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. The efficacy of the protein levels in predicting disease severity was further determined using machine learning models. The levels of bradykinins and SERPIN A were higher (P ≤ 0.001) in both severe and moderate cases on day 7 post-admission compared to DOA. All the soluble proteins studied were found to elevated (P ≤ 0.01) in severe compared to moderate in day 7 and were positively correlated (P ≤ 0.001) with D-dimer, a marker for coagulation. ROC analysis identified that SERPIN A, IL-18, and bradykinin could predict the clinical condition of COVID-19 with AUC values of 1, 0.979, and 1, respectively. Among the models trained using univariate model analysis, SERPIN A emerged as a strong prognostic biomarker for COVID-19 disease severity. The serum levels of SERPIN A in conjunction with the coagulation marker D-dimer, serve as a predictive indicator for COVID-19 clinical outcomes. However, studies are required to ascertain the role of these markers in disease virulence.

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. Methods: In the present study, a meta-analysis was carried out using dengue gene expression data obtained from Gene Expression Omnibus repository. The differentially expressed genes such as CCNB1 and CCNB2 (G2/mitotic-specific cyclin-B2 and B1) were upregulated in dengue fever to control (DF-CO) and severe dengue (dengue hemorrhagic fever [DHF]) to control (DHF-CO) were identified as key genes for controlling the major pathways (cell cycle, oocyte meiosis, p53 signaling pathway, cellular senescence and progesterone-mediated oocyte maturation). Similarly, interferon alpha-inducible (IFI27) genes, type-I and type-III interferon (IFN) signaling genes (STAT1 and STAT2), B cell activation and survival genes (TNFSF13B, TNFRSF17) and toll like receptor (TLR7) genes were differentially up activated during DF-CO and DHF-CO. Followed by, Cytoscape was used to identify the immune system process and topological analysis. Results: The results showed that the top differentially expressed genes under the statistical significance p <0.001, which is majorly involved in Kyoto Encyclopedia of Genes and Genomes orthology K05868 and K21770 with gene names CCNB1 and CCNB2. In addition to this, the immune system profile showed up-regulation of IL12A, CXCR3, TNFSF13B, IFI27, TNFRSF17, STAT, STAT2, and TLR7 genes in DF-CO and DHF-CO act as immunological signatures for inducing the immune response towards dengue infection. Conclusions: The current study could aid in understanding of molecular pathogenesis, genes and corresponding pathway upon dengue infection, and could facilitate for identification of novel drug targets and prognostic markers.

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 IC50 values 7.71±0.39 µg/ml and 23.93±2.27 µg/ml against P. falciparum and P. berghei respectively. These nanoparticles also showed less haemolysis activity suggesting their possible use in therapeutics. Further, P. berghei infected C57BL/6 mice were used for the four-day suppressive, curative and prophylactic assays where it was noticed that the Ag-ST nanoparticles significantly reduced the parasitaemia and there were no toxic effects observed in the biochemical and haematological parameters. Further to understand its possible toxic effects, both in vitro and in vivo genotoxicological studies were performed which revealed that these nanoparticles are non-genotoxic in nature. The possible antimalarial activity of Ag-ST may be due to the presence of bioactive phytochemicals and silver ions. Moreover, the phytochemicals prevent the nonspecific release of ions responsible for low genotoxicity. Together, the bio-efficacy and toxicology outcomes demonstrated that the green synthesized silver nanoparticles (Ag-ST) could be a cutting-edge alternative for therapeutic applications.

Genomic characterization and evolutionary analysis of dengue virus from Aedes 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. METHODS: Reverse transcriptase polymerase chain reaction (RT-PCR) was used for the identification and serotyping of the dengue virus. Bioinformatics analysis was performed using Mega 6.0 software. Followed by phylogenetic analysis, which was based on CprM structural genome sequence, was performed by using the Maximum-Likelihood method. RESULTS: The TaqMan RT-PCR assay was used to analyze the serotypes of 25 pools of Aedes mosquitoes and found that all four serotypes are circulating in Telangana. DENV1 (50%) was the most commonly detected serotype followed by DENV2 (16.6%), DENV3 (25%), and DENV4 (8.3%). Moreover, DENV1 has the highest MIR (16 per 1000 mosquitoes) compared with DENV2, 3, and 4. The CprM structural gene sequence was used for phylogenetic analysis, revealing that all four strains have a close relationship with strains isolated from India, Pakistan, China and Thailand. Similarly, two variations in amino acid sequence DENV1 at position 43 (K-R) and 86 (S-T) and a single mutation DENV2 at 111 amino acid position were observed. INTERPRETATION & CONCLUSION: The results of the study provide an in-depth transmission dynamic of the dengue virus and persistence of this emerging pathogen in Telangana, India that needs appropriate prevention programs.

