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Wireless Body Area Networks (WBANs) are a cost-effective, low-power technology that has been advancing due to the need to improve their performance. The predominant challenges encompass packet delivery ratio (PDR), packet loss ratio (PLR), and end-to-end (E2D) delay. Advances in wireless technology emphasize the urgency to surmount these issues. Channel congestion and collisions, increased latency, unfairness in access, high energy consumption, performance heterogeneity, QoS degradation, interference and reliability are some of the particular concerns largely caused by CW that influence WBAN performance. Researchers and developers strive to create adaptive CW techniques and protocols that dynamically modify CWsize in response to traffic loads, network conditions, and the particular needs of WBAN applications in order to maximise WBAN performance. In response, we propose GFuCWO—a genetic fuzzy logic technique—for optimizing contention windows in IEEE-802.15.6 WBANs. This study introduces three distinct algorithms to accomplish this. We evaluate the efficiency of the GFuCWO technique against the ABEB and Improved-CSMA/CA algorithms. The approach is implemented in Castalia OMNeT++. The study calculates PDR, PLR, and E2D delay using experimental data from four sensor nodes under different traffic conditions. The GFuCWO technique demonstrated superior performance in PDR, PLR, and E2D, with average enhancements of 4%-11% and 3%-13%, respectively, with a 95% confidence interval, indicating its potential for community benefit and medical performance improvements.

Identifying human diseases remains a difficult process, even in the age of advanced information technology and the smart healthcare industry 5.0. In the smart healthcare industry 5.0, precise prediction of human diseases, particularly lethal cancer diseases, is critical for human well-being. The global Internet of Medical Things sector has advanced at a breakneck pace in recent years, from small wristwatches to large aircraft. The critical aspects of the Internet of Medical Things include security and privacy, owing to the massive scale and deployment of the Internet of Medical Things networks. Transfer learning with a secure IoMT-based approach is considered. The Google net deep machine-learning model is used for accurate disease prediction in the smart healthcare industry 5.0. We can easily and reliably anticipate the lethal cancer disease in the human body by using the secure IoMT-based transfer learning approach. Furthermore, the results of the proposed secure IoMT-based Transfer learning techniques are used to validate the best cancer disease prediction in the smart healthcare industry 5.0. The proposed secure IoMT-based transfer learning methodology reached 98.8%, better than the state-of-the-art methodologies used previously for cancer disease prediction in the smart healthcare industry 5.0.

Cancer is a manifestation of disorders caused by the changes in the body's cells that go far beyond healthy development as well as stabilization. Breast cancer is a common disease. According to the stats given by the World Health Organization (WHO), 7.8 million women are diagnosed with breast cancer. Breast cancer is the name of the malignant tumor which is normally developed by the cells in the breast. Machine learning (ML) approaches, on the other hand, provide a variety of probabilistic and statistical ways for intelligent systems to learn from prior experiences to recognize patterns in a dataset that can be used, in the future, for decision making. This endeavor aims to build a deep learning-based model for the prediction of breast cancer with a better accuracy. A novel deep extreme gradient descent optimization (DEGDO) has been developed for the breast cancer detection. The proposed model consists of two stages of training and validation. The training phase, in turn, consists of three major layers data acquisition layer, preprocessing layer, and application layer. The data acquisition layer takes the data and passes it to preprocessing layer. In the preprocessing layer, noise and missing values are converted to the normalized which is then fed to the application layer. In application layer, the model is trained with a deep extreme gradient descent optimization technique. The trained model is stored on the server. In the validation phase, it is imported to process the actual data to diagnose. This study has used Wisconsin Breast Cancer Diagnostic dataset to train and test the model. The results obtained by the proposed model outperform many other approaches by attaining 98.73 % accuracy, 99.60% specificity, 99.43% sensitivity, and 99.48% precision.

