Human Epithelial Type 2 Cells Research Articles

IoMT-Based Automated Diagnosis of Autoimmune Diseases Using MultiStage Classification Scheme for Sustainable Smart Cities

The resolution of complex medical diagnoses using pattern recognition requires an artificial neural network-based expert system to automate autoimmune disease diagnosis in blood samples. This process is done using image-based computer-aided diagnosis (CAD) to reduce errors in the diagnosis process. This paper describes a Multistage Classification Scheme (MSCS), which uses antinuclear antibody (ANA) tests to identify and classify the existence of autoantibodies in the blood serum that bind to antigens found in the nuclei of mammalian cells. The MSCS classified HEp-2 cells into three stages by using Binary Tree (BT), Artificial Neural Network (ANN), and Support Vector Machine (SVM) as basic blocks. The Indirect Immunofluorescence (IIF) technique is used in the ANA test with Human Epithelial type-2 (HEp-2) cells as substrates. The efficiency of the proposed methodology is assessed using the dataset of ICPR 2016. The intermediate cells (IMC) and positive cells (PC) were separated in Stage 1 prior to preprocessing based on their total strength, and special preprocessing is applied to intermediate cells for improved output, and positive cells are subjected to mild preprocessing. The mean class accuracy (MCA) was 84.9% for intermediate cells and 95.8% for positive cells, although the carefully picked 24 features and SVM classifier were applied. ANN showed better performance by adjusting the weights using the SCGBP algorithm. So, the MCA is 88.4% and 97.1% for intermediate and positive cells, respectively. BT had an MCA of 95.3% for intermediate and 98.6% for positive. In Stage 2, the meta learners BT2, ANN2, and SVM2 were trained for an augmented feature set (24 + 3 results from base learners). Therefore, the performance of BT2, ANN2, and SV M2 was increased by 1.8%, 4.5%, and 4.1% as compared to Stage 1. In Stage 3, the final prediction was performed by majority voting among the results of the three meta learners to achieve 99.1% MCA. The proposed algorithm can be embedded into a CAD framework built for the ANA examination. The proposed model will improve operational efficiency, decrease medical expenses, expand accessibility to healthcare, and improve patient safety in the sector, enabling enterprises to lower unplanned downtime, develop new products or services, increase operational effectiveness, and enhance risk management.

HEp-2 image classification using a multi-class and multiple-binary classifier.

In medicine, identifying the indirect immunofluorescence of human epithelial type 2 (HEp-2) cells plays a decisive role in the diagnosis of autoimmune diseases. The manual interpretation of Hep-2 cell images may lead to some limitations, such as subjectivity, inconsistency and low efficiency. Therefore, it is very important to automatically identify HEp-2 images. Inspired by the outstanding performance of neural networks in image classification tasks, we propose a multi-class and multiple-binary classifier (MCMBC) for the classification of HEp-2 cells. MCMBC is an ensemble learner that contains two kinds of sub-classifiers: multi-class (MC) and multiple-binary (MB). The MC sub-classifier adopts a multi-scale convolutional neural network (MSCNN) that increases the efficiency of information transmission between layers. On the basis of classification results of the MC sub-classifier on validation sets, we can find easy-to-confuse class pairs. An easy-to-confuse class pair is two classes that are not easy to be identified from each other. The MB sub-classifiers adopt multiple-binary pre-trained VGG16 networks that are used to deal with these class pairs. The final prediction for a sample possibly belonging to an easy-to-confuse class is decided by the assembled features extracted from the last fully connected layer of MC and the output of MB sub-classifiers. To evaluate the proposed model, experiments were conducted on the ICPR 2014 Task-2 dataset. Experimental results show that MCMBC performs better than the state-of-the-art method (84.68% vs. 83.35% on the criterion of average classification accuracy (ACA) and 82.89% vs. 82.67% on the criterion of mean classification accuracy (MCA)).

Sol-gel synthesis of amorphous calcium phosphate nanoparticles in brown rice substrate and assessment of their cytotoxicity and antimicrobial activities.

