Severity prediction markers in dengue: a prospective cohort study using machine learning approach.

Background: Dengue virus causes illnesses with or without warning indicators for severe complications. There are no clear prognostic signs linked to the disease outcomes.

Methods: Clinical and laboratory parameters among 102 adult including 17 severe dengue (SD), 33 with warning and 52 without warning signs during early and critical phases were analysed by statistical and machine learning (ML) models.

Metabolomic profiling of dengue infection: unraveling molecular signatures by LC-MS/MS and machine learning models.

Background & Objective: The progression of dengue fever to severe dengue (SD) is a major public health concern that impairs the capacity of the medical system to predict and treat dengue patients. Hence, the present study used a metabolomic approach integrated with machine models to identify differentially expressed metabolites in patients with SD compared to nonsevere patients and healthy controls.

Methods: Comprehensively, the plasma was collected at different clinical phases during dengue without warning signs (DWOW, N = 10), dengue with warning signs (DWW, N = 10), and SD (N = 10) at different stages [i.

Increased shedding of PECAM-1 associated with elevated serum MMP-14 levels as new blood indicators of dengue disease manifestation.

Objectives: Host factors that regulate plasma leakage during severe dengue (SD) are under investigation. While PECAM-1 and MMP-14 have been reported to regulate vascular integrity, their role in dengue pathogenesis remains unexplored. This study aims to assess the association of soluble PECAM-1 and MMP-14 with dengue severity symptoms.

Intratympanic injections of emulsion-like dispersions to augment cinnarizine amount in a healthy rabbit inner ear model.

To investigate eutectic liquid-based emulsion-like dispersions for intratympanic injections to augment cinnarizine permeability across round window membrane in a healthy rabbit inner ear model. Two-tier systematic optimization was used to get the injection formula. The drug concentrations in perilymph and plasma were analyzed via.

Understanding COVID-19 outcome: Exploring the prognostic value of soluble biomarkers indicative of endothelial impairment.

Host proteins released by the activated endothelial cells during SARS-CoV-2 infection are implicated to be involved in coagulation and endothelial dysfunction. However, the underlying mechanism that governs the vascular dysfunction and disease severity in COVID-19 remains obscure. The study evaluated the serum levels of Bradykinin, Kallikrein, SERPIN A, and IL-18 in COVID-19 (N-42 with 20 moderate and 22 severe) patients compared to healthy controls (HC: N-10) using ELISA at the day of admission (DOA) and day 7 post-admission.

Analysis of gene expression profile for identification of novel gene signatures during dengue infection.

Background: Dengue is a major arthropod-borne viral disease spreading rapidly across the globe. The absence of vaccines and inadequate vector control measures leads to further expansion of dengue in many regions globally. Hence, the identification of genes involved in the pathogenesis of dengue will help to understand the molecular basis of the disease and the genes responsible for the disease progression.

Genomic characterization and evolutionary analysis of dengue virus from mosquitoes in Telangana, India.

Background & Objectives: Entomological surveillance for mosquito-borne viruses is vital for monitoring disease transmission and vector control programs. The vector control program is reliant not only on vector density but also on the timely detection of mosquito-borne infections. In the present study, we conducted an entomological surveillance in different locations of Hyderabad, Telangana, India during 2017-2018 and the collected mosquitoes were screened for dengue virus.

In vitro and in vivo antimalarial activity of green synthesized silver nanoparticles using Sargassum tenerrimum - a marine seaweed.

Green nanotechnology has recently attracted a lot of attention as a potential technique for drug development. In the present study, silver nanoparticles were synthesised by using Sargassum tenerrimum, a marine seaweed crude extract (Ag-ST), and evaluated for antimalarial activity in both in vitro and in vivo models. The results showed that Ag-ST nanoparticles exhibited good antiplasmodial activity with IC values 7.

The synergistic effect of climatic factors on malaria transmission: a predictive approach for northeastern states of India.