A major and essential issue in biomedical research is to predict genome disorder. Genome disorders cause multivariate diseases like cancer, dementia, diabetes, cystic fibrosis, leigh syndrome, etc. which are causes of high mortality rates around the world. In past, theoretical and explanatory-based approaches were introduced to predict genome disorder. With the development of technology, genetic data were improved to cover almost genome and protein then machine and deep learning-based approaches were introduced to predict genome disorder. Parallel machine and deep learning approaches were introduced. In past, many types of research were conducted on genome disorder prediction using supervised, unsupervised, and semi-supervised learning techniques, most of the approaches using binary problem prediction using genetic sequence data. The prediction results of these approaches were uncertain because of their lower accuracy rate and binary class prediction techniques using genome sequence data but not genome disorder patients’ data with his/her history. Most of the techniques used Ribonucleic acid (RNA) gene sequence and were not often capable of handling bid data effectively. Consequently, in this study, the AlexNet as an effective convolutional neural network architecture proposed to develop an advance genome disorder prediction model (AGDPM) for predicting genome multi classes disorder using a large amount of data. AGDPM tested and compare with the pre-trained AlexNet neural network model and AGDPM gives the best results with 89.89% & 81.25% accuracy of training and testing respectively. So, the advance genome disorder prediction model shows the ability to efficiently predict genome disorder and can process a large amount of patients’ genome disorder data with a multi-class prediction method. AGDPM has proved that it is capable to predict single gene inheritance disorder, mitochondrial gene inheritance disorder, and multifactorial gene inheritance disorder with respect to various statistical performance parameters. So, with the help of AGDPM biomedical research will be improved in terms to predict genetic disorders and put control on high mortality rates.

Cancer is a major public health issue in the modern world. Breast cancer is a type of cancer that starts in the breast and spreads to other parts of the body. One of the most common types of cancer that kill women is breast cancer. When cells become uncontrollably large, cancer develops. There are various types of breast cancer. The proposed model discussed benign and malignant breast cancer. In computer-aided diagnosis systems, the identification and classification of breast cancer using histopathology and ultrasound images are critical steps. Investigators have demonstrated the ability to automate the initial level identification and classification of the tumor throughout the last few decades. Breast cancer can be detected early, allowing patients to obtain proper therapy and thereby increase their chances of survival. Deep learning (DL), machine learning (ML), and transfer learning (TL) techniques are used to solve many medical issues. There are several scientific studies in the previous literature on the categorization and identification of cancer tumors using various types of models but with some limitations. However, research is hampered by the lack of a dataset. The proposed methodology is created to help with the automatic identification and diagnosis of breast cancer. Our main contribution is that the proposed model used the transfer learning technique on three datasets, A, B, C, and A2, A2 is the dataset A with two classes. In this study, ultrasound images and histopathology images are used. The model used in this work is a customized CNN-AlexNet, which was trained according to the requirements of the datasets. This is also one of the contributions of this work. The results have shown that the proposed system empowered with transfer learning achieved the highest accuracy than the existing models on datasets A, B, C, and A2.

Automatic information extraction from online published scientific documents is useful in various applications such as tagging, web indexing and search engine optimization. As a result, automatic information extraction has become among the hottest areas of research in text mining. Although various information extraction techniques have been proposed in the literature, their efficiency demands domain specific documents with static and well-defined format. Furthermore, their accuracy is challenged with a slight modification in the format. To overcome these issues, a novel ontological framework for information extraction (OFIE) using fuzzy rule-base (FRB) and word sense disambiguation (WSD) is proposed. The proposed approach is validated with a significantly wider document domains sourced from well-known publishing services such as IEEE, ACM, Elsevier, and Springer. We have also compared the proposed information extraction approach against state-of-the-art techniques. The results of the experiment show that the proposed approach is less sensitive to changes in the document format and has a significantly better average accuracy of 89.14% and F-score as 89%.

Seven new derivatives, 6α-hydroxy-1-methyl-3-oxo-5α-androst-1-en-17-yl acetate (2), 6α,17β-dihydroxy-1-methyl-3-oxo-5α-androst-1-en (3), 7β-hydroxy-1-methyl-3-oxo-5α-androst-1-en-17-yl acetate (4), 15β,20-dihydroxy-1-methyl-3-oxo-5α-androst-1-en-17-yl acetate (5), 15β-hydroxy-1-methyl-3-oxo-5α-androst-1-en-17-yl acetate (6), 12β,17β-dihydroxy-1-methyl-3-oxoandrosta-1,4-dien (11), and 7β,15β,17β-trihydroxy-1-methyl-3-oxo-5α-androst-1-en (14), along with six known metabolites, 17β-hydroxy-1-methyl-3-oxoandrosta-1,4-dien (7), 17β-hydroxy-1-methyl-3-oxo-5α-androst-1-en (8), 17β-hydroxy-1-methyl-3-oxo-5β-androst-1-en (9), 1-methyl-5β-androst-1-en-3,17-dione (10), 1-methyl-3-oxoandrosta-1,4-dien-3,17-dione (12), and 17β-hydroxy-1α-methyl-5α-androstan-3-one (13) of metenolone acetate (1), were synthesized through whole-cell biocatalysis with Rhizopus stolonifer, Aspergillus alliaceous, Fusarium lini, and Cunninghamella elegans. Atamestane (12), an aromatase inhibitor, was synthesized for the first time via F. lini-mediated transformation of 1 as the major product. Hydroxylation, dehydrogenation, and reduction were occurred during biocatalysis. Study indicated that F. lini was able to catalyze dehydrogenation reactions selectively. Structures of compounds 1–14 were determined through NMR, HRFAB-MS, and IR spectroscopic data. Compounds 1–14 were identified as non-cytotoxic against BJ human fibroblast cell line (ATCC CRL-2522). Metabolite 5 (81.0 ± 2.5%) showed a potent activity against TNF-α production, as compared to the substrate 1 (62.5 ± 4.4%). Metabolites 2 (73.4 ± 0.6%), 8 (69.7 ± 1.4%), 10 (73.2 ± 0.3%), 11 (60.1 ± 3.3%), and 12 (71.0 ± 7.2%), also showed a good inhibition of TNF-α production. Compounds 3 (IC50 = 4.4 ± 0.01 µg/mL), and 5 (IC50 = 10.2 ± 0.01 µg/mL) showed a significant activity against T-cell proliferation. Identification of selective inhibitors of TNF-α production, and T-cell proliferation is a step forward towards the development of anti-inflammatory drugs.

