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The metabolomic differences between the peels of Citrus reticulata ‘Chachi’ and different varieties of Citrus unshiu Marc. were analyzed to provide a theoretical basis and scientific evidence for the development and utilization of the peels of Citrus unshiu Marc.. The chemical composition of Citrus reticulata ‘Chachi’ (CZG) and three different varieties of Citrus unshiu Marc. (‘Youliang’ (YL), ‘Nangan 20’ (NG) and ‘Gongchuan’ (GC)) were determined by ultra-high performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS), and the metabolomic data were comparatively analyzed using principal component analysis (PCA), orthogonal partial least square-discriminant analysis (OPLS-DA) and cluster analysis (CA) to screen differential metabolites. Through database comparison, 790, 796 and 818 metabolites were identified between CZG and YL, NG and GC, respectively, with organic oxides, flavonoids, carboxylic acids and their derivatives accounting for the highest proportions, which are mainly involved in the metabolic pathways of flavonoid and flavanol biosynthesis, isoflavones biosynthesis, α-linolenic acid metabolism, and phenylpropanoid biosynthesis. Using the cutoff of variable importance in the projection (VIP) score > 1 with P value < 0.05, stable and reliable differential metabolites were selected. A total of 43 differential metabolites of 11 classes were identified between Citrus reticulata ‘Chachi’ and Citrus unshiu Marc., the predominant ones being flavonoids, amino acids and purine nucleosides. Most flavonoids in Citrus unshiu Marc. such as 5, 6, 7, 8, 3’, 4’, 5’-heptamethoxyflavone, quercetin, naringenin, and naringenin were up-regulated, which could be used as potential markers to differentiate Citrus reticulata ‘Chachi’ from Citrus unshiu Marc.. Furthermore, Citrus unshiu Marc. had a higher content of flavonoids and therefore could be used as a source of Citri Reticulatae Pericarpium.

BackgroundThe polygenic nature of Alzheimer's disease (AD) suggests that multiple variants jointly contribute to disease susceptibility. As an individual's genetic variants are constant throughout life, evaluating the combined effects of multiple disease-associated genetic risks enables reliable AD risk prediction. Because of the complexity of genomic data, current statistical analyses cannot comprehensively capture the polygenic risk of AD, resulting in unsatisfactory disease risk prediction. However, deep learning methods, which capture nonlinearity within high-dimensional genomic data, may enable more accurate disease risk prediction and improve our understanding of AD etiology. Accordingly, we developed deep learning neural network models for modeling AD polygenic risk.MethodsWe constructed neural network models to model AD polygenic risk and compared them with the widely used weighted polygenic risk score and lasso models. We conducted robust linear regression analysis to investigate the relationship between the AD polygenic risk derived from deep learning methods and AD endophenotypes (i.e., plasma biomarkers and individual cognitive performance). We stratified individuals by applying unsupervised clustering to the outputs from the hidden layers of the neural network model.ResultsThe deep learning models outperform other statistical models for modeling AD risk. Moreover, the polygenic risk derived from the deep learning models enables the identification of disease-associated biological pathways and the stratification of individuals according to distinct pathological mechanisms.ConclusionOur results suggest that deep learning methods are effective for modeling the genetic risks of AD and other diseases, classifying disease risks, and uncovering disease mechanisms.

Previous studies have shown that some anesthesia drugs can inhibit tumor growth and metastasis. As a clinical anesthetic drug, dezocine has been reported to play an important role in immune function. However, the effects of dezocine on ovarian cancer cell growth and metastasis are not fully understood. In this study, we found that dezocine dose-dependently inhibited the viability of ES-2 and SKOV3 cells. Dezocine suppressed the migration and invasion abilities of ovarian cancer cells, and promoted apoptosis. Moreover, the Akt/mTOR signaling pathway was also inhibited by dezocine. Furthermore, mechanism study showed that dezocine could significantly inhibit the expression of CRABP2, and CRABP2 overexpression reversed the inhibitory effects of dezocine on ovarian cancer cell proliferation and migration. In conclusion, dezocine has significant anti-tumor effects on the growth and metastatic potential of ovarian cancer cells, and CRABP2 functions as a downstream effector of dezocine.

INTRODUCTION: More robust non‐human primate models of Alzheimer's disease (AD) will provide new opportunities to better understand the pathogenesis and progression of AD. METHODS: We designed a CRISPR/Cas9 system to achieve precise genomic deletion of exon 9 in cynomolgus monkeys using two guide RNAs targeting the 3′ and 5′ intron sequences of PSEN1 exon 9. We performed biochemical, transcriptome, proteome, and biomarker analyses to characterize the cellular and molecular dysregulations of this non‐human primate model. RESULTS: We observed early changes of AD‐related pathological proteins (cerebrospinal fluid Aβ42 and phosphorylated tau) in PSEN1 mutant (ie, PSEN1‐ΔE9) monkeys. Blood transcriptome and proteome profiling revealed early changes in inflammatory and immune molecules in juvenile PSEN1‐ΔE9 cynomolgus monkeys. DISCUSSION: PSEN1 mutant cynomolgus monkeys recapitulate AD‐related pathological protein changes, and reveal early alterations in blood immune signaling. Thus, this model might mimic AD‐associated pathogenesis and has potential utility for developing early diagnostic and therapeutic interventions. Highlights: A dual‐guide CRISPR/Cas9 system successfully mimics AD PSEN1‐ΔE9 mutation by genomic excision of exon 9.PSEN1 mutant cynomolgus monkey‐derived fibroblasts exhibit disrupted PSEN1 endoproteolysis and increased Aβ secretion.Blood transcriptome and proteome profiling implicate early inflammatory and immune molecular dysregulation in juvenile PSEN1 mutant cynomolgus monkeys.Cerebrospinal fluid from juvenile PSEN1 mutant monkeys recapitulates early changes of AD‐related pathological proteins (increased Aβ42 and phosphorylated tau). [ABSTRACT FROM AUTHOR]