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Amidst the transition from agriculture to tourism, ethnic villages are facing issues of “misalignment” and “disequilibrium” between economic growth and cultural heritage conservation. Previous research has often isolated the effects of cultural heritage conservation or socio-economic development, neglecting their reciprocal influences within the coupling coordination. This study addresses this gap by assessing 43 villages in Leishan County, quantifying the preservation status of the cultural landscape (PSCL) and socio-economic level (SEL) using a comprehensive evaluation model and revealing the coupling coordination relationship between PSCL and SEL in each village with the coupling coordination degree (CCD) model. Spatial autocorrelation and the geographical detector model reveal the spatial agglomeration characteristics and influential factors of the CCD. The results highlight three findings. (1) The majority of villages (93%) are in a moderate imbalance stage or a serious imbalance stage, underscoring an urgent need for targeted policies. (2) Spatial autocorrelation analysis exposes significant clustering, with the northwest corner exhibiting the most pronounced high-value aggregation, which contrasts with the southern region’s low-value clustering. A ‘low–high’ clustering pattern is observed in the northern region, while the southwestern corner presents a ‘high–low’ distribution. (3) Key determinants affecting the CCD include the tourism revenue, tourist volume, resident participation in tourism, village hollowing level, and number of historical buildings. The interactions between driving factors show non-linear and two-factor enhancement effects. This study concludes with policy recommendations advocating for context-specific strategies to enhance the CCD, emphasizing the importance of aligning cultural preservation with economic progress in ethnic villages.

Abstract Background The circular RNA (circRNA) plays a vital role in the pathogenesis of tumors as a competitive endogenous RNA (ceRNA). Given the high aggressiveness and fatality rate of glioblastoma (GBM) as well as poor prognosis, it is necessary to construct a circRNA-related ceRNA network for further studies on the mechanism of GBM and identify possible biomarkers as well as therapeutic drugs. Methods Three datasets from the gene expression omnibus (GEO) database were downloaded to distinguish differential circRNAs, microRNAs, and messenger RNAs respectively in GBM. With the help of GEPIA2, circBank, CSCD, TargetScan, miRDB, and miRTarBase databases, we established a circRNAs-related ceRNA network in GBM. Functional enrichments were employed to profile the most relevant mRNAs to indirectly clarify the mechanisms of the ceRNA network. Based on the expression profile data and survival information of GBM patients from the GEO and the cancer genome atlas (TCGA) databases, we performed survival analysis to select prognostic mRNAs and constructed a novel circRNA-miRNA-mRNA central regulatory subnetwork. The DGIdb database was used to find potential drug–gene interactions. Results The datasets obtained from the GEO and TCGA databases were analyzed, and 504 differentially expressed mRNAs (DEmRNAs), 71 differentially expressed microRNAs (DEmiRNAs), and 270 differentially expressed circRNAs (DEcircRNAs) were screened out. The novel ceRNA regulatory network included 22 circRNAs, 11 miRNAs, and 15 mRNAs. FZD1 and KLF10 were significantly correlated with the overall survival rate of patients with GBM (P

Abstract Glioblastoma multiforme (GBM) is the most lethal primary brain tumor with a poor median survival of less than 15 months. However, clinical strategies and effective therapies are limited. Here, we found that the second-generation small molecule multi-CDK inhibitor AT7519 is a potential drug for GBM treatment according to high-throughput screening via the Approved Drug Library and Clinical Compound Library (2718 compounds). We found that AT7519 significantly inhibited the cell viability and proliferation of U87MG, U251, and patient-derived primary GBM cells in a dose-dependent manner. Furthermore, AT7519 also inhibited the phosphorylation of CDK1/2 and arrested the cell cycle at the G1-S and G2-M phases. More importantly, AT7519 induced intrinsic apoptosis and pyroptosis via caspase-3-mediated cleavage of gasdermin E (GSDME). In the glioblastoma intracranial and subcutaneous xenograft assays, tumor volume was significantly reduced after treatment with AT7519. In summary, AT7519 induces cell death through multiple pathways and inhibits glioblastoma growth, indicating that AT7519 is a potential chemical available for GBM treatment.

Objective: To discuss the techniques and methods in respective operation of brain gliomas located in eloquent brain region under awake anesthesia state Methods: 21 patients admitted into Department of Neurosurgery of the First Affiliated Hospital of Xiamen University were chosen as subject. Diagnosed with brain gliomas, they received operation with neuronavigation, intraoperative ultrasonography for locating the lesion and intraoperative direct electric stimulation for functional mapping of the eloquent brain region after receiving awake anesthesia. All patients were followed up from post-surgical 3 months to 18 months. Results: Applied with MRI scanning during post-surgical 60–90d, resection results shows that 5 cases (23.8%) received total resection of lesions, 10 cases (47.6%) received subtotal resection while 6 cases (28.6%) received partial resection. Post-surgical performance shows 8 cases (38.1%) of transitory postoperative aphasia, 5 cases(23.8%) of transitory postoperative dyskinesia and 1 case(4.8%) of permanent dyskinesia. Recovery was achieved in the patients except for the 1 case of permanent dyskinesia. Conclusions: Comprehensive application of awake anesthesia, neuronavigation, intraoperative ultrasonography and intraoperative direct electrical stimulation facilitates recognition of clear position relationship between gliomas and eloquent brain region, and maximum safe resection of gliomas in eloquent brain region with maximal protection of brain function.

