to build an integrated deep learning model based on a combination of clinical and radiomics features and analyze its application value in prognosis prediction. Methods: The research steps in this study include data source and feature extraction, data processing and feature fusion, model building and optimization, model training, and so on. Using data from 441 stroke patients, clinical and radiomics features were extracted, and feature selection was performed. Clinical, radiomics, and combined features were included to construct predictive models. We applied the concept of deep integration to the joint analysis of multiple deep learning methods, used a metaheuristic algorithm to improve the parameter search efficiency, and finally, developed an acute ischemic stroke (AIS) prognosis prediction method, namely, the optimized ensemble of deep learning (OEDL) method. Results: Among the clinical features, 17 features passed the correlation check. Among the radiomics features, 19 features were selected. In the comparison of the prediction performance of each method, the OEDL method based on the concept of ensemble optimization had the best classification performance. In the comparison to the predictive performance of each feature, the inclusion of the combined features resulted in better classification performance than that of the clinical and radiomics features. In the comparison to the prediction performance of each balanced method, SMOTEENN, which is based on a hybrid sampling method, achieved the best classification performance than that of the unbalanced, oversampled, and undersampled methods. The OEDL method with combined features and mixed sampling achieved the best classification performance, with 97.89, 95.74, 94.75, 94.03, and 94.35% for Macro-AUC, ACC, Macro-R, Macro-P, and Macro-F1, respectively, and achieved advanced performance in comparison with that of methods in previous studies. Conclusion: The OEDL approach proposed herein could effectively achieve improved stroke prognosis prediction performance, the effect of using combined data modeling was significantly better than that of single clinical or radiomics feature models, and the proposed method had a better intervention guidance value. Our approach is beneficial for optimizing the early clinical intervention process and providing the necessary clinical decision support for personalized treatment.

CircDLGAP4 overexpression relieves oxygen-glucose deprivation-induced neuronal injury by elevating NEGR1 through sponging miR-503-3p.

Circular RNAs (circRNAs) have been reported to play vital regulatory roles in human diseases. However, the functions of circRNAs in ischemic stroke (IS) are limited. In this study, we aimed to explore the functions and mechanisms of circRNA DLG associated protein 4 (circDLGAP4) in IS development. Oxygen-glucose deprivation (OGD)-treated HCN-2 cells were used to mimic IS environment in vitro. Quantitative real-time polymerase chain reaction (qRT-PCR) assay was used to detect the levels of circDLGAP4, microRNA-503-3p (miR-503-3p) and neuronal growth regulator 1 (NEGR1) mRNA. RNase R assay was conducted to analyze the stability of circDLGAP4. Cell Counting Kit-8 (CCK-8) assay and flow cytometry analysis were adopted for cell viability and death, respectively. Western blot assay was performed for protein levels. Enzyme-linked immunosorbent assay (ELISA) kits were used to examine the concentrations of inflammatory cytokines. Dual-luciferase reporter assay, RNA immunoprecipitation (RIP) assay and RNA pull-down assay were employed to analyze the relationships among circDLGAP4, miR-503-3p and NEGR1. CircDLGAP4 level was declined in HCN-2 cells after OGD treatment. CircDLGAP4 overexpression promoted cell viability and suppressed cell death and inflammatory cytokine concentrations in OGD-treated HCN-2 cells. CircDLGAP4 acted as the sponge for miR-503-3p and the impacts of circDLGAP4 overexpression on cell viability, death and inflammation in OGD-treated HCN-2 cells were reversed by miR-503-3p elevation. Furthermore, NEGR1 was the target gene of miR-503-3p. MiR-503-3p inhibition ameliorated OGD-induced HCN-2 cell impairments, but NEGR1 knockdown abolished the effects. CircDLGAP4 alleviated OGD-induced HCN-2 cell damage by regulating miR-503-3p/NEGR1 axis.

Classification of stomach adenocarcinoma based on fatty acid metabolism-related genes frofiling.

