Search Results (19)

Background: Cerebral venous sinus thrombosis (CVT) is a rare cause of stroke, constituting 0.5–3% of all strokes with an extremely varied spectrum of presentation, predisposing factors, neuroimaging findings, and eventual outcomes. A high index of suspicion is needed because timely diagnosis can significantly alter the natural course of the disease, reduce acute complications, and improve long-term outcomes. Due to its myriad causative factors, protean presentation, and association with several systemic diseases, CVT is encountered not only by neurologists but also by emergency care practitioners, internists, hematologists, obstetricians, and pediatricians. Discussion: Anticoagulation remains the mainstay of treatment for CVT. Heparin and warfarin previously had been the anticoagulation of choice. Recently there has been an increased interest in utilizing direct-acting oral anticoagulants in the treatment of CVT given comparable safety and efficacy with ease of utilization. However recent clinical guidelines given by multiple societies including the American Stroke guidelines and European guidelines do not include these agents so far in their treatment recommendations. Ongoing multicentric clinical trials are currently reviewing the role of these agents in both short-term as well as long-term. Our review of the literature supports the safety and reinforces the efficacy of DOAC in the treatment of CVT. Additionally, patient satisfaction has been shown to be better with the use of DOAC. In conclusion, DOAC continues to have a valid role in the management of CVT. Full article

Border disease virus (BDV) causes significant economic losses in sheep farming worldwide. In India, BDV has not yet been studied in sheep migrating for summer pasturing. This study aimed to determine the extent of BDV infection in migratory sheep and provide genetic characteristics of BDV. Blood and serum samples from 90 lambs of a migratory sheep flock (600) in Central India were collected and subjected to molecular detection, phylogenetic analysis and virus neutralization test (VNT). We detected BDV in two lambs through real-time RT-PCR, while 64.4% (58/90) of in-contact lambs had BDV neutralizing antibodies. One apparently healthy lamb was found to be persistently infected with BDV. Phylogenetic analysis of 5′-UTR and N^pro genes and the concatenated datasets typed the BDV isolate from PI sheep as BDV-3 genotype. However, it showed a closer relationship with BDV-3 strains from China than the previously reported Indian BDV-3 strains. This is the first report on the detection of BDV persistently infected migratory sheep in India. Additionally, we provided evidence of genetic variability among BDV-3 strains in India. The findings improve our understanding of epidemiology and genetic characteristics of BDV in India and highlight the potential risks associated with the traditional practice of sheep migration for summer pasturing. Full article

Background: Transfusional iron overload causes significant morbidity and mortality in sickle cell disease (SCD). Nevertheless, red blood cell transfusions continue to be essential in its management. This study describes the transfusion patterns among SCD hospitalizations. Methods: Hospitalizations for SCD in the 2017–2018 Nationwide Readmissions Database were divided into two groups based on whether they received transfusions. Descriptive analysis was performed to compare their demographics and complications. Multivariable logistic regression was performed to determine the factors associated with transfusions. Results: Out of 109,783 hospitalizations, 28,300 were transfused, and 81,483 were not transfused. Females and older individuals were higher in the transfused category than the non-transfused category (59.49% vs. 53.52% and 28.86% vs. 21.27%, respectively; p < 0.001 for both). The wealthiest population was more likely to be in the transfused category (11.27% vs. 8.34%; p < 0.001). Admissions to teaching hospitals, large metropolitan hospitals, and highest-volume hospitals were higher in the non-transfused category vs. transfused category (79.89% vs. 72.17%; p < 0.001, 69.26% vs. 65.35%; p 0.003 and 74.71% vs. 63.51%; p < 0.001, respectively). Most admissions were transfused once, with three or more transfusions being given more in the non-teaching hospitals than the teaching hospitals (1.27% vs. 0.41%; p 0.01). Furthermore, a higher proportion of early transfusions occurred in the non-teaching hospitals (65.6% vs. 57.82% for admission days 1 and 2; p < 0.001). Admission to a teaching hospital was associated with lower blood transfusion odds than a non-teaching hospital. Conclusion: A quarter of admissions for SCD receive a blood transfusion. In addition to performing more frequent and early transfusions, the odds of being transfused are higher in non-teaching hospitals. Full article

