Siddhartha Medical College

Nigella sativa (N. sativa) (Family Ranunculaceae) is a widely used medicinal plant throughout the world. It is very popular in various traditional systems of medicine like Unani and Tibb, Ayurveda and Siddha. Seeds and oil have a long history of folklore usage in various systems of medicines and food. The seeds of N. sativa have been widely used in the treatment of different diseases and ailments. In Islamic literature, it is considered as one of the greatest forms of healing medicine. It has been recommended for using on regular basis in Tibb-e-Nabwi (Prophetic Medicine). It has been widely used as antihypertensive, liver tonics, diuretics, digestive, anti-diarrheal, appetite stimulant, analgesics, anti-bacterial and in skin disorders. Extensive studies on N. sativa have been carried out by various researchers and a wide spectrum of its pharmacological actions have been explored which may include antidiabetic, anticancer, immunomodulator, analgesic, antimicrobial, anti-inflammatory, spasmolytic, bronchodilator, hepato-protective, renal protective, gastro-protective, antioxidant properties, etc. Due to its miraculous power of healing, N. sativa has got the place among the top ranked evidence based herbal medicines. This is also revealed that most of the therapeutic properties of this plant are due to the presence of thymoquinone which is major bioactive component of the essential oil. The present review is an effort to provide a detailed survey of the literature on scientific researches of pharmacognostical characteristics, chemical composition and pharmacological activities of the seeds of this plant.

Protein-protein interaction plays key role in predicting the protein function of target protein and drug ability of molecules. The majority of genes and proteins realize resulting phenotype functions as a set of interactions. The in vitro and in vivo methods like affinity purification, Y2H (yeast 2 hybrid), TAP (tandem affinity purification), and so forth have their own limitations like cost, time, and so forth, and the resultant data sets are noisy and have more false positives to annotate the function of drug molecules. Thus, in silico methods which include sequence-based approaches, structure-based approaches, chromosome proximity, gene fusion, in silico 2 hybrid, phylogenetic tree, phylogenetic profile, and gene expression-based approaches were developed. Elucidation of protein interaction networks also contributes greatly to the analysis of signal transduction pathways. Recent developments have also led to the construction of networks having all the protein-protein interactions using computational methods for signaling pathways and protein complex identification in specific diseases.

Self-medication is a global phenomenon and potential contributor to human pathogen resistance to antibiotics. The adverse consequences of such practices should always be emphasized to the community and steps to curb it. Rampant irrational use of antimicrobials without medical guidance may result in greater probability of inappropriate, incorrect, or undue therapy, missed diagnosis, delays in appropriate treatment, pathogen resistance and increased morbidity. This review focused on the self-medication of allopathic drugs, their use, its safety and reason for using it. It would be safe, if the people who are using it, have sufficient knowledge about its dose, time of intake, side effect on over dose, but due to lack of information it can cause serious effects such as antibiotic resistance, skin problem, hypersensitivity and allergy. There is need to augment awareness and implement legislations to promote judicious and safe practices. Improved knowledge and understanding about self-medication may result in rationale use and thus limit emerging microbial resistance issues. Articles which were published in peer reviewed journals, World Self-Medication Industry and World Health Organization websites relating to self-medication reviewed.

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 Internet of Things (IoT) has lately developed into an innovation for developing smart environments. Security and privacy are viewed as main problems in any technology's dependence on the IoT model. Privacy and security issues arise due to the different possible attacks caused by intruders. Thus, there is an essential need to develop an intrusion detection system for attack and anomaly identification in the IoT system. In this work, we have proposed a deep learning-based method Deep Belief Network (DBN) algorithm model for the intrusion detection system. Regarding the attacks and anomaly detection, the CICIDS 2017 dataset is utilized for the performance analysis of the present IDS model. The proposed method produced better results in all the parameters in relation to accuracy, recall, precision, F1-score, and detection rate. The proposed method has achieved 99.37% accuracy for normal class, 97.93% for Botnet class, 97.71% for Brute Force class, 96.67% for Dos/DDoS class, 96.37% for Infiltration class, 97.71% for Ports can class and 98.37% for Web attack, and these results were compared with various classifiers as shown in the results.

