Central University of Odisha (CUO), formerly Central University of Orissa, was established by parliament under the Central Universities Act, 2009 (No. 3C of 2009) by Government of India, situated at Sunabeda Town, Koraput District in the Indian state of Odisha. The territorial jurisdiction of the university is whole of the Odisha state.
The Internet of Things (IoT) is important to the success of modern-day e-Healthcare applications. It generates the e-Healthcare multimedia data and test reports, which are confidential to the individual. Creating a system to store and access confidential e-Healthcare data efficiently and securely is significantly challenging. In addition to that, the requirement of resource-constrained security mechanisms makes the design and implementation of the secure e-Healthcare system more difficult. Blockchain technology has been an interesting research area for reforming the management of e-Healthcare data, due to its unique properties such as immutability, confidentiality, and decentralization. This paper proposes a novel blockchain-based architecture to address the critical challenges in e-Healthcare data management. Specifically, the proposed work focuses on ensuring data integrity through blockchain-based hashing, enforcing fine-grained access control via smart contracts, and reducing storage and transaction overhead in resource-constrained IoT healthcare environments. Additionally, an Access Control Policy is introduced to facilitate secure and controlled data sharing among various entities within the e-Healthcare ecosystem. The proposed model is evaluated against existing systems using relevant blockchain performance metrics, demonstrating enhanced security and efficiency.
Sickle cell trait (SCT) has long been considered a benign carrier condition; however, emerging evidence suggests that individuals with SCT may experience adverse health outcomes, both physical and mental. These findings challenge the conventional notion of SCT as clinically insignificant. Therefore, the present study aims to capture the physical and mental health of SCT individuals among scheduled caste (SC) and scheduled tribe (ST) populations of Odisha, India. This cross-sectional study was conducted in the hard-to-reach regions, primarily located in the mountainous terraces of Koraput district, Odisha. A total of 382 individuals (182 SCT individuals and 200 controls) of either sex, aged between 30 and 58, were recruited for the present study. SCT individuals were identified by screening through the sickle cell slide technique. Somatometric, physiological, and biochemical data were obtained using standard protocols and techniques. Beck’s Depression Inventory (BDI) tool was utilized to assess mental health. Hemoglobin, SpO₂, blood sugar, and pulse rate were significantly affected in SCT individuals. However, they were found to be protected against overweight, obesity, and hypertension. Most importantly, a significantly higher percentage of SCT individuals (17.03
Sickle cell trait (SCT), long believed to be a benign carrier condition, is now being recognized for its association with various health complications. Despite its high prevalence across diverse social groups in India, research on SCT-related comorbidities remains limited. Therefore, this study aims to investigate the health complications associated with SCT among individuals residing in remote areas of Odisha, India. This community-based case-control study was conducted among 382 individuals aged 30 years and above from Scheduled Caste and Scheduled Tribe communities. It comprised 182 individuals with SCT identified through field-based screening (cases) and 200 age-, sex-, and community-matched individuals without SCT (controls). Data on comorbidities were collected through structured interviews, supplemented by available medical records. Odds ratios (ORs) with 95
Accurate assessment of genetic diversity using physiological and molecular marker-based approach helps to assess effective breeding activities. In our previous studies, eight indigenous finger millet genotypes were identified as having tolerant traits against drought after a comprehensive screening of over 32 indigenous genotypes from different locations of Koraput. In addition to two improved drought-tolerant check varieties (GPU 28 and Chillika), these eight genotypes were utilized for detailed molecular and physiological characterization under control and drought conditions. Drought stress led to a significant decrease in plant biomass, leaf gas exchange, PSII activity, leaf chlorophyll content, membrane stability index, and relative water content in studied finger millet genotypes relative to the control plants, with notable varietal differences observed. Remarkable increases in reactive oxygen species, proline content, and antioxidant enzymes were observed under drought stress. Genetic diversity of the investigated finger millet genotypes was evaluated using ten previously reported SSR markers associated with drought tolerance traits. The mean value for polymorphism information content (PIC), marker index (MI), and resolving power (RP) were 0.19, 0.47, and 0.72, respectively. Taken together, five traditional finger millet genotypes (Bati, Bhalu, Biri, Ladu, and Lala) are more genetically closer to tolerant check variety (GPU 28 and Arjuna). However, three genotypes (Telugu, Tumuka, and Dushera) exhibit a significant genetic distance from GPU 28. The degree of genetic variants obtained could be useful for germplasm conservation and global implications in relation to drought tolerance.
Reliable estimates of aboveground biomass (AGB) are essential for aboveground carbon (AGC) accounting, biodiversity monitoring, and sustainable forest management. However, limited efforts have been made to compare carbon fluxes over time using both field-based and remote sensing approaches. To address this gap, the present study investigates carbon-fluxes in the Southern Eastern Ghats of Odisha. Using the Vegetation carbon pool sampling method, we conducted phytosociological inventories during two periods: 2018–2019 (initial) and 2021–2022 (revisit). A sum of 180 sample plots (0.1 ha each) was surveyed across 45 stratified random locations, with each location comprising four nested plots within a 250 × 250-m macro plot. The initial inventory recorded 39,793 individual trees, representing 107 tree species from 89 genera and 33 families. During the revisit, previously tagged trees were re-measured. AGB estimates for both periods were derived through R BIOMASS package. The mean annual increments were calculated at 1.266 Mg ha⁻1 year⁻1 for AGB and 0.595 Mg C ha⁻1 year⁻1 for AGC, with highest growth observed in the 60–90 cm girth class. The field derived AGB estimates were compared with remote sensing AGB products for 2019 to 2022. The results showed modest positive correlations with field values (R2 = 0.37 for 2019; R2 = 0.34 for 2022), indicating notable uncertainties in remotely sensed AGB estimates. These findings underscore the importance of integrating field data with remote sensing products to enhance the accuracy of biomass assessment. Such integrative approaches are vital for accurate carbon stock assessment and informed forest management strategies.
