Rashtrasant Tukadoji Maharaj Nagpur University

Phenolic compounds form one of the main classes of secondary metabolites. They display a large range of structures and are responsible for the major organoleptic characteristics of plant-derived foods and beverages, particularly color and taste properties. They also contribute to the nutritional qualities of fruits and vegetables. Among these compounds, flavonoids constitute one of the most ubiquitous groups of plant phenolics. Owing to their importance in food organoleptic properties and human health, a better understanding of their structures and biological activities indicates their potentials as therapeutic agents and also for predicting and controlling food quality. Due to the variety of pharmacological activities in the mammalian body, flavonoids are more correctly referred as “nutraceuticals”. Keywords: Bioflavonoids, Structure-Classification, Nutraceuticals, Antimicrobial activities, Anti-oxidant activity, Metabolic effects Tropical Journal of Pharmaceutical Research Vol. 7 (3) 2008: pp. 1089-1099

BACKGROUND: For more than three decades, the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) has provided a framework to quantify health loss due to diseases, injuries, and associated risk factors. This paper presents GBD 2023 findings on disease and injury burden and risk-attributable health loss, offering a global audit of the state of world health to inform public health priorities. This work captures the evolving landscape of health metrics across age groups, sexes, and locations, while reflecting on the remaining post-COVID-19 challenges to achieving our collective global health ambitions. METHODS: The GBD 2023 combined analysis estimated years lived with disability (YLDs), years of life lost (YLLs), and disability-adjusted life-years (DALYs) for 375 diseases and injuries, and risk-attributable burden associated with 88 modifiable risk factors. Of the more than 310 000 total data sources used for all GBD 2023 (about 30% of which were new to this estimation round), more than 120 000 sources were used for estimation of disease and injury burden and 59 000 for risk factor estimation, and included vital registration systems, surveys, disease registries, and published scientific literature. Data were analysed using previously established modelling approaches, such as disease modelling meta-regression version 2.1 (DisMod-MR 2.1) and comparative risk assessment methods. Diseases and injuries were categorised into four levels on the basis of the established GBD cause hierarchy, as were risk factors using the GBD risk hierarchy. Estimates stratified by age, sex, location, and year from 1990 to 2023 were focused on disease-specific time trends over the 2010-23 period and presented as counts (to three significant figures) and age-standardised rates per 100 000 person-years (to one decimal place). For each measure, 95% uncertainty intervals [UIs] were calculated with the 2·5th and 97·5th percentile ordered values from a 250-draw distribution. FINDINGS: Total numbers of global DALYs grew 6·1% (95% UI 4·0-8·1), from 2·64 billion (2·46-2·86) in 2010 to 2·80 billion (2·57-3·08) in 2023, but age-standardised DALY rates, which account for population growth and ageing, decreased by 12·6% (11·0-14·1), revealing large long-term health improvements. Non-communicable diseases (NCDs) contributed 1·45 billion (1·31-1·61) global DALYs in 2010, increasing to 1·80 billion (1·63-2·03) in 2023, alongside a concurrent 4·1% (1·9-6·3) reduction in age-standardised rates. Based on DALY counts, the leading level 3 NCDs in 2023 were ischaemic heart disease (193 million [176-209] DALYs), stroke (157 million [141-172]), and diabetes (90·2 million [75·2-107]), with the largest increases in age-standardised rates since 2010 occurring for anxiety disorders (62·8% [34·0-107·5]), depressive disorders (26·3% [11·6-42·9]), and diabetes (14·9% [7·5-25·6]). Remarkable health gains were made for communicable, maternal, neonatal, and nutritional (CMNN) diseases, with DALYs falling from 874 million (837-917) in 2010 to 681 million (642-736) in 2023, and a 25·8% (22·6-28·7) reduction in age-standardised DALY rates. During the COVID-19 pandemic, DALYs due to CMNN diseases rose but returned to