Decoding the Impact of Acidification and Toxic Metal Stress on Tea Soil Bacteriome: Integrating 16S rRNA Sequencing with Multivariate Approaches - Riddhi Basu; Sonali Banerjee; Saibal Ghosh; Gourav Mondal; Sumit Kumar; Pradip Bhattacharyya (Biological Trace Element Research, 2026)

Comprehensive evaluation of surface water quality: heavy metals, speciation, and human health risks in an industrial region of West Bengal, India - Gourav Mondal; Riddhi Basu; Sumit Kumar; Shreya Chakraborty; Ambika Barman; Kasturi Charan; Jajati Mandal; Pradip Bhattacharyya (Environmental Monitoring and Assessment, 2026)
This study evaluates the hydrogeochemical characteristics, heavy metal (HM) contamination, irrigation suitability, and associated human health risks of surface water in an industrial area affected by dye-manufacturing activities in West Bengal, India. A total of 30 surface water collected in triplicate were analysed. Major ions were determined using standard American Public Health Association (APHA) methods, HMs were quantified using atomic absorption spectrometry (AAS), and aqueous speciation was modelled using Visual MINTEQ. The water was slightly alkaline, with pH values ranging from 7.18 to 8.92 (mean 7.79±0.41). Extremely high salinity was observed, as electrical conductivity ranged from 2500 to 21,000 μS cm⁻¹ (mean 11,300±4272 μS/cm), while total dissolved solids varied between 1500 and 12,600 mg/L (mean 6780 ± 2563 mg/L), exceeding World Health Organization (WHO) guideline values. Major cations followed the order Ca²⁺ > Mg²⁺ > Na⁺ > K⁺, whereas dominant anions were HCO 3 ⁻ > Cl⁻ > SO 4 2 ⁻, reflecting combined geogenic and anthropogenic influences. Mean concentrations of heavy metals were elevated, with values of Cr (0.97 mg/L), Pb (0.23 mg/L), Ni (0.19 mg/L), Cu (0.06 mg/L), and Cd (0.004 mg/L). Except for Cu, all metals exceeded WHO permissible limits, indicating substantial contamination. Pollution indices (Pi) confirmed severe degradation, with Pi > 1 for Pb, Ni, Cr, and Cd. High average values of C d (48.89), Heavy Metal Evaluation Index (HEI), 53.65, and Heavy Metal Pollution Index (HPI), 338.73, indicated high to extreme pollution, while Water Pollution Index (WPI) values (1.51–5.27) classified all samples as severely polluted. Speciation modelling showed Cd predominantly occurred as Cd 2+ (52.35%) and CdCl + (32.10%), suggesting high mobility. Lead (Pb) was predominantly present as PbCO 3 (aq) (39.47%) and PbHCO 3 + (31.31%), Ni largely as Ni 2+ (63.15%), and Cr(VI) mainly as CrO 4 2 ⁻. Copper was largely present as CuCO 3 (aq) (67.94%). Irrigation indices indicated suitability, although overall water quality remained poor. Human health risk assessment identified Pb and Cr as posing non-carcinogenic risks, while Cr presenting the highest carcinogenic risk, particularly for children (CR > 1 × 10⁻ 4 ). Overall, the findings highlight severe surface water degradation and underscore the urgent need for remediation measures and stricter regulatory intervention.

Deciphering stone mining-induced hazardous heavy metal contamination in agricultural soils using source attribution, health-dietary risk analysis, and machine learning-driven insights - Annewsa Acharya; Chetana Tunga; Sonali Banerjee; Riddhi Basu; Saibal Ghosh; Gourav Mondal; Sumit Kumar; Shreya Chakraborty; Gopinath Gorai; Sudipta Tripathi; Pradip Bhattacharyya (Environmental Geochemistry and Health, 2026)

Source apportionment and risk assessment of heavy metals in mangrove soils using multivariate and sensitivity analyses - Annewsa Acharya; Debatri Dewanjee; Gourav Mondal; Shreya Chakraborty; Sonali Banerjee; Sumit Kumar; Saibal Ghosh; Riddhi Basu; Sumit Roy; Pradip Bhattacharyya (Journal of Environmental Chemical Engineering, 2025)

