Methods最新文献

Inflammatory responses evident in many diseases involve the generation of oxidants which can cause oxidant-induced post-translational modifications to proteins. Albumin, the most abundant plasma protein, contains a free thiol group which is susceptible to oxidative modification. We propose that albumin thiol oxidation (AlbOx) could be a useful biomarker to monitor changes in inflammatory activity and oxidative stress. To measure AlbOx in humans and animal models, we developed a fast, sensitive, simple, and reproducible capillary electrophoresis method (CE-AlbOx). This method can analyse total, reversible, and irreversible oxidation of albumin. The method only requires a small volume of sample (<10 μL blood), has an intra/interday variation of <2 %, and has a total run time of 17 min. We validated the usefulness of AlbOx as a biomarker of chronic inflammation by analysing samples from patients with, and animal models of, Duchenne muscular dystrophy (DMD), a disease associated with chronic inflammation. The main findings in this study are (1) dystrophic humans and animals have higher oxidised albumin compared to healthy controls, (2) mouse albumin has two reactive cysteine groups, and (3) our method is the first to quantify the different oxidation states of mouse albumin. In conclusion, we have developed a new method to measure albumin oxidation in humans and animals using capillary electrophoresis. The simple methodology of the CE-AlbOx method makes it advantageous to current methods and can be readily used as a biomarker of inflammation and oxidative stress in both humans and animal models.

Early detection of Alzheimer's disease (AD) and its precursor, mild cognitive impairment (MCI), is paramount for timely intervention and effective disease management. This study introduces a novel computer-aided diagnostic model that leverages electroencephalogram (EEG) data to precisely identify and classify AD and MCI. A comprehensive preprocessing pipeline is employed, incorporating discrete wavelet transform (DWT) for EEG signal decomposition into relevant subbands and subsequent signal windowing to address non-stationarity. Spectrograms derived from these preprocessed signals serve as input for a deep ensemble feature learning and deep support vector machine (DEF-DSVM) architecture. The DEF-DSVM model significantly enhances the accuracy of diagnosing both MCI and AD, achieving an impressive 98.17% accuracy rate that surpasses contemporary state-of-the-art methods. Beyond diagnostic precision, the model effectively identifies specific EEG subbands-namely alpha, theta, and delta-instrumental in elucidating the pathophysiology of AD and MCI. The structure's generalizability and robustness are validated using the Figshare dataset, encompassing, AD, MCI, and control classes. To ensure a rigorous assessment of the model's performance, the Leave-One-Subject-Out (LOSO) cross-validation procedure is employed in lieu of the traditional K-fold approach, mitigating the risk of overoptimistic performance estimates and providing a more accurate reflection of the model's ability to generalize to novel, unseen subjects. Further evaluation of the method's generalizability through its application to an EEG dataset related to attention deficit hyperactivity disorder (ADHD) highlights its broader clinical utility across various neurodegenerative disorders. These findings establish the DEF-DSVM model as a reliable and potent tool for the early diagnosis and monitoring of AD and MCI, offering substantial accuracy gains and demonstrating its potential for widespread application across different neurological conditions.