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. The nonlinear associations between individual and synergistic effect of meteorological factors and malaria cases were assessed, and climate-based malaria prediction models were developed using the generalized additive model (GAM) with Gaussian distribution. During the study period, a total of 216,943 and 125,926 cases were recorded in Meghalaya and Tripura, respectively, and majority of the cases occurred due to the infection of Plasmodium falciparum in both the states. The temperature and relative humidity in Meghalaya and temperature, rainfall, relative humidity, and soil moisture in Tripura showed a significant nonlinear effect on malaria; moreover, the synergistic effects of temperature and relative humidity (SI=2.37, RERI=0.58, AP=0.29) and temperature and rainfall (SI=6.09, RERI=2.25, AP=0.61) were found to be the key determinants of malaria transmission in Meghalaya and Tripura, respectively. The developed climate-based malaria prediction models are able to predict the malaria cases accurately in both Meghalaya (RMSE: 0.0889; R2: 0.944) and Tripura (RMSE: 0.0451; R2: 0.884). The study found that not only the individual climatic factors can significantly increase the risk of malaria transmission but also the synergistic effects of climatic factors can drive the malaria transmission multifold. This reminds the policymakers to pay attention to the control of malaria in situations with high temperature and relative humidity and high temperature and rainfall in 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. This study aimed to investigate urine metabolic alterations among smokeless tobacco users using targeted LC-ESI-MS/MS metabolomics approaches to better understand the effects of smokeless tobacco on human metabolism. Smokeless tobacco users' specific urinary metabolomics signatures were extracted using univariate, multivariate analysis and machine learning methods. Statistical analysis identified 30 urine metabolites significantly associated with metabolomic alterations in humans who chew smokeless tobacco. Receiver operator characteristic (ROC) curve analysis evidenced the 5 most discriminatory metabolites from each approach that could differentiate between smokeless tobacco users and controls with higher sensitivity and specificity. An analysis of multiple-metabolite machine learning models and single-metabolite ROC curves revealed discriminatory metabolites capable of distinguishing smokeless tobacco users from nonusers more effectively with higher sensitivity and specificity. Furthermore, metabolic pathway analysis depicted several dysregulated pathways in smokeless tobacco users, including arginine biosynthesis, beta-alanine metabolism, TCA cycle, etc. This study devised a novel strategy to identify exposure biomarkers among smokeless tobacco users by combining metabolomics and machine learning algorithms.

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. Urinary metabolomics was performed using liquid chromatography-mass spectrometry. Environmental phenols-related metabolic biomarkers were investigated by comparing the low and high exposure of environmental phenols. Seven potential biomarkers, namely histidine, cysteine-s-sulfate, 12-KETE, malonic acid, p-hydroxybenzoic acid, PE (36:2), and PS (36:0), were identified, revealing that environmental phenol exposure altered the metabolic pathways such as histidine metabolism, beta-Alanine metabolism, glycerophospholipid metabolism, and other pathways. This study also conceived an innovative strategy for the early prediction of diseases by combining urinary metabolomics with machine learning (ML) algorithms. The differential metabolites predictive accuracy by ML models was >80%. This is the first mass spectrometry-based metabolomics study on young adult females from India with environmental phenols exposure. The study is valuable in demonstrating multiple urine metabolic changes linked to environmental phenol exposure and a better understanding of the mechanisms behind environmental phenol-induced effects in young female adults.

