Institute of Advanced Study in Science and Technology

In recent decades, Cold Atmospheric Plasma (CAP) has become increasingly popular in healthcare for managing diseases, especially skin cancer. This study aimed to assess the preclinical safety of an indigenously developed dielectric barrier discharge-CAP (DBD) device and its cytotoxic efficacy against melanoma cells while adhering to OECD 402 guidelines for acute dermal toxicity study. The safety evaluation includes ex vivo studies on mouse peritoneal exudates and in vivo acute dermal toxicity tests on Wistar rats. The ex vivo study of mice peritoneal cells treated for up to 120 seconds, showed a survival rate of over 90% up to 90 seconds of CAP treatment for applied voltage 18.6 kV at 20 kHz with no significant difference with control. In the acute dermal toxicity tests, CAP exposure for up to 30 seconds caused minimal inflammatory cell infiltration and no significant Dermal Inflammation Scoring (DIS) (60;1). The efficacy study against G361 human melanoma cells showed reduced cell viability by ~50% (MTT assay) upon 30 seconds of CAP treatment for applied voltage 24 kV at 20 kHz through ROS-mediated apoptosis, confirmed by a 3-fold increase in intracellular reactive oxygen species levels and nuclear fragmentation (4',6-diamidino-2-phenylindole staining). Annexin V/PI (propium iodide) staining further revealed ~30% apoptosis after 24 hours of incubation. These findings establish the developed DBD-CAP device is safe for rat skin exposure durations of up to 30 seconds and effective in inducing apoptosis in melanoma cells. This study supports CAP's optimization for clinical applications and its integration with existing therapies for enhanced outcomes. However, further study is needed to examine the possible risks associated with using CAP devices in the biomedical field.

A new Ni( ii ) complex of an unsymmetric Schiff base ligand was used as a photocatalyst in the degradation of cationic dyes in water under sunlight. Thus, providing a sustainable, cost-effective, and energy-efficient water remediation approach.

Air pollution is a leading cause of mortality in the developing world, with industrial regions experiencing particularly high levels of fine particulate matter (PM2.5). The Assam Oil Field Region, a significant industrial hub, is characterized by elevated PM2.5 concentrations, yet predictive modeling studies in such environments remain scarce. This study presents a preliminary exploration of statistical and machine learning techniques, aiming to identify potential tools for future environmental monitoring, in oil field localities. Using a limited dataset (n = 109), we modelled PM2.5 levels based on multiple predictor variables, including meteorological parameters (temperature, humidity, wind speed, and wind direction). We employed multiple linear regression alongside six machine learning models—Linear Regression, Decision Tree Regression, Support Vector Regression, KNN Regression, XGBoost Regression (XGBR), and Random Forest Regression, and one Feed Forward Deep Learning model. Additionally, Principal Component Analysis was employed to investigate the impact of dimensionality reduction on model performance. Among the models, XGBR consistently demonstrated superior predictive performance, achieving high R2 scores (0.78 in non-PCA, 0.59 in PCA) and minimal error metrics (MAE: 6.75, MSE: 62.72, RMSE: 7.92). Its robustness across scenarios and ability to handle complex relationships efficiently make it particularly effective compared to other ensemble methods like Random Forest Regression. While initial outcomes suggest the potential of machine learning models, especially XGBoost, for PM2.5 predictions in industrial contexts, further validation using larger and more diverse datasets is necessary. These early findings may serve as a basis for future work on data-driven air quality forecasting in oil field regions.

Abstract Long‐lead seasonal forecasts (>12 months) of the Indian summer monsoon rainfall (ISMR) are crucial for adaptive planning and damage minimization against climate change‐induced increasing threats of higher frequency of hydrological disasters in the coming decades. However, the growth of initial errors and drift of forecast climatology with lead month drive the seasonal forecast skill of ISMR by Atmosphere–Ocean General Circulation Models (AOGCMs) to decrease with lead time, making them useless beyond an approx. six‐month lead. Hope of overcoming the challenge is rekindled from recent advances in the application of deep‐learning models to weather and climate prediction that extend the skill of weather prediction beyond the limit of the best numerical weather prediction (NWP) models. Simultaneous to this advance, we have established the physical basis of high‐potential predictability of seasonal forecast of ISMR up to 24‐month leads. Here, we develop a physics‐guided deep‐learning (PGDL) model‐based ‘long‐lead forecast system’ for ISMR trained on the relationship between ISMR and the depth of the 20 °C isotherm in the tropics from a large ensemble of AOGCM historical simulations and past observations to overcome the challenge of poor forecast skill of ISMR at long leads. In contrast to the initialized physical AOGCMs, our model makes increasingly skillful seasonal forecasts up to 24‐month leads in accordance with potential predictability while demonstrating superior skill in predicting extreme excess/deficient ISMR between 1980 and 2023 at 18‐ and five‐month leads. Operational feasibility of the model is demonstrated by making an experimental 18‐month lead forecast of ISMR for 2024. Our findings establish a physical basis and methodology for long‐lead seasonal prediction of ISMR and a building block for three‐dimensional tropical seasonal predictions.