The northeast region of India is highlighted as the most vulnerable region for malaria. This study attempts to explore the epidemiological profile and quantify the climate-induced influence on malaria cases in the context of tropical states, taking Meghalaya and Tripura as study areas. Monthly malaria cases and meteorological data from 2011 to 2018 and 2013 to 2019 were collected from the states of Meghalaya and Tripura, respectively.

Urinary Metabolomics Identified Metabolic Perturbations Associated with Gutka, a Smokeless Form of Tobacco.

Gutka, a form of smokeless tobacco, is widely used in the Indian subcontinent and in other regions of South Asia. Smokeless tobacco exposure is most likely to increase the incidence of oral cancer in the Indian population, and metabolic changes are a hallmark of cancer. The development of biomarkers for early detection and better prevention measures for smokeless tobacco users at risk of oral cancer can be aided by studying urinary metabolomics and offering insight into altered metabolic profiles.

Environmental phenol exposure associates with urine metabolome alteration in young Northeast Indian females.

Urinary biomonitoring delivers the most accurate environmental phenols exposure assessment. However, environmental phenol exposure-related biomarkers are required to improve risk assessment to understand the internal processes perturbed, which may link exposure to specific health outcomes. This study aimed to investigate the association between environmental phenols exposure and the metabolome of young adult females from India.

Integrated metabolomics and machine learning approach to predict hypertensive disorders of pregnancy.

Background: Hypertensive disorders of pregnancy account for 3% to 10% of maternal-fetal morbidity and mortality worldwide. This condition has been considered one of the leading causes of maternal deaths in developing countries, such as India.

Objective: This study aimed to discover hypertensive disorders of pregnancy-specific candidate urine metabolites as markers for hypertensive disorders of pregnancy by applying integrated metabolomics and machine learning approaches.

Weather integrated multiple machine learning models for prediction of dengue prevalence in India.

Dengue is a rapidly spreading viral disease transmitted to humans by Aedes mosquitoes. Due to global urbanization and climate change, the number of dengue cases are gradually increasing in recent decades. Hence, an early prediction of dengue continues to be a major concern for public health in countries with high prevalence of dengue.

Weather integrated malaria prediction system using Bayesian structural time series model for northeast states of India.

Malaria is an endemic disease in India and targeted to eliminate by the year 2030. The present study is aimed at understanding the epidemiological patterns of malaria transmission dynamics in Assam and Arunachal Pradesh followed by the development of a malaria prediction model using monthly climate factors. A total of 144,055 cases in Assam during 2011-2018 and 42,970 cases in Arunachal Pradesh were reported during the 2011-2019 period observed, and Plasmodium falciparum (74.

Modelling the impact of perfect and imperfect vaccination strategy against SARS CoV-2 by assuming varied vaccine efficacy over India.

Background: The outbreak of Coronavirus disease (COVID-19) has swiftly spread globally and caused public health and socio-economic disruption in many countries. An epidemiological modelling studies in the susceptible-infectious-removed (SIR) has played an important role for making effective public health policy to mitigate the spread of COVID-19. The aim of the present study is to investigate the optimal vaccination strategy to control the COVID-19 pandemic in India.

and Anticancer and Genotoxicity Profiles of Green Synthesized and Chemically Synthesized Silver Nanoparticles.

Silver nanoparticles were green synthesized (Ag-PTs) employing the crude extract of , a marine alga, and their anticancer and safety profile were compared with those of chemically synthesized silver nanoparticles (Ag-NPs) by and models. Ag-PT exhibited potent cytotoxicity against B16-F10 (IC = 3.29), MCF-7 (IC = 4.

structural characterization of Cytochrome c oxidase Subunit 1: A transmembrane protein from .

Background & Objectives: The present study proposed a series of computational techniques such as homology modelling, molecular simulation, and molecular docking to be performed to explore the structural features and binding mechanism of Cytochrome c oxidase subunit I (COX1) protein with known inhibitors.

Methods: Elucidation of the three-dimensional structure of COX1 protein was carried out by using MODELLER software. The modelled protein was validated using GROMACS, structural qualitative tools and web servers.

