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The discovery of antibiotics was hailed as a historic breakthrough for the human race in the fight against bacterial and malignant infections. However, in a very short time, owing to their acute and aggressive nature, bacteria hav...
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The discovery of antibiotics was hailed as a historic breakthrough for the human race in the fight against bacterial and malignant infections. However, in a very short time, owing to their acute and aggressive nature, bacteria have developed resistance against antibiotics and other chemotherapeutics agents. Potentially, this situation could again result in bacterial infection outbreaks. Metal and metal oxide nanoparticles have been proven as better alternatives; the combination of antibiotics and metal oxide nanoparticles was shown to decrease the toxicity and enhance the antibacterial, antiviral, and anticancer efficacy of the agents. This review provides a detailed view about the role of metal and metal oxide nanoparticles in the treatment of infections in conjunction with antibiotics, their modes of action, and synergism. In addition, the problems of multidrug resistance are addressed and will allow the development of a comprehensive, reliable, and rational treatment plan. It is expected that this comprehensive review will lead to new research opportunities, which should be helpful for future applications in biomedical science.
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Brain-Computer Interface(BCI)is a system that provides a link between the brain of humans and the hardware directly.The recorded brain data is converted directly to the machine that can be used to control external devices.There ar...
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Brain-Computer Interface(BCI)is a system that provides a link between the brain of humans and the hardware directly.The recorded brain data is converted directly to the machine that can be used to control external devices.There are four major components of the BCI system:acquiring signals,preprocessing of acquired signals,features extraction,and classification.In traditional machine learning algorithms,the accuracy is insignificant and not up to the mark for the classification of multi-class motor imagery data.The major reason for this is,features are selected manually,and we are not able to get those features that give higher accuracy results.In this study,motor imagery(MI)signals have been classified using different deep learning algorithms.We have explored two different methods:Artificial Neural Network(ANN)and Long Short-Term Memory(LSTM).We test the classification accuracy on two datasets:BCI competition Ⅲ-dataset Ⅲa and BCI competition Ⅳ-dataset Ⅱa.The outcome proved that deep learning algorithms provide greater accuracy results than traditional machine learning algorithms.Amongst the deep learning classifiers,LSTM outperforms the ANN and gives higher classification accuracy of 96.2%.
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Abstract Internet usage is increasing day by day all over the world and as a result, technology is also developing to make daily life appliances as smart as possible. Millions of devices are connected using IoT technology, and the...
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Abstract Internet usage is increasing day by day all over the world and as a result, technology is also developing to make daily life appliances as smart as possible. Millions of devices are connected using IoT technology, and the vulnerabilities of these devices are still exploitable by attackers. Having access to IoT devices through a Bot Master allows the Bot Master to attack a targeted server with these devices. To detect malicious traffic in IoT networks, there is a need for an intelligent mechanism. Although there have been many studies on the detection of botnet malware, accuracy and efficiency remain a gap. This study focuses on an automatic system that can detect botnet malware with high accuracy. A new ensemble model has been proposed in this study, known as the Extra Tree Random Voting Ensemble Classifier (ER-VEC), which is a combination of two tree-based models called Extra Tree and Random Forest. The proposed model is tested on several malicious traffic in the IoT networks datasets such as IoTID20, MedBIoT, UNSW-NB15, N-BaIoT, and ER-VEC achieving 99.99%, 99.91%, 95.64%, and 100% accuracy scores, respectively. In comparison with the proposed model, other machine learning models were also employed, and ER-VEC significantly outperformed them in terms of accuracy, precision, recall, F1-score, and error rate across all datasets. In addition, we performed K-Fold cross-validation and found that ER-VEC achieved an accuracy score of 98% and a standard deviation of 0.04±.
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Introduction: Oncolytic viruses are genetically engineered viruses that target myeloma-affected cells by detecting specific cell surface receptors (CD46, CD138), causing cell death by activating the signaling pathway to induce apo...