Extreme weather events such as droughts are catastrophic and can have serious consequences for people and the environment. Drought may be managed if measures are taken in advance. The success of this endeavor depends on a number of factors, not the least of which is accurate descriptions and measurements of drought conditions. Reducing the negative consequences of droughts requires an early forecast of drought conditions. The primary objective of this research is, hence, to establish a process for the assessment and prediction of drought. The drought evaluation was carried out using the standards established by the SPI and the Indian Meteorological Department. Maps of drought severity were generated using severe drought data. Thirty years’ worth of SPI readings was analyzed. Fuzzy-based drought forecasting model parameters were determined during a 25-year period, and the model was validated throughout the remaining years. The findings of this study can be used by the community to help combat the drought. Before the drought worsens, the local government can implement lifesaving mitigating measures. Full article

As one of nature’s most destructive calamities, floods cause fatalities, property destruction, and infrastructure damage, affecting millions of people worldwide. Due to its ability to accurately anticipate and successfully mitigate the effects of floods, flood modeling is an important approach in flood control. This study provides a thorough summary of flood modeling’s current condition, problems, and probable future directions. The study of flood modeling includes models based on hydrologic, hydraulic, numerical, rainfall–runoff, remote sensing and GIS, artificial intelligence and machine learning, and multiple-criteria decision analysis. Additionally, it covers the heuristic and metaheuristic techniques employed in flood control. The evaluation examines the advantages and disadvantages of various models, and evaluates how well they are able to predict the course and impacts of floods. The constraints of the data, the unpredictable nature of the model, and the complexity of the model are some of the difficulties that flood modeling must overcome. In the study’s conclusion, prospects for development and advancement in the field of flood modeling are discussed, including the use of advanced technologies and integrated models. To improve flood risk management and lessen the effects of floods on society, the report emphasizes the necessity for ongoing research in flood modeling. Full article

Different soil characteristics in different parts of India affect agriculture growth. Crop growth and crop production are significantly impacted by healthy soil. Soil enzymes mediate almost all biochemical reactions in the soil. Understanding the biological processes of soil carbon and nitrogen cycling requires defining the significance of prospective elements at the play of soil enzymes and evaluating their activities. A combination of Multiple Linear Regression (MLR), Random Forest (RF) models, and Artificial Neural Networks (ANN) was employed in this study to assess soil enzyme activity, including amylase and urease activity, soil physical properties, such as sand, silt, clay, and soil chemical properties, including organic matter (SOM), nitrogen (N), phosphorus (P), soil organic carbon (SOC), pH, and fertility level. Compared to other methods for estimating soil phosphatase, cellulose, and urease activity, the RF model significantly outperforms the MLR model. In addition, due to its ability to manage dynamic and hierarchical relationships between enzyme activities, the RF model outperforms other models in evaluating soil enzyme activity. This study collected 3972 soil samples from 25 villages in the Bhandara district of Maharashtra, India, with chemical, physical, and biological parameters. Overall, 99% accuracy was achieved for cellulase enzyme activity and 94% for N-acetyl-glucosaminidase enzyme activity using the Random Forest model. Crops have been suggested based on the best performance accuracy algorithms and evaluation performance metrics. Full article

Across all countries, both developing and developed, women face the greatest risk of breast cancer. Patients who have their breast cancer diagnosed and staged early have a better chance of receiving treatment before the disease spreads. The automatic analysis and classification of medical images are made possible by today’s technology, allowing for quicker and more accurate data processing. The Internet of Things (IoT) is now crucial for the early and remote diagnosis of chronic diseases. In this study, mammography images from the publicly available online repository The Cancer Imaging Archive (TCIA) were used to train a deep transfer learning (DTL) model for an autonomous breast cancer diagnostic system. The data were pre-processed before being fed into the model. A popular deep learning (DL) technique, i.e., convolutional neural networks (CNNs), was combined with transfer learning (TL) techniques such as ResNet50, InceptionV3, AlexNet, VGG16, and VGG19 to boost prediction accuracy along with a support vector machine (SVM) classifier. Extensive simulations were analyzed by employing a variety of performances and network metrics to demonstrate the viability of the proposed paradigm. Outperforming some current works based on mammogram images, the experimental accuracy, precision, sensitivity, specificity, and f1-scores reached 97.99%, 99.51%, 98.43%, 80.08%, and 98.97%, respectively, on the huge dataset of mammography images categorized as benign and malignant, respectively. Incorporating Fog computing technologies, this model safeguards the privacy and security of patient data, reduces the load on centralized servers, and increases the output. Full article

