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Purpose: Early assessment of renal allograft function post‐transplantation is crucial to minimize and control allograft rejection. Biopsy — the gold standard — is used only as a last resort due to its invasiveness, high cost, adverse events (e.g., bleeding, infection, etc.), and the time for reporting. To overcome these limitations, a renal computer‐assisted diagnostic (Renal‐CAD) system was developed to assess kidney transplant function. Methods: The developed Renal‐CAD system integrates data collected from two image‐based sources and two clinical‐based sources to assess renal transplant function. The imaging sources were the apparent diffusion coefficients (ADCs) extracted from 47 diffusion‐weighted magnetic resonance imaging (DW‐MRI) scans at 11 different b‐values (b0, b50, b100,..., b1000 s/mm2), and the transverse relaxation rate (R2*) extracted from 30 blood oxygen level‐dependent MRI (BOLD‐MRI) scans at 5 different echo times (TEs = 2, 7, 12, 17, and 22 ms). Serum creatinine (SCr) and creatinine clearance (CrCl) were the clinical sources for kidney function evaluation. The Renal‐CAD system initially performed kidney segmentation using the level‐set method, followed by estimation of the ADCs from DW‐MRIs and the R2* from BOLD‐MRIs. ADCs and R2* estimates from 30 subjects that have both types of scans were integrated with their associated SCr and CrCl. The integrated biomarkers were then used as our discriminatory features to train and test a deep learning‐based classifier, namely stacked autoencoders (SAEs) to differentiate non‐rejection (NR) from acute rejection (AR) renal transplants. Results: Using a leave‐one‐subject‐out cross‐validation approach along with SAEs, the Renal‐CAD system demonstrated 93.3% accuracy, 90.0% sensitivity, and 95.0% specificity in differentiating AR from NR. Robustness of the Renal‐CAD system was also confirmed by the area under the curve value of 0.92. Using a stratified tenfold cross‐validation approach, the Renal‐CAD system demonstrated its reproducibility and robustness by a diagnostic accuracy of 86.7%, sensitivity of 80.0%, specificity of 90.0%, and AUC of 0.88. Conclusion: The obtained results demonstrate the feasibility and efficacy of accurate, noninvasive identification of AR at an early stage using the Renal‐CAD system. [ABSTRACT FROM AUTHOR]

Previous research on autism risk (ASD), developmental regulatory (DevReg), and central nervous system (CNS) genes suggests they tend to be large in size, enriched in nested repeats, and mutation intolerant. The relevance of these genomic features is intriguing yet poorly understood. In this study, we investigated the feature landscape of these gene groups to discover structural themes useful in interpreting their function, developmental patterns, and evolutionary history. ASD, DevReg, CNS, housekeeping, and whole genome control (WGC) groups were compiled using various resources. Multiple gene features of interest were extracted from NCBI/UCSC Bioinformatics. Residual variation intolerance scores, Exome Aggregation Consortium pLI scores, and copy number variation data from Decipher were used to estimate variation intolerance. Gene age and protein–protein interactions (PPI) were estimated using Ensembl and EBI Intact databases, respectively. Compared to WGC: ASD, DevReg, and CNS genes are longer, produce larger proteins, maintain greater numbers/density of conserved noncoding elements and transposable elements, produce more transcript variants, and are comparatively variation intolerant. After controlling for gene size, mutation tolerance, and clinical association, ASD genes still retain many of these same features. In addition, we also found that ASD genes that are extremely mutation intolerant have larger PPI networks. These data support many of the recent findings within the field of autism genetics but also expand our understanding of the evolution of these broad gene groups, their potential regulatory complexity, and the extent to which they interact with the cellular network. Autism Res 2019, 12: 860–869. © 2019 International Society for Autism Research, Wiley Periodicals, Inc. Lay Summary: Autism risk genes are more ancient compared to other genes in the genome. As such, they exhibit physical features related to their age, including long gene and protein size and regulatory sequences that help to control gene expression. They share many of these same features with other genes that are expressed in the brain and/or are associated with prenatal development. [ABSTRACT FROM AUTHOR]

It is possible that during long lasting chronic infections such as tuberculosis (TB) and leprosy individuals who generate a stronger immune response will produce a chronic shift in the systemic levels of inflammatory proteins. Consequently, the systemic immunological shift could affect inflammatory responses against other persistent pathogens such as Porphyromonas gingivalis associated with periodontal disease (PD). Objective To determine if in vitro exposure to Mycobacterium tuberculosis or M. leprae lysates impacts subsequent immune responses to P. gingivalis; and to propose a new dialogue between experimental immunology and paleopathology. Material and methods We sequentially (2 days protocol) exposed peripheral blood mononuclear cells (PBMCs) from healthy donors to bacterial lysates either from M. tuberculosis, or M. leprae, or P. gingivalis. After collecting all supernatants, we measured the expression of immune proteins TNFα and IFNγ using an enzyme-linked immunosorbent assay. Results Early exposure (day 1) of PBMCs to M. leprae or M. tuberculosis lysates induces an inflammatory shift detected by the increase of TNFα and IFNγ when the same cells are subsequently (day 2) exposed to oral pathogen P. gingivalis. Discussion By extrapolating these results, we suggest that chronic infections, such as TB and leprosy, could generate a systemic immunological shift that can affect other inflammatory processes such the one present in PD. We propose that the presence and severity of PD should be explored as a proxy for inflammatory status or competence when reconstructing the health profile in past populations. [ABSTRACT FROM AUTHOR]

Radially oriented ensembles of neurons and their projections, termed minicolumns, are hypothesized to be the basic microcircuit of mammalian cerebral cortex. Minicolumns can be divided into a core and a peripheral neuropil space compartment. The core of minicolumns is constrained by the migratory path of pyramidal cells and their attendant radially oriented projections. Variation in minicolumnar morphometry and density is observed both within and across species. Using a scale-independent measure of variability in minicolumnar width ( VCW), we demonstrated a significant increase in VCW in layers III–V of striate cortex in humans relative to macaques and chimpanzees. Despite changes in minicolumnar width ( CW) across species, their core space ( w) remained the same. Given that cellular elements and processes within the peripheral neuropil space of minicolumns are derived from assorted sources, cross-species differences in VCW may result from genetic and epigenetic influences acting primarily on this compartment of the minicolumn. [ABSTRACT FROM AUTHOR]

Analyzes morphological differences between minicolumns in human and nonhuman primate cortex. Column width; Neuropil space; Grey level ratio; Core grey level ratio; Linearity; Comparisons of columns in chimpanzee and rhesus monkey.