pre-pandemic levels by 2023. From 2010 to 2023, decreases in age-standardised rates for CMNN diseases were led by rate decreases of 49·1% (32·7-61·0) for diarrhoeal diseases, 42·9% (38·0-48·0) for HIV/AIDS, and 42·2% (23·6-56·6) for tuberculosis. Neonatal disorders and lower respiratory infections remained the leading level 3 CMNN causes globally in 2023, although both showed notable rate decreases from 2010, declining by 16·5% (10·6-22·0) and 24·8% (7·4-36·7), respectively. Injury-related age-standardised DALY rates decreased by 15·6% (10·7-19·8) over the same period. Differences in burden due to NCDs, CMNN diseases, and injuries persisted across age, sex, time, and location. Based on our risk analysis, nearly 50% (1·27 billion [1·18-1·38]) of the roughly 2·80 billion total global DALYs in 2023 were attributable to the 88 risk factors analysed in GBD. Globally, the five level 3 risk factors contributing the highest proportion of risk-attributable DALYs were high systolic blood pressure (SBP), particulate matter pollution, high fasting plasma glucose (FPG), smoking, and low birthweight and short gestation-with high SBP accounting for 8·4% (6·9-10·0) of total DALYs. Of the three overarching level 1 GBD risk factor categories-behavioural, metabolic, and environmental and occupational-risk-attributable DALYs rose between 2010 and 2023 only for metabolic risks, increasing by 30·7% (24·8-37·3); however, age-standardised DALY rates attributable to metabolic risks decreased by 6·7% (2·0-11·0) over the same period. For all but three of the 25 leading level 3 risk factors, age-standardised rates dropped between 2010 and 2023-eg, declining by 54·4% (38·7-65·3) for unsafe sanitation, 50·5% (33·3-63·1) for unsafe water source, and 45·2% (25·6-72·0) for no access to handwashing facility, and by 44·9% (37·3-53·5) for child growth failure. The three leading level 3 risk factors for which age-standardised attributable DALY rates rose were high BMI (10·5% [0·1 to 20·9]), drug use (8·4% [2·6 to 15·3]), and high FPG (6·2% [-2·7 to 15·6]; non-significant). INTERPRETATION: Our findings underscore the complex and dynamic nature of global health challenges. Since 2010, there have been large decreases in burden due to CMNN diseases and many environmental and behavioural risk factors, juxtaposed with sizeable increases in DALYs attributable to metabolic risk factors and NCDs in growing and ageing populations. This long-observed consequence of the global epidemiological transition was only temporarily interrupted by the COVID-19 pandemic. The substantially decreasing CMNN disease burden, despite the 2008 global financial crisis and pandemic-related disruptions, is one of the greatest collective public health successes known. However, these achievements are at risk of being reversed due to major cuts to development assistance for health globally, the effects of which will hit low-income countries with high burden the hardest. Without sustained investment in evidence-based interventions and policies, progress could stall or reverse, leading to widespread human costs and geopolitical instability. Moreover, the rising NCD burden necessitates intensified efforts to mitigate exposure to leading risk factors-eg, air pollution, smoking, and metabolic risks, such as high SBP, BMI, and FPG-including policies that promote food security, healthier diets, physical activity, and equitable and expanded access to potential treatments, such as GLP-1 receptor agonists. Decisive, coordinated action is needed to address long-standing yet growing health challenges, including depressive and anxiety disorders. Yet this can be only part of the solution. Our response to the NCD syndemic-the complex interaction of multiple health risks, social determinants, and systemic challenges-will define the future landscape of global health. To ensure human wellbeing, economic stability, and social equity, global action to sustain and advance health gains must prioritise reducing disparities by addressing socioeconomic and demographic determinants, ensuring equitable health-care access, tackling malnutrition, strengthening health systems, and improving vaccination coverage. We live in times of great opportunity. FUNDING: Gates Foundation and Bloomberg Philanthropies.