Objective:This study intended to perform a synthesizing procedure for amorphous calcium phosphate (ACP) through a green template by the usage of brown rice (BR).Materials and Methods:ACP nanoparticles were obtained by application of a sol-gel method and comprehensively characterized using X-ray powder diffraction (XRD), zeta potential, fourier-transform infrared spectroscopy (FTIR), field emission scanning electron microscope (FESEM), and atomic force microscopy (AFM). Cytotoxic activity of ACP was evaluated in human epithelial type 2 (HEp-2) cell lines. The antibacterial effects of nanoparticles were appraised against Gram-positive Streptococcus mutans and Enterococcus faecalis. Results: The procedures for the evaluation of the characterization outcomes, dispersion, and stability of our product were confirmed by observing the smooth and uniformed surfaces of ACP. The zeta potential value of the synthesized sample was -22 mV, which indicates its acceptable stable condition caused by electrostatic repulsion. The cytotoxicity of the ACP nanoparticles was investigated in HEp-2 cells, and results showed no cytotoxicity for the synthesized nanoparticles. Also, the obtained minimum inhibitory concentration (MIC) of ACP nanoparticles in opposition to S. mutans and E. faecalis was 15 and 20 µg/ml, respectively, indicating the resistance of E. faecalis in comparison to S. mutans and MBC for synthesized nanoparticles against S. mutans and E. faecalis strains was 20 and 25 µg/ml. Conclusion:The present study showed that this compound has no toxicity on the examined cell line. Also, the antibacterial properties of the synthesized ACP were approved by the obtained data, which enables the application of this material for therapeutic purposes in the pharmaceutical industry.

A Classification Method for the Cellular Images Based on Active Learning and Cross-Modal Transfer Learning.

In computer-aided diagnosis (CAD) systems, the automatic classification of the different types of the human epithelial type 2 (HEp-2) cells represents one of the critical steps in the diagnosis procedure of autoimmune diseases. Most of the methods prefer to tackle this task using the supervised learning paradigm. However, the necessity of having thousands of manually annotated examples constitutes a serious concern for the state-of-the-art HEp-2 cells classification methods. We present in this work a method that uses active learning in order to minimize the necessity of annotating the majority of the examples in the dataset. For this purpose, we use cross-modal transfer learning coupled with parallel deep residual networks. First, the parallel networks, which take simultaneously different wavelet coefficients as inputs, are trained in a fully supervised way by using a very small and already annotated dataset. Then, the trained networks are utilized on the targeted dataset, which is quite larger compared to the first one, using active learning techniques in order to only select the images that really need to be annotated among all the examples. The obtained results show that active learning, when mixed with an efficient transfer learning technique, can allow one to achieve a quite pleasant discrimination performance with only a few annotated examples in hands. This will help in building CAD systems by simplifying the burdensome task of labeling images while maintaining a similar performance with the state-of-the-art methods.

New Insights into the Antimicrobial Action of Cinnamaldehyde towards Escherichia coli and Its Effects on Intestinal Colonization of Mice

Escherichia coli is responsible for cases of diarrhea around the world, and some studies have shown the benefits of cinnamaldehyde in the treatment of bacterial disease. Therefore, the objective of this study was to evaluate the effects of cinnamaldehyde in mice colonized by pathogenic E. coli, as well as to provide more insights into its antimicrobial action mechanism. After determination of minimum inhibitory (MIC) and minimum bactericidal (MBC) concentrations, the interference of cinnamaldehyde in macromolecular pathways (synthesis of DNA, RNA, protein, and cell wall) was measured by incorporation of radioisotopes. The anti-adhesive properties of cinnamaldehyde towards E. coli 042 were evaluated using human epithelial type 2 (HEp-2) cells. Intestinal colonization was tested on mice, and the effect of cinnamaldehyde on Tenebrio molitor larvae. Cinnamaldehyde showed MIC and MBC values of 780 μg/mL and 1560 μg/mL, respectively; reduced the adhesion of E. coli 042 on HEp-2 cells; and affected all the synthetic pathways evaluated, suggesting that compost impairs the membrane/cell wall structure leading bacteria to total collapse. No effect on the expression of genes related to the SOS pathway (sulA and dinB1) was observed. The compound did not interfere with cell viability and was not toxic against T. molitor larvae. In addition, cinnamaldehyde-treated mice exhibited lower levels of colonization by E. coli 042 than the untreated group. Therefore, the results show that cinnamaldehyde is effective in treating the pathogenic E. coli strain 042 and confirm it as a promising lead molecule for the development of antimicrobial agents.

MiR-199a-5p suppresses proliferation and invasion of human laryngeal cancer cells.