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. STUDY DESIGN: The targeted urinary metabolomics study was conducted in 70 healthy pregnant controls and 133 pregnant patients having hypertension as cases. Hypertensive disorders of pregnancy-specific metabolites for disease prediction were further extracted using univariate and multivariate statistical analyses. For machine learning analysis, 80% of the data were used for training (79 for hypertensive disorders of pregnancy and 42 for healthy pregnancy) and validation (27 for hypertensive disorders of pregnancy and 14 for healthy pregnancy), and 20% of the data were used for test sets (27 for hypertensive disorders of pregnancy and 14 for healthy pregnancy). RESULTS: The statistical analysis using an unpaired t test revealed 44 differential metabolites. Pathway analysis showed mainly that purine and thiamine metabolism were altered in the group with hypertensive disorders of pregnancy compared with the healthy pregnancy group. The area under the receiver operating characteristic curves of the 5 most predominant metabolites were 0.98 (adenosine), 0.92 (adenosine monophosphate), 0.89 (deoxyadenosine), 0.81 (thiamine), and 0.81 (thiamine monophosphate). The best prediction accuracies were obtained using 2 machine learning models (95% for the gradient boost model and 98% for the decision tree) among the 5 used models. The machine learning models showed higher predictive performance for 3 metabolites (ie, thiamine monophosphate, adenosine monophosphate, and thiamine) among 5 metabolites. The combined accuracies of adenosine from all models were 98.6 in the training set and 95.6 in the test set. Moreover, the predictive performance of adenosine was higher than other metabolites. The relative feature importance of adenosine was also observed in the decision tree and the gradient boost model. CONCLUSION: Among other metabolites, adenosine and thiamine metabolites were found to differentiate participants with hypertensive disorders of pregnancy from participants with healthy pregnancies; hence, these metabolites can serve as a promising noninvasive marker for the detection of hypertensive disorders of pregnancy.

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. Creating a robust forecast model for the accurate prediction of dengue is a complex task and can be done through various data modelling approaches. In the present study, we have applied vector auto regression, generalized boosted models, support vector regression, and long short-term memory (LSTM) to predict the dengue prevalence in Kerala state of the Indian subcontinent. We consider the number of dengue cases as the target variable and weather variables viz., relative humidity, soil moisture, mean temperature, precipitation, and NINO3.4 as independent variables. Various analytical models have been applied on both datasets and predicted the dengue cases. Among all the models, the LSTM model was outperformed with superior prediction capability (RMSE: 0.345 and R2:0.86) than the other models. However, other models are able to capture the trend of dengue cases but failed in predicting the outbreak periods when compared to LSTM. The findings of this study will be helpful for public health agencies and policymakers to draw appropriate control measures before the onset of dengue. The proposed LSTM model for dengue prediction can be followed by other states of India as well.

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. Methods: We have applied compartment mathematical model susceptible-vaccination-infectious-removed (SVIR) with different range of vaccine efficacy scenarios and predicted the population to be covered for vaccination per day in India as well as state level was performed. Results: The model assumed that a vaccine has 100% efficacy, predicted that >5 million populace to be vaccinated per day to flatten the epidemic curve in India. Similarly, different vaccination mechanisms such as 'all-or-nothing' (AoN) and leaky vaccines does not have potential discordance in their effectiveness at higher efficacies (>70%). However, AoN vaccine was found to be marginally effective than leaky at lower efficacies (<70%) when administered at the higher coverage strategies. Further state level analyses were performed and it was found that 0.3, 0.3, 0.2 and 1 million vaccinations required per day in Andhra Pradesh, Gujarat, Kerala and Maharashtra as it assumes that the vaccine efficacy is 70%. Conclusion: The proposed modelling approach shows a range of assumptions on the efficacy of vaccine which helps the health authorities to prioritize the vaccination strategies to prevent the transmission as well as disease.

In Vitro and In Vivo Anticancer and Genotoxicity Profiles of Green Synthesized and Chemically Synthesized Silver Nanoparticles.

Silver nanoparticles were green synthesized (Ag-PTs) employing the crude extract of Padina tetrastromatica, a marine alga, and their anticancer and safety profile were compared with those of chemically synthesized silver nanoparticles (Ag-NPs) by in vitro and in vivo models. Ag-PT exhibited potent cytotoxicity against B16-F10 (IC50 = 3.29), MCF-7 (IC50 = 4.36), HEPG2 (IC50 =3.89), and HeLa (IC50 = 4.97) cancer cell lines, whereas they exhibited lower toxicity on normal CHO-K1 cells (IC50 = 5.16). The potent anticancer activity of Ag-PTs on cancer cells is due to the liberation of ions from the nanoparticles. Increased ion internalization to the cells promotes reactive oxygen species (ROS) production and ultimately leads to cell death. The in vitro anticancer results and in vivo melanoma tumor regression study showed significant inhibition of melanoma tumor growth due to Ag-PT treatment. Ag-PT is involved in the upregulation of the p53 protein and downregulation of Sox-2 along with the Ki-67 protein. The antitumor effects of Ag-PTs may be due to the additional release of ions at a lower pH of the tumor microenvironment than that of the normal tissue. The results of safety investigations of Ag-PT by studying mitotic chromosome aberrations (CAs), micronucleus (MN) induction, and mitotic index (MI) demonstrated Ag-PT to be less genotoxic compared to Ag-NP. The bioefficacy and toxicology outcomes together demonstrated that the green synthesized silver nanoparticles (Ag-PTs) could be explored to develop a biocompatible, therapeutic agent and a vehicle of drug delivery for various biomedical applications.