The investigation of a groundwater resource impacted with Cr(VI) requires analysis of groundwater for Cr(VI) and total Cr. Most notably, Cr(III) is considered to be a trace element essential for the proper functioning of living organisms, whereas Cr(VI) may exert toxic effects on biological systems. The nature and behavior of various Cr forms found in wastewater can be quite different from those present in natural waters because of altered physicochemical conditions of the eluents originating from various industrial sources. The chromium content in surface waters is usually at the low μgL−1 level, typically between 0.3 and 6 μgL−1. Speciation of Cr(III) and Cr(VI) has been a longstanding analytical challenge. The selective determination of Cr(VI) is of particular importance because of its toxicity. Due to the importance of Cr(III) and Cr(VI), the accurate and sensitive determinations of these ions are the important part of the analytical chemistry. Chromium speciation is very important in different branches of natural sciences. Therefore, total chromium measurements alone cannot determine the actual environmental impact. This requires speciation techniques with sufficient selectivity and high sensitivity. Speciation of trace levels of chromium in water sample requires high-capacity separation and high sensitivity detection. The authors present a review of presently available analytical possibilities of chromium speciation investigations in water samples.

The positive features of neural networks and fuzzy logic are combined together for the detection of stator inter-turn insulation and bearing wear faults in single-phase induction motor. The adaptive neural fuzzy inference systems (ANFISs) are developed for the detection of these two faults. These faults are created experimentally on a single-phase induction motor in the laboratory. The experimental data is generated for the five measurable parameters, viz, motor intakes current, speed, winding temperature, bearing temperature, and the noise of the machine. Earlier, the ANFIS fault detectors are trained for the two input parameters, i.e., speed and current, and the performance is tested. Later, the three remaining parameters are added and the five input ANFIS fault detector is trained and tested. It observed from the simulation results that the five input parameter system predicts more accurate results

TiO2 is a more effective photocatalyst for the photocatalytic degradation of organic pollutants. However it shows more reactivity under UV light and around 5% of solar spectrum contains UV radiations. A new approach for the degradation of pollutants present in wastewater is suggested by making use of nanocomposite photocatalysts. The technique has potential for the treatment of wastewater because of the use of doped graphene-based nanocomposite photocatalysts. Graphene is a one-atom-thick planar sheet of sp2-bonded carbon atoms that are densely packed in a honeycomb crystal lattice. Furthermore, graphene has high electron mobility and therefore it will supress the recombination of the electron-hole pair formed which in turn improves the effectiveness of the graphene-TiO2 photocatalyst. In addition, development of doped graphene-TiO2 photocatalyst will be useful as it can be effective for the degradation of pollutants in the visible sunlight. Recently, there has been an increase in interest in the preparation of high performance graphene-based TiO2 photocatalyst with doping of it using metal and non-metal ions. In this review, the preparation method and application of TiO2, graphene-TiO2 and doped raphene-TiO2 photocatalyst are presented.

Conducting polymers are promising materials for organic–inorganic composites in lithium-ion batteries due to electrical conductivity and high coulombic efficiency, and are able to be cycled hundreds or thousands of times with only small degradation.

computational molecular docking simulations. Docking was carried out on a Glide module of Schrodinger Maestro 2018-1 MM Share Version on NSP15 (PDB ID: 6W01) and Prefusion 2019-nCoV spike glycoprotein (PDB ID: 6VSB) from SARS-CoV-2. From the binding energy and interaction studies, the Saikosaponins U and V showed the best affinity towards both the proteins suggesting them to be future research molecule as they mark the desire interaction with NSP15, which is responsible for replication of RNA and also with 2019-nCoV spike glycoprotein which manage the connection with ACE2. [Formula: see text] Communicated by Ramaswamy H. Sarma.

Lake sediments constitute natural archives of past environmental changes. Historically, research has focused mainly on generating regional climate records, but records of human impacts caused by land use and exploitation of freshwater resources are now attracting scientific and management interests. Long-term environmental records are useful to establish ecosystem reference conditions, enabling comparisons with current environments and potentially allowing future trajectories to be more tightly constrained. Here we review the timing and onset of human disturbance in and around inland water ecosystems as revealed through sedimentary archives from around the world. Palaeolimnology provides access to a wealth of information reflecting early human activities and their corresponding aquatic ecological shifts. First human impacts on aquatic systems and their watersheds are highly variable in time and space. Landscape disturbance often constitutes the first anthropogenic signal in palaeolimnological records. While the effects of humans at the landscape level are relatively easily demonstrated, the earliest signals of human-induced changes in the structure and functioning of aquatic ecosystems need very careful investigation using multiple proxies. Additional studies will improve our understanding of linkages between human settlements, their exploitation of land and water resources, and the downstream effects on continental waters.