Background: Fatty acid metabolism (FAM)-related genes play a key role in the development of stomach adenocarcinoma (STAD). Although immunotherapy has led to a paradigm shift in STAD treatment, the overall response rate of immunotherapy for STAD is low due to heterogeneity of the tumor immune microenvironment (TIME). How FAM-related genes affect TIME in STAD remains unclear. Methods: The univariate Cox regression analysis was performed to screen prognostic FAM-related genes using transcriptomic profiles of the Cancer Genome Atlas (TCGA)-STAD cohort. Next, the consensus clustering analysis was performed to divide the STAD cohort into two groups based on the 13 identified prognostic genes. Then, gene set enrichment analysis (GSEA) was carried out to identify enriched pathways in the two groups. Furthermore, we developed a prognostic signature model based on 7 selected prognostic genes, which was validated to be capable in predicting the overall survival (OS) of STAD patients using the univariate Cox regression, least absolute shrinkage and selection operator (LASSO) regression, and multivariate Cox regression analyses. Finally, the "Estimation of STromal and Immune cells in MAlignant Tumours using Expression data" (ESTIMATE) algorithm was used to evaluate the stromal, immune, and ESTIMATE scores, and tumor purity of each STAD sample. Results: A total of 13 FAM-related genes were identified to be significantly associated with OS in STAD patients. Two molecular subtypes, which we named Group 1 and Group 2, were identified based on these FAM-related prognostic genes using the consensus clustering analysis. We showed that Group 2 was significantly correlated with poor prognosis and displayed higher programmed cell death ligand 1 (PD-L1) expressions and distinct immune cell infiltration patterns. Furthermore, using GSEA, we showed that apoptosis and HCM signaling pathways were significantly enriched in Group 2. We constructed a prognostic signature model using 7 selected FAM-related prognostic genes, which was proven to be effective for prediction of STAD (HR = 1.717, 95% CI = 1.105-1.240, p < 0.001). After classifying the patients into the high- and low-risk groups based on our model, we found that patients in the high-risk group tend to have more advanced T stages and higher tumor grades, as well as higher immune scores. We also found that the risk scores were positively correlated with the infiltration of certain immune cells, including resting dendritic cells (DCs), and M2 macrophages. We also demonstrated that elevated expression of gamma-glutamyltransferase 5 (GGT5) is significantly associated with worse OS and disease-free survival (DFS), more advanced T stage and higher tumor grade, and increased immune cell infiltration, suggesting that STAD patients with high GGT5 expression in the tumor tissues might have a better response to immunotherapy. Conclusion: FAM-related genes play critical roles in STAD prognosis by shaping the TIME. These genes can regulate the infiltration of various immune cells and thus are potential therapeutic targets worthy of further investigation. Furthermore, GGT5 was a promising marker for predicting immunotherapeutic response in STAD patients.

ARD1 contributes to IKKß-mediated breast cancer tumorigenesis.

The expression of IκB kinase ß (IKKß) promotes the growth of breast cancer cells. Meanwhile, IKKß mediates the phosphorylation and subsequent degradation of arrest-defective protein 1 (ARD1). However, the relationship between IKKß and ARD1 in the occurrence of breast cancer has not been reported. In this study, we found that IKKß not only acts directly on mammalian target of rapamycin (mTOR) activity but also indirectly acts on mTOR activity through posttranscriptional modification of ARD1, thereby effectively promoting the growth of breast cancer cells. ARD1 prevents mTOR activity and breast cancer cell growth by stabilizing tuberous sclerosis complex 2 (TSC2) to induce autophagy. Moreover, acetylation of heat shock protein 70 (Hsp70) also contributes to ARD1-mediated autophagy. Therefore, upstream IKKß can further promote the occurrence of breast cancer by mediating the function of ARD1.

GNAQ mutation R183Q as a potential cause of familial Sturge-Weber syndrome: A case report.

Sturge-Weber syndrome (SWS) is a rare neurocutaneous disorder whose etiology remains unclear. To investigate the genetic contribution underlying this disease, the genetic variants of a 4-generation family with a history of SWS was analyzed in the present study. SWS was diagnosed in 3 of the family members (II-1, III-11 and IV-6). Sanger sequencing was performed to identify mutations in G protein subunit αq (GNAQ) and RAS p21 protein activator 1 exons in the 3 patients with SWS and other unaffected family members. Notably, a non-synonymous single-nucleotide variant at codon 183 on exon 4 of the GNAQ gene was identified as the only pathogenic site. This variant generated a substitution of arginine (R) with glutamine and resulted in a change of function of the encoded protein. Evolutionary conservation analysis revealed that the mutated residue 183 (R) of GNAQ is highly conserved across several vertebrate species. Furthermore, an immunofluorescence staining assay demonstrated that the substitution of arginine with glutamine resulted in a change in the sub-cellular localization of the GNAQ recombinant protein in vitro. These findings may aid in the development of novel diagnostic markers and/or therapeutic targets for the treatment of patients with familial SWS.