Jogesh Chandra Chaudhuri College

In this study, we develop and analyze a novel mathematical model for colon cancer progression in intestinal epithelial cells incorporating the role of CD8^+ immune response. While traditional numerical techniques are commonly employed for studying such biological systems, we employ a machine learning–driven physics-informed neural network (PINN) approach to address the nonlinear system of ordinary differential equations arising from the model. The PINN approach integrates biological domain knowledge (e.g., tumor growth kinetics and immune interactions) into the training process of a neural network, thereby combining data-driven learning with governing biological laws to estimate system dynamics and unknown parameters with improved accuracy and efficiency. A rigorous analytical investigation ensures positivity, boundedness, and local stability of equilibrium states, characterized by a threshold parameter analogous to the basic reproduction number ( ℛ_0 ) from infectious disease modeling. Sensitivity analysis highlights the critical influence of mutation rates and immune efficacy on long-term disease dynamics. In particular, an enhanced CD8^+ immune response significantly reduces cancerous cell populations while promoting healthy epithelial cell survival. PINN-based simulations not only validate the theoretical predictions but also reveal critical thresholds for treatment effectiveness and early detection. Overall, this hybrid mathematical machine learning framework provides a powerful tool for modeling cancer progression, offering insights for improving early intervention strategies and optimizing therapeutic and screening policies in colon cancer management.

This editorial introduces the Special Issue on Translational Biosensing with 2D Nanomaterials: From Green Synthesis to Clinical Applications, emphasizing the shift from material innovation to deployable systems. Despite advances in ultrasensitive detection, translation remains limited by challenges in reproducibility, manufacturability, and stability. By bringing together contributions on material design, device engineering, and deployment-oriented validation, the issue aims to bridge the gap between laboratory-scale demonstrations and scalable, reliable biosensing technologies for clinical and environmental applications.

The ideas of sustainable development and corporate social responsibility (CSR) are being considered by corporations and nations to a considerable extent. Since the inception of Integrated Reporting (), it has achieved its basic objectives, and most companies are now taking initiatives to include in their annual reports. To make and CSR more useful to stakeholders, the Integrated Ratio Guidelines were proposed by the Center for ESG Research (2017). These guidelines combine financial and sustainability-related performance into ratios known as Integrated Ratios (INTR). The present paper has been prepared based on such INTRs. Data from 16 companies in the cement, automobile, and oil industries have been analysed using their published sustainability and annual reports for the years 2021 to 2025. The key financial metrics such as Return on Assets and Return on Equity, alongside environmental indicators like carbon emissions per sales amount earned and water efficiency etc. are being considered. Comparison is made through figures to industry averages as benchmark basis. The findings reveal that companies investing genuinely in sustainability—reducing emissions, supporting their workforce, and managing resources wisely—tend to perform better financially. This shows a strong link between responsible practices and shareholder value. For managers and investors, integrating sustainability and profitability provides a clearer picture of true performance, fostering responsible growth and long-term resilience.

Phthalate esters are widely used industrial plasticizers that enhance polymer flexibility. However, their non-covalent incorporation into polymer matrices, coupled with improper disposal and recycling, leads to their uncontrolled release into soil systems. Because of this phthalate esters emerge as contaminants of significant ecological and health concern. This review systematically analyzes graphene-based functional materials for the monitoring and remediation of phthalate-contamination, based on a rigorously screened dataset of 20 peer-reviewed articles (2014-2026) following PRISMA guidelines. Owing to ultrahigh specific surface area, pi-conjugated framework, and tunable surface chemistry, graphene and its derivatives enable multiple interaction pathways, including pi-pi stacking, hydrophobic partitioning, hydrogen bonding, and electrostatic interactions, thereby facilitating both high-affinity adsorption and sensitive detection. Reported graphene-based sensing platforms exhibit detection limits spanning from pg/L to & micro;M levels, while adsorption and photocatalytic systems achieve removal efficiencies up to similar to 99% with adsorption capacities up to similar to 60 mg/g. Furthermore, hybrid graphene-based architectures incorporating metal oxides, biochar, and catalytic nanostructures enhance degradation through reactive oxygen species (center dot OH, center dot O-2(-))-mediated pathways, enabling partial to near-complete mineralization of phthalates. Despite these advances, challenges related to performance variability in complex soil matrices, potential ecotoxicity of graphene derivatives, high production costs, and limited scalability persist, compounded by insufficient benchmarking and field-level validation. Addressing these limitations through green synthesis strategies, toxicity-aware design, and scalable material engineering will be critical for translating graphene-based technologies into sustainable, real-world soil remediation applications.

MXenes, a class of two-dimensional materials, have emerged as promising candidates for developing advanced electrochemical biosensors due to their exceptional electrical conductivity, large surface area, and rich surface chemistry. These unique properties enable high sensitivity, rapid response, and versatile functionalization, making MXene-based biosensors highly suitable for detecting biomolecules and pathogens in biomedical applications. This review explores recent advancements in MXene-based electrochemical biosensors from 2020 to 2024, focusing on their design principles, fabrication strategies, and integration with microfluidic platforms for enhanced performance. The potential of MXene sensors to achieve real-time and multiplexed detection is highlighted, alongside the associated challenges. Emphasis is placed on the role of MXenes in addressing critical needs in disease diagnostics, personalized medicine, and point-of-care testing, providing insights into future trends and transformative possibilities in the field of biomedical sensing technologies.