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Alzheimer’s is the most common cause of dementia worldwide and seriously affects patients’ daily tasks. Plant endophytic fungi are known for providing novel and unique secondary metabolites with diverse activities. This review focuses primarily on the published research regarding anti-Alzheimer’s natural products derived from endophytic fungi between 2002 and 2022. Following a thorough review of the literature, 468 compounds with anti-Alzheimer’s-related activities are reviewed and classified based on their structural skeletons, primarily including alkaloids, peptides, polyketides, terpenoids, and sterides. The classification, occurrences, and bioactivities of these natural products from endophytic fungi are summarized in detail. Our results provide a reference on endophytic fungi natural products that may assist in the development of new anti-Alzheimer’s compounds.

Background Postoperative cognitive dysfunction (POCD) is a common complication following anesthesia and surgery. General anesthetic isoflurane has potential neurotoxicity and induces cognitive impairments, but the exact mechanism remains unclear. Astrocytes form interconnected networks in the adult brain through gap junctions (GJs), which primarily comprise connexin 43 (Cx43), and play important roles in brain homeostasis and functions such as memory. However, the role of the GJ-Cx43-mediated astrocytic network in isoflurane-induced cognitive dysfunction has not been defined. Methods 4-month-old male C57BL/6 mice were exposure to long-term isoflurane to induce cognitive impairment. To simulate an in vitro isoflurane-induced cognitive dysfunction‐like condition, primary mouse astrocytes were subjected to long-term isoflurane exposure. Cognitive function was assessed by Y-maze and fear conditioning tests. Western blot was used to determine the expression levels of different functional configurations of Cx43. The morphology of the GJs-Cx43 was evaluated by immunofluorescence staining. Levels of IL-1β and IL-6 were examined by ELISA. The ability of GJs-Cx43-mediated intercellular communication was examined by lucifer yellow dye transfer assay. Ethidium bromide uptake assays were used to measure the activity of Cx43 hemichannels. The ultrastructural morphology of astrocyte gap junctions and tripartite synapse were observed by transmission electron microscopy. Results After long-term isoflurane anesthesia, the GJs formed by Cx43 in the mouse hippocampus and primary mouse astrocytes were significantly reduced, GJs function was impaired, hemichannel activity was enhanced, the levels of IL-1β and IL-6 were increased, and mice showed significant cognitive impairment. After treatment with the novel GJ-Cx43 enhancer ZP1609, GJ-Cx43-mediated astrocytic network function was enhanced, neuroinflammation was alleviated, and ameliorated cognition dysfunction induced by long-term isoflurane exposure. However, ZP1609 enhances the astrocytic network by promoting Cx43 to form GJs without affecting hemichannel activity. Additionally, our data showed that long-term isoflurane exposure does not alter the structure of tripartite synapse. Conclusion Our results reveal a novel mechanism of the GJ-Cx43-mediated astrocytic network involved in isoflurane-induced neuroinflammation and cognitive impairments, which provides new mechanistic insight into the pathogenesis of POCD and identifies potential targets for its treatment.

Stock market prediction has long attracted the attention of academia and industry due to its potential for substantial financial returns. Despite the availability of various forecasting methods, such as CNN, LSTM, BiLSTM, GRU, and Transformer, the hyperparameter optimization of these models often faces limitations, particularly in single-objective optimization, where they can easily fall into local optima. To address this issue, this paper proposes an innovative multi-objective optimization algorithm—the Multi-Objective Escape Bird Algorithm (MOEBS)—and introduces the MOEBS-Transformer architecture to enhance the efficiency and effectiveness of hyper-parameter optimization for Transformer models. This study first validates the performance of MOEBS through a series of multi-objective benchmark tests on standard problem sets such as ZDT, DTLZ, and WFG, comparing it with other multi-objective optimization algorithms (e.g., MOMVO, MSSA, and MOEAD) using evaluation metrics such as GD, Spacing, IGD, and HV for comprehensive analysis. In the context of stock price prediction, we select the closing price datasets of Amazon, Google, and Uniqlo, using MOEBS to optimize the core hyper parameters of the Transformer while considering multiple objectives, including training set RMSE, testing set RMSE, and testing set error variance. In the experiments, this paper first compares CNN, LSTM, BiLSTM, GRU, and traditional Transformer models to establish the Transformer as the optimal model for stock market prediction. Subsequently, the study compares the MOEBS-Transformer with Transformer models optimized using various hyperparameter optimization methods, including MOMVO-Transformer, MSSA-Transformer, and MOEAD-Transformer. Additionally, it evaluates Transformer models optimized through conventional methods: Random Search (RS-Transformer), Grid Search (GS-Transformer), and Bayesian Optimization (BO-Transformer). By assessing the performance of these models using R 2 , RMSE, and RPD metrics on both training and testing sets, the results demonstrate that the Transformer model optimized by MOEBS significantly outperforms the other methods in terms of prediction accuracy and prediction stability. This research offers a new solution for complex optimization scenarios and lays a foundation for advancements in stock market prediction technologies.