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Introduction: Oncolytic viruses are genetically engineered viruses that target myeloma-affected cells by detecting specific cell surface receptors (CD46, CD138), causing cell death by activating the signaling pathway to induce apoptosis or by immune-mediated cellular destruction. Areas covered: This article summarizes oncolytic virotherapy advancements such as the therapeutic use of viruses by targeting cell surface proteins of myeloma cells as well as the carriers to deliver viruses to the target tissues safely. The major classes of viruses that have been studied for this include measles, myxoma, adenovirus, reovirus, vaccinia, vesicular-stomatitis virus, coxsackie, and others. The measles virus acts as oncolytic viral therapy by binding to the CD46 receptors on the myeloma cells to utilize its surface H protein. These H-protein and CD46 interactions lead to cellular syncytia formation resulting in cellular apoptosis. Vesicular-stomatitis virus acts by downregulation of anti-apoptotic factors (Mcl-2, BCL-2). Based upon the published literature searches till December 2020, we have summarized the data supporting the advances in viral oncolytic for the treatment of MM. Expert opinion: Oncolytic virotherapy is an experimental approach in multiple myeloma (MM); many issues need to be addressed for safe viral delivery to the target tissue
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The outbreak of coronavirus disease 2019 (COVID-19) has had a tremendous effect on daily life and a great impact on the economy of the world. More than 200 countries have been affected. The diagnosis of coronavirus is a major chal...
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The outbreak of coronavirus disease 2019 (COVID-19) has had a tremendous effect on daily life and a great impact on the economy of the world. More than 200 countries have been affected. The diagnosis of coronavirus is a major challenge for medical experts. Early detection is one of the most effective ways to reduce the mortality rate and increase the chance of successful treatment. At this point in time, no antiviral drugs have been approved for use, and clinically approved vaccines have only recently become available in some countries. Hybrid artificial intelligence computer-aided systems for the diagnosis of disease are needed to help prevent the rapid spread of COVID-19. Various detection methods are being used to diagnose coronavirus. Deep extreme learning is the most successful artificial intelligence (AI) technique that efficiently supports medical experts in making smart decisions for the detection of COVID-19. In this study, a novel detection model to diagnose COVID-19 has been introduced to achieve a better accuracy rate. The study focuses on quantitative analysis and disease detection of COVID-19 empowered by a statistical real-time sequential deep extreme learning machine (D2C-RTS-DELM). The experimental results show 98.18% accuracy and 98.87% selectivity, and the probability of detection is 98.84%. The results demonstrate that the quantitative analysis and statistical real-time sequential deep extreme learning machine used in this study perform well in forecasting COVID-19 as well as in making timely decisions for treatment.
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The outbreak of coronavirus disease 2019 (COVID-19) has had a tremendous effect on daily life and a great impact on the economy of the world. More than 200 countries have been affected. The diagnosis of coronavirus is a major chal...
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The outbreak of coronavirus disease 2019 (COVID-19) has had a tremendous effect on daily life and a great impact on the economy of the world. More than 200 countries have been affected. The diagnosis of coronavirus is a major challenge for medical experts. Early detection is one of the most effective ways to reduce the mortality rate and increase the chance of successful treatment. At this point in time, no antiviral drugs have been approved for use, and clinically approved vaccines have only recently become available in some countries. Hybrid artificial intelligence computer-aided systems for the diagnosis of disease are needed to help prevent the rapid spread of COVID-19. Various detection methods are being used to diagnose coronavirus. Deep extreme learning is the most successful artificial intelligence (AI) technique that efficiently supports medical experts in making smart decisions for the detection of COVID-19. In this study, a novel detection model to diagnose COVID-19 has been introduced to achieve a better accuracy rate. The study focuses on quantitative analysis and disease detection of COVID-19 empowered by a statistical real-time sequential deep extreme learning machine (D2C-RTS-DELM). The experimental results show 98.18% accuracy and 98.87% selectivity, and the probability of detection is 98.84%. The results demonstrate that the quantitative analysis and statistical real-time sequential deep extreme learning machine used in this study perform well in forecasting COVID-19 as well as in making timely decisions for treatment.
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Pumpkin (Cucurbita maxima Lam.) is a well-known, extensively grown and consumed crop, world-wide. Pumpkins are natural and rich source of potential bioactive compounds. The presence of active phytochemicals makes these fruits a gr...
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Pumpkin (Cucurbita maxima Lam.) is a well-known, extensively grown and consumed crop, world-wide. Pumpkins are natural and rich source of potential bioactive compounds. The presence of active phytochemicals makes these fruits a great matrix to be further exploited for therapeutic purposes, beyond biotechnological applications. Peel, flesh and seeds of this fruit are heavily loaded with phenolics, flavonoids and carotenoids, which are the main tributes of this functional and medicinal food. Present study was designed to utilize these parts of pumpkin in the form of powders, at 0, 5, 10 and 15% replacement levels with white flour, to develop biscuits and to obtain methanolic extracts of these biscuits to determine their phytochemical parameters. Among the different treatment biscuits, highest amount of total phenolics (101.79 mg GAE/100 g), flavonoids (60.74 mg CE/ 100 g) and DPPH free radical scavenging activity (38.00 mg AAE/100 g) was found in biscuits with 15% replacement of pumpkin seeds powder, while biscuits with 15% replacement of pumpkin flesh powder exhibited highest amount of total carotenoid contents (6.95 mg/ 100 g) and β carotene (2.86 mg/100 g). Functional biscuits developed from replacement of pumpkin parts powders with wheat flour may be offered to patients facing oxidative stress, degenerative diseases and diabetes. These biscuits can be offered to children for better growth and development. Consumers awareness through proper marketing at commercial level with proper labelling of nutritional facts, may lead to increased demand of this functional and medicinal food rich in bio actives.
