SAJID FAROOQ

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Autor: DENISE MARIA ZEZELL × Agência de fomento: Coordenação de Aperfeiçoamento de Pessoal de Ensino Superior (CAPES) ×

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Agora exibindo 1 - 2 de 2
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    Artigo IPEN-doc 30501
    Advances in metallic‑based localized surface plasmon sensors for enhanced tropical disease detection
    2024 - FAROOQ, SAJID; ZEZELL, DENISE M.
    Tropical diseases present significant challenges to global health, particularly in resource-limited regions. Early and accurate detection of these diseases is vital for effective management and control. In recent years, metallic-based LSPR sensors have emerged as promising diagnostic tools for sensitive and rapid detection of tropical diseases. This comprehensive review aims to provide an in-depth analysis of the current state of research on metallic-based LSPR sensors for the detection of various tropical diseases. In this study, we focused on the connection between neglected tropical diseases (NTDs) and its risk using metallic-based LSPR sensors to identify potential inflammatory biomarkers. We conducted a literature search using PubMed, Web of Science, and Google Scholar. Only published materials written in English were considered, resulting in the identification of 220 articles. After a comprehensive evaluation, we selected 35 relevant ones. Our analysis revealed 35 links to neglected tropical diseases, providing valuable insights into their relationship using metallic-based LSPR sensors. Moreover, we explore the potential of metallic-based LSPR sensors in point-of-care testing and their integration with emerging technologies such as microfluidics and smartphone-based diagnostics. This review underscores the need for continued research efforts to develop affordable, sensitive, and user-friendly metallic-based LSPR sensors for early detection and surveillance of tropical diseases.
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    Artigo IPEN-doc 30368
    Recognition of breast cancer subtypes using FTIR hyperspectral data
    2024 - FAROOQ, SAJID; DEL-VALLE, MATHEUS; SANTOS, SOFIA N. dos; BERNARDES, EMERSON S.; ZEZELL, DENISE M.
    Fourier -transform infrared spectroscopy (FTIR) is a powerful, non-destructive, highly sensitive and a promising analytical technique to provide spectrochemical signatures of biological samples, where markers like carbohydrates, proteins, and phosphate groups of DNA can be recognized in biological micro -environment. However, method of measurements of large cells need an excessive time to achieve high quality images, making its clinical use difficult due to speed of data -acquisition and lack of optimized computational procedures. To address such challenges, Machine Learning (ML) based technologies can assist to assess an accurate prognostication of breast cancer (BC) subtypes with high performance. Here, we applied FTIR spectroscopy to identify breast cancer subtypes in order to differentiate between luminal (BT474) and nonluminal (SKBR3) molecular subtypes. For this reason, we tested multivariate classification technique to extract feature information employing three -dimension (3D) -discriminant analysis approach based on 3D -principle component analysis -linear discriminant analysis (3D-PCA-LDA) and 3D -principal component analysis -quadratic discriminant analysis (3D-PCA-QDA), showing an improvement in sensitivity (98%), specificity (94%) and accuracy (98%) parameters compared to conventional unfolded methods. Our results evidence that 3D-PCALDA and 3D-PCA-QDA are potential tools for discriminant analysis of hyperspectral dataset to obtain superior classification assessment.