Common mechanisms underlying axonal transport deficits in neurodegenerative diseases: a mini review

Many neurodegenerative diseases including Alzheimer’s disease, Parkinson’s disease, and amyotrophic lateral sclerosis are characterized by the accumulation of pathogenic proteins and abnormal localization of organelles. These pathological features may be related to axonal transport deficits in neurons, which lead to failures in pathological protein targeting to specific sites for degradation and organelle transportation to designated areas needed for normal physiological functioning. Axonal transport deficits are most likely early pathological events in such diseases and gradually lead to the loss of axonal integrity and other degenerative changes. In this review, we investigated reports of mechanisms underlying the development of axonal transport deficits in a variety of common neurodegenerative diseases, such as Alzheimer’s disease, amyotrophic lateral sclerosis, Parkinson’s disease and Huntington’s disease to provide new ideas for therapeutic targets that may be used early in the disease process. The mechanisms can be summarized as follows: (1) motor protein changes including expression levels and post-translational modification alteration; (2) changes in microtubules including reducing stability and disrupting tracks; (3) changes in cargoes including diminished binding to motor proteins. Future studies should determine which axonal transport defects are disease-specific and whether they are suitable therapeutic targets in neurodegenerative diseases

Risk estimation for postoperative nausea and vomiting: development and validation of a nomogram based on point-of-care gastric ultrasound

Abstract Background We aimed to develop a nomogram that can be combined with point-of-care gastric ultrasound and utilised to predict postoperative nausea and vomiting (PONV) in adult patients after emergency surgery. Methods Imaging and clinical data of 236 adult patients undergoing emergency surgery in a university hospital between April 2022 and February 2023 were prospectively collected. Patients were divided into a training cohort (n = 177) and a verification cohort (n = 59) in a ratio of 3:1, according to a random number table. After univariate analysis and multivariate logistic regression analysis of the training cohort, independent risk factors for PONV were screened to develop the nomogram model. The receiver operating characteristic curve, calibration curve, decision curve analysis (DCA) and clinical impact curve (CIC) were used to evaluate the prediction efficiency, accuracy, and clinical practicability of the model. Results Univariate analysis and multivariate logistic regression analysis showed that female sex, history of PONV, history of migraine and gastric cross-sectional area were independent risk factors for PONV. These four independent risk factors were utilised to construct the nomogram model, which achieved significant concordance indices of 0.832 (95% confidence interval [CI], 0.771–0.893) and 0.827 (95% CI, 0.722–0.932) for predicting PONV in the training and validation cohorts, respectively. The nomogram also had well-fitted calibration curves. DCA and CIC indicated that the nomogram had great clinical practicability. Conclusions This study demonstrated the prediction efficacy, differentiation, and clinical practicability of a nomogram for predicting PONV. This nomogram may serve as an intuitive and visual guide for rapid risk assessment in patients with PONV before emergency surgery

Identification of metabolism-related subtypes and feature genes in Alzheimer’s disease

Abstract Background Owing to the heterogeneity of Alzheimer's disease (AD), its pathogenic mechanisms are yet to be fully elucidated. Evidence suggests an important role of metabolism in the pathophysiology of AD. Herein, we identified the metabolism-related AD subtypes and feature genes. Methods The AD datasets were obtained from the Gene Expression Omnibus database and the metabolism-relevant genes were downloaded from a previously published compilation. Consensus clustering was performed to identify the AD subclasses. The clinical characteristics, correlations with metabolic signatures, and immune infiltration of the AD subclasses were evaluated. Feature genes were screened using weighted correlation network analysis (WGCNA) and processed via Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses. Furthermore, three machine-learning algorithms were used to narrow down the selection of the feature genes. Finally, we identified the diagnostic value and expression of the feature genes using the AD dataset and quantitative reverse-transcription polymerase chain reaction (qRT-PCR) analysis. Results Three AD subclasses were identified, namely Metabolism Correlated (MC) A (MCA), MCB, and MCC subclasses. MCA contained signatures associated with high AD progression and may represent a high-risk subclass compared with the other two subclasses. MCA exhibited a high expression of genes related to glycolysis, fructose, and galactose metabolism, whereas genes associated with the citrate cycle and pyruvate metabolism were downregulated and associated with high immune infiltration. Conversely, MCB was associated with citrate cycle genes and exhibited elevated expression of immune checkpoint genes. Using WGCNA, 101 metabolic genes were identified to exhibit the strongest association with poor AD progression. Finally, the application of machine-learning algorithms enabled us to successfully identify eight feature genes, which were employed to develop a nomogram model that could bring distinct clinical benefits for patients with AD. As indicated by the AD datasets and qRT-PCR analysis, these genes were intimately associated with AD progression. Conclusion Metabolic dysfunction is associated with AD. Hypothetical molecular subclasses of AD based on metabolic genes may provide new insights for developing individualized therapy for AD. The feature genes highly correlated with AD progression included GFAP, CYB5R3, DARS, KIAA0513, EZR, KCNC1, COLEC12, and TST