Kalinga University is a private university located in Naya Raipur, Chhattisgarh, India.
Millions of people worldwide suffer from asthma, a chronic respiratory disease. Conventional medication prescriptions are symptomatic, but side effects limit long-term use. But because of their long history and diverse range of molecular actions, herbal therapies may offer a supportive treatment option. This review examines how herbal remedies have developed for asthma, emphasizing how they work molecularly on primary receptor targets. Evidence for herbal remedies, including Ephedra sinica, Boswellia serrata, Glycyrrhiza glabra, Tylophora indica, Adhatoda vasica, Ginkgo biloba, and Scutellaria baicalensis, and many more is reviewed. It focuses on encouraging clinical evidence and effectiveness supporting the value of herbal remedies as therapeutic adjuncts or substitutes. In order to optimize the potential of herbal therapies in the management of asthma, we discuss future directions and emphasize the necessity of thorough research. This review aims to encourage more research and the use of herbal medicine in asthma treatment, giving patients who require supplemental treatments new hope.
The durability of fibre-reinforced polymer (FRP) rebars in seawater and sea sand concrete (SWSSC) is crucial for the design of marine and offshore structures. The retention of tensile strength in rebars within alkaline environments is a complex, nonlinear function dependent on several parameters. This study compares traditional and high-performance machine learning algorithms (MLAs) for estimating the tensile strength retention of glass FRP (GFRP) rebars aged in laboratory-simulated alkaline concrete conditions. Four widely used MLAs, namely artificial neural network, adaptive neuro-fuzzy inference system, extreme learning machine (ELM), and least squares support vector machine, were used. Multiple hybrid intelligence paradigms of these MLAs were constructed using swarm intelligence algorithms. Furthermore, an augmented grey wolf optimizer (AGWO), was utilized to develop high-performance soft-computing paradigms. Experimental results exhibit that the constructed ELM-AGWO model achieved 90.18% accuracy during the testing phase. Overall results indicate that the developed ELM-AGWO model outperforms all other models developed in this study. Moreover, the proposed ELM-AGWO model was employed to evaluate a simulated dataset representing a low alkaline concrete environment, demonstrating a significant reduction in the degradation of GFRP rebars with a decrease in the pH of the surrounding environment.
In this study, a novel brominated bithiazole compound (8) is designed, synthesized, and comprehensively evaluated alongside a previously reported analogue monothiazole (1) to check the effect of thiazole rings. The synthetic route involves sequential bromination, condensation, and NBS-mediated side-chain bromination, resulting compound (8) with 90% yield, further, confirmed by NMR and HRMS analyses. Density functional theory analysis (B3LYP/6-311G(d,p)) revealed distinct differences in HOMO-LUMO characteristics, electrostatic potential distribution, and molecular polarity of both the compounds. In vitro anticancer screening against MCF-7 breast cancer cells revealed that compound (1) exhibited potent cytotoxicity (IC50 = 1.769 & micro;M), whereas compound (8) showed significantly weaker activity (IC50 = 269.4 & micro;M). Antimicrobial assays demonstrated broad-spectrum activity of compound (1) against strain A. niger, B. subtilis, and E. coli, while compound (8) remained largely inactive up to 1000 & micro;mol/disc. SAR study supporting the in vitro results of anticancer and antimicrobial activity. The acetyl group attached with compound (1) is largely responsible for huge disparity in biological results. ADMET predictions further indicates favorable drug-likeness and safety for compound (1), whereas compound (8) exhibited solubility and toxicity liabilities. Overall, the study underscores the critical role of structural features in modulating bithiazole bioactivity and identifies compound (1) as a promising lead candidate for further anticancer and antimicrobial development.
The current investigation focuses on isolating and identifying soil fungi from pulp and paper mill wastes and their potential to produce cellulases. Out of 82 identified fungal colonies, 12 isolated species belong to the genera Aspergillus, Cladosporium, Fusarium, Penicillium, Humicola, Rhizopus, of which one was sterile mycelia. Moreover, 12 fungal isolates were further subjected to qualitative screening, confirming cellulase production from 10 isolates. The cellulolytic index (CI) ranged from 1.33 to 2.47. The highest CI was exhibited by Fusarium sp. (KU11), and the lowest CI was shown by Humicola sp. (KU9) and Cladosporium sp. (KU8), respectively. The present study's findings revealed that Fusarium sp. (KU11) was found to be a potent cellulase producer that can be effectively utilized in pulp and paper industries for waste recycling and other relevant applications.
The problem of accurate and interpretable automated classification of dermoscopic skin lesions is difficult because publicly available benchmark datasets are imbalanced, diagnostically heterogeneous, and prone to image-level confounding effects. While individual techniques such as pre-trained convolutional neural networks (CNNs), ensemble learning, and explainable artificial intelligence (XAI) are established, their combined effectiveness is reported without controlled comparison to determine whether ensemble consensus improves robustness and explanation quality in a unified framework. In this paper, we report a controlled comparison of six pre-trained CNN architectures, namely ResNet50, Xception, EfficientNetB3, MobileNetV2, DenseNet201, and InceptionV3, on the publicly available HAM10000 dataset, as well as their soft voting ensemble. The contribution of this paper is not in proposing a new deep learning architecture but rather in presenting a harmonized evaluation framework to determine whether diversity in architectures can be exploited to improve robustness on minority classes and qualitatively better explanations in terms of spatial coherence in seven-class classification of dermoscopic images of skin lesions. To this end, we used Grad-CAM, LIME, and occlusion sensitivity methods to evaluate the interpretability of each individual CNN as well as the ensemble. The soft voting ensemble outperformed all individual architectures by achieving a macro-average ROC curve of 0.985 and accuracy of 89.37%, while simultaneously providing qualitatively better spatially coherent explanations that are lesion-centric, suggesting better trustworthiness in transparent XAI-based automated screening in dermatological studies.
