Prospects of Using Machine Learning and Diamond Nanosensing for High Sensitivity SARS-CoV-2 Diagnosis

The worldwide death toll claimed by Acute Respiratory Syndrome Coronavirus Disease 2019 (SARS-CoV), including its prevailed variants, is 6,812,785 (worldometer.com accessed on 14 March 2023). Rapid, reliable, cost-effective, and accurate diagnostic procedures are required to manage pandemics. In this regard, we bring attention to quantum spin magnetic resonance detection using fluorescent nanodiamonds for biosensing, ensuring the benefits of artificial intelligence-based biosensor design on an individual patient level for disease prediction and data interpretation. We compile the relevant literature regarding fluorescent nanodiamonds-based SARS-CoV-2 detection along with a short description of viral proliferation and incubation in the cells. We also propose a potentially effective strategy for artificial intelligence-enhanced SARS-CoV-2 biosensing. A concise overview of the implementation of artificial intelligence algorithms with diamond magnetic nanosensing is included, covering this roadmap’s benefits, challenges, and prospects. Some mutations are alpha, beta, gamma, delta, and Omicron with possible symptoms, viz. runny nose, fever, sore throat, diarrhea, and difficulty breathing accompanied by severe body pain. The recommended strategy would deliver reliable and improved diagnostics against possible threats due to SARS-CoV mutations, including possible pathogens in the future.</p

Optimization of dilute acid pretreatment of water hyacinth biomass for enzymatic hydrolysis and ethanol production

The present study was conducted for the optimization of pretreatment process that was used for enzymatic hydrolysis of lignocellulosic biomass (Water Hyacinth, WH), which is a renewable resource for the production of bioethanol with decentralized availability. Response surface methodology has been employed for the optimization of temperature (oC), time (hr)and different concentrations of maleic acid (MA), sulfuric acid (SA) and phosphoric acid (PA) that seemed to be significant variables with P < 0.05. High F and R2 values and low P-value for hydrolysis yield indicated the model predictability. The pretreated biomass producing 39.96 g/l, 39.86 g/l and 37.9 g/l of reducing sugars during enzymatic hydrolysis with yield 79.93, 78.71 and 75.9 % from PA, MA and SA treated respectively. The order of catalytic effectiveness for hydrolysis yield was found to be phosphoric acid > maleic acid > sulfuric acid. Mixture of sugars was obtained during dilute acid pretreatment with glucose being the most prominent sugar while pure glucose was obtained during enzymatic hydrolysis. The resulting sugars, obtained during enzymatic hydrolysis were finally fermented to ethanol, with yield 0.484 g/g of reducing sugars which is 95 % of theoretical yield (0.51 g/g glucose) by using commercial baker’s yeast (Sacchromyces cerveasiae)

Foreign Direct Investment Lead to Exports of Pakistan: An Econometric Evidence

The objective of the study is to empirically analyze that whether foreign direct investment lead to exports of Pakistan for the period from 1972 to 2014. Econometric results are estimated using Partial Adjustment Model for long run as well short run and then some diagnostic statistics are also applied for reliability of results. Long run and short run results propose positive influence of foreign direct investment, exchange rate, trade openness, and real GDP of Pakistan while inflation is found to have inverse effect of exports of Pakistan. Further tests indicate regression model free from Autocorrelation, Heteroskedasticity, abnormality of residuals and dynamic instability problems. Keywords: Foreign Direct Investment, Real GDP, Exports, Exchange Rate, Inflation

Role of Flavonoids as Wound Healing Agent

Flavonoids are found as the most abundant bioactive compounds all around the world. It is found in a number of medicinal plants that are used as wound healing agents in traditional medicinal uses such as Buddleja globosa, Moringa oleifera, Lam, Butea monosperma, Parapiptadenia rigida and Ononis spinosa. Flavonoids nowadays are being used in different formulation and wound healing dressings. Inflammation, proliferation and reepithelialization are involved in wound healing. Most of the wound healing medicinal plants possess multiple flavonoids that act as synergistic effect or combined effect. This chapter briefly reviews the role of flavonoids as wound healing agent in traditional and modern medicine

Reverse Image Search Using Deep Unsupervised Generative Learning and Deep Convolutional Neural Network

Reverse image search has been a vital and emerging research area of information retrieval. One of the primary research foci of information retrieval is to increase the space and computational efficiency by converting a large image database into an efficiently computed feature database. This paper proposes a novel deep learning-based methodology, which captures channel-wise, low-level details of each image. In the first phase, sparse auto-encoder (SAE), a deep generative model, is applied to RGB channels of each image for unsupervised representational learning. In the second phase, transfer learning is utilized by using VGG-16, a variant of deep convolutional neural network (CNN). The output of SAE combined with the original RGB channel is forwarded to VGG-16, thereby producing a more effective feature database by the ensemble/collaboration of two effective models. The proposed method provides an information rich feature space that is a reduced dimensionality representation of the image database. Experiments are performed on a hybrid dataset that is developed by combining three standard publicly available datasets. The proposed approach has a retrieval accuracy (precision) of 98.46%, without using the metadata of images, by using a cosine similarity measure between the query image and the image database. Additionally, to further validate the proposed methodology’s effectiveness, image quality has been degraded by adding 5% noise (Speckle, Gaussian, and Salt pepper noise types) in the hybrid dataset. Retrieval accuracy has generally been found to be 97% for different variants of nois

