Bibliometric analysis of electroencephalogram research in Parkinson's disease from 2004 to 2023.

Background: Parkinson's disease (PD) is a prevalent neurodegenerative disorder affecting millions globally. It encompasses both motor and non-motor symptoms, with a notable impact on patients' quality of life. Electroencephalogram (EEG) is a non-invasive tool that is increasingly utilized to investigate neural mechanisms in PD, identify early diagnostic markers, and assess therapeutic responses. Methods: The data were sourced from the Science Citation Index Expanded within the Web of Science Core Collection database, focusing on publications related to EEG research in PD from 2004 to 2023. A comprehensive bibliometric analysis was conducted using CiteSpace and VOSviewer software. The analysis began with an evaluation of the selected publications, identifying leading countries, institutions, authors, and journals, as well as co-cited references, to summarize the current state of EEG research in PD. Keywords are employed to identify research topics that are currently of interest in this field through the analysis of high-frequency keyword co-occurrence and cluster analysis. Finally, burst keywords were identified to uncover emerging trends and research frontiers in the field, highlighting shifts in interest and identifying future research directions. Results: A total of 1,559 publications on EEG research in PD were identified. The United States, Germany, and England have made notable contributions to the field. The University of London is the leading institution in terms of publication output, with the University of California closely following. The most prolific authors are Brown P, Fuhr P, and Stam C In terms of total citations and per-article citations, Stam C has the highest number of citations, while Brown P has the highest H-index. In terms of the total number of publications, Clinical Neurophysiology is the leading journal, while Brain is the most highly cited. The most frequently cited articles pertain to software toolboxes for EEG analysis, neural oscillations, and PD pathophysiology. Through analyzing the keywords, four research hotspots were identified: research on the neural oscillations and connectivity, research on the innovations in EEG Analysis, impact of therapies on EEG, and research on cognitive and emotional assessments. Conclusion: This bibliometric analysis demonstrates a growing global interest in EEG research in PD. The investigation of neural oscillations and connectivity remains a primary focus of research. The application of machine learning, deep learning, and task analysis techniques offers promising avenues for future research in EEG and PD, suggesting the potential for advancements in this field. This study offers valuable insights into the major research trends, influential contributors, and evolving themes in this field, providing a roadmap for future exploration.

Altered dynamic amplitude of low-frequency fluctuation in individuals at high risk for Alzheimer's disease.

The brain's dynamic spontaneous neural activity is significant in supporting cognition; however, how brain dynamics go awry in subjective cognitive decline (SCD) and mild cognitive impairment (MCI) remains unclear. Thus, the current study aimed to investigate the dynamic amplitude of low-frequency fluctuation (dALFF) alterations in patients at high risk for Alzheimer's disease and to explore its correlation with clinical cognitive assessment scales, to identify an early imaging sign for these special populations. A total of 152 participants, including 72 SCD patients, 44 MCI patients and 36 healthy controls (HCs), underwent a resting-state functional magnetic resonance imaging and were assessed with various neuropsychological tests. The dALFF was measured using sliding-window analysis. We employed canonical correlation analysis (CCA) to examine the bi-multivariate correlations between neuropsychological scales and altered dALFF among multiple regions in SCD and MCI patients. Compared to those in the HC group, both the MCI and SCD groups showed higher dALFF values in the right opercular inferior frontal gyrus (voxel P < .001, cluster P < .05, correction). Moreover, the CCA models revealed that behavioural tests relevant to inattention correlated with the dALFF of the right middle frontal gyrus and right opercular inferior frontal gyrus, which are involved in frontoparietal networks (R = .43, P = .024). In conclusion, the brain dynamics of neural activity in frontal areas provide insights into the shared neural basis underlying SCD and MCI.

FIP-nha, a fungal immunomodulatory protein from Nectria haematococca, induces apoptosis and autophagy in human gastric cancer cells via blocking the EGFR-mediated STAT3/Akt signaling pathway.

FIP-nha, a fungal immunomodulatory protein from Nectria haematococca, has been demonstrated a broad spectrum of antitumor activity and cell selectivity against human cancers in our previous study. However, the effect and mechanism of FIP-nha on gastric cancer remains unclear. In this study, we systematically observed the cytotoxicity, biological effect, regulatory mechanism and interaction target of FIP-nha on human gastric cancer cell lines, AGS and SGC7901. Our results demonstrated that FIP-nha inhibited the growth of AGS and SGC7901 cells in a dose-dependent manner and exerted proapoptotic effects on both cells as confirmed by flow cytometry, DAPI staining and western blot analysis. Additionally, the exposure of AGS and SGC7901 to FIP-nha induced autophagy as indicated by western blot analysis, GFP-LC3 and mCherry-GFP-LC3 transfection and acridine orange staining. Furthermore, we found that FIP-nha decreased the phosphorylation of EGFR, STAT3 and Akt and inhibited activation effect of ligand factor EGF to EGFR and its downstream signal molecule STAT3 and Akt. Finally, we proved that FIP-nha located on the surface of gastric cancer cells and bound directly to the transmembrane protein of EGFR by immunoprecipitation, cellular localization, molecular docking, microscale thermophoresis assay. The above findings indicated that FIP-nha inhibited the growth of gastric cancer and induced apoptosis and autophagy through competitively binding to EGFR with EGF to blocking the EGFR-mediated STAT3/Akt pathway. In summary, our study provided novel insights regarding the activity of FIP-nha against gastric cancer and contributed to the clinical application of FIP-nha as a potential chemotherapy drugs that targeted EGFR for human gastric cancer.

1H NMR-based chemometric metabolomics characterization of soymilk fermented by Bacillus subtilis BSNK-5.

Microbial fermentation can endow food with unique flavors, increase its nutritional value and enhance functional characteristics. Our previous research has shown that liquid fermentation of soymilk by Bacillus subtilis BSNK-5 imparted new functional properties of to the fermented product via production of nattokinase. In this study, in order to further investigate the changes in the flavor, nutritional quality and functional characteristics of soymilk during fermentation using proton nuclear magnetic resonance (1H NMR) metabolomics to monitor the metabolite profile of BSNK-5-fermented soymilk. A total of 44 differential metabolites were identified between BSNK-5-fermented soymilk and uninoculated/unfermented soymilk, among which the levels of flavor-related substances (acetate, isovalerate and 2-methylbutyrate), nutrient-related substances (12 free amino acids), and functional substances (taurine, GABA and genistein) significantly increased after fermentation. These metabolites were closely associated with eight potential metabolic pathways. This work highlighted the significance of BSNK-5 strain in improving the nutritional quality and functional characteristics of fermented soymilk; however, the use of the strain also caused flavor deterioration. This study lays a theoretical foundation for the improvement and development of fermented soy products via liquid fermentation with B. subtilis.