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Skin cancer is a lethal disease, and its early detection plays a pivotal role in preventing its spread to other body organs and tissues. Artificial Intelligence (AI)-based automated methods can play a significant role in its early detection. This study presents an AI-based novel approach, termed 'DualAutoELM' for the effective identification of various types of skin cancers. The proposed method leverages a network of autoencoders, comprising two distinct autoencoders: the spatial autoencoder and the FFT (Fast Fourier Transform)-autoencoder. The spatial-autoencoder specializes in learning spatial features within input lesion images whereas the FFT-autoencoder learns to capture textural and distinguishing frequency patterns within transformed input skin lesion images through the reconstruction process. The use of attention modules at various levels within the encoder part of these autoencoders significantly improves their discriminative feature learning capabilities. An Extreme Learning Machine (ELM) with a single layer of feedforward is trained to classify skin malignancies using the characteristics that were recovered from the bottleneck layers of these autoencoders. The 'HAM10000' and 'ISIC-2017' are two publicly available datasets used to thoroughly assess the suggested approach. The experimental findings demonstrate the accuracy and robustness of the proposed technique, with AUC, precision, and accuracy values for the 'HAM10000' dataset being 0.98, 97.68% and 97.66%, and for the 'ISIC-2017' dataset being 0.95, 86.75% and 86.68%, respectively. This study highlights the possibility of the suggested approach for accurate detection of skin cancer., (© 2024. The Author(s).)

The COVID-19 pandemic has posed an unprecedented threat to the global public health system, primarily infecting the airway epithelial cells in the respiratory tract. Chest X-ray (CXR) is widely available, faster, and less expensive therefore it is preferred to monitor the lungs for COVID-19 diagnosis over other techniques such as molecular test, antigen test, antibody test, and chest computed tomography (CT). As the pandemic continues to reveal the limitations of our current ecosystems, researchers are coming together to share their knowledge and experience in order to develop new systems to tackle it. In this work, an end-to-end IoT infrastructure is designed and built to diagnose patients remotely in the case of a pandemic, limiting COVID-19 dissemination while also improving measurement science. The proposed framework comprises six steps. In the last step, a model is designed to interpret CXR images and intelligently measure the severity of COVID-19 lung infections using a novel deep neural network (DNN). The proposed DNN employs multi-scale sampling filters to extract reliable and noise-invariant features from a variety of image patches. Experiments are conducted on five publicly available databases, including COVIDx, COVID-19 Radiography, COVID-XRay-5K, COVID-19-CXR, and COVIDchestxray, with classification accuracies of 96.01%, 99.62%, 99.22%, 98.83%, and 100%, and testing times of 0.541, 0.692, 1.28, 0.461, and 0.202 s, respectively. The obtained results show that the proposed model surpasses fourteen baseline techniques. As a result, the newly developed model could be utilized to evaluate treatment efficacy, particularly in remote locations., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2022 Elsevier B.V. All rights reserved.)

Background: Oroxylum indicum (Bignoniaceae) also known as Sonapatha is an indigenous medicinal plant widely used in Ayurvedic medicine for over thousands of years. It is an active ingredient of well-known Ayurvedic formulations such as Chyawanprash and Dasamula. Root bark of this plant has tonic and astringent properties and it is also used in rheumatism., Objective: The present investigation was carried out to evaluate the anti-arthritic activity of different extracts of root bark of Oroxylum indicum against adjuvant - induced arthritis in rats., Materials and Methods: Male Wistar rats were used in this study. Arthritis was induced by injecting 0.1 ml Freund's complete adjuvant intra-dermally into the left hind paw of the rats. The paw volume, hematological, biochemical, radiographic and histopathological aspects were evaluated., Results: The relative percentage inhibition potential of paw volume in rats treated with various extracts of Oroxylum indicum was found to be ethyl acetate extract (67.69%) >chloroform extract (64.61%) >n-butanol extract (58.46%) respectively. The hematological parameters like RBC count, hemoglobin content showed significant increase while there was a significant decrease in total WBC count and ESR in all the groups of animals pretreated with root bark extracts. The biochemical parameters such as catalase, glutathione contents showed a significant increase while the lipid peroxide and Cathepsin-D content decreased significantly only in case of ethyl acetate pretreated rats when compared to others., Conclusion: The present study suggests that the chloroform, ethyl acetate and n-butanol extracts of root bark of Oroxylum indicum exhibit anti-arthritic activity. The order of activity of extracts was found to be ethyl acetate >chloroform >n-butanol respectively.