Wuhan Red Cross Hospital

The misfolding, aggregation, and deposition of alpha-synuclein into Lewy bodies are pivotal events that trigger pathological changes in Parkinson’s disease. Extracellular vesicles are nanosized lipid-bilayer vesicles secreted by cells that play a crucial role in intercellular communication due to their diverse cargo. Among these, brain-derived extracellular vesicles, which are secreted by various brain cells such as neurons, glial cells, and Schwann cells, have garnered increasing attention. They serve as a promising tool for elucidating Parkinson’s disease pathogenesis and for advancing diagnostic and therapeutic strategies. This review highlights the recent advancements in our understanding of brain-derived extracellular vesicles released into the blood and their role in the pathogenesis of Parkinson’s disease, with specific emphasis on their involvement in the aggregation and spread of alpha-synuclein. Brain-derived extracellular vesicles contribute to disease progression through multiple mechanisms, including autophagy-lysosome dysfunction, neuroinflammation, and oxidative stress, collectively driving neurodegeneration in Parkinson’s disease. Their application in Parkinson’s disease diagnosis is a primary focus of this review. Recent studies have demonstrated that brain-derived extracellular vesicles can be isolated from peripheral blood samples, as they carry α-synuclein and other key biomarkers such as DJ-1 and various microRNAs. These findings highlight the potential of brain-derived extracellular vesicles, not only for the early diagnosis of Parkinson’s disease but also for disease progression monitoring and differential diagnosis. Additionally, an overview of explorations into the potential therapeutic applications of brain-derived extracellular vesicles for Parkinson’s disease is provided. Therapeutic strategies targeting brain-derived extracellular vesicles involve modulating the release and uptake of pathological alpha-synuclein -containing brain-derived extracellular vesicles to inhibit the spread of the protein. Moreover, brain-derived extracellular vesicles show immense promise as therapeutic delivery vehicles capable of transporting drugs into the central nervous system. Importantly, brain-derived extracellular vesicles also play a crucial role in neural regeneration by promoting neuronal protection, supporting axonal regeneration, and facilitating myelin repair, further enhancing their therapeutic potential in Parkinson’s disease and other neurological disorders. Further clarification is needed of the methods for identifying and extracting brain-derived extracellular vesicles, and large-scale cohort studies are necessary to validate the accuracy and specificity of these biomarkers. Future research should focus on systematically elucidating the unique mechanistic roles of brain-derived extracellular vesicles, as well as their distinct advantages in the clinical translation of methods for early detection and therapeutic development.

Cardiovascular disease (CVD) remains a major contributor to the global disease burden. Previous studies have established a link between the atherogenic index of plasma (AIP) and CVD. However, it remains unclear whether cumulative AIP and AIP control influence the future incidence of CVD in individuals with Cardiovascular-Kidney-Metabolic (CKM) syndrome. This study aims to explore the association between cumulative AIP, AIP control levels, and the risk of CVD in individuals with CKM syndrome from stages 1 to 3. Participants with CKM syndrome were drawn from the China Health and Retirement Longitudinal Study (CHARLS). Cumulative AIP was calculated using triglycerides (TG) and high-density lipoprotein cholesterol (HDL-C), while AIP control levels were categorized into four groups via k-means clustering. CVD was defined by self-reported heart disease or stroke. Multivariable logistic regression and restricted cubic spline analysis were employed to examine the association between AIP and incident CVD in individuals with CKM syndrome. A total of 793 participants (18.84%) developed CVD. After adjusting for confounders, cumulative AIP were associated with the developing CVD (OR=1.139, 95% CI: 1.017-1.275, P=0.0245). Compared to group 1 (best AIP control), the OR (95% CI) for incident CVD were 1.278 (0.959-1.702) for group 2, 1.329 (1.076-1.641) for group 3, and 1.195 (0.974-1.465) for group 4. Restricted cubic spline regression indicated the relationship between cumulative AIP and CVD risk is linear (P for nonlinear = 0.3377). In middle-aged and elderly individuals with CKM syndrome, higher cumulative AIP and poorer AIP control were associated with an elevated incidence of CVD. These findings suggest that enhanced assessment of the AIP index could inform targeted prevention strategies for CVD in the context of CKM syndrome.

