SAJID FAROOQ
3 resultados
Resultados de Busca
Agora exibindo 1 - 3 de 3
Artigo IPEN-doc 30368 Recognition of breast cancer subtypes using FTIR hyperspectral data2024 - 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.Artigo IPEN-doc 30236 Quantitative analysis of high performance plasmonic metamolecules for targeted deep tissues applications2023 - FAROOQ, SAJID; RATIVA, DIEGO; ARAUJO, RENATO E. deRationally designed gold nanoparticles (Au NPs) show a great potential for biomedical applications. Specifically, for optically induced heating of deep tissues facilitated by plasmonic-assisted lasers, nanostructures with high optical absorption coefficient in biological window are required. Plasmonic metamolecules, such as gold nanodimers (NDs), exhibit a robust localized field enhancement with strong infrared optical absorption. However, an exclusive investigation of the optical/ thermal features of high-performance Au NDs for optical infrared heating remains a challenge. Here, we focus on Au NDs for optothermal characteristics in deep tissues heating procedures. Our analysis encompasses parameters such as absorption cross-sections, field enhancement, and temperature rise with a systematic methodology selecting optimal NDs. Our findings reveal a non-uniform spatial distribution of temperature at the nano-scale and show that short-pulsed laser excitation enhances the temperature near the dimer’s junction. Remarkably, when compared to monomeric gold nanorods under the same excitation resonance mode, optically generated heating of Au NDs leads a threefold higher temperature increase. These results evidence valuable insights for using Au NDs as efficient plasmonic nanoheaters in photothermal-assisted applications.Artigo IPEN-doc 30225 Diabetes monitoring through urine analysis using ATR-FTIR spectroscopy and machine learning2023 - FAROOQ, SAJID; ZEZELL, DENISE M.Diabetes mellitus (DM) is a widespread and rapidly growing disease, and it is estimated that it will impact up to 693 million adults by 2045. To cope this challenge, the innovative advances in non-destructive progressive urine glucose-monitoring platforms are important for improving diabetes surveillance technologies. In this study, we aim to better evaluate DM by analyzing 149 urine spectral samples (86 diabetes and 63 healthy control male Wistar rats) utilizing attenuated total reflection–Fourier transform infrared (ATR-FTIR) spectroscopy combined with machine learning (ML) methods, including a 3D discriminant analysis approach—3D–Principal Component Analysis–Linear Discriminant Analysis (3D-PCA-LDA)—in the ‘bio-fingerprint’ region of 1800–900 cm−1 . The 3D discriminant analysis technique demonstrated superior performance compared to the conventional PCA-LDA approach with the 3D-PCA-LDA method achieving 100% accuracy, sensitivity, and specificity. Our results show that this study contributes to the existing methodologies on non-destructive diagnostic methods for DM and also highlights the promising potential of ATR-FTIR spectroscopy with an ML-driven 3D-discriminant analysis approach in disease classification and monitoring.