Abstract Groundwater plays a vital role in the sustainable development of agriculture, society and economy, and it's demand is increasing due to low rainfall, especially in arid and semiarid regions. In this context, delineation of groundwater potential zones is essential for meeting the demand of different sectors. In this research, the integrated approach consisting of analytical hierarchy process (AHP), multiple influence factors (MIF) and receiver operating characteristics (ROC) was applied. The demarcation of groundwater potential zones is based on thematic maps, namely Land Use/Land Cover (LULC), Digital Elevation Model (DEM), hillshade, soil texture, slope, groundwater depth, geomorphology, Normalized Difference Vegetation Index (NDVI), and flow direction and accumulation. The pairwise comparison matrix has been created, and weights are assigned to each thematic layer. The comparative score to every factor was calculated from the overall weight of two major and minor influences. Groundwater potential zones were classified into five classes, namely very poor, poor, moderate, good and very good, which cover an area as follows: 3.33 km 2 , 785.84 km 2 , 1147.47 km 2 , 595.82 km 2 and 302.65 km 2 , respectively, based on AHP method. However, the MIF groundwater potential zones map was classified into five classes: very poor, poor, moderate, good and very good areas covered 3.049 km 2 , 567.42 km 2 , 1124.50 km 2 868.86 km 2 and 266.67 km 2 , respectively. The results of MIF and AHP techniques were validated using receiver operating characteristics (ROC). The result of this research would be helpful to prepare the sustainable groundwater planning map and policy. The proposed framework has admitted to test and could be implemented in different in various regions around the world to maintain the sustainable practices.

The polyelectrolyte complexes (PECs) are versatile formulations formed by electrostatic interactions between oppositely charged biopolymers. PECs have been investigated widely by the researchers to explore the virtues of this formulation viz. high biocompatibility, excellent biodegradability, low toxicity, cost-effective, environment-friendly, and energy-efficient production. The prime object of the present review is to present the prominent features of PECs including mechanism of PEC formation, structural models of PECs, interactions involved in PEC formation, steps involved in PEC fabrication, factors affecting the formation of PECs and applications of PECs. The patents pertaining to PECs have briefly been tabulated as well.

Dietary foods and fruits possess an array of flavonoids with unique chemical structure and diverse bioactivities relevant to cancer. Numerous epidemiological studies have validated the inverse relation between the consumption of flavonoids and the risk of cancer. Flavonoids possess cancer blocking and suppressing effects. Flavonoids modulate various CYPs involved in carcinogen activation and scavenging reactive species formed from carcinogens by CYP-mediated reactions. They induce biosynthesis of several CYPs. They are involved in the regulation of enzymes of phase-II responsible for xenobiotic biotransformation and colon microflora. Since cytochromes P450, P-gp and phase-II enzymes are involved in the metabolism of drugs and in the processes of chemical carcinogenesis, interactions of flavonoids with these systems hold great promise for their therapeutic potential. The role of flavonoids also includes the inhibition of activation of pro-carcinogens, inhibition of proliferation of cancer cells, selective death of cancer cells by apoptosis, inhibition of metastasis and angiogenesis, activation of immune response against cancer cells, modulation of the inflammatory cascade and the modulation of drug resistance. This has greatly extended the goal of cancer therapy from eradicating the affected cells to control of the cancer phenotype. Phytotherapy is being used in combination with other therapies as phytonutrients have been shown to work by nutrient synergy.