Abstract Big data and cloud computing technology appeared on the scene as new trends due to the rapid growth of social media usage over the last decade. Big data represent the immense volume of complex data that show more details about behaviours, activities, and events that occur around the world. As a result, big data analytics needs to access diverse types of resources within a decreased response time to produce accurate and stable business experimentation that could help make brilliant decisions for organizations in real-time. These developments have spurred a revolutionary transformation in research, inventions, and business marketing. User behaviour analysis for classification and prediction is one of the hottest topics in data science. This type of analysis is performed for several purposes, such as finding users’ interests about a product (for marketing, e-commerce, etc.) or toward an event (elections, championships, etc.) and observing suspicious activities (security and privacy) based on their traits over the Internet. In this paper, a neuro-fuzzy approach for the classification and prediction of user behaviour is proposed. A dataset, composed of users’ temporal logs containing three types of information, namely, local machine, network and web usage logs, is targeted. To complement the analysis, each user’s 360-degree feedback is also utilized. Various rules have been implemented to address the company’s policy for determining the precise behaviour of a user, which could be helpful in managerial decisions. For prediction, a Gaussian Radial Basis Function Neural Network (GRBF-NN) is trained based on the example set generated by a Fuzzy Rule Based System (FRBS) and the 360-degree feedback of the user. The results are obtained and compared with other state-of-the-art schemes in the literature, and the scheme is found to be promising in terms of classification as well as prediction accuracy.

Optimal site selection of a dam is one of the crucial tasks in water resource management. In this study, a dam suitability stream model (DSSM) is utilized to identify potential sites for constructing multi-purpose dams. In DSSM, each input parameter is weighted using the analytic hierarchy process (AHP), and then weighted overlay analysis is performed in a Geographical Information System (GIS) environment. Compared to the previous studies, this study showed different results based on the crucial parameter that is “stream order”. Two resultant site suitability maps are prepared to differentiate the importance of stream order. Each of the resulting maps visualizes four classes of suitability from highly suitable to least suitable. The proposed sites will store water for a variety of uses at the local and regional level and reduce flood risk, which can be very useful for hydrologists and disaster risk managers.

Natural products have served as primary remedies since ancient times due to their cultural acceptance and outstanding biodiversity. To investigate whether Tamarix aphylla L. modulates an inflammatory process, we carried out bioassay-guided isolation where the extracts and isolated compounds were tested for their modulatory effects on several inflammatory indicators, such as nitric oxide (NO), reactive oxygen species (ROS), proinflammatory cytokine; tumour necrosis factor (TNF-α), as well as the proliferation of the lymphocyte T-cells. The aqueous ethanolic extract of the plant inhibited the intracellular ROS production, NO generation, and T-cell proliferation. The aqueous ethanolic crude extract was partitioned by liquid-liquid fractionation using n-hexane (n-C6H6), dichloromethane (DCM), ethyl acetate (EtOAc), n-butanol (n-BuOH), and water (H2O). The DCM and n-BuOH extracts showed the highest activity against most inflammatory indicators and were further purified to obtain compounds 1–4. The structures of 3,5-dihydroxy-4’,7-dimethoxyflavone (1) and 3,5-dihydroxy-4-methoxybenzoic acid methyl ester (2) from the DCM extracts; and kaempferol (3), and 3-hydroxy-4-methoxy-(E)-cinnamic acid (4) from the n-BuOH extract were elucidated by different spectroscopic tools, including MS, NMR, UV, and IR. Compound 2 inhibited the production of ROS and TNF-α, whereas compound 3 showed inhibitory activity against all the tested mediators. A better understanding of the potential aspect of Tamarix aphylla L. derivatives as anti-inflammatory agents could open the door for the development of advanced anti-inflammatory entities.