Abstract Glioblastoma (GBM) patients present poor prognosis. Deubiquitination by deubiquitinating enzymes (DUBs) is a critical process in cancer progression. Ubiquitin-specific proteases (USPs) constitute the largest sub-family of DUBs. Evaluate the role of USP32 in GBM progression and provide a potential target for GBM treatment. Clinical significance of USP32 was investigated using Gene Expression Omnibus databases. Effects of USP32 on cell growth and metastasis were studied in vitro and in vivo. Differentially expressive genes between USP32-knockdown U-87 MG cells and negative control cells were detected using RNA sequencing and used for Gene Ontology and Kyoto Encyclopedia of Genes and Genomic pathway enrichment analyses. Finally, RT-qPCR was used to validate the divergent expression of genes involved in the enriched pathways. USP32 was upregulated in GBM patients, being correlated to poor prognosis. USP32 downregulation inhibited cell growth and metastasis in vitro. Furthermore, USP32 knockdown inhibited tumorigenesis in vivo. In addition, UPS32 was identified as a crucial regulator in different pathways including cell cycle, cellular senescence, DNA replication, base excision repair, and mismatch repair pathways. USP32 acts as an oncogene in GBM through regulating several biological processes/pathways. It could be a potential target for GBM treatment.

Abstract Background Although chronic periodontitis has previously been reported to be linked with Alzheimer's disease (AD), the pathogenesis between the two is unclear. The purpose of this study is to analyze and screen the relevant and promising molecular markers between chronic periodontitis and Alzheimer's disease (AD). Methods In this paper, we analyzed three AD expression datasets and extracted differentially expressed genes (DEGs), then intersected them with chronic periodontitis genes obtained from text mining, and finally obtained integrated DEGs. We followed that by enriching the matching the matching cell signal cascade through DAVID analysis. Moreover, the MCODE of Cytoscape software was employed to uncover the protein–protein interaction (PPI) network and the matching hub gene. Finally, we verified our data using a different independent AD cohort. Results The chronic periodontitis gene set acquired from text abstracting was intersected with the previously obtained three AD groups, and 12 common genes were obtained. Functional enrichment assessment uncovered 12 cross-genes, which were mainly linked to cell morphogenesis involved in neuron differentiation, leading edge membrane, and receptor ligand activity. After PPI network creation, the ten hub genes linked to AD were retrieved, consisting of SPP1, THY1, CD44, ITGB1, HSPB3, CREB1, SST, UCHL1, CCL5 and BMP7. Finally, the function terms in the new independent dataset were used to verify the previous dataset, and we found 22 GO terms and one pathway, "ECM-receptor interaction pathways", in the overlapping functional terms. Conclusions The establishment of the above-mentioned candidate key genes, as well as the enriched signaling cascades, provides promising molecular markers for chronic periodontitis-related AD, which may help the diagnosis and treatment of AD patients in the future.

Background: Ferroptosis plays an important role in glioma and significantly affects the prognosis, but the specific mechanism has not yet been elucidated. Recent studies suggest that autophagy regulates the process of ferroptosis. This study aimed to find potential autophagy-ferroptosis genes and explore the prognostic significance in glioma.Methods: Ferroptosis and autophagy genes were obtained from two online databases (zhounan.org/ferrdb and autophagy.lu/). The RNAseq data and clinical information were obtained from the Chinese Glioma Genome Atlas (CGGA) database (http://www.cgga.org.cn/). Univariate, multivariate, lasso and Cox regression analysis screened out prognosis-related genes, and a risk model was constructed. Receiver operating characteristic (ROC) curve analysis evaluated the predictive efficiency of the model. Finally, a nomogram was constructed to more accurately predict the prognosis of glioma.Results: We developed a Venn diagram showing 23 autophagy-ferroptosis genes. A total of 660 cases (including RNA sequences and complete clinical information) from two different cohorts (training group n = 413, verification group n = 247) of the CGGA database was acquired. Cohorts were screened to include five prognosis-related genes (MTOR, BID, HSPA5, CDKN2A, GABARAPLA2). Kaplan-Meier curves showed that the risk model was a good prognostic indicator (p < 0.001). ROC analysis showed good efficacy of the risk model. Multivariate Cox analysis also revealed that the risk model was suitable for clinical factors related to prognosis, including type of disease (primary, recurrence), grade (III-IV), age, temozolomide treatment, and 1p19q state. Using the five prognosis-related genes and the risk score, we constructed a nomogram assessed by C-index (0.7205) and a calibration plot that could more accurately predict glioma prognosis.Conclusion: Using a current database of autophagy and ferroptosis genes, we confirmed the prognostic significance of autophagy-ferroptosis genes in glioma, and we constructed a prognostic model to help guide treatment for high grade glioma in the future.