Diabetes is a chronic condition caused by an uncontrolled blood sugar levels in the human body. Its early diagnosis may prevent severe complications such as diabetic foot ulcers (DFUs). A DFU is a critical condition that can lead to the amputation of a diabetic patient’s lower limb. The diagnosis of DFU is very complicated for the medical professional as it often goes through several costly and time-consuming clinical procedures. In the age of data deluge, the application of deep learning, machine learning, and computer vision techniques have provided various solutions for assisting clinicians in making more reliable and faster diagnostic decisions. Therefore, the automatic identification of DFU has recently received more attention from the research community. The wound characteristics and visual perceptions with respect to computer vision and deep learning, especially convolutional neural network (CNN) approaches, have provided potential solutions for DFU diagnosis. These approaches have the potential to be quite helpful in current medical practices. Therefore, a detailed comprehensive study of such existing approaches was required. The article aimed to provide researchers with a detailed current status of automatic DFU identification tasks. Multiple observations have been made from existing works, such as the use of traditional ML and advanced DL techniques being necessary to help clinicians make faster and more reliable diagnostic decisions. In traditional ML approaches, image features provide signification information about DFU wounds and help with accurate identification. However, advanced DL approaches have proven to be more promising than ML approaches. The CNN-based solutions proposed by various authors have dominated the problem domain. An interested researcher will successfully be able identify the overall idea in the DFU identification task, and this article will help them finalize the future research goal. Full article

Imaging data fusion is becoming a bottleneck in clinical applications and translational research in medical imaging. This study aims to incorporate a novel multimodality medical image fusion technique into the shearlet domain. The proposed method uses the non-subsampled shearlet transform (NSST) to extract both low- and high-frequency image components. A novel approach is proposed for fusing low-frequency components using a modified sum-modified Laplacian (MSML)-based clustered dictionary learning technique. In the NSST domain, directed contrast can be used to fuse high-frequency coefficients. Using the inverse NSST method, a multimodal medical image is obtained. Compared to state-of-the-art fusion techniques, the proposed method provides superior edge preservation. According to performance metrics, the proposed method is shown to be approximately 10% better than existing methods in terms of standard deviation, mutual information, etc. Additionally, the proposed method produces excellent visual results regarding edge preservation, texture preservation, and more information. Full article

In the area of text summarization, there have been significant advances recently. In the meantime, the current trend in text summarization is focused more on news summarization. Therefore, developing a synthesis approach capable of extracting, comparing, and ranking sentences is vital to create a summary of various news articles in the context of erroneous online data. It is necessary, however, for the news summarization system to be able to deal with multi-document summaries due to content redundancy. This paper presents a method for summarizing multi-document news web pages based on similarity models and sentence ranking, where relevant sentences are extracted from the original article. English-language articles are collected from five news websites that cover the same topic and event. According to our experimental results, our approach provides better results than other recent methods for summarizing news. Full article

Over recent years, machine translation has achieved astounding accomplishments. Machine translation has become more evident with the need to understand the information available on the internet in different languages and due to the up-scaled exchange in international trade. The enhanced computing speed due to advancements in the hardware components and easy accessibility of the monolingual and bilingual data are the significant factors that have added up to boost the success of machine translation. This paper investigates the machine translation models developed so far to the current state-of-the-art providing a solid understanding of different architectures with the comparative evaluation and future directions for the translation task. Because hybrid models, neural machine translation, and statistical machine translation are the types of machine translation that are utilized the most frequently, it is essential to have an understanding of how each one functions. A comprehensive comprehension of the several approaches to machine translation would be made possible as a result of this. In order to understand the advantages and disadvantages of the various approaches, it is necessary to conduct an in-depth comparison of several models on a variety of benchmark datasets. The accuracy of translations from multiple models is compared using metrics such as the BLEU score, TER score, and METEOR score. Full article