As part of our continual efforts to exploit ‘Tujia Ethnomedicine’ for their pharmacophoric functionalities, we herein investigated Kadsura heteroclita collected from a deep Wulin mountain area in northern Hunan province. The current study resulted in the isolation of three new sesquiterpenes: 6α,9α,15-trihydroxycadinan-4-en-3-one (1), (+)-3,11,12-trihydroxycalamenene (2), (−)-3,10,11,12-tetrahydroxy-calamenene (3), along with four known sesquiterpenes (4−7), and a cytochalasin H (8). Their chemical structures were elucidated by 1D-, and 2D-NMR spectroscopy, and HRESI-MS, CD spectrometry. The antioxidant, and cytotoxic activities of the compounds were evaluated. Compound 8 exhibited a strong antioxidant effect with an IC50 value of 3.67 µM on isolated human polymorphonuclear cells or neutrophils.

A new geranylated xanthone (1) was isolated from the root bark extract of Garcinia smeathmannii Oliver along with known guttiferone I, isoxanthochymol, smeathxanthones A and B, and triacontanyl caffeate. The structures of these compounds were elucidated by spectral analysis and by comparison with the reported data. These compounds showed significant antioxidant DPPH radical scavenging activities.

Two hundred and fifty samples of mesenteric lymph nodes were collected from goats of Damani and Teddy breeds slaughtered at Dera Ismail Khan slaughter house. Each of these samples was triturated and vigorously shaken with sterilized normal saline and the suspension was centrifuged. The supernatant was subjected to counter immunoelectrophoresis with Peste des petits ruminants hyperimmune serum. Out of 250 samples, 47.34% samples of Damani and 43% of Teddy goats were positive for peste des petits ruminants. In both the breeds, 47.05% males and 42.50% females were positive. Agewise results showed that young animals gave higher positive percentages than old ones.

Abstract Background Biotransformation of organic compounds by using microbial whole cells provides an efficient approach to obtain novel analogues which are often difficult to synthesize chemically. In this manuscript, we report for the first time the microbial transformation of a synthetic anabolic steroidal drug, oxymetholone, by fungal cell cultures. Results Incubation of oxymetholone (1) with Macrophomina phaseolina, Aspergillus niger, Rhizopus stolonifer, and Fusarium lini produced 17β-hydroxy-2-(hydroxy-methyl)-17α-methyl-5α-androstan-1-en-3-one (2), 2α,17α-di(hydroxyl-methyl)-5α-androstan-3β,17β-diol (3), 17α-methyl-5α-androstan-2α,3β,17β-triol (4), 17β-hydroxy-2-(hydroxymethyl)-17α-methyl-androst-1,4-dien-3-one (5), 17β-hydroxy-2α-(hydroxy-methyl)-17α-methyl-5α-androstan-3-one (6), and 2α-(hydroxymethyl)-17α-methyl-5α-androstan-3β-17β-diol (7). Their structures were deduced by spectral analyses, as well as single-crystal X-ray diffraction studies. Compounds 2–5 were identified as the new metabolites of 1. The immunomodulatory, and anti-inflammatory activities and cytotoxicity of compounds 1–7 were evaluated by observing their effects on T-cell proliferation, reactive oxygen species (ROS) production, and normal cell growth in MTT assays, respectively. These compounds showed immunosuppressant effect in the T-cell proliferation assay with IC50 values between 31.2 to 2.7 μg/mL, while the IC50 values for ROS inhibition, representing anti-inflammatory effect, were in the range of 25.6 to 2.0 μg/mL. All the compounds were found to be non-toxic in a cell-based cytotoxicity assay. Conclusion Microbial transformation of oxymetholone (1) provides an efficient method for structural transformation of 1. The transformed products were obtained as a result of de novo stereoselective reduction of the enone system, isomerization of double bond, insertion of double bond and hydroxylation. The transformed products, which showed significant immunosuppressant and anti-inflammatory activities, can be further studied for their potential as novel drugs.