To explore the roles of micro ribonucleic acid (miR)-199a-5p in the proliferation, apoptosis, invasion and metastasis of laryngeal cancer cells, and its molecular mechanisms. The expression of miR-199a-5p in 25 cases of laryngeal cancer tissues and paracancerous tissues was detected via quantitative real-time polymerase chain reaction (qRT-PCR). Its expression in TU212, TU686 and human epithelial type 2 (HEp-2) laryngeal cancer cell lines and normal nasopharyngeal epithelial cell line NP69 was also detected via qRT-PCR. HEp-2 cells were transiently transfected with miR-199a-5p mimic or miR-199a-5p inhibitor, and the expression of miR-199a-5p was verified using RT-PCR after transfection. The regulatory effects of miR-199a-5p on the proliferation, apoptosis, invasion and migration abilities of HEp-2 cells were observed through methyl thiazolyl tetrazolium (MTT) assay, flow cytometry, wound healing assay and transwell assay, respectively. Then, the mechanisms of miR-199a-5p in regulating Caspase-3 activity and epithelial-mesenchymal transition (EMT)-related proteins were further explored. The qRT-PCR results revealed that miR-199a-5p was significantly lowly expressed in the laryngeal cancer tissues and tumor cell lines, and overexpression of miR-199a-5p substantially inhibited the proliferation of HEp-2 cells. According to the results of flow cytometry, overexpression of miR-199a-5p promoted the apoptosis of HEp-2 cells, whereas down-regulating miR-199a-5p suppressed their apoptosis. It was found that the activity of Caspase-3 was notably enhanced after overexpression of miR-199a-5p, which was evidently weakened after down-regulating miR-199a-5p. Wound healing assay and transwell assay results manifested that overexpressing miR-199a-5p weakened the invasion and migration abilities of HEp-2 cells, which were facilitated by down-regulating miR-199a-5p. Based on Western blotting results, miR-199a-5p regulated the expressions of E-cadherin, N-cadherin and vimentin. Overexpression of miR-199a-5p could inhibit EMT process, whereas suppressing miR-199a-5p could accelerate the process. The expression of miR-199a-5p in laryngeal cancer tissues is substantially lower than that in the paracancerous tissues. MiR-199a-5p suppresses proliferation, invasion and migration in laryngeal cancer cell proliferation, while triggers cell apoptosis.

A study on indirect immunofluorescence image classification methods for bioinformatics

The indirect immunofluorescence (IIF) test with human epithelial type-2 (HEp-2) cells as substrates is the gold standard for anti-nuclear antibodies (ANA) test to diagnose autoimmune diseases. The specialists in the laboratory visually examine the specimen under microscope to recognise the staining patterns and generate the report. So ANA test is subjective and needs systemic automation for bioinformatics. In this view international benchmarking initiatives were organised by IAPR in the last six years. In this paper the state of the art on IIF HEp-2 cells classification task was analysed. This paper highlighted the original aspects with the detailed discussion of the published methods. Design choice verses performance was analysed.

A study on indirect immunofluorescence image classification methods for bioinformatics

The indirect immunofluorescence (IIF) test with human epithelial type-2 (HEp-2) cells as substrates is the gold standard for anti-nuclear antibodies (ANA) test to diagnose autoimmune diseases. The specialists in the laboratory visually examine the specimen under microscope to recognise the staining patterns and generate the report. So ANA test is subjective and needs systemic automation for bioinformatics. In this view international benchmarking initiatives were organised by IAPR in the last six years. In this paper the state of the art on IIF HEp-2 cells classification task was analysed. This paper highlighted the original aspects with the detailed discussion of the published methods. Design choice verses performance was analysed.

In Vitro Cytotoxicity of Parasporins from Native Algerian Bacillus thuringiensis Strains Against Laryngeal and Alveolar Cancers.