Abstract Nowadays, diabetic retinopathy is a prominent reason for blindness among the people who suffer from diabetes. Early and timely detection of this problem is critical for a good prognosis. An automated system for this purpose contains several phases like identification and classification of lesions in fundus images. Machine learning techniques based on manual extraction of features and automatic extraction of features with convolution neural network have been presented for diabetic retinopathy detection. The recent developments like capsule networks in deep learning and their significant success over traditional machine learning methods for a variety of applications inspired the researchers to apply them for diabetic retinopathy diagnosis. In this paper, a reformed capsule network is developed for the detection and classification of diabetic retinopathy. Using the convolution and primary capsule layer, the features are extracted from the fundus images and then using the class capsule layer and softmax layer the probability that the image belongs to a specific class is estimated. The efficiency of the proposed reformed network is validated concerning four performance measures by considering the Messidor dataset. The constructed capsule network attains an accuracy of 97.98%, 97.65%, 97.65%, and 98.64% on the healthy retina, stage 1, stage 2, and stage 3 fundus images.

Diseases and disorders that damage the mouth and face can disturb well-being and his self-esteem. Oral health-related quality of life (OHRQOL) is a relatively new but rapidly growing notion. The concept of OHRQOL can become a tool to understand and shape not only the state of clinical practice, dental research and dental education but also that of community at large. There are different approaches to measure OHRQOL; the most popular one is multiple item questionnaires. OHRQOL should be the basis for any oral health programme development. Moreover, research at the conceptual level is needed in countries where OHRQOL has not been previously assessed, including India.

BACKGROUND: Timely and comprehensive analyses of causes of death stratified by age, sex, and location are essential for shaping effective health policies aimed at reducing global mortality. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2023 provides cause-specific mortality estimates measured in counts, rates, and years of life lost (YLLs). GBD 2023 aimed to enhance our understanding of the relationship between age and cause of death by quantifying the probability of dying before age 70 years (70q0) and the mean age at death by cause and sex. This study enables comparisons of the impact of causes of death over time, offering a deeper understanding of how these causes affect global populations. METHODS: GBD 2023 produced estimates for 292 causes of death disaggregated by age-sex-location-year in 204 countries and territories and 660 subnational locations for each year from 1990 until 2023. We used a modelling tool developed for GBD, the Cause of Death Ensemble model (CODEm), to estimate cause-specific death rates for most causes. We computed YLLs as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. Probability of death was calculated as the chance of dying from a given cause in a specific age period, for a specific population. Mean age at death was calculated by first assigning the midpoint age of each age group for every death, followed by computing the mean of all midpoint ages across all deaths attributed to a given cause. We used GBD death estimates to calculate the observed mean age at death and to model the expected mean age across causes, sexes, years, and locations. The expected mean age reflects the expected mean age at death for individuals within a population, based on global mortality rates and the population's age structure. Comparatively, the observed mean age represents the actual mean age at death, influenced by all factors unique to a location-specific population, including its age structure. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 250-draw distribution for each metric. Findings are reported as counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2023 include a correction for the misclassification of deaths due to COVID-19, updates to the method used to estimate COVID-19, and updates to the CODEm modelling framework. This analysis used 55 761 data sources, including vital registration and verbal autopsy data as well as data from surveys, censuses, surveillance systems, and cancer registries, among others. For GBD 2023, there were 312 new country-years of vital registration cause-of-death data, 3 country-years of surveillance data, 51 country-years of verbal autopsy data, and 144 country-years of other data types that were added to those used in previous GBD rounds. FINDINGS: The initial years of the COVID-19 pandemic caused shifts in long-standing rankings of the leading causes of global deaths: it ranked as the number one age-standardised cause of death at Level 3 of the GBD cause classification hierarchy in 2021. By 2023, COVID-19 dropped to the 20th place among the leading global causes, returning the rankings of the leading two causes to those typical across the time series (ie, ischaemic heart disease and stroke). While ischaemic heart disease and stroke persist as leading causes of death, there has been progress in reducing their age-standardised mortality rates globally. Four other leading causes have also shown large declines in global age-standardised mortality rates across the study period: diarrhoeal diseases, tuberculosis, stomach cancer, and measles. Other causes of death showed disparate patterns between sexes, notably for deaths from conflict and terrorism in some locations. A large reduction in age-standardised rates of YLLs occurred for neonatal disorders. Despite this, neonatal disorders remained the leading cause of global YLLs over the period studied, except in 2021, when COVID-19 was temporarily the leading cause. Compared to 1990, there has been a considerable reduction in total YLLs in many vaccine-preventable diseases, most notably diphtheria, pertussis, tetanus, and measles. In addition, this study quantified the mean age at death for all-cause mortality and cause-specific mortality and found noticeable variation by sex and location. The global all-cause mean age at death increased from 46·8 years (95% UI 46·6-47·0) in 1990 to 63·4 years (63·1-63·7) in 2023. For males, mean age increased from 45·4 years (45·1-45·7) to 61·2 years (60·7-61·6), and for females it increased from 48·5 years (48·1-48·8) to 65·9 years (65·5-66·3), from 1990 to 2023. The highest all-cause mean age at death in 2023 was found in the high-income super-region, where the mean age for females reached 80·9 years (80·9-81·0) and for males 74·8 years (74·8-74·9). By comparison, the lowest all-cause mean age at death occurred in sub-Saharan Africa, where it was 38·0 years (37·5-38·4) for females and 35·6 years (35·2-35·9) for males in 2023. Lastly, our study found that all-cause 70q0 decreased across each GBD super-region and region from 2000 to 2023, although with large variability between them. For females, we found that 70q0 notably increased from drug use disorders and conflict and terrorism. Leading causes that increased 70q0 for males also included drug use disorders, as well as diabetes. In sub-Saharan Africa, there was an increase in 70q0 for many non-communicable diseases (NCDs). Additionally, the mean age at death from NCDs was lower than the expected mean age at death for this super-region. By comparison, there was an increase in 70q0 for drug use disorders in the high-income super-region, which also had an observed mean age at death lower than the expected value. INTERPRETATION: We examined global mortality patterns over the past three decades, highlighting-with enhanced estimation methods-the impacts of major events such as the COVID-19 pandemic, in addition to broader trends such as increasing NCDs in low-income regions that reflect ongoing shifts in the global epidemiological transition. This study also delves into premature mortality patterns, exploring the interplay between age and causes of death and deepening our understanding of where targeted resources could be applied to further reduce preventable sources of mortality. We provide essential insights into global and regional health disparities, identifying locations in need of targeted interventions to address both communicable and non-communicable diseases. There is an ever-present need for strengthened health-care systems that are resilient to future pandemics and the shifting burden of disease, particularly among ageing populations in regions with high mortality rates. Robust estimates of causes of death are increasingly essential to inform health priorities and guide efforts toward achieving global health equity. The need for global collaboration to reduce preventable mortality is more important than ever, as shifting burdens of disease are affecting all nations, albeit at different paces and scales. FUNDING: Gates Foundation.