Application of Design of Experiments® Approach-Driven Artificial Intelligence and Machine Learning for Systematic Optimization of Reverse Phase High Performance Liquid Chromatography Method to Analyze Simultaneously Two Drugs (Cyclosporin A and Etodolac) in Solution, Human Plasma, Nanocapsules, and Emulsions.

The objectives of current investigation are (1) to find out wavelength of maximum absorbance (λ) for combined cyclosporin A and etodolac solution followed by selection of mobile phase suitable for the RP-HPLC method, (2) to define analytical target profile and critical analytical attributes (CAAs) for the analytical quality by design, (3) to screen critical method parameters with the help of full factorial design followed by optimization with face-centered central composite design (CCD) approach-driven artificial neural network (ANN)-linked with the Levenberg-Marquardt (LM) algorithm for finding the RP-HPLC conditions, (4) to perform validation of analytical procedures (trueness, linearity, precision, robustness, specificity and sensitivity) using combined drug solution, and (5) to determine drug entrapment efficiency value in dual drug-loaded nanocapsules/emulsions, percentage recovery value in human plasma spiked with two drugs and solution state stability analysis at different stress conditions for substantiating the double-stage systematically optimized RP-HPLC method conditions. Through isobestic point and scouting step, 205 nm and ACN:HO mixture (74:26) were selected respectively as the λ and mobile phase. The ANN topology (3:10:4) indicating the input, hidden and output layers were generated by taking the 20 trials produced from the face-centered CCD model.

Prediction of COVID-19 cases using the weather integrated deep learning approach for India.

Advanced and accurate forecasting of COVID-19 cases plays a crucial role in planning and supplying resources effectively. Artificial Intelligence (AI) techniques have proved their capability in time series forecasting non-linear problems. In the present study, the relationship between weather factor and COVID-19 cases was assessed, and also developed a forecasting model using long short-term memory (LSTM), a deep learning model.

Climate based malaria forecasting system for Andhra Pradesh, India.

Malaria is a major public health problem in tropical and subtropical countries of the World. During the year 1999, Visakhapatnam district of Andhra Pradesh, India experienced a major epidemic of malaria, and nearly 41,805 cases were reported. Hence, a retrospective malaria surveillance study was conducted from 2001 to 2016 and reported nearly a total of 149,317 malaria cases during the study period.

Dengue situation in India: Suitability and transmission potential model for present and projected climate change scenarios.

Dengue fever is mosquito borne viral disease caused by dengue virus and transmitted by Aedes mosquitoes. In recent years the dengue has spread rapidly to several regions and it becomes a major public health menace globally. Dengue transmission is strongly influenced by environmental factors such as temperature and rainfall.

UPLC-MS Guided Isolation of New Antifeedant Limonoids from Fruits of .

UPLC-MS guided isolation of CHCl extract from the fruits of yielded two new mexicanolide-type limonoids trichanolide F () and trichanolide G () along with a known compound carapanolide U (). The structures of the limonoids were characterized by extensive spectroscopic analysis (MS, IR, 2D NMR). These limonoids (-) were evaluated for their antifeedancy against F.

Applications of machine learning techniques to predict filariasis using socio-economic factors.

Filariasis is one of the major public health concerns in India. Approximately 600 million people spread across 250 districts of India are at risk of filariasis. To predict this disease, a pilot scale study was carried out in 30 villages of Karimnagar district of Telangana from 2004 to 2007 to collect epidemiological and socio-economic data.

Lag effect of climatic variables on dengue burden in India.

Dengue is a widespread vector-borne disease believed to affect between 100 and 390 million people every year. The interaction between vector, host and pathogen is influenced by various climatic factors and the relationship between dengue and climatic conditions has been poorly explored in India. This study explores the relationship between El Niño Southern Oscillation (ENSO), the Indian Ocean Dipole (IOD) and dengue cases in India.

Temperature dependent transmission potential model for chikungunya in India.

Chikungunya is a major public health problem in tropical and subtropical countries of the world. During 2016, the National Capital Territory of Delhi experienced an epidemic caused by chikungunya virus with >12,000 cases. Similarly, other parts of India also reported a large number of chikungunya cases, highest incidence rate was observed during 2016 in comparison with last 10 years of epidemiological data.