Parasporins (PS), a class of non-insecticidal and non-hemolytic crystal proteins of Bacillus thuringiensis (Bt), are being explored as promising anti-cancer agents due to their specific toxicity to cancer cells. This work is considered as a first initiative aiming at investigating Algerian soil Bt isolates' activity and cytotoxic potential against cancer cells. A total of 48 Bacillus spp. were isolated from different sites in Algeria. Phenotypic and biochemical tests, 16S rDNA molecular identification, and microscopic observation of crystal have confirmed the identification of Bt for ten strains. A screening for non-hemolytic crystalline proteins was performed. Extraction, purification, and activation of non-hemolytic proteins by chromatographic analysis yielded several polypeptides of different molecular weights. A purified PS1, with pro-protein of 81kDa and several peptides with different molecular weights (18-58kDa) after activation by trypsin, has been identified from the strain BDzG. The NH2-terminal sequence deciphered in BLAST analysis showed homology to a Bt PS1 protein. Moreover, the screening of parasporin-1 (PS1) gene has also been performed. Cytocidal activity against human epithelial type 2 (HEp2) cells, considered to originate from a human laryngeal carcinoma, was observed with an IC50 equal to 2.33μg/ml, while moderate cytotoxicity against adenocarcinomic human alveolar basal epithelial (A549) cells has been shown with IC50 equal to 18.54μg/ml. No cytotoxicity against normal cells was noted. Fluorescence microscopy revealed a condensed or fragmented chromatin indicating the apoptotic death of HEp2 cells. Thus, Bt PS-producer isolated from Algerian soil might have a potential to join the arsenal of natural anti-cancer drugs with high therapeutic potential.

Central nervous system-specific antinuclear antibodies in patients with multiple sclerosis

BackgroundNuclear antigen released from central nervous system (CNS) cells undergoing destruction may induce production of antinuclear antibodies (ANA). We characterized the CNS-specific production of ANA in multiple sclerosis (MS). MethodsWe assessed CNS-ANA binding to mouse cerebellar cell nuclei by immunofluorescence assay (IFA) with sera from 104 MS patients (91 relapsing-remitting; 13 secondary progressive), 30 patients with neuromyelitis optica spectrum disorders (NMOSD), and 30 healthy controls (HCs). Conventional ANA (cANA) was detected by IFA using human epithelial type-2 cells. CNS-ANA-positive cANA-negative patients were termed CNS-specific ANA-positive. Western blotting (WB) was performed using mouse cerebellar nuclear fractions. ResultsCNS-specific ANA were more frequent in MS than in NMOSD patients or HCs (13.5% vs 0% for both comparisons, both p < .05) and were associated with HLA-DRB1*15:01 (p = .0174). WB revealed a common 55 kDa band in seven MS patients. Compared with CNS-specific ANA-negative MS patients, those with 55 kDa band-immunoreactive CNS-specific ANA showed a higher frequency of secondary progressive MS (42.9% vs 10.0%, p = .0387) and greater Expanded Disability Status Scale scores (4.50 ± 2.02 vs 2.92 ± 2.27, p = .0506). ConclusionsThe CNS-specific ANA was more frequently detected in MS patients than NMOSD patients or HCs. 55 kDa band-reactive CNS-specific ANA may reflect clinical disease progression in MS.

HEp-2 CELL CLASSIFICATION BY ADAPTIVE CONVOLUTIONAL LAYER BASED CONVOLUTIONAL NEURAL NETWORK

Objective: The antinuclear antibodies (ANA) that present in the human serum have a link with various autoimmune diseases. Human Epithelial type-2 (HEp-2) cells acts as a substance in the Indirect Immuno fluorescence (IIF) test for diagnosing these autoimmune diseases. In recent times, the computer-aided diagnosis of autoimmune diseases by the HEp-2 cell classification has drawn more interest. Though, they often pose limitations like large intra-class and small inter-class variations. Hence, various efforts have been performed to automate the procedure of HEp-2 cell classification. To overcome these problems, this research work intends to propose a new HEp-2 classification process. Materials and Methods: This is regulated by integrating two processes, namely, segmentation and classification. Initially, the segmentation of the HEp-2 cells is carried out by deploying the morphological operations. In this paper, two morphology operations are deployed called opening and closing. Further, the classification process is exploited by proposing a modified Convolutional Neural Network (CNN). The main objective is to classify the HEp-2 cells effectively (Centromere, Golgi, Homogeneous, Nucleolar, NuMem, and Speckled) and is made by exploiting the optimization concept. This is implanted by developing a new algorithm called Distance Sorting Lion Algorithm (DSLA), which selects the optimal convolutional layer in CNN. Results: Through the performance analysis, the performance of the proposed model for test case 1 at learning percentage 60 is 3.84%, 1.79%, 6.22%, 1.69%, and 5.53% better than PSO, FF, GWO, WOA, and LA, respectively. At 80, the performance of the proposed model is 5.77%, 6.46%, 3.95%, 3.24%, and 5.55% better from PSO, FF, GWO, WOA, and LA, respectively. Hence, the performance of the proposed work is proved over other models under different measures. Conclusion: Finally, the performance is evaluated by comparing it with the other conventional algorithms in terms of accuracy, sensitivity, specificity, precision, FPR, FNR, NPV, MCC, F1-Score and FDR, and proves the efficacy of the proposed model.