Dental simulation head model laboratories are crucial for clinical simulation training for stomatological students, yet the maintenance of their dental unit waterlines (DUWLs) has been overlooked. This study investigated water contamination in DUWLs within these laboratories and proposed solutions. Water samples were collected from 12 dental chairs in three laboratories at three time points: the beginning, middle, and end of the semester. At the start of the semester, severe contamination was observed, with colony counts of 11,586 1715 CFU/ml for high-speed handpieces and 5375 874 CFU/ml for three ways syringes. As the semester progressed, colony counts gradually decreased but remained above clinical thresholds. Both 20 mg/L organochlorine disinfectant and 20 mg/L chlorine dioxide were effective in reducing bacterial contamination below standard ranges three days post-disinfection. Microbial diversity analysis revealed Proteobacteria and Bacteroidota as the dominant bacterial phyla, with Ascomycota as the dominant fungal phylum. Potentially pathogenic bacteria such as Pseudomonas , Burkholderia-Caballeronia-Paraburkholderia , Ralstonia , Mycobacterium , Legionella , Paenibacillus , Streptomyces , Acinetobacter , and Prevotella , as well as fungi like Fusarium and Penicillium , were detected. Therefore, urgent attention is needed to address DUWL contamination in dental laboratories, and it is recommended to disinfect DUWLs using either 20 mg/L organochlorine disinfectant or 20 mg/L chlorine dioxide every three days.

In the context of global economic transformation, high-quality enterprise development (HQED) is crucial for driving economic growth, particularly through enhancing Total Factor Productivity (TFPLP). Digital Inclusive Finance (DIF), as a classical financial model, plays an important role in promoting high-quality enterprise development. To explore the relationship between TFP and DIF, we first applied traditional double fixed-effects models, along with robustness and heterogeneity tests, for modeling experiments. This series of tests effectively revealed the theoretical linear relationships between economic variables. However, the double fixed-effects model has limitations in capturing nonlinear relationships and making predictions. Given the growing body of research on existing hybrid models, we acknowledge the importance of exploring and contributing to this evolving area. To address this issue, based on the results of traditional economic analysis, we introduced improved time series models. These advanced deep learning models allow us to better capture the complex nonlinear relationship between DIF and TFP. The experiment initially explored the preliminary structural relationship between DIF and TFP using double fixed-effects models combined with robustness and heterogeneity tests. Then, based on the results of these tests, we selected deep learning features and combined Kolmogorov–Arnold Neural Network (KAN), Graph Neural Network (GNN) models with classic time series deep learning models (Transformer, LSTM, BiLSTM, GRU) to capture the latent nonlinear features in the data for prediction. The results show that, compared to traditional time series forecasting methods, the improved deep learning models perform better in capturing the nonlinear relationships of economic variables, improving prediction accuracy, and reducing prediction errors. Finally, paired t -tests and Cohen’s d effect size tests were used to evaluate error metrics, and the results indicate that the introduction of KAN and GNN models significantly improved the performance of time series forecasting models.

MADS-box transcription factors play a crucial role in plant growth and development. Although previous genome-wide analyses have investigated the MADS-box family in cucumber, this study provides the first comprehensive reannotation of the MADS-box gene family in Cucumis sativus using updated Cucurbitaceae genome data, offering novel insights into the gene family’s evolution and functional diversity. The results show that a total of 48 CsMADS-box genes were identified in the V3 version of cucumber, while 3 of the 43 genes identified in the V1 version were duplicated. The V1 version actually has only 40 genes. Additionally, we analyzed the variability in protein sequences and found that the amino acid sequences of 14 genes showed no differences between the two versions of the database, while the amino acid sequences of 29 genes exhibited significant differences. The further analysis of conserved motifs revealed that although the amino acid lengths of 15 genes had changed, their conserved motifs remained unchanged; however, the conserved motifs of 12 genes had altered. Furthermore we found that motif1 and motif2 were present in most proteins, indicating that they are highly conserved. Gene structure analysis revealed that most type I (Mα, Mβ) MADS-box genes lack introns, whereas type II (MIKC) genes exhibit a similar structure with a higher number of introns. Chromosomal localization analysis indicated that CsMADS-box genes are unevenly distributed across the seven chromosomes of cucumber. Promoter region analysis showed that the promoter regions of CsMADS-box genes contain response elements related to plant growth and development, suggesting that CsMADS-box genes may be extensively involved in plant growth and development. Different CsMADS-box genes exhibit specific high expression in roots, stems, leaves, tendrils, male flowers, female flowers, and ovaries, suggesting that these genes play crucial roles in the growth, development, reproduction and morphogenesis of cucumber. Moreover, 26, 18, 8, and 10 CsMADS-box genes were differentially expressed under high temperature, NaCl and/or silicon, downy mildew, and powdery mildew treatments, respectively. Interestingly, CsMADS07 and CsMADS16 responded to all tested stress conditions. These findings provide a reference and basis for further investigation into the function and mechanisms of the MADS-box genes for resistance breeding in cucumber.