Hybrid-modality brain-computer Interfaces (BCIs), which combine motor imagery (MI) bio-signals and steady-state visual evoked potentials (SSVEPs), has attracted wide attention in the research field of neural engineering. The number of channels should be as small as possible for real-life applications. However, most of recent works about channel selection only focus on either the performance of classification task or the effectiveness of device control. Few works conduct channel selection for MI and SSVEP classification tasks simultaneously. In this paper, a multitasking-based multiobjective evolutionary algorithm (EMMOA) was proposed to select appropriate channels for these two classification tasks at the same time. Moreover, a two-stage framework was introduced to balance the number of selected channels and the classification accuracy in the proposed algorithm. The experimental results verified the feasibility of multiobjective optimization methodology for channel selection of hybrid BCI tasks.

Accurate identification of the type of seizure is very important for the treatment plan and drug prescription of epileptic patients. Artificial intelligence has shown considerable potential in the fields of automated EEG analysis and seizure classification. However, the highly personalized representation of epileptic seizures in EEG has led to many research results that are not satisfactory in clinical applications. In order to improve the clinical adaptability of the algorithm, this paper proposes an adversarial learning-driven domain-invariant deep feature representation method, which enables the hybrid deep networks (HDN) to reliably identify seizure types. In the train phase, we first use the labeled multi-lead EEG short samples to train squeeze-and-excitation networks (SENet) to extract short-term features, and then use the compressed samples to train the long short-term memory networks (LSTM) to extract long-time features and construct a classifier. In the inference phase, we first adjust the feature mapping of LSTM through the adversarial learning between LSTM and clustering subnet so that the EEG of the target patient and the EEG in the database obey the same distribution in the deep feature space. Finally, we use the adjusted classifier to identify the type of seizure. Experiments were carried out based on the TUH EEG Seizure Corpus and CHB-MIT seizure database. The experimental results show that the proposed domain adaptive deep feature representation improves the classification accuracy of the hybrid deep model in the target set by 5%. It is of great significance for the clinical application of EEG automatic analysis equipment.

Objective: To address the feasibility and predictive value of multimodal image-based 3D reconstruction in assessing features of neurovascular relationship particularly regarding the detection of offending vessels, degree of compression exerted on the nerve root, in patients who underwent microvascular decompression for primary trigeminal neuralgia. Methods: This study includes 58 patients who underwent microvascular decompression for primary trigeminal neuralgia. All patients underwent preoperative 3.0-T magnetic resonance imaging (MRI) scan of 3D-TOF MRA and 3D-FIESTA sequences in combination. Multimodal MRIs were then registered with open-source software 3D Slicer, followed by 3D image reconstruction to get 3D models for detection of possible neurovascular relationship in the cerebellopontine angle (CPA). Evaluations were performed by 2 doctors and compared with the intraoperative findings and surgery video. Results: For detection of offending vessels, multimodal image-based 3D reconstruction sensitivity was 93.1% (54/58). Compared with the intraoperative findings, the k coefficients for evaluating the offending vessel and the degree of compression were >0.70 (P

Background: Although the tumor microenvironment (TME) is known to influence the prognosis of glioblastoma (GBM), the underlying mechanisms are not clear. This study aims to identify hub genes in the TME that affect the prognosis of GBM.Methods: The transcriptome profiles of the central nervous systems of GBM patients were downloaded from The Cancer Genome Atlas (TCGA). The ESTIMATE scoring algorithm was used to calculate immune and stromal scores. The application of these scores in histology classification was tested. Univariate Cox regression analysis was conducted to identify genes with prognostic value. Subsequently, functional enrichment analysis and protein–protein interaction (PPI) network analysis were performed to reveal the pathways and biological functions associated with the genes. Next, these prognosis genes were validated in an independent GBM cohort from the Chinese Glioma Genome Atlas (CGGA). Finally, the efficacy of current antitumor drugs targeting these genes against glioma was evaluated.Results: Gene expression profiles and clinical data of 309 GBM samples were obtained from TCGA database. Higher immune and stromal scores were found to be significantly correlated with tissue type and poor overall survival (OS) (p = 0.15 and 0.77, respectively). Functional enrichment analysis identified 860 upregulated and 162 downregulated cross genes, which were mainly linked to immune response, inflammatory response, cell membrane, and receptor activity. Survival analysis identified 228 differentially expressed genes associated with the prognosis of GBM (p ≤ 0.05). A total of 48 hub genes were identified by the Cytoscape tool, and pathway enrichment analysis of the genes was performed using Database for Annotation, Visualization and Integrated Discovery (DAVID). The 228 genes were validated in an independent GBM cohort from the CGGA. In total, 10 genes were found to be significantly associated with prognosis of GBM. Finally, 14 antitumor drugs were identified by drug–gene interaction analysis.Conclusions: Here, 10 TME-related genes and 14 corresponding antitumor agents were found to be associated with the prognosis and OS of GBM.