Evaluation of aqueous-extracts from four aromatic plants for their activity against Candida albicans adhesion to human HEp-2 epithelial cells

The present study was aimed to examine the aqueous extracts of Eugenia caryophyllata, Cichorium intybus, Punica granatum and Rhus coriaria as anti-adhesion agents of Candida albicans (C. albicans) to human epithelial type 2 (HEp-2) cells. The anti-C. albicans activity of extracts was evaluated using two methods including a standard agar well diffusion assay and monitoring C. albicans growth in the presence of extracts via optical density. The cytotoxic concentrations of the obtained aqueous extracts against HEp-2 cells were assessed using the MTT assay. Sub-cytotoxic concentrations of extracts were evaluated for their effect on the adhesion of C. albicans to HEp-2 cells. The aqueous extract of Cichorium intybus showed a promising inhibitory activity on the growth of C. albicans in a concentration-dependent manner. In addition, these plant extracts did not show cytotoxic activity against HEp-2 cells at the specific concentration. Among the four extracts, Rhus coriaria was more effective in reducing adhesion. It seems that anti-adhesion activities of the extracts could be attributed to further mechanisms rather than their anti-Candida activities. No direct correlation was found between the examined biological activities and total polyphone content in all samples. Our data demonstrate that Rhus coriaria can interfered with the growth of C. albicans and its adhesion to the epithelial cells, which could motivate researchers to make more use it in potential clinical applications.

Benchmarking HEp-2 specimen cells classification using linear discriminant analysis on higher order spectra features of cell shape

Human Epithelial Type-2 (HEp-2) specimen patterns in microscopic images have been widely used for diagnosing autoimmune diseases. Manual cell classification is inefficient, time consuming and subject to human error, therefore, researchers have attempted to automate the procedure of HEp-2 cell classification. Challenges include large data volumes, variation in cell densities, shape, rotation, scaling and shifting, and overlapped cells. A computer-aided diagnosis (CAD) system, based on image-based classification, can help in terms of effort, time, and reliability of classification HEp-2 specimens. HEp-2 cells are classified into seven classes: speckled, homogeneous, centromere, nuclear membrane, nucleolar, golgi, and mitotic spindle. In this work, we focus on the task of HEp-2 specimen cell classification in terms of feature extraction representations, features selection and classifier design. We propose a method of linear discriminant analysis relying on bispectral invariant features from a segmented HEp-2 specimen cell shape that allows us to generalize features to being variation in shape, rotation, scaling and shifting. HEp-2 specimen cell shape segmentation is determined using level set based forces in normal direction and geometric active contours. Both linear and kernel support vector machines as well as fuzzy C mean are employed for fast classification of specimens and achieved Mean Class Rates (MCAs) of 91.25%, 86.02% and 89.14%, respectively, over the ICPR2014 and ICPR2016 contests HEp-2 cell task-2 datasets. The results validate the competitive performance of the proposed system compared with other approaches reported on both contests.

CanSuR: a robust method for staining pattern recognition of HEp-2 cell IIF images

The recognition of staining patterns present in human epithelial type 2 (HEp-2) cells helps to diagnose connective tissue disease. In this context, the paper introduces a robust method, termed as CanSuR, for automatic recognition of antinuclear autoantibodies by HEp-2 cell indirect immunofluorescence (IIF) image analysis. The proposed method combines the advantages of a new sequential supervised canonical correlation analysis (CCA), introduced in this paper, with the theory of rough hypercuboid approach. While the proposed CCA efficiently combines the local textural information of HEp-2 cells, derived from various scales of rotation-invariant local binary patterns, the relevant and significant features of HEp-2 cell for staining pattern recognition are extracted using rough hypercuboid approach. Finally, the support vector machine, with radial basis function kernel, is used to recognize one of the known staining patterns present in IIF images. The effectiveness of the proposed staining pattern recognition method, along with a comparison with related approaches, is demonstrated on MIVIA, SNP and ICPR HEp-2 cell image databases. An important finding is that the proposed method performs significantly better than state-of-the art methods, on three HEp-2 cell image databases with respect to both classification accuracy and F1 score.