Restless legs syndrome (RLS) and Parkinson's disease (PD) are both classified as neurological movement disorders, and they may be interconnected due to abnormalities in the dopaminergic system. The incidence of RLS in PD patients is higher than that in the general population. And PD patients with RLS exhibit a higher age of onset, more severe depression, worse nutritional status, lower quality of life, and a relatively lower frequency and severity of RLS symptom. The current research has unveiled several potential mechanisms underlying RLS in PD patients, such as iron deficiency, reduced function of the dopamine system in the A11 region, and decreased connectivity of related pathways and so on. By systematically summarizing the pathogenesis, clinical manifestations, diagnosis and treatment of PD with RLS, this paper not only provides ideas for the precise diagnosis and treatment of PD with RLS, but also lays a theoretical foundation for clarifying the intricate relationship between PD and RLS.

Early exposure to sevoflurane may cause brain tissue degeneration; however, the mechanism involved in this process has not been explored. In this study, we investigated the role of long non-coding RNA small nucleolar RNA host gene 3 (lncRNA SNHG3) in sevoflurane-induced neuronal injury. The injury models of HT22 and primary cultures of neurons were constructed using sevoflurane treatment. The WST-8 reduction was detected by CCK-8 assay, the level of inflammatory factors was detected by enzyme-linked immunosorbent assay (ELISA), and cell pyroptosis was detected by flow cytometry. The expression of genes and proteins was detected by qRT-PCR and Western blot, respectively. The level of β-tubulin III in primary cultures of hippocampal neurons was analyzed by immunofluorescence. The relationship among SNHG3, PTBP1 and NEK7 was confirmed by RIP assay. The expression of SNHG3 and NEK7 were enhanced in sevoflurane-treated HT22 cells. Sevoflurane inhibited the WST-8 reduction in a concentration-dependent manner, promoted the pyroptosis, and increased pyroptosis-related protein expression. SNHG3 knockdown significantly inhibited sevoflurane-induced pyroptosis and inflammatory injury in HT22 cells and primary cultures of neurons. Furthermore, SNHG3 regulated NEK7 expression by binding to PTBP1. NEK7 knockdown reversed the decrease in WST-8 reduction, inhibited pyroptosis, and decreased the release of inflammatory factors and pyroptosis-related protein expression by inactivation of NLRP3 signaling in sevoflurane-induced HT22 cells. Moreover, NEK7 overexpression attenuated the effect of SNHG3 knockdown on neuronal pyroptosis and inflammation injury. Downregulation of SNHG3 attenuates sevoflurane-induced neuronal inflammation and pyroptosis by mediating the NEK7/NLRP3 axis, suggesting that SNHG3 could be a potential target gene for neuronal injury.

Background and AimsDistinguishing pancreatic cancer from nonneoplastic masses is critical and remains a clinical challenge. The study aims to construct a deep learning‐based artificial intelligence system to facilitate pancreatic mass diagnosis, and to guide EUS‐guided fine‐needle aspiration (EUS‐FNA) in real time.MethodsThis is a prospective study. The CH‐EUS MASTER system is composed of Model 1 (real‐time capture and segmentation) and Model 2 (benign and malignant identification). It was developed using deep convolutional neural networks and Random Forest algorithm. Patients with pancreatic masses undergoing CH‐EUS examinations followed by EUS‐FNA were recruited. All patients underwent CH‐EUS and were diagnosed both by endoscopists and CH‐EUS MASTER. After diagnosis, they were randomly assigned to undergo EUS‐FNA with or without CH‐EUS MASTER guidance.ResultsCompared with manual labeling by experts, the average overlap rate of Model 1 was 0.708. In the independent CH‐EUS video testing set, Model 2 generated an accuracy of 88.9% in identifying malignant tumors. In clinical trial, the accuracy, sensitivity, and specificity for diagnosing pancreatic masses by CH‐EUS MASTER were significantly better than that of endoscopists. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were respectively 93.8%, 90.9%, 100%, 100%, and 83.3% by CH‐EUS MASTER guided EUS‐FNA, and were not significantly different compared to the control group. CH‐EUS MASTER‐guided EUS‐FNA significantly improved the first‐pass diagnostic yield.ConclusionCH‐EUS MASTER is a promising artificial intelligence system diagnosing malignant and benign pancreatic masses and may guide FNA in real time.Trial registration number: NCT04607720.