Abstract Object detection is a critical and complex problem in computer vision, and deep neural networks have significantly enhanced their performance in the last decade. There are two primary types of object detectors: two stage and one stage. Two-stage detectors use a complex architecture to select regions for detection, while one-stage detectors can detect all potential regions in a single shot. When evaluating the effectiveness of an object detector, both detection accuracy and inference speed are essential considerations. Two-stage detectors usually outperform one-stage detectors in terms of detection accuracy. However, YOLO and its predecessor architectures have substantially improved detection accuracy. In some scenarios, the speed at which YOLO detectors produce inferences is more critical than detection accuracy. This study explores the performance metrics, regression formulations, and single-stage object detectors for YOLO detectors. Additionally, it briefly discusses various YOLO variations, including their design, performance, and use cases.

Credit card fraud detection is presently the most frequently occurring problem in the present world. This is due to the rise in both online transactions and e-commerce platforms. Credit card fraud generally happens when the card was stolen for any of the unauthorized purposes or even when the fraudster uses the credit card information for his use. In the present world, we are facing a lot of credit card problems. To detect the fraudulent activities the credit card fraud detection system was introduced. This project aims to focus mainly on machine learning algorithms. The algorithms used are random forest algorithm and the Adaboost algorithm. The results of the two algorithms are based on accuracy, precision, recall, and F1-score. The ROC curve is plotted based on the confusion matrix. The Random Forest and the Adaboost algorithms are compared and the algorithm that has the greatest accuracy, precision, recall, and F1-score is considered as the best algorithm that is used to detect the fraud.