An Automatic HEp-2 Specimen Analysis System Based on an Active Contours Model and an SVM Classification

The antinuclear antibody (ANA) test is widely used for screening, diagnosing, and monitoring of autoimmune diseases. The most common methods to determine ANA are indirect immunofluorescence (IIF), performed by human epithelial type 2 (HEp-2) cells, as substrate antigen. The evaluation of ANA consist an analysis of fluorescence intensity and staining patterns. This paper presents a complete and fully automatic system able to characterize IIF images. The fluorescence intensity classification was obtained by performing an image preprocessing phase and implementing a Support Vector Machines (SVM) classifier. The cells identification problem has been addressed by developing a flexible segmentation methods, based on the Hough transform for ellipses, and on an active contours model. In order to classify the HEp-2 cells, six SVM and one k-nearest neighbors (KNN)classifiers were developed. The system was tested on a public database consisting of 2080 IIF images. Unlike almost all work presented on this topic, the proposed system automatically addresses all phases of the HEp-2 image analysis process. All results have been evaluated by comparing them with some of the most representative state-of-the-art work, demonstrating the goodness of the system in the characterization of HEp-2 images.

Protective Activity of Probiotic Bacteria Against Candida albicans: An In Vitro Study

Background: Therapeutic applications of probiotics against human candida infections remain controversial. Candida species are the most common human fungal pathogens that cause both superficial and systemic infection. Given the low number of appropriate and effective antifungal drugs, the continuing increase in the incidence of Candida infections, and increased drug resistance, it is required to explore new and better factors targeting essential biological processes and pathogenic determinants of C. albicans. Objective: In this context, a laboratory study was conducted to investigate the effects of probiotic Lactobacillus acidophilus on the adherence of C. albicans to the human epithelial cell line known as human epithelial type 2 (HEp-2) cells and the potential protective effects of probiotic bacteria on the infected cells. Materials and Methods: To evaluate the effect of L. acidophilus on the adherence of C. albicans to HEp-2 cells, either yeast cells, probiotic bacteria, or both were added to each well of a 12-well plate, with a coverslip at the bottom, covered with a semiconfluent layer of HEp-2 cells. After 2 hours of incubation, the number of adhered pathogens was counted using light microscopy. In order to determine the effect of C. albicans on the viability of the HEp-2 cells, in the presence and absence of L. acidophilus, MTT (3-(4,5-dimethylthiazolyl-2)-2,5-diphenyltetrazolium bromide) assay was conducted. Results: The results revealed that either L. acidophilus strain La5 or C. albicans adhered to the (HEp-2) cells. In addition, cell association of C. albicans with Hep2 cells decreased by up to 80% when probiotic bacteria were added. The most interesting finding was that in the presence of L. acidophilus La-5, a significant decrease was observed in the adhesion of C. albicans to the cell line or cell mortality. Conclusion: According to the results of the study, the use of probiotics is a promising method to decrease the pathogenicity of opportunistic mycoses.

Human Epithelial Type-2 Cell Image Classification Using an Artificial Neural Network with Hybrid Descriptors

ABSTRACTAntinuclear antibody (ANA) testing is best performed using the indirect immunofluorescence (IIF) method with human epithelial type-2 (HEp-2) cells as the substrate. IIF is a subjective procedure in which HEp-2 patterns are analyzed manually from the microscope. Therefore, ANA test results greatly rely on the experience and expertise of pathologists. Hence, complete automation of the ANA test is required to avoid incorrect diagnoses. This paper represents an algorithm for the complex HEp-2 cell classification problem. The proposed algorithm used a small hybrid feature set that characterizes the texture and morphology of the HEp-2 cells along with artificial neural network (ANN). The hybrid features were extracted by breaking up the image into eight binary images. The proposed hybrid descriptors were more efficient than the popular co-occurrence matrix descriptor and local binary pattern descriptors for texture analysis. The proposed algorithm was evaluated on the ICPR 2016 IIF HEp-2 cell image dataset. The results indicated that the hybrid descriptor with an ANN approach achieved improved performance, with “96.8%” mean class accuracy.