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Abstract Industrial effluents have caused water bodies to suffer from accumulation of heavy metals such as cadmium (Cd), lead (Pb), and mercury (Hg). This study focuses on determination of the potential use of mycofiltration in th...
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Abstract Industrial effluents have caused water bodies to suffer from accumulation of heavy metals such as cadmium (Cd), lead (Pb), and mercury (Hg). This study focuses on determination of the potential use of mycofiltration in the removal of heavy metals, the effect of contact time, and the efficiency of mushroom species in mycofiltration from an artificial wet pond. In search of effective biosorbents, mycofilters were developed by adding spawns of P. ostreatus and A. bisporus, which were left for incubation and then installed in synthetic ponds for a certain period. Thereafter, mushrooms were harvested, post treated and their biosorption rate was analyzed through atomic absorption spectrophotometer. Significant differences were evaluated in the biosorption rate of mycofilters of P. ostreatus and A. bisporus. A dependent relationship was also established between biosorption and the temperature, namely, an increase in biosorption with an increase in temperature. The comparison of all results for the two species revealed that P. ostreatus showed superiority in the biosorption capacity for Pb 9–189 mg g–1 and Cd 1–21.4 mg g–1 over A. bisporus. However, A. bisporus has shown significant results in the case of Hg 0.6–10 mg g–1 absorption. Overall, in this study, P. ostreatus demonstrated the highest range in biosorption efficiency for metals Pb 99–100% and Cd 97–100% as compared to A. bisporus. At the same time, A. bisporus showed a slightly higher level of removal efficiency in the case of Hg 85–100%. Considering these findings, industrialists may use this cheap and ecofriendly treatment technology for the uptake of heavy metals.
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The whole world is concerned about the pandemic of coronavirus disease (COVID-19), caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), due to fatality of this condition. This has become a public health emer...
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The whole world is concerned about the pandemic of coronavirus disease (COVID-19), caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), due to fatality of this condition. This has become a public health emergency of international concern. No specific vaccine and medicine have proven effective in large-sized trials at this time. With the rapidly increasing number of positive cases and deaths, there is a dire need for effective treatments and an effective vaccine for prevention. An urgent unmet need led to the planning and opening of multiple drug development trials for treatment and vaccine development. In this article, we have summarized data on cell receptor interactions and data on prospects of new vaccines targeting the deoxyribonucleic acid (DNA), messenger ribonucleic acid?(mRNA), and viral minigenes. We have tabulated the available data on various clinical trials testing various aspects of COVID-19 vaccines.
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Background: Tissue margin marking with India ink is important in decision making for surgeons. The present study was conducted to examine the reliability of different shades of locally available poster colours in tissue marking an...
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Background: Tissue margin marking with India ink is important in decision making for surgeons. The present study was conducted to examine the reliability of different shades of locally available poster colours in tissue marking and to evaluate the colour perceptibility microscopically in comparison with similar tissues marked by India ink. Methodology: This experimental study was conducted at Department of Pathology, HBS Medical & Dental College & Hospital, Islamabad from 27th February 2021 to 29th April 2021. Sample size was fifty, collected through convenient sampling technique. Five types of formalin fixed tissue specimens were selected for the study so as to evaluate the effectiveness of poster ink marking on different tissue surfaces. From each specimen, four sections were taken from the margins. Three shades of poster colour (black, blue and green) were used to ink three sections while one section from each tissue type was marked with India ink. After complete tissue processing and routine haematoxylin and eosin (H&E) staining, slides were examined microscopically. Scoring was done on a scale 0 to 3 on the basis of visibility. Results: The present study showed that poster colours inking of the tissues was quite reliable as compared to India ink. Most consistent results were achieved with black and blue colours as compared to green colour. Conclusion: Poster colours are reliable tool for tissue marking when India ink is not available. Their availability in a variety of colours provides them an edge over India ink.
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