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Globally, cancer is the second leading cause of mortality, surpassed only by cardiovascular disorders. Among its various types, Non-Small Cell Lung Cancer (NSCLC) poses a significant global health risk. Chemotherapy is the most common therapeutic approach for treating cancer. However, chemotherapeutic drugs can cause serious toxicity by reducing the survival of both tumor and normal cells, thereby diminishing the survival chances of patients. Natural products extracted from medicinal plants serve as effective anticancer agents with minimal side effects. Among natural compounds, flavonoids have been reported as effective anticancer agents. In this study, two flavonoids, viscosine and quercetin 7-rutinoside, were evaluated for their antitumor potential using in silico models. These flavonoids were tested for their inhibitory activity against DEAD-box RNA helicase DDX3X (referred to as DDX3) and B-cell lymphoma-extra-large (Bcl-XL) enzymes through molecular docking and dynamic simulation analyses. The docking analysis revealed low docking scores and binding energies for both viscosine and quercetin 7-rutinoside. The overall docking results revealed that the selected flavonoids exhibited a favorable binding affinity for DDX3 and Bcl-XL. They bind firmly to the key active sites and remain stably within the pocket regions of DDX3 and Bcl-XL, thereby inhibiting their activity. This data was further supported by the RMSF and RMSD analyses. The preliminary molecular docking and simulation analyses confirm the promising inhibitory effects of viscosine and quercetin 7-rutinoside against DDX3 and Bcl-XL. Therefore, these flavonoids could serve as potential antitumor drug candidates in the future. However, further experimental studies are needed to validate their potential for clinical trials. [ABSTRACT FROM AUTHOR]

Research and development of speech technology applications in low-resource languages (LRL) are challenging due to the non-availability of proper speech corpus. Especially, for most of the Indian languages, the amount and type of data found in different digital sources are sparse and prior works are too few to serve the purpose of large-scale development needs. This paper illustrates the creation process of such an LRL corpus comprising of sixteen rarely studied Eastern and Northeastern (E&NE) Indian languages and presents the data variability with different statistics. Furthermore, several experiments are carried out using the collected LRL corpus to build baseline speaker identification (SID) and language identification (LID) system for acceptance evaluation. For investigating the presence of speaker and language-specific information, spectral features like Mel frequency cepstral coefficients (MFCCs), shifted delta cepstral (SDC), and relative spectral transform-perceptual linear prediction (RASTA-PLP) features are used here. Vector quantization (VQ), Gaussian mixture models (GMMs), support vector machine (SVM), and multilayer perceptron (MLP)-based models are developed to represent the speaker and language-specific information captured through the spectral features. Apart from this, i-vectors, time delay neural networks (TDNN), and recurrent neural network with long short-term memory (LSTM-RNN) method-based SID and LID models are being experimented with to comply with the recent approaches. Performances of the developed systems are analyzed with LRL corpus in terms of SID and LID accuracy. The best SID and LID performances are observed to be 94.49% and 95.69%, respectively, for the baseline systems using LSTM-RNN with MFCC + SDC feature. [ABSTRACT FROM AUTHOR]