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Your search keyword '"Maulina, Novi"' showing total 15 results
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15 results on '"Maulina, Novi"'

6. Current perspective of gut-lung axis (GLA) in pulmonary tuberculosis (PTB).

Author

Maulina, Novi, Hayati, Zinatul, Hasballah, Kartini, and Zulkarnain

Abstract

The potential organic cross-talk of gut-lung and complex pathways including their corresponding microbiota have emphasized the existent of a gut–lung axis (GLA). Evidences considered the indispensable role of inter-kingdom crosstalk in maintaining host homeostasis by shaping local cell function, immune responses, and intercede the course of respiratory diseases, including pulmonary tuberculosis infection (PTB). A significant decrease of gut microbiota species number and diversity in TB patients compared to healthy group has conveyed that the gut microbiota may impact on M. tuberculosis (MTB) infection. In this review, we will provide an overview of the possible functional part of the gut microbiota in TB immunity and its role as the host-microbiota directed therapy (HMDT) in TB management. The literature search of this review was conducted using PubMed and Google Scholar with specific terms and no specification of article publication time. Microbial-host immune interaction in TB infection studies and potential use of microbial-based therapy in TB are listed and described. In sum, further researches are needed to identify strain-specific effect, gut microbiota anti-tubercular mechanism, and the role of other non-bacteria kingdom in immune function and disease. [ABSTRACT FROM AUTHOR]

Published
2024
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14. Performance Evaluation of Binary Classification of Diabetic Retinopathy through Deep Learning Techniques using Texture Feature.

Author

Adriman, Ramzi, Muchtar, Kahlil, and Maulina, Novi

Subjects
DIABETIC retinopathy, CLASSIFICATION, PEOPLE with diabetes, TEXTURES, RETINA
Abstract

One of the main causes of loss of vision in diabetic patients is Diabetic retinopathy (DR). Automated methods are important medical applications for detecting and classifying the disease type into normal or abnormal ones. Fundus images are obtained from the retina using a retinal camera, one of a non-invasive diagnostic technique that offers a way of examining the retina in diabetes patients. We present in this paper a system for the detection and classification of DRs. Our approach is divided into two main steps: in the first step, we use local binary patterns (LBP) to extract texture features, while in the second stage, we analyze extensively the state-of-the-art deep learning techniques for the detection and classification tasks. ResNet, DenseNet, and DetNet are used as deep learning techniques. Preliminary results show that ResNet, DenseNet and DetNet can obtain 0,9635%, 0,8405% and 0,9399% of accuracy, respectively. In addition, we also evaluate the performance of each detection configuration. [ABSTRACT FROM AUTHOR]

Published
2020
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