Simple Summary Leishmaniasis is considered a neglected tropical disease due to limited treatment options, increasing resistance to prioritized compounds, and high toxicity associated with treatment, necessitating the exploration of new potential treatment approaches with improved safety profiles. In this research, computational tools were used to evaluate the efficacy of certain natural bioactive compounds on Ornithine Decarboxylase, which is a key enzyme involved in parasite viability. Molecular docking found several compounds, including Sanguinarine, Rutin, Evodiamine, Cannabinol, and β-sitosterol, with good binding energy and interaction patterns. Further, molecular dynamics studies showed that Rutin and Evodiamine had relatively stable interactions with the protein target. Overall, the current findings suggest that selected natural compounds could serve as potential candidates for prioritized compound development in the treatment of leishmaniasis.
Abdul Haseeb Khan, Munazza Kanwal, S. B. Jamal et al.· Biology· 0 citations
Objective This study explores the potential of artificial intelligence (AI) using a hybrid deep learning Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) framework, for EEG-based classification and analysis of Dravet Syndrome (DS). Method The study cohort comprised nine pediatric patients with DS, confirmed through either a heterozygous pathogenic mutation in the SCN1A gene or a clinical diagnosis consistent with established diagnostic criteria. In addition, EEG recordings from age-matched healthy control subjects and pediatric patients with non-Dravet epilepsy (“abnormal” EEG) were included. Data on demographic information, seizure characteristics, developmental skills, cognitive functions, and genetic results were gathered from patient records. EEG recordings were analyzed using a subject-independent leave-one-subject-out validation strategy, spatial and temporal features by employing this model on preprocessed EEG data, effectively differentiating DS patients from Abnormal cases and healthy controls. Result Among the evaluated CNN-LSTM models, the pre-trained architecture achieved superior performance with improved stability across most subjects, with an overall accuracy of 85%, balanced accuracy of 85%, a macro-averaged F1-score of 0.85, and a macro-averaged ROC–AUC of 0.87, demonstrating stable performance for multi-class EEG classification of DS, Abnormal, and control subjects. The non-pretrained model showed reduced sensitivity and increased inter-class confusion, particularly for DS and Abnormal classes. Conclusion This study demonstrates that a pre-trained CNN-LSTM framework can support automated EEG-based classification of DS-related patterns as a proof-of-concept methodological approach, even in the context of limited subject availability. EEG-specific pretraining improves classification consistency and feature separability compared with training from scratch, highlighting the value of representation learning for rare epilepsy syndromes. Larger multi-center datasets and prospective validation will be required to assess robustness, generalizability, and clinical utility.
Sikandar Hussain, Soyiba Jawed, Ali Mir et al.· PLoS ONE· 0 citations
