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Percorrer por autor "Nawaz, Muhammad"

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  • Amoebicidal, anti-adhesive, and low-cytotoxic effects of Mangifera indica L. leaf extract against ocular Acanthamoeba spp.: first evidence supporting plant-based therapeutic potential
    Publication . Mendonça, Diana; Tabo, Hazel A.; Chimplee, Siriphorn; Oliveira, Sónia M. R.; Kwankaew, Pattamaporn; Girol, Ana Paula; Dungca, Julieta Z.; Sulaiman, Mazdida; Bhassu, Subha; Nawaz, Muhammad; Wilairatana, Polrat; Wiart, Christophe; Dolma, Karma G.; Kayesth, Sunil; Nissapatorn, Veeranoot; Pereira, Maria de Lourdes
    Background and Aim: Acanthamoeba spp. is free-living protozoa capable of causing severe infections, notably Acanthamoeba keratitis, which is difficult to manage due to cyst resistance and the cytotoxicity of current treatments. Plant-derived compounds represent a promising alternative strategy. This study investigated the amoebicidal, anti-adhesive, and cytotoxic properties of Mangifera indica L. (mango) leaf extract against ocularly relevant Acanthamoeba spp. Materials and Methods: Crude ethanolic leaf extract of M. indica was prepared and evaluated against Acanthamoeba polyphaga American Type Culture Collection (ATCC) 30461 and Acanthamoeba castellanii ATCC 50739. Minimum inhibitory concentration (MIC) and minimum parasiticidal concentration were determined for trophozoites and cysts. Morphological changes were analyzed by scanning electron microscopy (SEM). Anti-adhesion assays were conducted using polystyrene surfaces, with a commercial multipurpose contact lens (CL) solution as a control. Cytotoxicity was tested in Vero cells using the 3-(4, 5-Dimethylthiazol-2-yl)-2, 5-Diphenyltetrazolium Bromide assay to establish the minimum cytotoxic concentration. Results: The extract inhibited trophozoite growth at 2 mg/mL and demonstrated cysticidal activity at 4 mg/mL for A. polyphaga and 32 mg/mL for A. castellanii. SEM revealed disruption of trophozoite morphology, loss of acanthopodia, and surface perforations in cysts. At MIC levels, adhesion was reduced by >70%, and even at 1/8 MIC, inhibition remained above 50%, comparable to a commercial multipurpose solution. Cytotoxicity assessment showed >80% Vero cell viability at 0.125 mg/mL, indicating a favorable therapeutic window. Conclusion: This is the first report demonstrating amoebicidal and anti-adhesive effects of M. indica L. leaf extract against ocular Acanthamoeba species. The dual trophozoiticidal and anti-adhesive actions, combined with low cytotoxicity, highlight its potential for development as a plant-based therapeutic agent, particularly in ocular formulations or CL disinfectants. Future work should focus on phytochemical isolation, mechanistic studies, and novel delivery systems to enhance efficacy and safety.
    2025-11-06Artigo de investigação Acesso aberto Ver mais
  • Identification of depressing tweets using natural language processing and machine learning: application of grey relational grades
    Publication . Ullah, Wusat; Oliveira-Silva, Patrícia; Nawaz, Muhammad; Zulqarnain, Rana Muhammad; Siddique, Imran; Sallah, Mohammed
    Depression is a global public health concern that affects millions of people worldwide. Social media platforms, where individuals connect and share personal data, have emerged as potential sources for mental health detection. This study explored the use of computational models to identify individuals with depression based on Twitter posts. We retrieved and cleaned 1.6 million tweets using Natural Language Processing (NLP) techniques for feature extraction. The Grey Relational Grade (GRG) technique was applied to investigate the association between likes and shares of Twitter posts. Furthermore, the significant values of GRG in both cases, when data is limited and when data is large, represent that GRG provides better results at large data sets. The equal distri bution and selection approach (EDSA) can extract a small sample to describe the large data set and apply the GRG technique. Subsequently, we applied various machine learning models to classify user tweets into "stressed" or "not stressed" categories. These models achieved promising results, demonstrating high accuracy, precision, recall, and F1-score. Specifically, Logistic Regression, Support Vector Machine, XGBoost Classifier, and Random Forest Classifier yielded accuracies of 96, 95, 96, and 97%, respectively. These findings suggest the potential of social media data and computational models for mental health detection, thus opening avenues for further research and development.
    2025-03Artigo científico Acesso aberto Ver mais