Artificial intelligence-derived neurofibrillary tangle burden is associated with antemortem cognitive impairment.

Tauopathies are a category of neurodegenerative diseases characterized by the presence of abnormal tau protein-containing neurofibrillary tangles (NFTs). NFTs are universally observed in aging, occurring with or without the concomitant accumulation of amyloid-beta peptide (Aß) in plaques that typifies Alzheimer disease (AD), the most common tauopathy. Primary age-related tauopathy (PART) is an Aß-independent process that affects the medial temporal lobe in both cognitively normal and impaired subjects. Determinants of symptomology in subjects with PART are poorly understood and require clinicopathologic correlation; however, classical approaches to staging tau pathology have limited quantitative reproducibility. As such, there is a critical need for unbiased methods to quantitatively analyze tau pathology on the histological level. Artificial intelligence (AI)-based convolutional neural networks (CNNs) generate highly accurate and precise computer vision assessments of digitized pathology slides, yielding novel histology metrics at scale. Here, we performed a retrospective autopsy study of a large cohort (n = 706) of human post-mortem brain tissues from normal and cognitively impaired elderly individuals with mild or no Aß plaques (average age of death of 83.1 yr, range 55-110). We utilized a CNN trained to segment NFTs on hippocampus sections immunohistochemically stained with antisera recognizing abnormal hyperphosphorylated tau (p-tau), which yielded metrics of regional NFT counts, NFT positive pixel density, as well as a novel graph-theory based metric measuring the spatial distribution of NFTs. We found that several AI-derived NFT metrics significantly predicted the presence of cognitive impairment in both the hippocampus proper and entorhinal cortex (p < 0.0001). When controlling for age, AI-derived NFT counts still significantly predicted the presence of cognitive impairment (p = 0.04 in the entorhinal cortex; p = 0.04 overall). In contrast, Braak stage did not predict cognitive impairment in either age-adjusted or unadjusted models. These findings support the hypothesis that NFT burden correlates with cognitive impairment in PART. Furthermore, our analysis strongly suggests that AI-derived metrics of tau pathology provide a powerful tool that can deepen our understanding of the role of neurofibrillary degeneration in cognitive impairment.

SARS-CoV-2 detection with aptamer-functionalized gold nanoparticles.

A rapid detection test for SARS-CoV-2 is urgently required to monitor virus spread and containment. Here, we describe a test that uses nanoprobes, which are gold nanoparticles functionalized with an aptamer specific to the spike membrane protein of SARS-CoV-2. An enzyme-linked immunosorbent assay confirms aptamer binding with the spike protein on gold surfaces. Protein recognition occurs by adding a coagulant, where nanoprobes with no bound protein agglomerate while those with sufficient bound protein do not. Using plasmon absorbance spectra, the nanoprobes detect 16 nM and higher concentrations of spike protein in phosphate-buffered saline. The time-varying light absorbance is examined at 540 nm to determine the critical coagulant concentration required to agglomerates the nanoprobes, which depends on the protein concentration. This approach detects 3540 genome copies/µl of inactivated SARS-CoV-2.

Low-level blast exposure disrupts gliovascular and neurovascular connections and induces a chronic vascular pathology in rat brain.

Much concern exists over the role of blast-induced traumatic brain injury (TBI) in the chronic cognitive and mental health problems that develop in veterans and active duty military personnel. The brain vasculature is particularly sensitive to blast injury. The aim of this study was to characterize the evolving molecular and histologic alterations in the neurovascular unit induced by three repetitive low-energy blast exposures (3 × 74.5 kPa) in a rat model mimicking human mild TBI or subclinical blast exposure. High-resolution two-dimensional differential gel electrophoresis (2D-DIGE) and matrix-assisted laser desorption/ionization (MALDI) mass spectrometry of purified brain vascular fractions from blast-exposed animals 6 weeks post-exposure showed decreased levels of vascular-associated glial fibrillary acidic protein (GFAP) and several neuronal intermediate filament proteins (α-internexin and the low, middle, and high molecular weight neurofilament subunits). Loss of these proteins suggested that blast exposure disrupts gliovascular and neurovascular interactions. Electron microscopy confirmed blast-induced effects on perivascular astrocytes including swelling and degeneration of astrocytic endfeet in the brain cortical vasculature. Because the astrocyte is a major sensor of neuronal activity and regulator of cerebral blood flow, structural disruption of gliovascular integrity within the neurovascular unit should impair cerebral autoregulation. Disrupted neurovascular connections to pial and parenchymal blood vessels might also affect brain circulation. Blast exposures also induced structural and functional alterations in the arterial smooth muscle layer. Interestingly, by 8 months after blast exposure, GFAP and neuronal intermediate filament expression had recovered to control levels in isolated brain vascular fractions. However, despite this recovery, a widespread vascular pathology was still apparent at 10 months after blast exposure histologically and on micro-computed tomography scanning. Thus, low-level blast exposure disrupts gliovascular and neurovascular connections while inducing a chronic vascular pathology.

In silico prediction of tumor antigens derived from functional missense mutations of the cancer gene census.

Antigen-specific immune responses against peptides derived from missense gene mutations have been identified in multiple cancers. The application of personalized peptide vaccines based on the tumor mutation repertoire of each cancer patient is a near-term clinical reality. These peptides can be identified for pre-validation by leveraging the results of massive gene sequencing efforts in cancer. In this study, we utilized NetMHC 3.2 to predict nanomolar peptide binding affinity to 57 human HLA-A and B alleles. All peptides were derived from 5,685 missense mutations in 312 genes annotated as functionally relevant in the Cancer Genome Project. Of the 26,672,189 potential 8-11 mer peptide-HLA pairs evaluated, 0.4% (127,800) display binding affinities < 50 nM, predicting high affinity interactions. These peptides can be segregated into two groups based on the binding affinity to HLA proteins relative to germline-encoded sequences: peptides for which both the mutant and wild-type forms are high affinity binders, and peptides for which only the mutant form is a high affinity binder. Current evidence directs the attention to mutations that increase HLA binding affinity, as compared with cognate wild-type peptide sequences, as these potentially are more relevant for vaccine development from a clinical perspective. Our analysis generated a database including all predicted HLA binding peptides and the corresponding change in binding affinity as a result of point mutations. Our study constitutes a broad foundation for the development of personalized peptide vaccines that hone-in on functionally relevant targets in multiple cancers in individuals with diverse HLA haplotypes.