Clinical Characteristics and Histopathology of Idiopathic Epiretinal Membrane in Vietnam

BACKGROUND: Idiopathic epiretinal membrane (iERM) is an avascular proliferation of different types of cells between the posterior vitreous cortex and the internal limiting membrane. That causes visual impairment including blurry, distortion, scotoma. Many studies of iERM were done to describe the clinical characteristics and investigate the histopathology of this disease. Nonetheless, there has not been a study of iERM histopathology in Vietnam. AIMS: To describe clinical characteristics and histopathological results of idiopathic retinal membrane and the association between them. METHODS: A cross sectional decriptive study of 35 iERMs (33 patients) in Vietnam National Institute of Ophthalmology (VNIO). RESULTS: High morbidity incidence was in group age >50 years (32/35), female gender (26/35), limited movement works (27/35), and high educational levels (28/35). Distortion was the highest (77.14%), scotoma and floater was less frequent (28.5%, 45.7%). Macular edema in all cases and PVD and exudate were high frequent (65.7%, 62.8%). Symptom duration was 8.2 ± 4.7 months, (1-21 months). Mean of central macular thickness was 468.51 ± 97.24 µm (656-274 µm). Six types of cell were detected, including glial cell (35/35), fibroblast (23/35), myofibroblast (23/35), macrophage (13/35), lymphocyte (5/35) and neutrophil (2/35). The number of cell types in one sample ranged from 1-5 types (2.85 ± 1.28 cell types). Number of cell types were correlated to symptom duration (r = 0.47, p = 0.004, Pearson's test) and central macular thickness (r = 0.72, p < 0.001, Pearson's test). CONCLUSION: There were 6 types of cells in iERM. Glial cell was the most frequent cell, inflammatory cells (macrophage, lymphocyte, neutrophil) was also detected. The number of cell types was stastitically correlated to symptom duration and CMT

Penicillium digitatum as a Model Fungus for Detecting Antifungal Activity of Botanicals: An Evaluation on Vietnamese Medicinal Plant Extracts.

peer reviewedMedicinal plants play important roles in traditional medicine, and numerous compounds among them have been recognized for their antimicrobial activity. However, little is known about the potential of Vietnamese medicinal plants for antifungal activity. In this study, we examined the antagonistic activity of twelve medicinal plant species collected in Northern Vietnam against Penicillium digitatum, Aspergillus flavus, Aspergillus fumigatus, and Candida albicans. The results showed that the antifungal activities of the crude extracts from Mahonia bealei, Ficus semicordata, and Gnetum montanum were clearly detected with the citrus postharvest pathogen P. digitatum. These extracts could fully inhibit the growth of P. digitatum on the agar medium, and on the infected citrus fruits at concentrations of 300-1000 µg/mL. Meanwhile, the other tested fungi were less sensitive to the antagonistic activity of the plant extracts. In particular, we found that the ethanolic extract of M. bealei displayed a broad-spectrum antifungal activity against all four pathogenic fungi. Analysis of this crude extract by enrichment coupled with high-performance liquid chromatography revealed that berberine and palmatine are major metabolites. Additional inspections indicated berberine as the key compound responsible for the antifungal activity of the M. bealei ethanolic extract. Our study provides a better understanding of the potential of Vietnamese medicinal plant resources for combating fungal pathogens. This work also highlights that the citrus pathogen P. digitatum can be employed as a model fungus for screening the antifungal activity of botanicals

Protective and Enhancing HLA Alleles, HLA-DRB1*0901 and HLA-A*24, for Severe Forms of Dengue Virus Infection, Dengue Hemorrhagic Fever and Dengue Shock Syndrome

Dengue has become one of the most common viral diseases transmitted by infected mosquitoes (with any of the four dengue virus serotypes: DEN-1, -2, -3, or -4). It may present as asymptomatic or illness, ranging from mild to severe disease. Recently, the severe forms, dengue hemorrhagic fever (DHF) and dengue shock syndrome (DSS), have become the leading cause of death among children in Southern Vietnam. The pathogenesis of DHF/DSS, however, is not yet completely understood. The immune response, virus virulence, and host genetic background are considered to be risk factors contributing to disease severity. Human leucocyte antigens (HLA) expressed on the cell surface function as antigen presenting molecules and those polymorphism can change individuals' immune response. We investigated the HLA-A, -B (class I), and -DRB1 (class II) polymorphism in Vietnamese children with different severity (DHF/DSS) by a hospital-based case-control study. The study showed persons carrying HLA-A*2402/03/10 are about 2 times more likely to have severe dengue infection than others. On the other hand, HLA-DRB1*0901 persons are less likely to develop DSS with DEN-2 virus infection. These results clearly demonstrated that HLA controlled the susceptibility to severe forms of DV infection

Cell phase classification using Markov and Gaussian mixture models

We present Gaussian mixture and Markov modelling methods for the computerized classification of cell nuclei in different mitotic phases. The methods were tested with the data set containing 379519 cells in 892 cell sequences for 5 phases extracted from real image sequences recorded at every fifteen minutes with a time-lapse fluorescence microscopy. The experimental results have shown that the proposed methods are effective and have potential for higher performance with better cellular feature extraction strategy

Gaussian mixture and Markov models for cell-phase classification in microscopic imaging

Studies of drug effects on cancer cells are performed through measuring cell cycle progression such as inter phase, prophase, metaphase and anaphase in individual cells. Such studies require the processing and analysis of huge amounts of image data. Manual image analysis is very time consuming thus costly, potentially inaccurate and poorly reproducible. Stages of an automated cellular imaging analysis consist of segmentation, feature extraction, classification, and tracking of individual cells in a dynamic cellular population. Image classification of cell phases in a fully automatic manner presents the most difficult task of such analysis. We considered applying several versions of Gaussian mixture and Markov models for automating the classification of cell nuclei in different mitotic phases recorded over a period of twenty-four hours at every fifteen minutes with a time-lapse fluorescence microscopy. The experimental results have shown that the proposed methods are effective and have potential for higher performance

Fuzzy estimation of priors in speaker recognition

This paper proposes a method to estimate the a priori probability for speakers based on the training data set, speaker models and a fuzzy estimation technique. Speaker identification experiments performed on 138 Gaussian mixture speaker models in the YOHO database using the priors estimated by the fuzzy estimation method showed lower error rates than using those estimated by the probabilistic estimation method

Relaxation labeling for cell phase identification

Gaussian mixture model (GMM) is used in cell phase identification to model the distribution of cell feature vectors. The model parameters, which are mean vectors, covariance matrices and mixture weights, are trained in an unsupervised learning method using the expectation maximization algorithm. Experiments have shown that the GMM is an effective method capable of achieving high identification rate. However, the GMM approach is not always effective because of ambiguity inherently existing in the cell phase data. To enhance the effectiveness of the GMM for solving this specific problem, the relaxation labeling (RL) is proposed to be used with the GMM. The RL algorithm is a parallel algorithm that updates the probabilities of cell phases by using correlation or mutual information between cell phases to reduce uncertainty among GMMs having overlapping properties