Confronted with the spatial heterogeneity of the real estate market, some traditional research has utilized geographically weighted regression (GWR) to estimate house prices. However, its predictive power still has some room to improve, and its kernel function is limited in some simple forms. Therefore, we propose a novel house price valuation model, which is combined with geographically neural network weighted regression (GNNWR) to improve the accuracy of real estate appraisal with the help of neural networks. Based on the Shenzhen house price dataset, this work conspicuously captures the variable spatial regression relationships at different regions of different variables, which GWR has difficulty realizing. Moreover, we focus on the performance of GNNWR, verify its robustness and superiority, and refine the experiment process with 10-fold cross-validation. In contrast with the ordinary least squares (OLS) model, our model achieves an improvement of about 50% on most of the metrics. Compared with the best GWR model, our thorough experiments reveal that our model improves the mean absolute error (MAE) by 13.5% and attains a decrease of the mean absolute percentage error (MAPE) by 13.0% in the evaluation on the validation dataset. It is a practical and powerful way to assess house prices, and we believe our model could be applied to other valuation problems concerning geographical data to promote the prediction accuracy of socioeconomic phenomena.

Seven cyclic depsipeptides, including two new cyclic pentadepsipeptides avenamides A (1) and B (2), were isolated from a plant-derived fungus Fusarium avenaceum W8 by using the bioassay-guided fractionation method. The planar structures were elucidated by using comprehensive spectroscopic analyses, including 1D and 2D NMR, as well as MS/MS spectrometry. The absolute configuration of the amino acid and hydroxy acid residues was confirmed by using the advanced Marfey’s method and chiral HPLC analysis, respectively. Compounds 1–7 were evaluated for their cytotoxic activities against A549 and NCI-H1944 human lung adenocarcinoma cell lines and their antimicrobial activities against Staphylococcus aureus and Saccharomyces cerevisiae. As a result, compounds 1–4 showed moderate cytotoxicity, with IC50 values of 6.52~45.20 µM. Compounds 1 and 3 exhibited significant antimicrobial activities against S. aureus and S. cerevisiae, with an MIC80 of 11.1~30.0 µg/mL.

Background Ensuring the safety of dental unit waterlines (DUWLs) has become a pivotal issue in dental care practices, focusing on the health implications for both patients and healthcare providers. The inherent structure and usage conditions of DUWLs contribute to the risk of biofilm formation and bacterial growth, highlighting the need for effective disinfection solutions.The quest for a disinfection method that is both safe for clinical use and effective against pathogens such as Staphylococcus aureus and Escherichia coli in DUWLs underscores the urgency of this research. Materials Chlorine dioxide disinfectants at concentrations of 5, 20, and 80 mg/L were used to treat biofilms of S. aureus and E. coli cultured in DUWLs. The disinfection effectiveness was assessed through bacterial counts and culturing. Simultaneously, human skin fibroblast cells were treated with the disinfectant to observe changes in cell morphology and cytotoxicity. Additionally, the study included corrosion tests on various metals (carbon steel, brass, stainless steel, aluminum, etc.). Results Experimental results showed that chlorine dioxide disinfectants at concentrations of 20 mg/L and 80 mg/L significantly reduced the bacterial count of S. aureus and E. coli, indicating effective disinfection. In terms of cytotoxicity, higher concentrations were more harmful to cellular safety, but even at 80 mg/L, the cytotoxicity of chlorine dioxide remained within controllable limits. Corrosion tests revealed that chlorine dioxide disinfectants had a certain corrosive effect on carbon steel and brass, and the degree of corrosion increased with the concentration of the disinfectant. Conclusion After thorough research, we recommend using chlorine dioxide disinfectant at a concentration of 20 mg/L for significantly reducing bacterial biofilms in dental unit waterlines (DUWLs). This concentration also ensures satisfactory cell safety and metal corrosion resistance.

Volume muscle loss is a severe injury often caused by trauma, fracture, tumor resection, or degenerative disease, leading to long-term dysfunction or disability. The current gold-standard treatment is autologous muscle tissue transplantation, with limitations due to donor site restrictions, complications, and low regeneration efficiency. Tissue engineering shows potential to overcome these challenges and achieve optimal muscle regeneration, vascularization, nerve repair, and immunomodulation. In the field of muscle tissue engineering, skeletal muscle decellularized matrices are regarded as an ideal material due to their similarity to the defect site environment, yet they suffer from difficulties in preparation, severe fibrosis, and inconsistent experimental findings. Adipose decellularized matrices (AdECMs) have demonstrated consistent efficacy in promoting muscle regeneration, and their ease of preparation and abundant availability make them even more attractive. The full potential of AdECMs for muscle regeneration remains to be explored. The aim of this review is to summarize the relevant studies on using AdECMs to promote muscle regeneration, to summarize the preparation methods of various applied forms, and to analyze their advantages and shortcomings, as well as to further explore their mechanisms and to propose possible improvements, so as to provide new ideas for the clinical solution of the problem of volume muscle loss.