Refine
Document Type
- Article (3)
- Conference Proceeding (1)
Language
- English (4)
Has Fulltext
- yes (4)
Is part of the Bibliography
- no (4)
Keywords
- CAPON (1)
- Cellulase (1)
- Cellulases (1)
- Computational biology and bioinformatics (1)
- Diseases (1)
- Flagellaten (1)
- Flagellates (1)
- Image processing (1)
- Infectious diseases (1)
- Mastotermes (1)
- NOS-I (1)
- NOS1AP (1)
- Spirocheten (1)
- Spirochetes (1)
- Termiten (1)
- Termites (1)
- nNOS (1)
- nitric oxide (1)
- psychiatric disorders (1)
Institute
- Medizin (2)
- Biochemie, Chemie und Pharmazie (1)
- Biowissenschaften (1)
Background: Nitric oxide synthase 1 adaptor protein (NOS1AP; previously named CAPON) is linked to the glutamatergic postsynaptic density through interaction with neuronal nitric oxide synthase (nNOS). NOS1AP and its interaction with nNOS have been associated with several mental disorders. Despite the high levels of NOS1AP expression in the hippocampus and the relevance of this brain region in glutamatergic signalling as well as mental disorders, a potential role of hippocampal NOS1AP in the pathophysiology of these disorders has not been investigated yet.
Methods: To uncover the function of NOS1AP in hippocampus, we made use of recombinant adeno-associated viruses to overexpress murine full-length NOS1AP or the NOS1AP carboxyterminus in the hippocampus of mice. We investigated these mice for changes in gene expression, neuronal morphology, and relevant behavioural phenotypes.
Findings: We found that hippocampal overexpression of NOS1AP markedly increased the interaction of nNOS with PSD-95, reduced dendritic spine density, and changed dendritic spine morphology at CA1 synapses. At the behavioural level, we observed an impairment in social memory and decreased spatial working memory capacity.
Interpretation: Our data provide a mechanistic explanation for a highly selective and specific contribution of hippocampal NOS1AP and its interaction with the glutamatergic postsynaptic density to cross-disorder pathophysiology. Our findings allude to therapeutic relevance due to the druggability of this molecule.
The lower wood-feeding Australian termite Mastotermes darwiniensis Froggatt (Fig. 1) is the only living member of the family Mastotermitidae. The complex symbiotic hindgut flora consists of protozoa (formerly named Archaezoa; Cleveland & Grimstone 1964; Brugerolle & al. 1994; Berchtold & König 1995; Fröhlich & König 1999a, b), bacteria (Berchtold & König 1996; Berchtold & al. 1999), archaea (Fröhlich & König 1999a, b) and yeasts (Prillinger & al. 1996; Schäfer & al. 1996). The digestive system of Mastotermes darwiniensis consists of the foregut with the crop and the gizzard, the midgut, and the hindgut (Noirot & Noirot-Timothée 1969; 1995). The hindgut consists of five segments (P1 – P5): the proctodeal segment, the enteric valve, the paunch, the colon and the rectum. The paunch is the main microbial fermentation chamber, but the colon also contains microorganisms. The paunch is subdivided into a dilated thin-walled region (P3a) and a thick walled more tubular region (P3b) (Fig. 1c). In the case of Mastotermes darwiniensis oxygen diffusion gradients could be detected up to 100 μm below the epithelium (Berchtold & al., 1999).
Coronavirus disease 2019 (COVID-19) is a global pandemic posing significant health risks. The diagnostic test sensitivity of COVID-19 is limited due to irregularities in specimen handling. We propose a deep learning framework that identifies COVID-19 from medical images as an auxiliary testing method to improve diagnostic sensitivity. We use pseudo-coloring methods and a platform for annotating X-ray and computed tomography images to train the convolutional neural network, which achieves a performance similar to that of experts and provides high scores for multiple statistical indices (F1 scores > 96.72% (0.9307, 0.9890) and specificity >99.33% (0.9792, 1.0000)). Heatmaps are used to visualize the salient features extracted by the neural network. The neural network-based regression provides strong correlations between the lesion areas in the images and five clinical indicators, resulting in high accuracy of the classification framework. The proposed method represents a potential computer-aided diagnosis method for COVID-19 in clinical practice.