EMERSON SOARES BERNARDES

EMERSON SOARES BERNARDES

(Fonte: Lattes)

Resumo

Bachelor's at Farmácia from Universidade Federal de Ouro Preto (1998) and doctorate at Applied Imunology from Universidade de São Paulo (2004). Has experience in Medicine, acting on the following subjects: galectina-3, carboidratos, trypanosoma cruzi, carcinogênesis and macrophage. (Text obtained from the Currículo Lattes on October 8th 2021)

Possui graduação em Farmácia pela Universidade Federal de Ouro Preto (1998), mestrado e doutorado em Imunologia Básica e Aplicada pela Faculdade de Medicina de Ribeirão Preto, Universidade de São Paulo (2004), com período de Doutorado Sanduíche pela Universidade da California, Davis, USA. Realizou pós-doutoramento durante o período de 2004 a 2008 pela Faculdade de Medicina da USP-Ribeirão Preto. Trabalhou como pesquisador contratado pelo Instituto de Patologia e Imunologia Molecular da Universidade do Porto - IPATIMUP em Portugal no período de 2008 a 2011. Retornou ao Brasil como pesquisador visitante na Faculdade de Medicina da USP - São Paulo (2011-2012) e foi posteriormente contratado como pesquisador no Instituto do Câncer do Estado de São Paulo (2012-2013). Coordenou um projeto Jovem Pesquisador financiado pela FAPESP (2012-2016 - Desenvolvimento e Produção de Radiofármacos Emissores de Pósitrons com Aplicações Diagnósticas em Oncologia) e está integrado como pesquisador Colaborador no Instituto de Pesquisas Energéticas e Nucleares (IPEN). Tem atuado na área da Glicobiologia, com ênfase na participação de proteínas ligantes de carboidratos em processos inflamatórios e no Câncer. Atualmente é professor do Programa de Pós-Graduação do IPEN-USP Tecnologia Nuclear - Aplicações, tem experiência na área de Radiofarmácia, com ênfase no desenvolvimento de Radiofármacos inéditos para diagnóstico e terapia em Oncologia. (Texto extraído do Currículo Lattes em 08 out. 2021)

