Sarguja University

Kanger National Park is home to a tropical mixed deciduous forest in central India, which has great floral diversity and is a key carbon reservoir. Survey was conducted which looked at tree species composition, structural features, diversity indices, biomass, and carbon stock in 3 representative sites within the park: Tirathgarh, Kamanar, and Kudruwada. At Tirathgarh, Kamanar and Kudruwada, 27, 22, and 25 tree species were recorded, respectively, with stand densities that ranged from 694.46 to 1166.69 stems/ha and basal areas from 19.23 to 26.25 m2/ha. At all sites, Shorea robusta and Tectona grandis were the dominant species, whereas Buchanania cochinchinensis exhibited very high abundance specifically at Kudruwada. Species diversity peaked at Tirathgarh (H′ = 3.42), while Kudruwada recorded maximum biomass (279.02 t·ha⁻¹; carbon stock = 117.33 t·ha⁻¹). When compared to other dry deciduous reserves across the country, Kanger National Park exhibits analogous structural complexity and high biomass. These results underline the protected area’s regional ecological value. The data also suggest that the park could effectively contribute to broader carbon sequestration efforts.

Industrial expansion in coal-based thermal power generation, aluminium processing, and mining sectors has intensified concerns regarding water quality deterioration in the industrial cluster of Korba. The present study aims to assess the physico-chemical characteristics and heavy metal contamination of industrial water discharged into and around the Hasdeo River. A systematic sampling strategy was adopted covering effluent outlets, ash pond discharge points, upstream and downstream river locations, and selected groundwater borewells. Samples were collected during pre-monsoon and post-monsoon periods and analyzed using standard methods prescribed by APHA. Key physical parameters such as temperature, turbidity, and Total Suspended Solids (TSS) were evaluated to determine thermal and particulate pollution. Chemical parameters including pH, Electrical Conductivity (EC), Total Dissolved Solids (TDS), Dissolved Oxygen (DO), Biological Oxygen Demand (BOD), and Chemical Oxygen Demand (COD) were analyzed to assess organic and inorganic pollution load. Heavy metals such as lead (Pb), cadmium (Cd), chromium (Cr), iron (Fe), and zinc (Zn) were quantified using Atomic Absorption Spectrophotometry. Results indicated elevated turbidity, TSS, BOD, COD, and TDS levels at effluent discharge points compared to upstream reference sites. The BOD/COD ratio suggested partially biodegradable effluent with significant chemical contamination. Cadmium and chromium concentrations exceeded Central Pollution Control Board (CPCB) permissible limits at certain discharge locations, indicating potential ecological and public health risks. Although dilution reduced pollutant concentration downstream, cumulative impacts remain evident. Comparative evaluation with CPCB discharge standards and World Health Organization (WHO) drinking water guidelines revealed partial compliance, with heavy metals and organic load parameters showing notable deviations. The study highlights moderate to high pollution intensity in localized zones and underscores the need for strengthened effluent treatment, periodic monitoring, and strict regulatory enforcement to ensure environmental sustainability in the Korba industrial region.

This study investigates the development and evaluation of catalase-loaded chitosan nanoparticles (CNPs) as a sustained drug delivery system for anti-arthritic therapy. Chitosan nanoparticles were prepared using ionotropic gelation, achieving an optimal particle size of approximately 190 nm and a drug entrapment efficiency of 67-68%. The nanoparticles exhibited a zeta potential of 40.26 ± 1.4 mV, indicating good stability, and were characterized for their spherical morphology via transmission electron microscopy. In vitro release studies demonstrated a controlled release profile, with 58.42% of catalase released over 24 hours in phosphate buffer saline (pH 7.4). Stability studies revealed minimal changes in particle size and drug content at 4°C, though aggregation was observed at 27°C. In vivo pharmacodynamic studies in a carrageenan-induced arthritis model in albino rats showed that CNPs significantly enhanced anti-inflammatory effects, achieving up to 67.38% inhibition of arthritis compared to 41.26% for free catalase. Biodistribution analysis indicated higher drug retention in the arthritic joint and prolonged blood levels with CNPs compared to free catalase, suggesting improved targeting and sustained release. These findings highlight the potential of chitosan nanoparticles as an effective delivery system for catalase in the treatment of rheumatoid arthritis, offering enhanced biodistribution and therapeutic efficacy.

BackgroundCervical cancer (CC) is a leading cause of cancer-related deaths worldwide, emphasizing the need for accurate and efficient diagnostic tools. Traditional methods of cervical cell classification are time-consuming and susceptible to human error, highlighting the need for automated solutions.Materials and methodsThis study introduces the modified hierarchical deep feature fusion (HDFF) method for cervical cell classification using the SIPaKMeD and Herlev datasets. The novelty of this research lies in the integration of hierarchical deep learning features, which allows for more accurate and robust classification. By enhancing the feature extraction process and combining multiple layers of deep learning models, the Modified HDFF method improves classification performance across various tasks, ranging from binary to multi-class problems.ResultsOur results demonstrate that the Modified HDFF method significantly outperforms existing models. In the 2-class task, it achieves an impressive accuracy of 98.88%, surpassing other approaches such as RF-based hierarchical classification (98.43%). Additionally, it maintains high precision, recall, and F1-scores in multi-class tasks, with 98.8% accuracy in the 3-class problem and 98.5% in the 7-class problem.ConclusionsOverall, the Modified HDFF method shows great promise as a reliable and efficient diagnostic tool for cervical cancer screening. Its superior accuracy across multiple classification tasks highlights its potential for improving early detection and public health outcomes. Further refinement and expanded training datasets can further enhance its performance, making it an invaluable asset in automated cervical cancer detection.