Automated facial characterization and image retrieval by convolutional neural networks

IntroductionDeveloping efficient methods to infer relations among different faces consisting of numerous expressions or on the same face at different times (e.g., disease progression) is an open issue in imaging related research. In this study, we present a novel method for facial feature extraction, characterization, and identification based on classical computer vision coupled with deep learning and, more specifically, convolutional neural networks.MethodsWe describe the hybrid face characterization system named FRetrAIval (FRAI), which is a hybrid of the GoogleNet and the AlexNet Neural Network (NN) models. Images analyzed by the FRAI network are preprocessed by computer vision techniques such as the oriented gradient-based algorithm that can extract only the face region from any kind of picture. The Aligned Face dataset (AFD) was used to train and test the FRAI solution for extracting image features. The Labeled Faces in the Wild (LFW) holdout dataset has been used for external validation.Results and discussionOverall, in comparison to previous techniques, our methodology has shown much better results on k-Nearest Neighbors (KNN) by yielding the maximum precision, recall, F1, and F2 score values (92.00, 92.66, 92.33, and 92.52%, respectively) for AFD and (95.00% for each variable) for LFW dataset, which were used as training and testing datasets. The FRAI model may be potentially used in healthcare and criminology as well as many other applications where it is important to quickly identify face features such as fingerprint for a specific identification target

Antioxidants: Natural Antibiotics

The aim of this current piece of writing is to draw the attention of readers and researchers toward the natural antioxidants that can take the place of synthetic antibiotics to avoid bacterial resistance and gastrotoxicity/nephrotoxicity. Antioxidants such as polyphenols, vitamins, and carotenoids are the organic compounds mainly extracted from natural sources and dominantly involved in boosting the defense system of organisms. The main public health-related issue over the globe is ever-growing bacterial resistance to synthetic antibiotics, which is being continuously reported during the last decade. Further, the pipeline of the development of new synthetic antibacterial agents to replace the resistant antibiotics in clinical set-up is gradually drying up. This scenario originated the concept to revive the interest toward natural antibacterial products due to their chemical diversity, which provide important therapeutic effect and make the microbes unable to copy them for creating resistance. Natural products, especially polyphenols had been seen in antioxidant, antibacterial, anticancer, anti-inflammation, and antiviral activities with encouraging results. In this chapter, we will focus over the role of natural antioxidants as antibacterial agents

Intelligent ultra-light deep learning model for multi-class brain tumor detection

The diagnosis and surgical resection using Magnetic Resonance (MR) images in brain tumors is a challenging task to minimize the neurological defects after surgery owing to the non-linear nature of the size, shape, and textural variation. Radiologists, clinical experts, and brain surgeons examine brain MRI scans using the available methods, which are tedious, error-prone, time-consuming, and still exhibit positional accuracy up to 2−3 mm, which is very high in the case of brain cells. In this context, we propose an automated Ultra-Light Brain Tumor Detection (UL-BTD) system based on a novel Ultra-Light Deep Learning Architecture (UL-DLA) for deep features, integrated with highly distinctive textural features, extracted by Gray Level Co-occurrence Matrix (GLCM). It forms a Hybrid Feature Space (HFS), which is used for tumor detection using Support Vector Machine (SVM), culminating in high prediction accuracy and optimum false negatives with limited network size to fit within the average GPU resources of a modern PC system. The objective of this study is to categorize multi-class publicly available MRI brain tumor datasets with a minimum time thus real-time tumor detection can be carried out without compromising accuracy. Our proposed framework includes a sensitivity analysis of image size, One-versus-All and One-versus-One coding schemes with stringent efforts to assess the complexity and reliability performance of the proposed system with K-fold cross-validation as a part of the evaluation protocol. The best generalization achieved using SVM has an average detection rate of 99.23% (99.18%, 98.86%, and 99.67%), and F-measure of 0.99 (0.99, 0.98, and 0.99) for (glioma, meningioma, and pituitary tumors), respectively. Our results have been found to improve the state-of-the-art (97.30%) by 2%, indicating that the system exhibits capability for translation in modern hospitals during real-time surgical brain applications. The method needs 11.69 ms with an accuracy of 99.23% compared to 15 ms achieved by the state-of-the-art to earlier to detect tumors on a test image without any dedicated hardware providing a route for a desktop application in brain surgery