Rat endomannosidase is a glycosidic enzyme that catalyzes the cleavage of di-, tri-, or tetrasaccharides (Glc(1-3)Man), from N-glycosylation intermediates with terminal glucose residues. To date it is the only characterized member of this class of endomannosidic enzymes. Although this protein has been demonstrated to localize to the Golgi lumenal membrane, the mechanism by which this occurs has not yet been determined. Using the rat endomannosidase sequence, we identified three homologs, one each in the human, mouse, and rat genomes. Alignment of the four encoded protein sequences demonstrated that the newly identified sequences are highly conserved but differed significantly at the N-terminus from the previously reported protein. In this study we have cloned two novel endomannosidase sequences from rat and human cDNA libraries, but were unable to amplify the open reading frame of the previously reported rat sequence. Analysis of the rat genome confirmed that the 59- and 39-termini of the previously reported sequence were in fact located on different chromosomes. This, in combination with our inability to amplify the previously reported sequence, indicated that the N-terminus of the rat endomannosidase sequence previously published was likely in error (a cloning artifact), and that the sequences reported in the current study encode the intact proteins. Furthermore, unlike the previous sequence, the three ORFs identified in this study encode proteins containing a single N-terminal transmembrane domain. Here we demonstrate that this region is responsible for Golgi localization and in doing so confirm that endomannosidase is a type II membrane protein, like the majority of other secretory pathway glycosylation enzymes.

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Diabetic Retinopathy (DR) is a commonly occurring disease among diabetic patients that affects retina lesions and vision. Since DR is irreversible, an earlier diagnosis of DR can considerably decrease the risk of vision loss. Manual detection and classification of DR from retinal fundus images is time-consuming, expensive, and prone to errors, contrasting to CAD models. In recent times, DL models have become a familiar topic in several applications, particularly medical image classification. With this motivation, this paper presents new deep learning-empowered diabetic retinopathy detection and classification (DL-DRDC) model. The DL-DRDC technique aims to recognize and categorize different grades of DR using retinal fundus images. The proposed model involves the Contrast Limited Adaptive Histogram Equalization (CLAHE) technique as a pre-processing stage, which is used to enhance the contrast of the fundus images and improve the low contrast of medical images. Besides, the CLAHE is applied to the L channel of the retina images that have higher contrast. In addition, a deep learning-based Efficient Net-based feature extractor is used to generate feature vectors from pre-processed images. Moreover, a deep neural network (DNN) is used as a classifier model to allocate proper DR stages. An extensive set of experimental analyses takes place using a benchmark MESSIDOR dataset and the results are examined interms of different evaluation parameters. The simulation values highlighted the better DR diagnostic efficiency of the DL-DRDC technique over the recent techniques.

Bovine rhodopsin was subjected to reductive methylation in the dark using formaldehyde and high specific activity sodium borotritide. After purification by gel filtration and affinity chromatography on Concanavalin A-Sepharose, the product retained its immunoreactive properties. [3H]-Reductively methylated rhodopsin (specific activity, 32 Ci/mmole) was suitable for use in radioimmunoassays for rhodopsin, having many advantages over radioiodinated rhodopsin for this purpose. The site of the reductive methylation was shown to be the non-active site lysines with the production of tritiated N-epsilon-dimethyllysine and tritiated N-epsilon-methyllysine in a molar ratio of about 1.3:1, respectively. In terms of stability, ease of preparation, and specificity, tritiated, reductively methylated rhodopsin presents itself as a preferable ligand to radioiodinated rhodopsin in many applications, such as the radioimmunoassay.