University of Central Punjab

Calcium (Ca2⁺) signaling plays a pivotal role in plant defense responses against pests and pathogens, serving as an early and highly coordinated intracellular messenger. Upon biotic stress perception, Ca2⁺ channels mediate rapid Ca2⁺ influx, triggering downstream signaling pathways that activate defense-associated genes, secondary metabolite production, and hormonal pathways. This review explores the molecular mechanisms underlying Ca2⁺ channel activation, the spatiotemporal dynamics of intracellular Ca2⁺ fluxes, and their crosstalk with phytohormones and other signaling pathways. We further highlight the role of key Ca2⁺ sensors, such as calmodulins (CaMs), calcineurin B-like proteins (CBLs) and CBL-interacting protein kinase (CBL-CIPK) complexes, and calcium-dependent protein kinases (CDPKs) in decoding Ca2⁺ signals. In addition to this, emerging biotechnological approaches to enhance Ca2⁺-mediated resistance in crops like Nicotiana tabacum, Zea mays, and Arabidopsis thaliana has also been discussed. Understanding these mechanisms will provide valuable insights for developing new strategies to enhance plant resilience against evolving pest and pathogen threats under changing environmental conditions.

Alzheimer’s disease (AD) is a chronic, progressive neurodegenerative disorder marked by cognitive decline, memory impairment, and behavioural changes, ultimately leading to mortality. With over 45 million cases globally, the prevalence of AD is projected to triple by 2050, posing a significant healthcare challenge due to the absence of curative therapies. The plant kingdom is a rich source of bioactive metabolites with neuroprotective properties. Recent studies have highlighted the potential of various phytochemicals in modulating key molecular mechanisms involved in AD pathogenesis, including reducing oxidative stress, inhibiting aberrant glial activation to attenuate neuroinflammation, promoting amyloid-beta (Aβ) clearance, inhibiting cholinesterase activity, and enhancing acetylcholine levels. This review provides an in-depth analysis of secondary metabolites from plants with promising anti-Alzheimer’s potential, emphasizing their mechanisms of action, challenges, and future research directions. The findings aim to promote further investigation into phytochemical-based interventions for AD management.

NAFLD and T2DM are metabolic diseases with overlapping pathogenic mechanisms, including insulin resistance, lipid imbalance, and chronic inflammation. miRNAs have emerged as reliable biomarkers for NAFLD and T2DM. The expression levels of miRNAs hold the potential to contribute to bridging the gap between metabolic diseases. miR-122 and miR-34a are significant regulators of metabolic pathways among various biomarkers. miR-122, a liver-enriched microRNA involved in hepatic lipid metabolism, inflammation, and insulin signaling, whereas miR-34a is closely linked to β-cell dysfunction and hepatic steatosis through SIRT1 suppression. Growing echelons of these miRNAs in the bloodstream of patients with T2DM and NAFLD show their potential use as prognostic and diagnostic indicators. These enable a non-invasive early disease monitoring and detection alternative. miR-122 and miR-34a are involved in disease progression by influencing key metabolic processes such as lipid metabolism, pro-inflammatory cytokine regulation, the SIRT1-SREBP1c pathway, and AMPK phosphorylation. Their changed expression levels in various metabolic conditions contribute to advancing pathophysiological processes, explaining their clinical value as therapeutic targets. This review investigates the molecular functions of miR-122 and miR-34a in NAFLD and T2DM, emphasizing their effect on various genes and interrelated pathways, showing their diagnostic potential and therapeutic implications. Knowing their regulatory roles might lead the way for miRNA-based precision medicine techniques that improve early diagnosis, risk assessment, and treatment strategies for metabolic illnesses. Future research should focus on their therapeutic use and make targeted interventions to change their expression levels for disease management.

The most common autoimmune illness in both developed and under developed countries is rheumatoid arthritis (RA). In the past, RA could not be effectively treated with any existing strategies. However, new medications and therapeutic approaches have been created to combat this condition in the present day as a result of advancements in drug delivery science. Numerous advancements have been made in the therapy formulations for RA that are currently on the market in the last few years. One well-known medication for the treatment of RA is methotrexate (MTX). However, most current treatment formulations have several adverse effects and are unable to completely alleviate all RA symptoms. Combination therapy for the treatment of RA may be more successful than MTX therapy alone in addressing such problems. We discovered that MTX plus infliximab (INF), golimumab (GOL), leflunomide (LEF), folic acid (FA), and azathioprine was more successful than MTX alone because it had less adverse effects and a lower risk of RA relapses. MTX monotherapy has been shown to be less effective than combinations with one or more drugs. We examined the many injectable treatments for RA, focusing on thermo-responsive targeted drug delivery systems as cutting-edge means of delivering MTX, either alone or in conjunction with other treatments. Thermo-responsive in situ hydrogel-based injectables in latest drug delivery systems have the potential to address many of the challenges related to RA treatment and therapy.

Floods are among the natural disasters that cause the most damage worldwide, making it imperative to identify flood prone areas for effective flood hazard management. However, accurate modeling and forecasting of floods is challenging due to their complex nature. This study presents a novel methodology for the flood susceptibility modelling (FSM) in the Uttar Dinajpur district of West Bengal, India using five boosting-based machine learning (ML) models: AdaBoost, CatBoost, Gradient Boosting Machine (GBM), LightGBM, and XGBoost. A total of twenty Flood Influential Factors (FIFs) were initially considered. Following multicollinearity analysis and Recursive Feature Elimination (RFE) using a Random Forest (RF) model, three factors—geology, normalized vegetation index and stream power index—were excluded, resulting in seventeen key FIFs. Hyperparameter optimization was performed using GridSearchCV, and the models were trained on these selected FIFs and a flood inventory map generated from Sentinel-1 SAR Ground Range Detected (GRD) C-band data. The land use land cover (LULC) map, an essential FIF, was derived using four ML classifiers: SVM, RF, DT, and KNN, with RF chosen as the final method due to its highest kappa accuracy. All boosting models demonstrated strong performance, as validated using statistical metrics such as accuracy, recall, specificity, precision, F1-score, Cohen’s kappa, confusion matrix, and ROC-AUC. Among them, XGBoost outperformed the others, achieving the highest testing accuracy (0.9133) and AUC value (0.9649). Consequently, XGBoost was used for block-level FSM in the district. The results highlight the robustness and reliability of boosting-based ML techniques for FSM. The proposed framework can assist policymakers and planners in developing effective flood mitigation and land-use management strategies in similar flood-prone regions.