Phenotyping Superagers Using Resting-State fMRI

BACKGROUND AND PURPOSE: Superagers are defined as older adults with episodic memory performance similar or superior to that in middle-aged adults. This study aimed to investigate the key differences in discriminative networks and their main nodes between superagers and cognitively average elderly controls. In addition, we sought to explore differences in sensitivity in detecting these functional activities across the networks at 3T and 7T MR imaging fields. MATERIALS AND METHODS: Fifty-five subjects 80 years of age or older were screened using a detailed neuropsychological protocol, and 31 participants, comprising 14 superagers and 17 cognitively average elderly controls, were included for analysis. Participants underwent resting-state-fMRI at 3T and 7T MR imaging. A prediction classification algorithm using a penalized regression model on the measurements of the network was used to calculate the probabilities of a healthy older adult being a superager. Additionally, ORs quantified the influence of each node across preselected networks. RESULTS: The key networks that differentiated superagers and elderly controls were the default mode, salience, and language networks. The most discriminative nodes (ORs > 1) in superagers encompassed areas in the precuneus posterior cingulate cortex, prefrontal cortex, temporoparietal junction, temporal pole, extrastriate superior cortex, and insula. The prediction classification model for being a superager showed better performance using the 7T compared with 3T resting-state-fMRI data set. CONCLUSIONS: Our findings suggest that the functional connectivity in the default mode, salience, and language networks can provide potential imaging biomarkers for predicting superagers. The 7T field holds promise for the most appropriate study setting to accurately detect the functional connectivity patterns in superagers

Identification of Markers that Distinguish Monocyte-Derived Fibrocytes from Monocytes, Macrophages, and Fibroblasts

The processes that drive fibrotic diseases are complex and include an influx of peripheral blood monocytes that can differentiate into fibroblast-like cells called fibrocytes. Monocytes can also differentiate into other cell types, such as tissue macrophages. The ability to discriminate between monocytes, macrophages, fibrocytes, and fibroblasts in fibrotic lesions could be beneficial in identifying therapies that target either stromal fibroblasts or fibrocytes. and in sections from human lung. We found that markers such as CD34, CD68, and collagen do not effectively discriminate between the four cell types. In addition, IL-4, IL-12, IL-13, IFN-γ, and SAP differentially regulate the expression of CD32, CD163, CD172a, and CD206 on both macrophages and fibrocytes. Finally, CD49c (α3 integrin) expression identifies a subset of fibrocytes, and this subset increases with time in culture.These results suggest that discrimination of monocytes, macrophages, fibrocytes, and fibroblasts in fibrotic lesions is possible, and this may allow for an assessment of fibrocytes in fibrotic diseases

Noise Cancellation: Viral Fine Tuning of the Cellular Environment for Its Own Genome Replication

Productive replication of DNA viruses elicits host cell DNA damage responses, which cause both beneficial and detrimental effects on viral replication. In response to the viral productive replication, host cells attempt to attenuate the S-phase cyclin-dependent kinase (CDK) activities to inhibit viral replication. However, accumulating evidence regarding interactions between viral factors and cellular signaling molecules indicate that viruses utilize them and selectively block the downstream signaling pathways that lead to attenuation of the high S-phase CDK activities required for viral replication. In this review, we describe the sophisticated strategy of Epstein-Barr virus to cancel such “noisy” host defense signals in order to hijack the cellular environment

Gene-centric meta-analysis of lipid traits in African, East Asian and Hispanic populations.

Meta-analyses of European populations has successfully identified genetic variants in over 100 loci associated with lipid levels, but our knowledge in other ethnicities remains limited. To address this, we performed dense genotyping of ∼2,000 candidate genes in 7,657 African Americans, 1,315 Hispanics and 841 East Asians, using the IBC array, a custom ∼50,000 SNP genotyping array. Meta-analyses confirmed 16 lipid loci previously established in European populations at genome-wide significance level, and found multiple independent association signals within these lipid loci. Initial discovery and in silico follow-up in 7,000 additional African American samples, confirmed two novel loci: rs5030359 within ICAM1 is associated with total cholesterol (TC) and low-density lipoprotein cholesterol (LDL-C) (p = 8.8×10(-7) and p = 1.5×10(-6) respectively) and a nonsense mutation rs3211938 within CD36 is associated with high-density lipoprotein cholesterol (HDL-C) levels (p = 13.5×10(-12)). The rs3211938-G allele, which is nearly absent in European and Asian populations, has been previously found to be associated with CD36 deficiency and shows a signature of selection in Africans and African Americans. Finally, we have evaluated the effect of SNPs established in European populations on lipid levels in multi-ethnic populations and show that most known lipid association signals span across ethnicities. However, differences between populations, especially differences in allele frequency, can be leveraged to identify novel signals, as shown by the discovery of ICAM1 and CD36 in the current report

Decoding the morphological diversity in two dimensional crystalline porous polymers by core planarity modulation

Two new chemically stable triazine- and phenylcore-based crystalline porous polymers (CPPs) have been synthesized using a single-step template-free solvothermal route. Unique morphological diversities were observed for these CPPs [2,3-DhaTta (ribbon) and 2,3-DhaTab (hollow sphere)] by simply altering the linker planarity. A detailed time-dependent study established a significant correlation between the molecular level structures of building blocks with the morphology of CPPs. Moreover, a DFT study was done for calculating the interlayer stacking energy, which revealed that the extent of stacking efficiency is responsible for governing the morphological diversity in these CPPs