BackgroundConsidering the high invasiveness and mortality of glioma as well as the unclear key genes and signaling pathways involved in the development of gliomas, there is a strong need to find potential gene biomarkers and available drugs.MethodsEight glioma samples and twelve control samples were analyzed on the GSE31095 datasets, and differentially expressed genes (DEGs) were obtained via the R software. The related glioma genes were further acquired from the text mining. Additionally, Venny program was used to screen out the common genes of the two gene sets and DAVID analysis was used to conduct the corresponding gene ontology analysis and cell signal pathway enrichment. We also constructed the protein interaction network of common genes through STRING, and selected the important modules for further drug-gene analysis. The existing antitumor drugs that targeted these module genes were screened to explore their efficacy in glioma treatment.ResultsThe gene set obtained from text mining was intersected with the previously obtained DEGs, and 128 common genes were obtained. Through the functional enrichment analysis of the identified 128 DEGs, a hub gene module containing 25 genes was obtained. Combined with the functional terms in GSE109857 dataset, some overlap of the enriched function terms are both in GSE31095 and GSE109857. Finally, 4 antitumor drugs were identified through drug-gene interaction analysis.ConclusionsIn this study, we identified that two potential genes and their corresponding four antitumor agents could be used as targets and drugs for glioma exploration.

Oncolytic viruses represent a promising therapeutic modality, but they have yet to live up to their therapeutic potential. Safety and efficacy concerns impel us to identify least toxic oncolytic agents that would generate durable and multifaceted anti-tumor immune responses to disrupt the tumors. Here we describe a rational engineered oncolytic herpes virus (OVH) that is a selective killer for targeting tumors, has strong safety records, induces complete regression of tumors in multiple tumor models, and elicits potent antitumor immunity. By far, the potential of OVs in promoting the tumor antigen-specific humoral immune responses remains obscure. In this study, we found that effective treatment by OVH induced immunogenic cell death, which facilitates to elicit humoral immune responses. Depletion experiments revealed that B cells were required for maximal antitumor efficacy of oncolytic immunotherapy. Both serum transfer and antibody treatment experiments revealed that endogenous oncolysis-induced antigen-targeting therapeutic antibodies can lead to systemic tumor regression. Our data demonstrate that tumor-targeting immune modulatory properties confer oncolytic OVH virotherapy as potent immunotherapeutic cancer vaccines that can generate specific and efficacious antitumor humoral responses by eliciting endogenous tumor antigen-targeting therapeutic antibodies in situ, resulting in an efficacious and tumor-specific therapeutic effect.

To address the heat transfer degradation that occurs in the top section of horizontal pipes when sCO2 fluid flows are heated, the upper half wall with higher temperatures is particularly roughened using uniform sand-grain roughness. The SST k−ω model is validated and used to simulate the horizontal sCO2 heat transfer involving a variety of operating parameters and roughness heights. Results indicate that the half-roughen scheme is effective in significantly reducing higher temperatures of the entire upper wall and improving heat transfer homogeneity. The roughness height plays a crucial role, and an optimal value exists, which is largely influenced by the buoyancy strength and its local variations of horizontal sCO2 flows under different conditions.

Background: Reactive gliosis had been implicated in injury and recovery patterns associated with hydrocephalus. Our aim is to determine the efficacy of minocycline, an antibiotic known for its anti-inflammatory properties, to reduce reactive gliosis and inhibit the development of hydrocephalus. Results: The ventricular dilatation were evaluated by MRI at 1-week post drugs treated, while GFAP and Iba-1 were detected by RT-PCR, Immunohistochemistry and Western blot. The expression of GFAP and Iba-1 was significantly higher in hydrocephalic group compared with saline control group (p < 0.05). Minocycline treatment of hydrocephalic animals reduced the expression of GFAP and Iba-1 significantly (p < 0.05). Likewise, the severity of ventricular dilatation is lower in minocycline treated hydrocephalic animals compared with the no minocyclinegroup (p < 0.05). Conclusion: Minocycline treatment is effective in reducing the gliosis and delaying the development of hydrocephalus with prospective to be the auxiliary therapeutic method of hydrocephalus. [ABSTRACT FROM AUTHOR]