Recent advances of ambient pressure X-ray photoelectron spectroscopy (AP-XPS) have enabled the chemical composition and the electrical potential profile at a liquid/electrode interface under electrochemical reaction conditions to be directly probed. In this work, we apply this operando technique to study the surface chemical composition evolution on a Co metal electrode in 0.1 M KOH aqueous solution under various electrical biases. It is found that an ∼12.2 nm-thick layer of Co(OH)₂ forms at a potential of about -0.4 V_Ag/AgCl, and upon increasing the anodic potential to about +0.4 V_Ag/AgCl, this layer is partially oxidized into cobalt oxyhydroxide (CoOOH). A CoOOH/Co(OH)₂ mixture layer is formed on the top of the electrode surface. Finally, the oxidized surface layer can be reduced to Co⁰ at a cathodic potential of -1.35 V_Ag/Cl. These observations indicate that the ultrathin layer containing cobalt oxyhydroxide is the active phase for oxygen evolution reaction (OER) on a Co electrode in an alkaline electrolyte, consistent with previous studies.

We report a new method to probe the solid-liquid interface through the use of a thin liquid layer on a solid surface. An ambient pressure XPS (AP-XPS) endstation that is capable of detecting high kinetic energy photoelectrons (7 keV) at a pressure up to 110 Torr has been constructed and commissioned. Additionally, we have deployed a "dip &pull" method to create a stable nanometers-thick aqueous electrolyte on platinum working electrode surface. Combining the newly constructed AP-XPS system, "dip &pull" approach, with a "tender" X-ray synchrotron source (2 keV-7 keV), we are able to access the interface between liquid and solid dense phases with photoelectrons and directly probe important phenomena occurring at the narrow solid-liquid interface region in an electrochemical system. Using this approach, we have performed electrochemical oxidation of the Pt electrode at an oxygen evolution reaction (OER) potential. Under this potential, we observe the formation of both Pt(2+) and Pt(4+) interfacial species on the Pt working electrode in situ. We believe this thin-film approach and the use of "tender" AP-XPS highlighted in this study is an innovative new approach to probe this key solid-liquid interface region of electrochemistry.

Late-onset Alzheimer's disease (AD) can, in part, be considered a metabolic disease. Besides age, female sex and APOE ε4 genotype represent strong risk factors for AD that also give rise to large metabolic differences. We systematically investigated group-specific metabolic alterations by conducting stratified association analyses of 139 serum metabolites in 1,517 individuals from the AD Neuroimaging Initiative with AD biomarkers. We observed substantial sex differences in effects of 15 metabolites with partially overlapping differences for APOE ε4 status groups. Several group-specific metabolic alterations were not observed in unstratified analyses using sex and APOE ε4 as covariates. Combined stratification revealed further subgroup-specific metabolic effects limited to APOE ε4+ females. The observed metabolic alterations suggest that females experience greater impairment of mitochondrial energy production than males. Dissecting metabolic heterogeneity in AD pathogenesis can therefore enable grading the biomedical relevance for specific pathways within specific subgroups, guiding the way to personalized medicine.

Introduction

The Alzheimer's Disease Research Summits of 2012 and 2015 incorporated experts from academia, industry, and nonprofit organizations to develop new research directions to transform our understanding of Alzheimer's disease (AD) and propel the development of critically needed therapies. In response to their recommendations, big data at multiple levels are being generated and integrated to study network failures in disease. We used metabolomics as a global biochemical approach to identify peripheral metabolic changes in AD patients and correlate them to cerebrospinal fluid pathology markers, imaging features, and cognitive performance.

Methods

Fasting serum samples from the Alzheimer's Disease Neuroimaging Initiative (199 control, 356 mild cognitive impairment, and 175 AD participants) were analyzed using the AbsoluteIDQ-p180 kit. Performance was validated in blinded replicates, and values were medication adjusted.

Results

Multivariable-adjusted analyses showed that sphingomyelins and ether-containing phosphatidylcholines were altered in preclinical biomarker-defined AD stages, whereas acylcarnitines and several amines, including the branched-chain amino acid valine and α-aminoadipic acid, changed in symptomatic stages. Several of the analytes showed consistent associations in the Rotterdam, Erasmus Rucphen Family, and Indiana Memory and Aging Studies. Partial correlation networks constructed for Aβ_1-42, tau, imaging, and cognitive changes provided initial biochemical insights for disease-related processes. Coexpression networks interconnected key metabolic effectors of disease.

Discussion

Metabolomics identified key disease-related metabolic changes and disease-progression-related changes. Defining metabolic changes during AD disease trajectory and its relationship to clinical phenotypes provides a powerful roadmap for drug and biomarker discovery.

Background

Deep learning has shown potential in various scientific domains but faces challenges when applied to complex, high-dimensional multi-omics data. Alzheimer's Disease (AD) is a neurodegenerative disorder that lacks targeted therapeutic options. This study introduces the Circular-Sliding Window Association Test (c-SWAT) to improve the classification accuracy in predicting AD using serum-based metabolomics data, specifically lipidomics.

Methods

The c-SWAT methodology builds upon the existing Sliding Window Association Test (SWAT) and utilizes a three-step approach: feature correlation analysis, feature selection, and classification. Data from 997 participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) served as the basis for model training and validation. Feature correlations were analyzed using Weighted Gene Co-expression Network Analysis (WGCNA), and Convolutional Neural Networks (CNN) were employed for feature selection. Random Forest was used for the final classification.

Findings

The application of c-SWAT resulted in a classification accuracy of up to 80.8% and an AUC of 0.808 for distinguishing AD from cognitively normal older adults. This marks a 9.4% improvement in accuracy and a 0.169 increase in AUC compared to methods without c-SWAT. These results were statistically significant, with a p-value of 1.04 × 10ˆ-4. The approach also identified key lipids associated with AD, such as Cer(d16:1/22:0) and PI(37:6).

Interpretation

Our results indicate that c-SWAT is effective in improving classification accuracy and in identifying potential lipid biomarkers for AD. These identified lipids offer new avenues for understanding AD and warrant further investigation.

Funding

The specific funding of this article is provided in the acknowledgements section.