Associations of visual, hearing, and dual sensory impairment with motoric cognitive risk syndrome: Observational and Mendelian randomization analyses.

BACKGROUND: Dementia is associated with individual vision impairment (VI) and hearing impairment (HI). However, little is known about their associations with motoric cognitive risk syndrome (MCR), a pre-dementia stage. We investigated the association of VI, HI, and dual sensory impairment (DSI) with MCR and to further evaluate causal relationships using Mendelian randomization (MR) approach. METHODS: First, an observational study was conducted in the China Health and Retirement Longitudinal Study (CHARLS). Evaluate the cross-sectional and longitudinal associations of VI, HI, and DSI with MCR using the logistic regression models and Cox proportional hazard models, respectively. Second, evaluate the causal association between VI and HI with MCR using MR analysis. The GWAS data was used for genetic instruments, including 88,250 of European ancestry (43,877 cases and 44,373 controls) and 504,307 with "white British" ancestry (100,234 cases and 404,073 controls), respectively; MCR information was obtained from the GWAS with 22,593 individuals. Inverse variance weighted was the primary method and sensitivity analysis was used to evaluate the robustness of MR methods. RESULTS: In the observational study, VI (HR: 1.767, 95%CI: 1.331-2.346; p < 0.001), HI (HR: 1.461, 95%CI: 1.196-1.783; p < 0.001), and DSI (HR: 1.507, 95%CI: 1.245-1.823; p < 0.001) were significantly associated with increased risk of MCR. For the MR, no causal relationship between VI (OR: 0.902, 95% CI: 0.593-1.372; p = 0.631) and HI (OR: 1.016, 95% CI: 0.989-1.043; p = 0.248) with MCR risk, which is consistent with the sensitivity analysis. CONCLUSION: VI, HI, and DSI were significantly associated with MCR, but MR analysis failed to provide evidence of their causal relationship. Emphasized the importance of sensory impairment screening in identifying high-risk populations for dementia.

HuNAC20 and HuNAC25, Two Novel NAC Genes from Pitaya, Confer Cold Tolerance in Transgenic Arabidopsis.

NAC transcription factors are one of the largest families of transcriptional regulators in plants, and members of the gene family play vital roles in regulating plant growth and development processes including biotic/abiotic stress responses. However, little information is available about the NAC family in pitaya. In this study, we conducted a genome-wide analysis and a total of 64 NACs (named HuNAC1-HuNAC64) were identified in pitaya (Hylocereus). These genes were grouped into fifteen subgroups with diversities in gene proportions, exon-intron structures, and conserved motifs. Genome mapping analysis revealed that HuNAC genes were unevenly scattered on all eleven chromosomes. Synteny analysis indicated that the segmental duplication events played key roles in the expansion of the pitaya NAC gene family. Expression levels of these HuNAC genes were analyzed under cold treatments using qRT-PCR. Four HuNAC genes, i.e., HuNAC7, HuNAC20, HuNAC25, and HuNAC30, were highly induced by cold stress. HuNAC7, HuNAC20, HuNAC25, and HuNAC30 were localized exclusively in the nucleus. HuNAC20, HuNAC25, and HuNAC30 were transcriptional activators while HuNAC7 was a transcriptional repressor. Overexpression of HuNAC20 and HuNAC25 in Arabidopsis thaliana significantly enhanced tolerance to cold stress through decreasing ion leakage, malondialdehyde (MDA), and H2O2 and O2- accumulation, accompanied by upregulating the expression of cold-responsive genes (AtRD29A, AtCOR15A, AtCOR47, and AtKIN1). This study presents comprehensive information on the understanding of the NAC gene family and provides candidate genes to breed new pitaya cultivars with tolerance to cold conditions through genetic transformation.

HRadNet: A Hierarchical Radiomics-Based Network for Multicenter Breast Cancer Molecular Subtypes Prediction.

Breast cancer is a heterogeneous disease, where molecular subtypes of breast cancer are closely related to the treatment and prognosis. Therefore, the goal of this work is to differentiate between luminal and non-luminal subtypes of breast cancer. The hierarchical radiomics network (HRadNet) is proposed for breast cancer molecular subtypes prediction based on dynamic contrast-enhanced magnetic resonance imaging. HRadNet fuses multilayer features with the metadata of images to take advantage of conventional radiomics methods and general convolutional neural networks. A two-stage training mechanism is adopted to improve the generalization capability of the network for multicenter breast cancer data. The ablation study shows the effectiveness of each component of HRadNet. Furthermore, the influence of features from different layers and metadata fusion are also analyzed. It reveals that selecting certain layers of features for a specified domain can make further performance improvements. Experimental results on three data sets from different devices demonstrate the effectiveness of the proposed network. HRadNet also has good performance when transferring to other domains without fine-tuning.