CC-GAN: A Robust Transfer-Learning Framework for HEp-2 Specimen Image Segmentation

Human epithelial type 2 (HEp-2) cell images play an important role for the detection of antinuclear autoantibodies in autoimmune diseases. As the HEp-2 cell has hundreds of different patterns, none of currently available HEp-2 datasets contain all of the types. Therefore, existing automatic processing systems for HEp-2 cells, e.g., cell segmentation and classification, needs to be transferred between different data sets. However, the performances of transferred system often dramatically decrease, especially when transferring supervised-approaches, e.g., deep learning network, from large dataset to the small but similar ones. In this paper, a novel transfer-learning framework using generative adversarial networks (cC-GAN) is proposed for robust segmentation of different HEp-2 datasets. The proposed cC-GAN tries to solve the overfitting problem of most deep learning networks and improves their transfer-capacity. An improved U-net, so-called Residual U-net (RU-net), is developed to work as the generator for cC-GAN model. The cC-GAN was first trained and tested using I3A dataset and then directly evaluated using MIVIA dataset, which is much smaller than I3A. The segmentation result demonstrates the excellent transferring-capacity of our cC-GAN framework, i.e., a new state-of-the-art segmentation accuracy of 75.27% was achieved on MIVIA without finetuning.

Synthesis, characterization of ZnO-chitosan nanocomposites and evaluation of its antifungal activity against pathogenic Candida albicans

In this study, we investigated the antifungal activity and cytotoxicity of ZnO-chitosan nanocomposites (ZnO-C NCs) against Candida albicans and human epithelial type 2 (HEp2) cells, respectively. The crystalline phase, morphology, composition, particle size and optical absorption properties of the synthesized ZnO-C NCs were systematically investigated by various contemporary methods. The X-ray diffraction analysis results showed characteristic diffraction peaks corresponding to both ZnO and chitosan, while field-emission scanning electron microscopy (FESEM) displayed clusters of spherical shaped particulate morphology. UV–vis absorption spectra showed a shift in the optical absorption towards lower wavelength for ZnO-C NCs when compared to ZnO nanoparticles (NPs). The antifungal activity results (against C. albicans) showed that the minimum inhibitory concentration of ZnO NPs and ZnO-C NCs were 200μg/mL and 75μg/mL, respectively, suggesting the greater therapeutic potential of ZnO-C NCs. FESEM analysis results showed the substantial change in the external morphology of C. albicans after treatment with both ZnO NPs and ZnO-C NCs due to the fungal cell membrane damage. ZnO-C NCs displayed lower cytotoxicity with HEp2 cells indicating the good cytocompatibility of the synthesized ZnO-C NCs. It is expected that ZnO and chitosan complement each other and exhibit synergistic effects potential for antimicrobial and biomedical applications.

Bifidobacteria-derived lipoproteins inhibit infection with coxsackievirus B4 in vitro

The aim of the present study was to investigate the potential of bifidobacteria in protecting cells from coxsackievirus B4 (CV-B4) infection. Bifidobacterial screening identified two of five strains that protected human epithelial type 2 (HEp-2) cell viability when bifidobacteria were incubated with viral particles prior to inoculation. In contrast, no effect was shown by incubating HEp-2 cells with bifidobacteria prior to CV-B4 inoculation. Cell wall lipoprotein aggregates (LpAs) secreted by the selected strains were assayed for their antiviral activity. The two LpAs exhibited antiviral activity when they were incubated with viral particles prior to inoculation of HEp-2 cells. Recombinant LpA-derived protein exhibited identical antiviral activity. To identify the peptide sequences interacting with the virus particles, LpA proteins were aligned with the peptide sequences of the north canyon rim and puff footprint onto coxsackievirus and adenovirus receptor (CAR). The in silico molecular docking study using CV-B3 as template showed low-energy binding, indicating a stable system for the selected peptides and consequently a likely binding interaction with CV-B. Bifidobacterium longum and Bifidobacterium breve peptides homologous to the viral north rim footprint onto CAR sequence formed hydrogen bonds with several viral residues in the north rim of the canyon, which were already predicted as interacting with CAR. In conclusion, proteins from bifidobacterial LpAs can inhibit infection with CV-B4, likely through binding to the capsid amino acids that interact with CAR.