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. The study found that the specific humidity has a strong positive correlation, whereas there is a negative correlation with maximum temperature, and a positive correlation with minimum temperature was observed in various geographic locations of India. The weather data and COVID-19 confirmed case data (1 April to 30 June 2020) were used to optimize univariate and multivariate LSTM time series forecast models. The optimized models were utilized to forecast the daily COVID-19 cases for the period 1 July 2020 to 31 July 2020 with 1 to 14 days of lead time. The results showed that the univariate LSTM model was reasonably good for the short-term (1 day lead) forecast of COVID-19 cases (relative error <20%). Moreover, the multivariate LSTM model improved the medium-range forecast skill (1-7 days lead) after including the weather factors. The study observed that the specific humidity played a crucial role in improving the forecast skill majorly in the West and northwest region of India. Similarly, the temperature played a significant role in model enhancement in the Southern and Eastern regions of India.

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 (λmax) 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:H2O mixture (74:26) were selected respectively as the λmax 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. The ANN-linked LM model produced minimal differences between predicted and observed values of output parameters (or CAAs), low mean squared error and higher correlation coefficient values in comparison to the respective values produced by face-centered CCD model. The optimized RP-HPLC method could be applied to analyze two drugs concurrently in different formulations, human plasma and solution state stability checking.

In silico structural characterization of Cytochrome c oxidase Subunit 1: A transmembrane protein from Aedes aegypti.

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. Finally the model was docked with carbon monoxide (CO) and nitric oxide (NO) using Auto Dock Tools. RESULTS: The three-dimensional structure of mitochondrial transmembrane protein COX1 was built using homology modelling based on high-resolution crystal structures of Bos taurus. Followed by inserting the lipid bilayer, molecular dynamics simulation was performed on the modelled protein structure. The modelled protein was validated using qualitative structural indices. Known inhibitors such as carbon monoxide (CO) and nitric oxide (NO) inhibit their active binding sites of mitochondrial COX1 and the inhibitors were docked into the active site of attained model. A structure-based virtual screening was performed on the basis of the active site inhibition with best scoring hits. The COX1 model was submitted and can be accessible from the Model Archive site through the following link https://www.modelarchive.org/doi/10.5452/ma-at44v. INTERPRETATION & CONCLUSION: Structural characterization and active site identification can be further used as target for the planning of potent mosquitocidal compounds, thereby assisting the information in the field of research.

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. Of which, Plasmodium vivax contributes 32%, and Plasmodium falciparum contributes 68% of the total cases. Malaria cases follow a strong seasonal variation and 70% of cases are reported during the monsoon periods. In the present study, we exploited multi step polynomial regression and seasonal autoregressive integrated moving average (SARIMA) models to forecast the malaria cases in the study area. The polynomial model predicted malaria cases with high predictive power and found that malaria cases at lag one, and population played a vital role in malaria transmission. Similarly, mean temperature, rainfall and Normalized Difference Vegetation Index build a significant impact on malaria cases. The best fit model was SARIMA (1, 1, 2) (2, 1, 1)12 which was used for forecasting monthly malaria incidence for the period of January 2015 to December 2016. The performance accuracy of both models are similar, however lowest Akaike information criterion score was observed by the polynomial model, and this approach can be helpful further for forecasting malaria incidence to implement effective control measures in advance for combating malaria in India.

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. In the present study, a climate driven dengue model was developed and predicted areas vulnerable for dengue transmission under the present and future climate change scenarios in India. The study also projected the dengue distribution risk map using representative concentration pathways (RCP4.5 and RCP8.5) in India in 2018-2030 (forthcoming period), 2031-2050 (intermediate period) and 2051-2080 (long period). The dengue cases assessed in India from 1998 to 2018 and found that the dengue transmission is gradually increasing year over year. The temperature data from 1980 to 2017 shows that, the mean temperatures are raising in the Southern region of India. During 2000-2017 periods the dengue transmission is steadily increasing across the India in compare with 1980-1999 periods. The dengue distribution risk is predicted and it is revealed that the coastal states have yearlong transmission possibility, but the high transmission potential is observed throughout the monsoon period. Due to the climate change, the expansion two more months of dengue transmission risk occurs in many regions of India. Both RCP4.5 and RCP8.5 scenarios revealed that dengue outbreaks might occur at larger volume in Southern, Eastern, and Central regions of India. Furthermore a sensitivity analysis was performed to explore the impact of climate change on dengue transmission. These results helps to suggest appropriate control measures should be implemented to limit the spread in future warmer climates. Besides these, a proper plan is required to mitigate greenhouse gas emissions to reduce the epidemic potential of dengue in India.

