National Institute of Cancer Prevention and Research

Forecasting malaria incidence is vital for effective resource allocation during malaria elimination. In this study, we highlight robust models for forecasting incidence using climatic and malaria data from Goa, India. Multi-collinearity and Shapley Additive Explanations (SHAP) were used to identify most important predictors of malaria transmission among 15 climatic variables. Three machine-learning models (Support vector machines, Random Forest, Extreme gradient boosting), three time-series models (ARIMA, SARIMA, SARIMAX), and three hybrid models (RF-ARMA, SVM-ARMA, XGB-ARMA) were then trained and tested on data spanning from 2010 to 2019. Climatic extremes have stronger influence on malaria transmission than average values in Goa. Machine learning models exhibit lower accuracy (Root Mean Square Error (RMSE):13–37) but high precision (lower confidence intervals). Conversely, time series models, yielded more accurate results (RMSE: 5–41) albeit with less precision (wider confidence interval). To address this, we augmented machine learning models by incorporating time series variables which significantly bolstered their accuracy while retaining their inherent precision (RMSE: 0·5-15). Integrating time-series components into machine learning models harnesses the strengths of both approaches resulting in a substantial enhancement in accuracy and precision of forecasts. This technique has potential for wider use in planning malaria elimination, and routine epidemiological data analysis.

SummaryBackgroundIndia accounts for one-fifth of the global hepatitis A virus (HAV) infections and half of HAV-related deaths. There is a lack of nationally representative population-based data on the endemicity of HAV to inform vaccination policy. We aimed to estimate the age-specific seroprevalence of HAV infection among individuals aged 6–30 years.MethodsWe used serum samples collected during the fourth national COVID-19 serosurvey conducted between 14 June and 6 July 2021 to estimate the seroprevalence of HAV infection. The survey was conducted in 70 randomly selected districts across 20 Indian states and one union territory. We tested the serum samples from individuals aged six to 30 years for IgG antibodies against HAV. We estimated the overall and state-specific seroprevalence, along with 95% CIs, for the age groups of 6–10, 11–15 and 16–30 years. We classified the HAV endemicity in India using WHO classification (high, intermediate, low and very low).FindingsWe tested 14,778 serum samples from individuals aged six to 30 years for IgG antibodies against HAV. Of these, 12,236 (90.0%, 95% CI 88.5–91.4) were found to be reactive. The seroprevalence increased with age, from 74.7% (71.1–77.9) among children aged 6–10 years to 85.2% (82.7–87.4) among those aged 11–15 years and 96.9% (96.3–97.5) among individuals aged 16–30 years. India was categorized as having intermediate endemicity for HAV infection as per the WHO classification. Of the 21 states or union territories included in the survey, 18 had intermediate endemicity.InterpretationOur study findings indicate an intermediate level of endemicity for HAV infection in India. While these findings support consideration of hepatitis-A vaccination, further evidence on disease burden and cost-effectiveness is needed to inform policy decisions.Funding10.13039/100000865Gates Foundation & 10.13039/501100001411Indian Council of Medical Research.

Smokeless tobacco (SLT) consumption has several adverse impacts on pregnancy and child health outcomes, particularly among women in low-income settings. SLT use during pregnancy heightens the risks, like preterm births, stillbirth, babies with low birth weight, and small for gestational age. The present qualitative study explored the patterns and contributing factors associated with SLT use behavior among pregnant and lactating mothers in slum settings. We conducted a qualitative study using in-depth interviews among pregnant and lactating women aged 18–49 years in the slums of Bhubaneswar. All participants were current users of smokeless tobacco (SLT) with a history of more than one year of consumption. The interviews were analyzed using a thematic analysis approach. Participants primarily consumed SLT products such as Paan, Khaini, Areca Nut, Gundi, Dukta, Gudakhu, and Gutkha, with consumption patterns varying based on personal preference, cravings, and affordability. 45% of pregnant women and 55% of lactating women reported consuming SLT immediately after waking up. Economic constraints influenced product preferences and consumption frequency. Key factors influencing SLT use included peer and family influence, stress relief, pregnancy-induced craving, curiosity, individual attitude and beliefs, to remaining engaged in work. Also, the study finding shows long-term effects of SLT use among pregnant and lactating women. Notably, 52.5% (n = 21) of participants started SLT use during their adolescence and 57.5% (n = 23) had no formal education. For the enrolled pregnant women and lactating mothers, the mean age of SLT initiation was 14.95 years and 12.58 years, respectively, indicating a significantly longer history of tobacco consumption. The present study provides a detailed qualitative understanding of the use of smokeless tobacco among pregnant and lactating mothers living in low-income settings like slums. The findings of this study have strong explanations to support healthcare professionals and policymakers in undertaking interventions and promoting anti-tobacco campaigns and awareness programmes addressing the health hazards for maternal and neonatal health. Supplementary InformationThe online version contains supplementary material available at 10.1038/s41598-025-15853-5.

