Molecular Characterization, Developmental Expression and Immunolocalization of Clathrin Heavy Chain in the Ovary of the American Cockroach, Periplaneta Americana During Oogenesis

Clathrin is the principal protein involved in receptor mediate endocytosis and the main component of the coated vesicles. It is composed of three identical clathrin heavy chains (CHC), each with an attached light chain. We characterized the deduced amino acid sequence of the partial cDNA clone of the American cockroach, Periplaneta americana (Pam) CHC. The analysis showed that this sequence is represented as multiple alpha helical repeats occurred in the arm region of the CHC and displayed a high level of identity and similarity to mosquitoes and Drosophila melanogaster CHCs. This is the first report on CHC from a hemimetabolous insect. The amplified CHC probe could hybridize two CHC transcripts in the current preparations, 6.3 kb and 7.3 kb. The Northern blot analysis confirmed that a 6.3 kb transcript is specifically expressed in ovarian tissues at high levels throughout the ovarian development, especially in previtellogenic ovaries (Days 1-4) but dropped during the vitellogenic period (days 5-7) and ultimately no transcript was detected in fully vitellogenic ovaries (days 9-13). Immunoblot analysis detected an ovary specific CHC protein of ~175 kDa that was present in previtellogenic ovaries on the day of female emergence and after initiation of vitellogenesis and onset of Vg uptake. Immunocytochemistry localized CHC protein to germ-line derived cells, oocytes, and revealed that CHC translation begins very early during oocyte differentiation in the germarium. The present work suggested a possible role for clathrin in the early fluid phase endocytosis (pinocytosis) in addition to its role in receptor-mediated endocytosis

Differential Pulse Voltammetric Determination of Loperamide in a Pharmaceutical Dosaqe Form

A voltammetric study of the oxidation of loperamide has been carried out at the glassy carbon electrode. This compound exhibited a single peak in Britton-Robinson buffer solutions of pH 5.0–11.0, with a maximum current at pH 8.0. The electrochemical oxidation of loperamide is identified as an irreversible, diffusion-controlled process. Based on this study, a simple, rapid and sensitive voltammetric method was applied, without any interference from the excipients, to the determination of the drug in a capsule dosage form

Multiple Ocular Disease Diagnosis Using Fundus Images Based on Multi-Label Deep Learning Classification

Designing computer-aided diagnosis (CAD) systems that can automatically detect ocular diseases (ODs) has become an active research field in the health domain. Although the human eye might have more than one OD simultaneously, most existing systems are designed to detect specific eye diseases. Therefore, it is crucial to develop new CAD systems that can detect multiple ODs simultaneously. This paper presents a novel multi-label convolutional neural network (ML-CNN) system based on ML classification (MLC) to diagnose various ODs from color fundus images. The proposed ML-CNN-based system consists of three main phases: the preprocessing phase, which includes normalization and augmentation using several transformation processes, the modeling phase, and the prediction phase. The proposed ML-CNN consists of three convolution (CONV) layers and one max pooling (MP) layer. Then, two CONV layers are performed, followed by one MP and dropout (DO). After that, one flatten layer is performed, followed by one fully connected (FC) layer. We added another DO once again, and finally, one FC layer with 45 nodes is performed. The system outputs the probabilities of all 45 diseases in each image. We validated the model by using cross-validation (CV) and measured the performance by five different metrics: accuracy (ACC), recall, precision, Dice similarity coefficient (DSC), and area under the curve (AUC). The results are 94.3%, 80%, 91.5%, 99%, and 96.7%, respectively. The comparisons with the existing built-in models, such as MobileNetV2, DenseNet201, SeResNext50, InceptionV3, and InceptionresNetv2, demonstrate the superiority of the proposed ML-CNN model

Multiple Ocular Disease Diagnosis Using Fundus Images Based on Multi-Label Deep Learning Classification

Designing computer-aided diagnosis (CAD) systems that can automatically detect ocular diseases (ODs) has become an active research field in the health domain. Although the human eye might have more than one OD simultaneously, most existing systems are designed to detect specific eye diseases. Therefore, it is crucial to develop new CAD systems that can detect multiple ODs simultaneously. This paper presents a novel multi-label convolutional neural network (ML-CNN) system based on ML classification (MLC) to diagnose various ODs from color fundus images. The proposed ML-CNN-based system consists of three main phases: the preprocessing phase, which includes normalization and augmentation using several transformation processes, the modeling phase, and the prediction phase. The proposed ML-CNN consists of three convolution (CONV) layers and one max pooling (MP) layer. Then, two CONV layers are performed, followed by one MP and dropout (DO). After that, one flatten layer is performed, followed by one fully connected (FC) layer. We added another DO once again, and finally, one FC layer with 45 nodes is performed. The system outputs the probabilities of all 45 diseases in each image. We validated the model by using cross-validation (CV) and measured the performance by five different metrics: accuracy (ACC), recall, precision, Dice similarity coefficient (DSC), and area under the curve (AUC). The results are 94.3%, 80%, 91.5%, 99%, and 96.7%, respectively. The comparisons with the existing built-in models, such as MobileNetV2, DenseNet201, SeResNext50, InceptionV3, and InceptionresNetv2, demonstrate the superiority of the proposed ML-CNN model