UPLC-MSE Guided Isolation of New Antifeedant Limonoids from Fruits of Trichilia connaroides.

UPLC-MSE guided isolation of CHCl3 extract from the fruits of Trichilia connaroides yielded two new mexicanolide-type limonoids trichanolide F (1) and trichanolide G (2) along with a known compound carapanolide U (3). The structures of the limonoids were characterized by extensive spectroscopic analysis (MS, IR, 2D NMR). These limonoids (1-3) were evaluated for their antifeedancy against Spodoptera litura F. To further explore and draw the meaningful structure activity relationship studies, secophragmalin-type limonoids, namely, secotrichagmalin B, C (4, 5) and semisynthetic derivatives (5a-5l) were also screened for antifeedancy. The results revealed that trichanolide F (1) displayed highest antifeedant index (AFI) and caused larval mortality at 24 h. Derivative 5b caused larval toxicity, whereas 3, 5a, 5d, and 5g lead to pupal mortality and 2, 5f, 5k, and 5l caused adult deformities. Overall, the study provided new insights into the antifeedant potential of isolated and chemically modified limonoids from T. connaroides for the control of spodopteran pests.

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. The collected data are analysed by employing various machine learning techniques such as Naïve Bayes (NB), logistic model tree, probabilistic neural network, J48 (C4.5), classification and regression tree, JRip and gradient boosting machine. The performances of these algorithms are reported using sensitivity, specificity, accuracy and area under ROC curve (AUC). Among all employed classification methods, NB yielded the best AUC of 64% and was equally statistically significant with the rest of the classifiers. Similarly, the J48 algorithm generated 23 decision rules that help in developing an early warning system to implement better prevention and control efforts in the management of filariasis.

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. Additionally, distributed lag non-linear model was used to assess the delayed effects of climatic factors on dengue cases. The weekly dengue cases reported by the Integrated Disease Surveillance Program (IDSP) over India during the period 2010-2017 were analysed. The study shows that dengue cases usually follow a seasonal pattern, with most cases reported in August and September. Both temperature and rainfall were positively associated with the number of dengue cases. The precipitation shows the higher transmission risk of dengue was observed between 8 and 15 weeks of lag. The highest relative risk (RR) of dengue was observed at 60 mm rainfall with a 12-week lag period when compared with 40 and 80 mm rainfall. The RR of dengue tends to increase with increasing mean temperature above 24 °C. The largest transmission risk of dengue was observed at 30 °C with a 0-3 weeks of lag. Similarly, the transmission risk increases more than twofold when the minimum temperature reaches 26 °C with a 2-week lag period. The dengue cases and El Niño were positively correlated with a 3-6 months lag period. The significant correlation observed between the IOD and dengue cases was shown for a 0-2 months lag period.

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. In the present study we exploited R0 mathematical model to understand the transmission risk of chikungunya virus which is transmitted by Aedes vectors. This mechanistic transmission model is climate driven and it predicts how the probability and transmission risk of chikungunya occurs in India. The gridded temperature data from 1948 to 2016 shows that the mean temperatures are gradually increasing in South India from 1982 to 2016 when compared with data of 1948-1981 time scale. During 1982-2016 period many states have reported gradual increase in risk of chikungunya transmission when compared with the 1948-1981 period. The highest transmission risk of chikungunya in India due to favourable ecoclimatic conditions, increasing temperature leads to low extrinsic incubation period, mortality rates and high biting rate were predicted for the year 2016. The epidemics in 2010 and 2016 are also strongly connected to El Nino conditions which favours transmission of chikungunya in India. The study shows that transmission of chikungunya occurs between 20 and 34 °C but the peak transmission occurs at 29 °C. The infections of chikungunya in India are due to availability of vectors and optimum temperature conditions influence chikungunya transmission faster in India. This climate based empirical model helps the public health authorities to assess the risk of chikungunya and one can implement necessary control measures before onset of disease outbreak.