Plant growth regulators (PGRs) are forest tree seed germinator, it is necessary to demonstrate that PGRs are involved in the mechanisms that control both the induction of dormancy and the release from it, and that these mechanisms operate in woody plant seeds. The establishment of plantations, recognizing the current need for sustainable alternatives for depleting natural forests, has gained significance for industrial and domestic wood production on both large and small scales. This review examines four known concepts of seed dormancy and discusses the published evidence linking PGRs to tree seed germination. Tree seed germination influenced by a variety of external and internal factors, with PGRs playing a key role among them. However, the mechanisms through which this control occurs can differ greatly, ranging from physical to metabolic processes. The successful establishment of tree species depends on factors such as the quality of planting material, soil condition, water availability for irrigation, and protection measures in place. Many tree species face challenges with seed germination due to both external and internal factors, leading to seed dormancy. Plant growth regulators such as Auxins (IAA, IBA, 2-4D, 4-CPA), Gibberellic acid (GA3), Cytokinins (Kinetin, Zeatin, Benzyl adenine), Ethylene (Etheral), and Abscisic acid (Dormins, Phaseic Acid) are used in various concentrations to improve tree seed germination. It can be concluded that seed morphological parameters are directly linked to germination, with a higher vigor index leading to faster germination. Additionally, seeds collected shortly after dispersal or ripening tend to be recalcitrant, but sowing them within a month of collection ensures high germination rates.

Wireless Sensor Networks (WSNs) are essential in the Internet of Things (IoT). These are widely used in modern industrial applications in which various security attacks are encountered to prevent and countermeasure the problems in WSN, such as data processing, analysis, and efficient utilization of network resources. To ensure secure data processing, techniques are proposed for protecting data privacy. Therefore, we propose a novel data-driven model by using a benchmark dataset BOT-IoT based on machine learning, which improves the performance of WSNs. Moreover, kernel of this proposed model evolved from regression based approach in use hyper-plane efficiently classified dataset into different classes. The proposed model's performance analysis has been analyzed using different metrics. The results obtained in the experiments depict that the proposed model effectively performs better than contemporary models in the state of the art.

A retrospective observational study was undertaken to assess the changing trends in the incidence of metabolic syndrome (MetS) in Asian-Indian patients with newly diagnosed type 2 diabetes (T2D) using Adult Treatment Panel III of the National Cholesterol Education Program (NCEP-ATP III), World Health Organization (WHO), and the International Diabetes Federation (IDF) criteria. The overall and gender-wise pattern of MetS and its components were also evaluated. Newly diagnosed T2D patients (n = 10,950)visiting Dr Panikar's Diabetes Care Centre from 2004 to 2019 with retrievable electronic medical records were selected. The incidence of MetS in these patients was studied using NCEP-ATP III, WHO, and IDF criteria in three separate timelines, namely, group I (2004-2008), group II (2009-2013), and group III (2014-2019). Overall and gender-wise, the incidence of various components of the MetS was also studied and compared across the three groups. All data were analyzed by using the Statistical Software Statistical Package for the Social Sciences (SPSS) version 10.0. Continuous variables were summarized by the number of observations (mean, standard deviation or median with minimum and maximum) and categorical values (calculating frequencies with percentages). Chi-square was used to estimate the incidence of MetS using different criteria and gender-wise patterns of the MetS components for the three periods. Other variables, such as mean body mass index (BMI), were assessed by applying variance analysis (ANOVA test). All values were reported based on a two-sided ANOVA test, and all the statistical tests were interpreted at a 5% level of significance. In the current study, the overall incidence of MetS observed among the study population was 80.9, 65.4, and 69.8% using NCEP-ATP III, WHO, and IDF criteria, respectively. The incidence of MetS across the three timelines (i.e., from 2004 to 2019) with all the diagnostic criteria showed a steady increase. An analysis of the individual components of MetS revealed a high incidence of central obesity across all subgroups, followed by hypertension and dyslipidemia. Central obesity was prevalent in nearly 85.9% of patients in 2014-2019 vs 78.6% in the 2004-2008 subgroup. Similarly, the incidence of hypertension and overall dyslipidemia [i.e., high triglycerides (TGs) and low high-density lipoprotein-cholesterol (HDL-C)] was 77.8% and 68.2% in the former vs 67.9% and 59.6% in the latter, respectively. The incidence of all three MetS components, along with fasting sugar, showed a statistically significant and progressive increase over the years, with prevalence in group III (2014-2019) being the highest. Women were found to be more centrally obese and more dyslipidemic compared to men, whereas men were found to be more hypertensive. The study shows a high incidence of MetS in Asian-Indian patients with newly diagnosed T2D. The incidence of MetS was significantly higher with the NCEP-ATP III diagnostic criteria than with WHO and IDF criteria. A steady rise in the incidence of MetS was observed over the study period of 2004-2019. Among the components of MetS, the incidence of central obesity, elevated TG levels, and low HDL-C were found to be higher in the female population than in males, whereas the incidence of hypertension was higher in males. Stringent lifestyle measures, along with appropriate pharmacological management, might help mitigate the risks associated with MetS.