BACKGROUND: The exact pro-oxidant and antioxidant status in osteoarthritis patients is still not clear. To add a new insight to the question, changes in the erythrocyte lipid peroxidation products (MDA), levels of glutathione (GSH), ascorbic acid and plasma vitamin E (nonenzymatic antioxidant parameters); and activities of antioxidant enzymes superoxide dismutase (SOD), glutathione peroxidase (GPX), catalase in erythrocytes and plasma glutathione - S - transferase (GST) were measured in patients with osteoarthritis. AIM: This work was undertaken to assess oxidative stress and antioxidant status in patients with osteoarthritis. SETTINGS AND DESIGN: The study was conducted in 20 patients and compared to controls. Levels of erythrocyte MDA, GSH, ascorbic acid, plasma vitamin E; and activities of antioxidant enzymes were measured in patients with osteoarthritis. MATERIALS AND METHODS: Erythrocyte GSH was measured by the method of Beutler et al. Ascorbic acid levels were measured by the method of Tietz. Plasma vitamin E levels were measured by the method of Baker et al. MDA was determined as the measure of thio barbituric acid reactive substances (TBARS). SOD activity in the hemolysate was measured by the method of Misra and Fridovich. Activity of catalase was measured by the method of Beers and Sizer. GPX activity was measured as described by Paglia and Valentine in erythrocytes and Plasma GST activity was measured as described by Warholm et al. These parameters were measured in 20 patients and compared to controls. STATISTICAL ANALYSIS: Statistical analysis between group 1 (controls) and group 2 (patients) was performed by the student's t - test using the stat -view package. RESULTS: It was observed that there was a significant increase in erythrocyte MDA levels; SOD, GPX and plasma GST activities; and a significant decrease in erythrocyte GSH, ascorbic acid, plasma vitamin E levels and catalase activity in patients with osteoarthritis when compared to controls. CONCLUSIONS: The results of our study suggest higher oxygen-free radical production, evidenced by increased MDA and decreased GSH, ascorbic acid, vitamin E and catalase activity, support to the oxidative stress in osteoarthritis. The increased activities of antioxidant enzymes may be a compensatory regulation in response to increased oxidative stress.

Breast cancer is the most lethal type of cancer for all women worldwide. At the moment, there are no effective techniques for preventing or curing breast cancer, as the source of the disease is unclear. Early diagnosis is a highly successful means of detecting and managing breast cancer, and early identification may result in a greater likelihood of complete recovery. Mammography is the most effective method of detecting breast cancer early. Additionally, this instrument enables the detection of additional illnesses and may provide information about the nature of cancer, such as benign, malignant, or normal. This article discusses an evolutionary approach for classifying and detecting breast cancer that is based on machine learning and image processing. This model combines image preprocessing, feature extraction, feature selection, and machine learning techniques to aid in the classification and identification of skin diseases. To enhance the image’s quality, a geometric mean filter is used. AlexNet is used for extracting features. Feature selection is performed using the relief algorithm. For disease categorization and detection, the model makes use of the machine learning techniques such as least square support vector machine, KNN, random forest, and Naïve Bayes. The experimental investigation makes use of MIAS data collection. This proposed technology is advantageous for accurately identifying breast cancer disease using image analysis.

Mobile ad hoc networks (MANETs) are self-organizing nodes in a mobile network that collaborate to form dynamic network architecture to establish connections. In MANET, data must traverse several intermediary nodes before reaching its destination. There must be security in place to prevent hostile nodes from accessing this data. Multiple methods were suggested in literature for securing routing; these techniques tackle different aspects of security. In order to enhance fault tolerance, wireless network multipath routing is typically used instead of the original single path routing. The routing protocol Genetic Algorithm with Hill climbing (GAHC) described in this article shows a hybrid GA-Hill Climbing algorithm that picks the optimal route in multipath. Prior to this in the beginning, the Improved fuzzy C-means algorithm method was built on density peak, and cluster heads (CHs) were chosen in a predicted manner, based on recent, indirect, and direct trust. The computation is based worth nodes are at the trust threshold found in addition. Even CHs take part in the alternate paths, the blend of all the many paths from these Cluster Heads that chooses the optimal route, which is based on the predicted hybrid protocol, as well as the optimum route’s aggregate features such as throughput, latency, and connection. This suggested technique requires a minimum amount of energy of 0.10 m joules and a small amount of delay time of 0.004 msec, which also yields a maximum throughput of 0.85 bits per second, a maximum detection rate of 91 percent and maximum packet delivery ratio of 89percent. The suggested approach was put through the paces with the selective packet dropping attack.

