Trend analysis in seroprevalence of dengue, chikungunya and malaria: A seven-year serological study from a tertiary care hospital of Maharashtra, India.

BACKGROUND & OBJECTIVES: Dengue, chikungunya and malaria are mosquito-borne infections, which have shared endemicity and similar clinical presentation. Simultaneous co-infection with more than one infectious agent complicates the diagnosis and further course of treatment. This study aims to determine the seroprevalence and trend of malaria, dengue and chikungunya from 2014-2020 in a tertiary care hospital of western India. METHODS: The present study was retrospective descriptive record-based. Serum samples from clinically suspected dengue and chikungunya were subjected to both IgM antibody capture ELISA kits produced by National Institute of Virology (NIV), Pune, India. They were also subjected to ELISA based NS1Ag testing. In Suspected malaria cases, blood collected in EDTA tubes was subjected for Rapid Malaria antigen testing. Statistical analysis was performed using MS Excel and JMP Software. RESULTS: Seropositivity of malaria was comparatively higher in 2014 (5.53%) and a decreasing trend was observed in subsequent years. Majority of malarial infections were caused by Plasmodium vivax (81.67%). There is drastic increase in seropositivity of chikungunya from 2016 (23.67%) and thereafter as compared to 2014 (6.57%) and 2015 (7.29%) indicating its re-emergence. The dengue seropositivity in 2019 (40.19%) was highest in last seven years. Males were predominantly affected, and most affected age group was 21-30 years. Peak transmission was observed in post-monsoon seasons. Dengue and chikungunya co-infection was observed to be 5.79%. INTERPRETATION & CONCLUSION: This study emphasizes the importance of surveillance studies to understand the trend of vector-borne diseases for prompt diagnosis, management of patients in hospital setup and for early detection and curtailment of outbreaks and epidemics by public health sectors through appropriate vector control programs.

An Efficient CNN-Based Deep Learning Model to Detect Malware Attacks (CNN-DMA) in 5G-IoT Healthcare Applications.

The role of 5G-IoT has become indispensable in smart applications and it plays a crucial part in e-health applications. E-health applications require intelligent schemes and architectures to overcome the security threats against the sensitive data of patients. The information in e-healthcare applications is stored in the cloud which is vulnerable to security attacks. However, with deep learning techniques, these attacks can be detected, which needs hybrid models. In this article, a new deep learning model (CNN-DMA) is proposed to detect malware attacks based on a classifier-Convolution Neural Network (CNN). The model uses three layers, i.e., Dense, Dropout, and Flatten. Batch sizes of 64, 20 epoch, and 25 classes are used to train the network. An input image of 32 × 32 × 1 is used for the initial convolutional layer. Results are retrieved on the Malimg dataset where 25 families of malware are fed as input and our model has detected is Alueron.gen!J malware. The proposed model CNN-DMA is 99% accurate and it is validated with state-of-the-art techniques.