Uncovering deep trouble through multi-indices assessment: Geochemical fingerprints and human health risks of groundwater affected by coal mining - Sumit Kumar; Gourav Mondal; Vikas Sonkar; Shreya Chakraborty; Rupsha Nandi; Riddhi Basu; Debatri Dewanjee; Pankaj Kumar Roy; Pradip Bhattacharyya (Process Safety and Environmental Protection, 2025)

Comprehensive Evaluation of Potentially Toxic Elements in Tea: Bioavailability, Bio-accessibility, and Health Risk Analysis Using Multimodal Statistics - Riddhi Basu; Saibal Ghosh; Gourav Mondal; Sonali Banerjee; Shreya Chakraborty; Sumit Kumar; Pradip Bhattacharyya (Biological Trace Element Research, 2025)

A Multifaceted Approach Assessing Heavy Metal Contamination and Health Risk in Tea Garden Soils: Insight Through Hot-Spot Analysis and Machine Learning Techniques - Riddhi Basu; Sonali Banerjee; Saibal Ghosh; Gourav Mondal; Sumit Kumar; Jajati Mandal; Pradip Bhattacharyya (Water, Air, & Soil Pollution, 2025)

The Flower Colour Influences Spontaneous Nectaring in Butterflies: a Case Study with Twenty Subtropical Butterflies - Sumit Kumar Sinha; Avishek Dolai; Arjan Basu Roy; Sourav Manna; Amlan Das (Neotropical Entomology, 2023)

Streamlining Food Effect Assessment — Are Repeated Food Effect Studies Needed? An IQ Analysis - Filippos Kesisoglou; Sumit Basu; Tejashree Belubbi; Philip Bransford; John Chung; Stephanie Dodd; Michael Dolton; Tycho Heimbach; Priyanka Kulkarni; Wen Lin; Andrea Moir; Neil Parrott; et al. (The AAPS Journal, 2023)

Understanding Mechanisms of Food Effect and Developing Reliable PBPK Models Using a Middle-out Approach - Xavier J. H. Pepin; James E. Huckle; Ravindra V. Alluri; Sumit Basu; Stephanie Dodd; Neil Parrott; Arian Emami Riedmaier (The AAPS Journal, 2021)

Correction to: Use of Physiologically Based Pharmacokinetic (PBPK) Modeling for Predicting Drug-Food Interactions: an Industry Perspective - Arian Emami Riedmaier; Kevin DeMent; James Huckle; Phil Bransford; Cordula Stillhart; Richard Lloyd; Ravindra Alluri; Sumit Basu; Yuan Chen; Varsha Dhamankar; Stephanie Dodd; Priyanka Kulkarni; et al. (The AAPS Journal, 2021)

Use of Physiologically Based Pharmacokinetic (PBPK) Modeling for Predicting Drug-Food Interactions: an Industry Perspective - Arian Emami Riedmaier; Kevin DeMent; James Huckle; Phil Bransford; Cordula Stillhart; Richard Lloyd; Ravindra Alluri; Sumit Basu; Yuan Chen; Varsha Dhamankar; Stephanie Dodd; Priyanka Kulkarni; et al. (The AAPS Journal, 2020)
The effect of food on pharmacokinetic properties of drugs is a commonly observed occurrence affecting about 40% of orally administered drugs. Within the pharmaceutical industry, significant resources are invested to predict and characterize a clinically relevant food effect. Here, the predictive performance of physiologically based pharmacokinetic (PBPK) food effect models was assessed via de novo mechanistic absorption models for 30 compounds using controlled, pre-defined in vitro , and modeling methodology. Compounds for which absorption was known to be limited by intestinal transporters were excluded in this analysis. A decision tree for model verification and optimization was followed, leading to high, moderate, or low food effect prediction confidence. High (within 0.8- to 1.25-fold) to moderate confidence (within 0.5- to 2-fold) was achieved for most of the compounds (15 and 8, respectively). While for 7 compounds, prediction confidence was found to be low (> 2-fold). There was no clear difference in prediction success for positive or negative food effects and no clear relationship to the BCS category of tested drug molecules. However, an association could be demonstrated when the food effect was mainly related to changes in the gastrointestinal luminal fluids or physiology, including fluid volume, motility, pH, micellar entrapment, and bile salts. Considering these findings, it is recommended that appropriately verified mechanistic PBPK modeling can be leveraged with high to moderate confidence as a key approach to predicting potential food effect, especially related to mechanisms highlighted here.