An experimental study has been carried out to investigate and characterize the properties of elephant grass fiber reinforced fully biodegradable poly lactic acid (PLA) composites. The composites were prepared with various weight fractions of untreated and treated fibers in PLA matrix using injection moulding technique. The tensile strength of PLA composite with treated elephant grass at 20% fiber loading was 18.14% and 24% higher than that of treated jute/PLA composite and plain PLA, respectively. While the flexural strength of treated elephant grass/PLA composite at same fiber loading was 4% and 22% higher than that of treated sisal composite and plain PLA, respectively. The impact strength of composites with untreated elephant grass, sisal and jute fibers were 129.5%, 111.5% and 22.3%, respectively higher when compared with plain PLA. The water absorption rate increased in all the composites as the fiber content increased and the absorption rate reduced with successive alkali treatment on the fibers. The thermal stability of the composite had been reduced with successive alkali treatments as evident from the TGA analysis. The percentage weight loss in all the composites was linearly increasing with number of days of soil burial. The degradation was high in composite with untreated fibers at highest weight fraction. Using enzymatic environment, the degradation was much faster compared to soil burial. Significant effect of surface modification was evident during observing surface morphology of tensile fractured and soil degraded surfaces of the composites using SEM. POLYM. COMPOS., 39:1125–1136, 2018. © 2016 Society of Plastics Engineers

BACKGROUND: Unlike chemical techniques, the combination of metal oxide nanoparticles utilizing plant concentrate is a promising choice. The purpose of this work was to synthesize magnesium oxide nanoparticles (MgO-NPs) utilizing heartwood aqueous extract of Pterocarpus marsupium. The heartwood extract of Pterocarpus marsupium is rich in polyphenolic compounds and flavonoids that can be used as a green source for large-scale, simple, and eco-friendly production of MgO-NPs. The phytoassisted synthesis of MgO is characterized by UV-Visible spectroscopy, X-ray diffraction (XRD), dynamic light scattering (DLS), Fourier transform infrared spectroscopy (FT-IR), scanning electron microscopy (SEM) with EDS (energy dispersive X-ray spectroscopy), and transmission electron microscopy (TEM). RESULTS: The formation of MgO-NPs is confirmed by a visual color change from colorless to dark brown and they displayed a wavelength of 310 nm in UV-Spectrophotometry analysis. The crystalline nature of the obtained biosynthesized nanoparticles are revealed by X-ray diffraction analysis. SEM results revealed the synthesized magnesium oxide nanoparticles formed by this cost-effective method are spherically shaped with an average size of < 20 nm. The presence of magnesium and oxygen were confirmed by the EDS data. TEM analysis proved the spherical shape of the nanoparticles with average particle size of 13.28 nm and SAED analysis confirms the crystalline nature of MgO-NPs. FT-IR investigation confirms the existence of the active compounds required to stabilize the magnesium oxide nanoparticles with hydroxyl and carboxyl and phenolic groups that act as reducing, stabilizing, and capping agent. All the nanoparticles vary in particle sizes between 15 and 25 nm and obtained a polydispersity index value of 0.248. The zeta-potential was measured and found to be - 2.9 mV. Further, MgO-NPs were tested for antibacterial action against Staphylococcus aureus (Gram-positive bacteria) and Escherichia coli (Gram-negative bacteria) by minimum inhibitory concentration technique were found to be potent against both the bacteria. The blended nanoparticles showed good antioxidant activity examined by the DPPH radical scavenging method, showed good anti-diabetic activity determined by alpha-amylase inhibitory activity, and displayed strong anti-inflammatory activity evaluated by the albumin denaturation method. CONCLUSIONS: The investigation reports the eco-friendly, cost-effective method for synthesizing magnesium oxide nanoparticles from Pterocarpus marsupium Rox.b heartwood extract with biomedical applications.

Existing routing protocols for wireless sensor networks (WSNs) focus primarily either on energy efficiency, quality of service (QoS), or security issues. However, a more holistic view of WSNs is needed, as many applications require both QoS and security guarantees along with the requirement of prolonging the lifetime of the network. The limited energy capacity of sensor nodes forces a tradeoff to be made between network lifetime, QoS, and security. To address these issues, an ant colony optimization based QoS aware energy balancing secure routing (QEBSR) algorithm for WSNs is proposed in this article. Improved heuristics for calculating the end-to-end delay of transmission and the trust factor of the nodes on the routing path are proposed. The proposed algorithm is compared with two existing algorithms: distributed energy balanced routing and energy efficient routing with node compromised resistance. Simulation results show that the proposed QEBSR algorithm performed comparatively better than the other two algorithms.