Manufacturing of metal and metal oxide nanoparticles (M/MO NPs) in large quantities needed a strong reliable, sustainable, and eco-friendly protocol. Present work represents on biogenic approaches to fabricate green nanoparticles using green technology. The fabrications of M/MO NPs using natural bio-resources were engaged by means of alternative technique in place of conventional methods. These methods are naturally benign, straightforward, economical, and renewed technology; they does not content harmful chemicals, zero contaminants, and eco-friendly. The extracts from the biogenic resources are widely accepted owing to its capability to minimise and control the size and shape of metal and metal oxides NPs because of different structure directing agents, usually bioorganic phyto-chemicals. In this present review, we have summarized fabrication of different NPs like silver, gold, copper oxide, cobalt oxide, titanium oxide, cerium oxide, bismuth oxide, zinc oxide and nickel oxide nanoparticles using natural resources. The challenges, limiting factors and future directions of the bioinspired synthesis of metal/metal oxide NPs are also highlighted in this review. Moreover, biogenic materials has explored for further environmental remediation in terms of photocatalytic activity, elimination of organic waste, and antibacterial, antioxidant assay, and protein-metal complexes binding affinities by molecular docking.

The present study was carried out using artificial neural network (ANN) model for predicting the sodium hazardness, i.e., sodium adsorption ratio (SAR), percent sodium (%Na) residual, Kelly’s ratio (KR), and residual sodium carbonate (RSC) in the groundwater of the Pratapgarh district of Southern Rajasthan, India. This study focuses on verifying the suitability of water for irrigational purpose, wherein more groundwater decline coupled with water quality problems compared to the other areas are observed. The southern part of the Rajasthan State is more populated as compared to the rest of the parts. The southern part of the Rajasthan is more populated as compared to the rest of the Rajasthan, which leads to the industrialization, urbanization, and evolutionary changes in the agricultural production in the southern region. Therefore, it is necessary to propose innovative methods for analyzing and predicting the water quality (WQ) for agricultural use. The study aims to develop an optimized artificial neural network (ANN) model to predict the sodium hazardness of groundwater for irrigation purposes. The ANN model was developed using ‘nntool’ in MATLAB software. The ANN model was trained and validated for ten years (2010–2020) of water quality data. An L-M 3-layer back propagation technique was adopted in ANN architecture to develop a reliable and accurate model for predicting the suitability of groundwater for irrigation. Furthermore, statistical performance indicators, such as RMSE, IA, R, and MBE, were used to check the consistency of ANN prediction results. The developed ANN model, i.e., ANN4 (3-12-1), ANN4 (4-15-1), ANN1 (4-5-1), and ANN4 (3-12-1), were found best suited for SAR, %Na, RSC, and KR water quality indicators for the Pratapgarh district. The performance analysis of the developed model (3-12-1) led to a correlation coefficient = 1, IA = 1, RMS = 0.14, and MBE = 0.0050. Hence, the proposed model provides a satisfactory match to the empirically generated datasets in the observed wells. This development of water quality modeling using an ANN model may help to useful for the planning of sustainable management and groundwater resources with crop suitability plans as per water quality.

This paper proposes an intellectual classification system to recognize normal and abnormal MRI brain images. Nowadays, decision and treatment of brain tumors is based on symptoms and radiological appearance. Magnetic resonance imaging (MRI) is a most important controlled tool for the anatomical judgment of tumors in brain. In the present investigation, various techniques were used for the classification of brain cancer. Under these techniques, image preprocessing, image feature extraction and subsequent classification of brain cancer is successfully performed. When different machine learning techniques: Support Vector Machine (SVM), K- Nearest Neighbor (KNN) and Hybrid Classifier (SVM-KNN) is used to classify 50 images, it is observed from the results that the Hybrid classifier SVM-KNN demonstrated the highest classification accuracy rate of 98% among others. The main goal of this paper is to give an excellent outcome of MRI brain cancer classification rate using SVM-KNN.

photocatalytic activity.

