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Exploring individual hallmarks of brain ageing is important. Here, we propose the age-related glucose metabolism pattern (ARGMP) as a potential index to characterize brain ageing in cognitively normal (CN) elderly people. We collected 18F-fluorodeoxyglucose (18F-FDG) PET brain images from two independent cohorts: the Alzheimer's Disease Neuroimaging Initiative (ADNI, N = 127) and the Xuanwu Hospital of Capital Medical University, Beijing, China (N = 84). During follow-up (mean 80.60 months), 23 participants in the ADNI cohort converted to cognitive impairment. ARGMPs were identified using the scaled subprofile model/principal component analysis method, and cross-validations were conducted in both independent cohorts. A survival analysis was further conducted to calculate the predictive effect of conversion risk by using ARGMPs. The results showed that ARGMPs were characterized by hypometabolism with increasing age primarily in the bilateral medial superior frontal gyrus, anterior cingulate and paracingulate gyri, caudate nucleus, and left supplementary motor area and hypermetabolism in part of the left inferior cerebellum. The expression network scores of ARGMPs were significantly associated with chronological age (R = 0.808, p

Over the past decades, accumulating evidence suggests that the gut microbiome exerts a key role in Alzheimer's disease (AD). The Alzheimer's Association Workgroup is updating the diagnostic criteria for AD, which changed the profiles and categorization of biomarkers from "AT(N)" to "ATNIVS." Previously, most of studies focus on the correlation between the gut microbiome and amyloid beta deposition ("A"), the initial AD pathological feature triggering the "downstream" tauopathy and neurodegeneration. However, limited research investigated the interactions between the gut microbiome and other AD pathogenesis ("TNIVS"). In this review, we summarize current findings of the gut microbial characteristics in the whole spectrum of AD. Then, we describe the association of the gut microbiome with updated biomarker categories of AD pathogenesis. In addition, we outline the gut microbiome‐related therapeutic strategies for AD. Finally, we discuss current key issues of the gut microbiome research in the AD field and future research directions. Highlights: The new revised criteria for Alzheimer's disease (AD) proposed by the Alzheimer's Association Workgroup have updated the profiles and categorization of biomarkers from "AT(N)" to "ATNIVS."The associations of the gut microbiome with updated biomarker categories of AD pathogenesis are described.Current findings of the gut microbial characteristics in the whole spectrum of AD are summarized.Therapeutic strategies for AD based on the gut microbiome are proposed. [ABSTRACT FROM AUTHOR]

Epoxy resins are used in electronic packaging. However, its low thermal conductivity is a disadvantage and the high moisture absorption leads to defects such as cracking and delamination. In this work, the effect of thermo‐mechanical properties of epoxy resins under moisture‐heat coupling is investigated by molecular dynamics (MD) simulations. The diglycidyl ether of bisphenol A (DGEBA) and polyetheramine D230 are used as epoxy resin matrix and curing agents, respectively. The degree of cross–linking is set from 0% to 98% and the moisture mass fraction is set from 0 wt.% to 10 wt.%. The radius distribution function(RDF) shows that water molecules tend to appear ≈2.75 Å from the epoxy resin. The thermal conductivity of the epoxy resin increases by 54.55% for the maximum cross–linking degrees studied. And it increases by 19.23% when the moisture mass fraction is increased from 0 wt.% to 10 wt.%. The deformation of the epoxy resin is calculated which includes the coefficient of thermal expansion(CTE), coefficient of moisture expansion(CME), and coefficient of linear expansion. It is found that moisture expansion predominated at low temperatures and thermal expansion dominates at high temperatures. The analysis provides a theoretical basis for the study of hygrothermal coupling effects. [ABSTRACT FROM AUTHOR]

Exploring individual brain atrophy patterns is of great value in precision medicine for Alzheimer's disease (AD) and mild cognitive impairment (MCI). However, the current individual brain atrophy detection models are deficient. Here, we proposed a framework called generative adversarial network constrained multiple loss autoencoder (GANCMLAE) for precisely depicting individual atrophy patterns. The GANCMLAE model was trained using normal controls (NCs) from the Alzheimer's Disease Neuroimaging Initiative cohort, and the Xuanwu cohort was employed to validate the robustness of the model. The potential of the model for identifying different atrophy patterns of MCI subtypes was also assessed. Furthermore, the clinical application potential of the GANCMLAE model was investigated. The results showed that the model can achieve good image reconstruction performance on the structural similarity index measure (0.929 ± 0.003), peak signal‐to‐noise ratio (31.04 ± 0.09), and mean squared error (0.0014 ± 0.0001) with less latent loss in the Xuanwu cohort. The individual atrophy patterns extracted from this model are more precise in reflecting the clinical symptoms of MCI subtypes. The individual atrophy patterns exhibit a better discriminative power in identifying patients with AD and MCI from NCs than those of the t‐test model, with areas under the receiver operating characteristic curve of 0.867 (95%: 0.837–0.897) and 0.752 (95%: 0.71–0.790), respectively. Similar findings are also reported in the AD and MCI subgroups. In conclusion, the GANCMLAE model can serve as an effective tool for individualised atrophy detection. [ABSTRACT FROM AUTHOR]