One of the biggest challenges for discovering more efficacious drugs for the control of neuropathic pain has been the diversity of chronic pain states in humans. It is now acceptable that different mechanisms contribute to normal physiologic pain, pain arising from tissue damage and pain arising from injury to the nervous system. To study pain transmission, spot novel pain targets and characterize the potential analgesic profile of new chemical entities, numerous experimental animal pain models have been developed that attempt to simulate the many human pain conditions. Among the neuropathic pain models, surgical models have paramount importance in the induction of pain states. Many surgical animal models exist, like the chronic constriction injury (CCI) to the sciatic nerve, partial sciatic nerve ligation (pSNL), spinal nerve ligation (SNL), spared nerve injury (SNI), brachial plexus avulsion (BPA), sciatic nerve transaction (SNT) and sciatic nerve trisection. Most of these models induce responses similar to those found in causalgia, a syndrome of sustained burning pain often seen in the distal extremity after partial peripheral nerve injury in humans. Researchers most commonly use these surgical models in both rats and mice during drug discovery to screen new chemical entities for efficacy in the area of neuropathic pain. However, there is scant literature that provides a comparative discussion of all these surgical models. Each surgical model has its own benefits and limitations. It is very difficult for a researcher to choose a suitable surgical animal model to suit their experimental set-up. Therefore, particular attention has been given in this review to comparatively provide the pros and cons of each model of surgically induced neuropathic pain.

Abstract Recently, researchers are investing more fervently in fault diagnosis area of electrical machines. The users and manufacturers of these various efforts are strong to contain diagnostic features in software for improving reliability and scalability. Internet of Things (IoT) has grown immensely and contributing for the development of recent technological advancements in industries, medical and various environmental applications. It provides efficient processing power through cloud, and presents various new opportunities for industrial automation by implementing IoT and industrial wireless sensor networks. The process of regular monitoring enables early detection of machine faults and hence beneficial for Industrial automation by providing efficient process control. The performance of fault detection and its classification by implementing machine-learning algorithms highly dependent on the amount of features involved. The accuracy of classification will adversely affect by the dimensionality features increment. To address these problems, the proposed work presents the extraction of relevant features based on oriented sport vector machine (FO-SVM). The proposed algorithm is capable for extracting the most relevant feature set and hence presenting the accurate classification of faults accordingly. The extraction of most relevant features before the process of classification results in higher classification accuracy. Moreover it is observed that the lesser dimensionality of propose process consumes less time and more suitable for cloud. The experimental analysis based on the implementation of proposed approach provides and solution for the monitoring of machine condition and prediction of fault accurately based on cloud platform using industrial wireless sensor networks and IoT service.

In this correspondence, we present an accurate Magnetic Resonance (MR) image Super-Resolution (SR) method that uses a Very Deep Residual network (VDR-net) in the training phase. By applying 2D Stationary Wavelet Transform (SWT), we decompose each Low Resolution (LR)-High Resolution (HR) example image pair into its low-frequency and high-frequency subbands. These LR-HR subbands are used to train the VDR-net through the input and output channels. The trained parameters are then used to generate residual subbands of a given LR test image. The obtained residuals are added with their LR subbands to produce the SR subbands. Finally, we attempt to maintain the intrinsic structure of images by implementing the Gaussian edge-preservation step on the SR subbands. Our extensive experimental results show that the proposed MR-SR method outperforms the existing methods in terms of four different objective metrics and subjective quality.

A Novel dual notched 4-element MIMO (Multi-Input-Multi-Output) antenna with gap sleeves and H-slot is proposed and fabricated for UWB (ultra wide-band) applications. The proposed antenna is CPW (Co-Planar Waveguide) fed and consists of four orthogonal elements with good isolation. It has low profile and small dimensions of 80 × 80 × 1.6 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> . The proposed MIMO antenna achieved an impedance bandwidth (S11 <; -10$ dB) from 2.1GHz - 20GHz with notches from 3.3GHz - 4.1GHz and 8.2GHz - 8.6GHz frequency bands. These achieved notches can filter the interference of WiMAX(3.3GHz - 3.7GHz), and military/radar applications band (8.2GHz - 8.6GHz). Mutual coupling among the elements is also below -25dB. The performance parameters of proposed MIMO antenna are relatively good with very low ECC (Envelop Correlation Coefficient) less than 0.02 except at notches and DG (Diversity Gain) nearly 10. Peak gain of 5.8dB is achieved by the proposed antenna and the radiation efficiency is also above 80% except at notches.The computer-generated and experimental results are in accord and therefore, the proposed four element MIMO antenna can be suggested as a suitable aspirant for UWB applications with stop bands for WiMAX and military/radar applications.