Agriculture is a backbone of global economy and most of the population relies on this sector for their livelihood. Chitosan as a biodegradable material thus can be explored for in various fields in its nano form to replace non-biodegradable and toxic compounds. The chitosan has appealing properties like biocompatibility, non-toxicity, biodegradability, and low allergenic, making it useful in several applications including in agriculture sector. Because of their unique properties, chitosan nanoparticles (ChNPs) are extensively applied as a bioagent in various biological and biomedical processes, including wastewater treatment, plant growth promoter, fungicidal agent, wound healing, and scaffold for tissue engineering. Furthermore, the biocompatibility of chitosan nanoparticles (ChNPs) is reported to have other biological properties such as anti-cancerous, antifungal, antioxidant activities, even induces an immune response in the plant, and helps manage biotic and abiotic stresses. Chitosan can also find its application in wastewater treatment, hydrating agents in cosmetics, the food industry, paper, and the textile industry as adhesive, drug-delivering agent in medical as well as for bioimaging. Since chitosan has low toxicity, the nano-formulation of chitosan can be used for the controlled release of fertilizers, pesticides, and plant growth promoters in agriculture fields. The ChNPs applications in precision farming being a novel approach in recent developments. Here we have comprehensively reviewed the major points in this review are; the synthesis of ChNPs by biological resources, their modification and formulation for increasing its applicability, their modified types, and the different agricultural applications of ChNPs.

In this paper we report the highly sensitive at low temperature and low concentration of liquefied petroleum gas (LPG) sensor based on D-Camphor-10-Sulphonic acid (CSA) doped Polyaniline/Polyethylene Oxide (PANI/PEO) and PANI/ZnO/PEO nanofibers fabricated by electrospinning technique. Nano-sized particles of zinc oxide (ZnO) were synthesized using sol-gel method. The emeraldine base form of polyaniline (PANI) and PANI/ZnO synthesized by chemical oxidative polymerization were treated with CSA for better solubility and conductivity and then blended with PEO solution to get nanofibers using electrospinning. The morphology and structure of electrospun nanofibers of CSA doped PANI/PEO and PANI/ZnO/PEO were investigated by SEM, UV-VIS, FTIR and XRD. The change in electrical resistance of CSA doped PANI/PEO and PANI/ZnO/PEO for 1000 ppm of LPG was measured. Both the samples showed a rapid and reversible resistance change upon exposure to LPG gas at 1000 ppm concentration. Maximum sensitivity was achieved at 36 °C for PANI/ZnO/PEO indicating suitability of nanofibrous PANI/ZnO/PEO towards LPG sensing as compared to pure ZnO and PANI.

As the global population and economy grow, so does the energy demand. Over-reliance on non-renewable resources leads to depletion and price spikes, making renewable alternatives necessary. Biodiesel is an eco-friendly and non-toxic fuel that closely resembles traditional fossil fuels. It is produced from various sources, including animal fat, palm oil, and non-edible plant oil. Biodiesel releases fewer harmful air pollutants and greenhouse gases than fossil fuels and is simpler to manage. Despite these advantages, it cannot replace traditional diesel fuel on a large scale. This overview summarizes biodiesel production, explaining the different types of feedstock utilized and their benefits and drawbacks. Various biodiesel production methodologies are discussed. The primary objective of this article is to inform engineers, industrialists, and researchers involved in waste biodiesel, as well as to highlight waste biodiesel as a potential substitute for fossil fuels. This review article discusses the nano-additives in biodiesel and applications of internet of things, artificial intelligence, and machine learning in biofuel. This review shows that nano-additives can potentially improve biodiesel fuel properties, favorable economic and policy environments promoting biodiesel production, and internet of things, artificial intelligence, and machine learning technologies optimize the biodiesel production processes. These advances can help promote biodiesel as a cleaner, renewable energy source, lowering the consumption of fossil fuels. It also suggests further biofuel development by improving efficiency, expanding feedstock options, creating policy support, developing infrastructure, and increasing public awareness.

Minireview on recent advances of application of MIPs prepared by precipitation polymerization for recognition of target analytes in complex matrices.

Abstract The study of long‐term precipitation record is critically important for a country, whose food security and economy rely on the timely availability of water. In this study, the historical 102‐year (1901–2002) rainfall data of the Sindh River basin ( SRB ), India, were analyzed for seasonal and annual trends. The Mann–Kendall test and Sen's slope model were used to identify the trend and the magnitude of the change, respectively. Spatial interpolation technique such as Kriging was used for interpolating the spatial pattern over SRB in GIS environment. The analysis revealed the significantly increasing precipitation trend in both seasonal and annual rainfall in the span of 102 years.