Choriocapillaris reduction accurately discriminates against early-onset Alzheimer's disease.

INTRODUCTION: This study addresses the urgent need for non-invasive early-onset Alzheimer's disease (EOAD) prediction. Using optical coherence tomography angiography (OCTA), we present a choriocapillaris model sensitive to EOAD, correlating with serum biomarkers. METHODS: Eighty-four EOAD patients and 73 controls were assigned to swept-source OCTA (SS-OCTA) or the spectral domain OCTA (SD-OCTA) cohorts. Our hypothesis on choriocapillaris predictive potential in EOAD was tested and validated in these two cohorts. RESULTS: Both cohorts revealed diminished choriocapillaris signals, demonstrating the highest discriminatory capability (area under the receiver operating characteristic curve: SS-OCTA 0.913, SD-OCTA 0.991; P < 0.001). A sparser SS-OCTA choriocapillaris correlated with increased serum amyloid beta (Aß)42, Aß42/40, and phosphorylated tau (p-tau)181 levels (all P < 0.05). Apolipoprotein E status did not affect choriocapillaris measurement. DISCUSSION: The choriocapillaris, observed in both cohorts, proves sensitive to EOAD diagnosis, and correlates with serum Aß and p-tau181 levels, suggesting its potential as a diagnostic tool for identifying and tracking microvascular changes in EOAD. HIGHLIGHTS: Optical coherence tomography angiography may be applied for non-invasive screening of Alzheimer's disease (AD). Choriocapillaris demonstrates high sensitivity and specificity for early-onset AD diagnosis. Microvascular dynamics abnormalities are associated with AD.

Protein posttranslational modifications in health and diseases: Functions, regulatory mechanisms, and therapeutic implications.

Protein posttranslational modifications (PTMs) refer to the breaking or generation of covalent bonds on the backbones or amino acid side chains of proteins and expand the diversity of proteins, which provides the basis for the emergence of organismal complexity. To date, more than 650 types of protein modifications, such as the most well-known phosphorylation, ubiquitination, glycosylation, methylation, SUMOylation, short-chain and long-chain acylation modifications, redox modifications, and irreversible modifications, have been described, and the inventory is still increasing. By changing the protein conformation, localization, activity, stability, charges, and interactions with other biomolecules, PTMs ultimately alter the phenotypes and biological processes of cells. The homeostasis of protein modifications is important to human health. Abnormal PTMs may cause changes in protein properties and loss of protein functions, which are closely related to the occurrence and development of various diseases. In this review, we systematically introduce the characteristics, regulatory mechanisms, and functions of various PTMs in health and diseases. In addition, the therapeutic prospects in various diseases by targeting PTMs and associated regulatory enzymes are also summarized. This work will deepen the understanding of protein modifications in health and diseases and promote the discovery of diagnostic and prognostic markers and drug targets for diseases.

An all-round AI-Chemist with a scientific mind.

The realization of automated chemical experiments by robots unveiled the prelude to an artificial intelligence (AI) laboratory. Several AI-based systems or robots with specific chemical skills have been demonstrated, but conducting all-round scientific research remains challenging. Here, we present an all-round AI-Chemist equipped with scientific data intelligence that is capable of performing basic tasks generally required in chemical research. Based on a service platform, the AI-Chemist is able to automatically read the literatures from a cloud database and propose experimental plans accordingly. It can control a mobile robot in-house or online to automatically execute the complete experimental process on 14 workstations, including synthesis, characterization and performance tests. The experimental data can be simultaneously analysed by the computational brain of the AI-Chemist through machine learning and Bayesian optimization, allowing a new hypothesis for the next iteration to be proposed. The competence of the AI-Chemist has been scrutinized by three different chemical tasks. In the future, the more advanced all-round AI-Chemists equipped with scientific data intelligence may cause changes to the landscape of the chemical laboratory.