Cancer, ranging from early stages to metastatic spread, is one of the leading causes of death globally. Current treatment options, including chemotherapy, radiotherapy, and targeted drugs, have limitations, such as significant side effects, drug resistance, and high cost. To overcome these challenges, extensive studies have explored the anticancer potential of various drugs such as clotrimazole (CLZ), which has shown promising anticancer effects. CLZ was first developed as an antifungal agent. Recently significant anticancer effects have been observed making it a suitable candidate for drug repurposing. Compared with other azole-based antifungals, CLZ has shown distinct therapeutic effects on cancer cells via several pathways. Its ability to disrupt glycolysis by inhibiting phosphofructokinase (PFK) and hexokinase (HK) distinguishes it from other azoles. Furthermore, CLZ obstructs calcium homeostasis and critical survival pathways, such as extracellular signal-regulated kinase (ERK)-p65, phosphatidylinositol 3-kinase (PI3K), and mitochondrial apoptotic pathways, inhibiting tumor growth, inducing apoptosis, and attenuating metastasis. This review explores the potential of repurposing CLZ in cancer and its well-established safety profile and cost-effectiveness to highlight current treatment gaps. It briefly examines in vitro and in vivo assessments to understand the mechanisms and effects of CLZ on various cancer types. Furthermore, novel strategies such as nanoformulations and combination therapies with existing chemotherapeutic drugs have been highlighted to improve therapeutic outcomes. Preclinical studies have provided promising evidence for the efficacy of CLZ in different cancers, showing tumor regression and improved responses to conventional chemotherapy or targeted therapies. Given its evident preclinical results and diverse mechanisms of action, CLZ may be considered an antineoplastic agent. Further clinical research is required to fully elucidate its anticancer potential, potentially positing it as a valuable addition to currently available cancer treatments.

Anthocyanins, a class of water-soluble pigments responsible for the vibrant red, purple, and blue hues in various fruits and vegetables, offer significant aesthetic and nutritional benefits. In purple cauliflower (Brassica oleracea var. botrytis), the striking pigmentation is primarily due to a mutation in the Pr gene, which encodes an R2R3 MYB transcription factor critical for regulating anthocyanin biosynthesis. This Pr-D mutation leads to enhanced accumulation of anthocyanins, particularly cyanidin-based compounds, by promoting the expression of key biosynthetic genes such as BoF3'H, BoDFR, and BoANS. The insertion of a Harbinger DNA transposon into the Pr gene's regulatory region further increases its expression, resulting in the ectopic accumulation of anthocyanins in various tissues, including curds, leaves, and seeds. Anthocyanins not only enhance the visual appeal of purple cauliflower but also confer numerous health benefits, acting as powerful antioxidants that combat oxidative stress and inflammation. Research indicates that regular consumption of anthocyanin-rich foods may reduce the risk of chronic diseases, including cardiovascular disease, cancer, and neurodegenerative disorders. However, challenges remain in cultivation, including environmental factors, soil management, and pest control, which can affect anthocyanin production and overall plant health. Market demand for nutrient-dense vegetables is increasing, driven by consumer interest in functional foods. The unique nutritional profile of purple cauliflower positions it as a promising candidate in specialty crop markets. Future research should focus on the genetic and physiological aspects of anthocyanin biosynthesis, tissue-specific regulation, and the identification of genetic markers to improve breeding strategies. By integrating genetic insights with agricultural practices, the potential for developing enhanced varieties of purple cauliflower can be realized, contributing to healthier diets and sustainable agricultural practices.

Emerging and re-emerging viruses (like Spanish flu, SARS-CoV-2, etc.) have substantially impacted global public health since the early twentieth century. These outbreaks are unpredictable and novel viruses are difficult to understand due to emerging variations. Advanced virology and diagnostic technologies have revolutionized viral diagnostics, enabling accurate early identification and successful treatment and containment. Next-generation sequencing (NGS) technologies, such as metagenomics and whole-genome sequencing, have played a crucial role in the detection and monitoring of emerging viruses, such as SARS-CoV-2 and its variants. Advanced diagnostic methods, such as digital PCR, CRISPR-based tools, and serological techniques like ELISA, enhance viral detection's sensitivity, specificity, and speed. Research has shown that innovations such as lateral flow immunoassays, biosensors, and aptamers have the potential to significantly enhance diagnostic accuracy in various fields. The integration of AI in diagnostics aids researchers in understanding viral evolution and outbreak management, offering new avenues for rapid response. This review aims to examine the latest advancements in virus diagnosis technologies, identify unresolved accuracy and detection issues, and discuss emerging ideas that are transforming the future of viral diagnostics. It is important to improve early identification, rendering the system more cost-effective and adaptable to new viral threats.