Computer-Aided Diagnosis System for Blood Diseases Using EfficientNet-B3 Based on a Dynamic Learning Algorithm

The immune system’s overproduction of white blood cells (WBCs) results in the most common blood cancer, leukemia. It accounts for about 25% of childhood cancers and is one of the primary causes of death worldwide. The most well-known type of leukemia found in the human bone marrow is acute lymphoblastic leukemia (ALL). It is a disease that affects the bone marrow and kills white blood cells. Better treatment and a higher likelihood of survival can be helped by early and precise cancer detection. As a result, doctors can use computer-aided diagnostic (CAD) models to detect early leukemia effectively. In this research, we proposed a classification model based on the EfficientNet-B3 convolutional neural network (CNN) model to distinguish ALL as an automated model that automatically changes the learning rate (LR). We set up a custom LR that compared the loss value and training accuracy at the beginning of each epoch. We evaluated the proposed model on the C-NMC_Leukemia dataset. The dataset was pre-processed with normalization and balancing. The proposed model was evaluated and compared with recent classifiers. The proposed model’s average precision, recall, specificity, accuracy, and Disc similarity coefficient (DSC) were 98.29%, 97.83%, 97.82%, 98.31%, and 98.05%, respectively. Moreover, the proposed model was used to examine microscopic images of the blood to identify the malaria parasite. Our proposed model’s average precision, recall, specificity, accuracy, and DSC were 97.69%, 97.68%, 97.67%, 97.68%, and 97.68%, respectively. Therefore, the evaluation of the proposed model showed that it is an unrivaled perceptive outcome with tuning as opposed to other ongoing existing models

Molecular characterization of a c-type lysozyme from the desert locust, Schistocerca gregaria (Orthoptera: Acrididae)

Lysozymes are bacteriolytic peptides that are implicated in the insect nonspecific innate immune responses. In this study, a full-length cDNA encoding a c-type lysozyme from Schistocerca gregaria (SgLys) has been cloned and characterized from the fat body of immune-challenged 5th instar. The deduced mature lysozyme is 119 amino acid residues in length, has a calculated molecular mass of 13.4 kDa and an isoelectric point of 9.2. SgLys showed high identities with other insect lysozymes, ranging from 41.5% to 93.3% by BLASTp search in NCBI. Eukaryotic in vitro expression of the SgLys ORF (rSgLys) with an apparent molecular mass of ~ 16 kDa under SDS-PAGE is close to the calculated molecular weight of the full-length protein. rSgLys displayed growth inhibitory activity against Gram-negative and Gram-positive bacteria. 3D structure modeling of SgLys, based on comparison with that of silkworm lysozyme, and sequence comparison with the helix-loop-helix (α- hairpin) structure of hen egg white lysozyme (HEWL) were employed to interpret the antibacterial potencies. Phylogenetic alignments indicate that SgLys aligns well with insect c-type lysozymes that expressed principally in fat body and hemocytes and whose role has been defined as immune related. Western blot analysis showed that SgLys expression was highest at 6-12 h post-bacterial challenge and subsequently decreased with time. Transcriptional profiles of SgLys were determined by semi-quantitative RT-PCR analysis. SgLys transcript was upregulated at the highest level in fat body, hemocytes, salivary gland, thoracic muscles, and epidermal tissue. It was expressed in all developmental stages from egg to adult. These data indicate that SgLys is a predominant acute phase protein that is expressed and upregulated upon immune challenge

A Novel Early Diagnosis System for Mild Cognitive Impairment Based on Local Region Analysis: A Pilot Study

Alzheimer's disease (AD) is an irreversible neurodegenerative disorder that accounts for 60–70% of cases of dementia in the elderly. An early diagnosis of AD is usually hampered for many reasons including the variable clinical and pathological features exhibited among affected individuals. This paper presents a computer-aided diagnosis (CAD) system with the primary goal of improving the accuracy, specificity, and sensitivity of diagnosis. In this system, PiB-PET scans, which were obtained from the ADNI database, underwent five essential stages. First, the scans were standardized and de-noised. Second, an Automated Anatomical Labeling (AAL) atlas was utilized to partition the brain into 116 regions or labels that served for local (region-based) diagnosis. Third, scale-invariant Laplacian of Gaussian (LoG) was used, per brain label, to detect the discriminant features. Fourth, the regions' features were analyzed using a general linear model in the form of a two-sample t-test. Fifth, the support vector machines (SVM) and their probabilistic variant (pSVM) were constructed to provide local, followed by global diagnosis. The system was evaluated on scans of normal control (NC) vs. mild cognitive impairment (MCI) (19 NC and 65 MCI scans). The proposed system showed superior accuracy, specificity, and sensitivity as compared to other related work