HoBi-like pestivirus (HoBiPeV), classified under Pestivirus H species, is an emerging cattle pathogen of high economic impact. However, the origin and evolution of HoBiPeV are not very clear due to a lack of full genomic sequences from diverse clades. This study aimed to determine full-genome sequences of HoBiPeV strains of three novel clades (c, d and e) and perform full-genome-based genetic and evolutionary analyses. Bayesian phylogenetic analyses herein confirmed the existence and independent evolution of four main HoBiPeV clades (a, c, d and e) globally, with genetic divergence ranging from 13.0% to 18.2%. Our Bayesian molecular clock estimates revealed that HoBiPeV most likely originated in India, with a dated tMRCA of 1938 (1762–2000), evidencing a more recent origin of HoBiPeV. The evolution rate of HoBiPeV was estimated to be 2.133 × 10⁻³ subs/site/year at full-genome level but varied widely among individual genes. Selection pressure analyses identified most of the positively selected sites in E2. Additionally, 21.8% of the ORF codon sites were found under strong episodic diversifying selection, providing first evidence of negative selection in HoBiPeV evolution. No recombination event was evident for HoBiPeV-c, d and e strains. These findings provide new insights into HoBiPeV origin and evolutionary history for better understanding the epidemiology and host–pathogen interactions and stimulate vaccine research. Full article

Biological treatment methods for the biodegradation of anthropogenic toxic pollutants are eco-friendly in nature and are powered by a variety of microbial enzymes. Green chemistry and enzymes play a crucial role in catalyzing the biodegradation of organic and inorganic pollutants including azo dyes; polyaromatic hydrocarbons; lead; organic cyanides; aromatic amines; mono-, di-, and polyphenols; polymers; and mercury. Laccases form a prospective group of multifunctional oxidoreductase enzymes with great potential for oxidizing different categories of organic and inorganic pollutants and their diversified functions, such as pigment formation, lignin degradation, and detoxification of industrial wastes including xenobiotics mainly from the pharmaceutical, paper textile, and petrochemical industries. Therefore, it is very important to study laccases as green and environmentally friendly alternatives for the degradation of xenobiotics. This review article will cover comprehensive information about the functions and properties of bacterial laccases for a deep understanding of their scope and applications for effective bioremediation of recalcitrant xenobiotics. Full article

The green, cost-effective and sustainable synthesis of nanomaterials has been a key concern of scientists and researchers. In this view, MNPs were prepared using a sapota plant leaf extract and the surface of the magnetite nanoparticles was engineered with unsaturated fatty acids. The first report on the effect of unsaturation on the size and magnetic properties of magnetite nanoparticles (MNPs), prepared by the co-precipitation method, has been studied by coating surfactants on MNPs based on their unsaturation from zero to three (lauric acid, oleic acid, linoleic acid, linolenic acid). The size effect and magnetic properties of MNPs coated with a surfactant have been studied in comparison with uncoated magnetite nanoparticles. After the surface modification of the magnetite particle, it is necessary to check whether the magnetic property has been restored or not. Therefore, the magnetic property was studied. The presence of a surfactant on the surface of MNPs was confirmed by Fourier-transform infrared spectroscopy (FTIR), which was later confirmed by scanning electron microscope (SEM) and thermogravimetric analysis (TGA). The atomic structure was studied by X-ray diffraction (XRD) and the size of uncoated and surfactant-coated MNPs was determined by transmission electron microscopy (TEM) and the Scherrer equation by following XRD data. The magnetization property was analyzed by a vibrating sample magnetometer (VSM) at 10, 100 and 300 K and both bared and surfactant-coated MNPs exhibited a superparamagnetic nature at room temperature. The saturation magnetization (M_s) study shows that MNPs coated with a surfactant have a lower saturation magnetization value in comparison to uncoated NPs, confirming surface layering. Because the magnetic fluid has been stabilized in the aqueous medium, the double-layer model is expected to prevail. Full article

The green synthesis of nanoparticles is an emerging branch of nanotechnology in recent times, as it has numerous advantages such as sustainability, cost-effectiveness, biocompatibility, and eco-friendliness. In the present research work, the authors synthesized ZnO nanoparticles (ZnO NPs) by a green and eco-friendly method. The synthesized ZnO NPs were characterized by X-ray diffraction (XRD), field emission scanning electron microscopy (FESEM), and Fourier transform infrared (FTIR) spectroscopic techniques. The calculated average crystallite size of ZnO NPs was observed at 36.73 nm and FESEM images clearly showed the cylindrical shape of nanoparticles. The absorption peak at 531 cm⁻¹ was observed in the FTIR spectrum of the ZnO NPs sample, which also supports the formation of the ZnO wurtzite structure. Finally, the synthesized ZnO NPs potential was analyzed for the remediation of malachite green from an aqueous solution. The ZnO NPs showed a desirable photocatalytic nature under LEDs irradiation. Full article