North Orissa University

Chemotherapeutic drugs like Mitoxantrone (MITX) are a practical therapeutic choice despite its adverse effects. So, monitoring of MITX in the patient's body is essential for effective treatment with minimal toxicity. In this work, we adopted a cost-effective, energy-efficient synthetic method to prepare highly fluorescent N-doped carbon dots (N-CDs) with a fluorescence quantum yield of 58%. The synthesized photostable N-CDs were demonstrated as a fluorescence/colorimetric sensor for MITX. With increasing concentration of MITX, the fluorescence emission of N-CDs at 520 nm gradually decreased and the limit of detection was found to be 0.47 & micro;M. Additionally, these N-CDs showed a good linear response toward MITX in the colorimetric method. The observed color change was quantified through a UV-visible spectrophotometer and smartphone with LOD values of 1.92 and 1.88 & micro;M respectively. The use of portable, easy-to-handle smartphones will facilitate the on-site quantitative detection and naked eye visualization of MITX. The N-CDs exhibited high selectivity toward MITX and negligible interference in both the fluorescence and colorimetry modes. To ensure the practical applicability of the multi-mode N-CDs probe, it was tested with real samples like human blood serum, artificial urine, and tap water that showed reasonable recovery of MITX, ranging from 95% to 105%.

The mangrove ecosystem has the highest carbon sink potential which significantly contributes to bringing carbon neutrality. Understanding the carbon stock dynamics along the age of forest stands in the mangrove forest ecosystem is of significance for managing the forests and their carbon accumulation. This study aimed to estimate the forest structural attributes, biomass and total ecosystem carbon stock (TECS) of old natural (age > 50 years) and young planted (age 20 years) mangrove forest stands at Bichitrapur Mangrove Reserve Forest in eastern coast of India. We also attempted to understand the interrelationship of structural attributes, biomass and soil properties in the mangrove forests. To achieve the results, twenty random plots were established (size: 20 m × 25 m) and suitable allometric equations along with species-specific wood density values were used to estimate the biomass and carbon stock. Altogether, 29 plant species (18 exclusive and 11 associate species) were recorded. The mean total biomass (± SE) and soil organic carbon (at 30 cm depth) were 165.31 ± 20.89 t ha−1 and 40.20 ± 1.24 t C ha−1 for young stands, and 586.12 ± 56.74 t ha−1 and 49.68 ± 2.39 t C ha−1 for old stands, respectively. Among mangrove species, Avicennia marina contributed the highest vegetation biomass in both forest stands (59.72 t ha−1 and 262.28 t ha−1 in young and old stands, respectively), followed by Avicennia officinalis (35.05 t ha−1) and Sonneratia apetala (26.09 t ha−1) in young stand and Avicennia alba (169.28 t ha−1) and Avicennia officinalis (115.58 t ha−1) in old stand. The mean TECS was 235.62 ± 27.34 t C ha−1. The contribution of vegetation and soil to TECS was 63

Forest fires are major disturbances that can have a significant impact on forest structure, species diversity and regeneration potential, which is poorly understood in tropical dry forests. The present study was conducted in a tropical dry deciduous forest of Similipal Biosphere Reserve (SBR), Eastern India to know how a forest fire event affects on species diversity and regeneration potential. Normalized burn ratio (NBR) and differenced normalized burn ratio (dNBR) were estimated to understand the overall effect of forest fire and post fire regrowth in SBR. Permanent plotting (50 m × 20 m) was employed to track the changes in burned and unburned forest plots. The comparative NBR between pre and post-fire conditions showed most of the area in SBR under moderately affected (highest frequency of pixel values near − 0.28) by the fire. The results of the study found that seedling and sapling diversity and density increased linearly in burned forest plots after the fire event in respect to time. The result was well supported by the NBR and dNBR indices. The severity of the fire was low to moderate (dNBR between 0.10 and 0.27). The highest intensity of pixel value of dNBR between − 0.10 to − 0.20 indicated good vegetation regrowth after 6 months of fire. Tree species had good regeneration potential (65.38

The ATP2B4 gene, encoding the PMCA4b Ca²+-ATPase in erythrocytes, has been linked to malaria protection via genome-wide association studies, though the proposed dehydration mechanism remains unclear. This study evaluates ATP2B4 genotypes and PMCA4b expression in malaria susceptibility and artemisinin sensitivity. ATP2B4 genotypes were compared across severe malaria, uncomplicated malaria, and healthy controls in Indian population. PMCA4b expression, intra-erythrocytic calcium, oxidative stress markers, Gardos channel activity, and Plasmodium falciparum growth dynamics were analyzed. Artemisinin sensitivity was assessed using growth inhibition and ring survival assays. ATP2B4 genotypes showed no significant association with malaria protection. Regardless of genotype, low PMCA4b expression correlated with increased intra-erythrocytic calcium, oxidative stress, and reduced in-vitro parasite growth. Gardos channel activity inversely correlated with PMCA4b but did not induce dehydration. Instead, oxidative-stress regulation by PMCA4b emerged as a key factor in malaria protection. Treatment of RBCs with resveratrol, a PMCA inhibitor, further validates the functional role of PMCA in regulation of intracellular calcium and oxidative stress. Notably, low PMCA4b expression also reduced P. falciparum artemisinin sensitivity, providing evidence that host genetic variation can influence drug efficacy. These findings suggest a role for PMCA4b in oxidative stress and drug response as critical to malaria pathophysiology. We further emphasize that many redox modulating RBC polymorphisms in malaria endemic areas could also influence artemisinin efficacy and serve as potential biomarkers for predicting therapeutic response.IMPORTANCEDiscovery of the mechanism by which human host variations affect the sensitivity of artemisinin in parasite provides an interestingly important view for optimizing anti-malarial treatment strategies. The human host and malaria parasite share a closely interconnected relationship; hence, we propose that parasite physiology or drug resistance cannot be studied alone without considering the host's biological factors. This study demonstrates that increase in intracellular calcium of RBCs is associated with a proportionate increase in intracellular oxidative stress, which affects the artemisinin sensitivity. Thus, variations in PMCA4b expression, the primary calcium efflux pump in RBCs, significantly alter the erythrocytic ROS levels, thereby affecting the P. falciparum growth and artemisinin sensitivity. Notably, intracellular redox imbalance is a common phenotype of multiple erythrocytic polymorphisms prevalent in malaria endemic areas, such as sickle cell, thalassemia, G6PD deficiency, etc. This study advocates the need for a widespread population-based investigation that associates the importance of host erythrocyte oxidative microenvironment surveillance in monitoring antimalarial drug resistance.

The accurate estimation of road widths on unmarked and unstructured roads is critical for Advanced Driver Assistance Systems (ADAS), autonomous navigation, and intelligent transportation planning. In Indian driving conditions, irregular boundaries and missing lane markings make conventional lane detection ineffective. We propose a multi-camera framework integrating a dashboard monocular camera with dual Outside Rear View Mirror (ORVM) cameras for dynamic road-width estimation. The scheme employs deep learning segmentation to extract road boundaries and fuses monocular depth and stereo disparity using a Kalman-based dynamic model. From a quantitative perspective, the recursive Kalman fusion functions analogously to dynamic optimization and equilibrium modeling in 'Quantitative Economics, allowing predictive analysis of traffic density, flow efficiency, and infrastructure utilization. Trained on a diverse urban–rural Indian road dataset, the model achieves high estimation accuracy and predictive stability, demonstrating improved performance over single-camera baselines and enabling data-driven optimization for safe, efficient transportation systems.