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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.
Breast cancer subtypes diagnostic via high performance supervised machine learning
2022 - FAROOQ, SAJID; DEL-VALLE, MATHEUS; SANTOS, MOISES O. dos; NASCIMENTO, SOFIA; BERNARDES, EMERSON S.; ZEZELL, DENISE M.
Aim: Breast cancer molecular subtypes are being used to improve clinical decision. The Fourier transform infrared (FTIR) spectroscopic imaging, which is a powerful and non-destructive technique, allows performing a non-perturbative and labelling free extraction of biochemical information towards diagnosis and evaluation for cell functionality. However, methods of measurements of large areas of cells demand a long time to achieve high quality images, making its clinical use impractical because of speed of data acquisition and dearth of optimized computational procedures. In order to cope with these challenges, Machine learning (ML) technologies can facilitate to obtain accurate prognosis of Breast Cancer (BC) subtypes with high action ability and accuracy. Methods: Here we propose a ML algorithm based method to distinguish computationally BC cell lines. The method is developed by coupling K neighbors Classifier (KNN) with Neighborhood Component Analysis (NCA) and NCA-KNN methods enables to identify BC subtypes without increasing model size as well additional parameters. Results: By incorporating FTIR imaging data, we show that using NCA-KNN method, the classification accuracies, specificities and sensitivities improve up to 97%, even at very low co-added scan (S_4). Moreover, a clear distinctive accuracy difference of our proposed method was obtained in comparison with other ML supervised models. Conclusion: For confirming our model results performance, the cross validation (k fold = 10) and receiver operation characteristics (ROC) curve were used and found in great agreement, suggest a potential diagnostic method for BC subtypes, even with small co-added scan < 8 at low spectral resolution (4 cm-1).
Identifying breast cancer cell lines using high performance machine learning methods
2022 - FAROOQ, SAJID; DEL-VALLE, MATHEUS; SANTOS, SOFIA; BERNARDES, EMERSON S.; ZEZELL, DENISE M.
We present a computational framework based on machine learning classifiers K-Nearest Neighbors and Neighborhood Component analysis for breast cancer (BC) subtypes prognostic. Our results has up to 97% accuracy for prognostic stratification of BC subtypes.
Breast cancer subtype classification using a one-dimensional convolutional neural network in hyperspectral images
2022 - DEL-VALLE, MATHEUS; SANTOS, MOISES O. dos; BERNARDES, EMERSON S.; ZEZELL, DENISE M.
FTIR spectroscopy imaging in addition to deep learning is a potential tool for breast cancer subtype classification, where accuracies higher than 86% can be achieved to predict among all subtypes.
Superior Machine Learning Method for breast cancer cell lines identification
2022 - FAROOQ, SAJID; CARAMEL-JUVINO, AMANDA; DEL-VALLE, MATHEUS; SANTOS, SOFIA; BERNARDES, EMERSON S.; ZEZELL, DENISE M.
We propose an artificial intelligence platform based on machine learning (ML) algorithm using Neighborhood Component analysis and K-Nearest Neighbors for breast cancer cell lines recognition. Our model presents up to 97% accuracy for identification of breast cancer cell lines.
A deep learning approach for breast tissue malignancy diagnosis using micro-FTIR hyperspectral imaging
2021 - DEL-VALLE, MATHEUS; SANTOS, MOISES O. dos; SANTOS, SOFIA N. dos; BERNARDES, EMERSON S.; ZEZELL, DENISE M.
The breast cancer is the most incident cancer in women with an estimative of 2.1 million new cases in 2018. With the grown of deep learning techniques, several approaches in vibrational spectroscopy have been studied. In this way, this work aimed to classify breast samples as breast cancer or adenosis using a deep learning model. It was used the human breast cancer microarray BR804b (Biomax, Inc., USA), where one core of each group, cancer and adenosis, was imaged by a Cary Series 600 micro-FTIR imaging system (Agilent Technologies, USA). The system has a spatial resolution of 5.5 μm and about 100 thousand spectra were acquired for each group. The regions of interest were selected by two k-means clustering using amide I/II (1700 to 1500 cm-1) and highest paraffin intensity (1480 to 1450 cm-1) bands. Spectra were preprocessed by five steps: outlier removal using Hotelling’s T2 versus Q residuals; biofingerprint truncation; Savitzky–Golay filtering for smoothing and second derivative; Extended multiplicative signal correction (EMSC) with digital de-waxing; another outlier removal. The deep learning model was a convolutional neural network (CNN) fused with a fully connected neural network (FCNN). The CNN was built with 2 Conv1D-ReLU-MaxPooling1D-Dropout layers. The kernel size was set to 5 and dropout of 0.5. Dense layers were built by two layers of neurons-BatchNorm-ReLU-Dropout, with 100 and 50 neurons, dropout of 0.2. The output was a single neuron with sigmoid activation. Binary cross-entropy loss function was adopted with Adam optimizer. Accuracy metric was calculated during the training, where a threshold of 0.5 was applied on the output predictions. Model was trained by a 4-fold cross-validation by 20 epochs and using a batch size of 250. The train accuracy was 0.978/0.004 (mean/std), while the testing accuracy was 0.969/0.008, demonstrating a generalized model without overfitting. Accuracies near one indicate the proposed model as a potential technique for the breast cancer vs adenosis classification, where hyperparameters and the architecture should be optimized along higher sample number acquisition.
The impact of scan number and its preprocessing in micro-FTIR imaging when applying machine learning for breast cancer subtypes classification
2021 - DEL-VALLE, MATHEUS; SANTOS, MOISES O. dos; SANTOS, SOFIA N. dos; CASTRO, PEDRO A.A. de; BERNARDES, EMERSON S.; ZEZELL, DENISE M.
The breast cancer molecular subtype is an important classification to outline the prognostic. Gold-standard assessing using immunohistochemistry adds subjectivity due to interlaboratory and interobserver variations. In order to increase the diagnosis confidence, other techniques need to be examined, where the FTIR spectroscopy imaging allied with machine learning techniques may provide additional and quantitative information regarding the molecular composition. However, the impact of co-added scans acquisition parameter into machine learning classifications still needs better evaluation. In this study, FTIR images of Luminal B and HER2 subtypes were acquired varying the scan number and preprocessing techniques. It was demonstrated a spectral quality improvement when the scan number was increased, decreasing the standard deviation and outliers. Six machine learning models were used to classify the subtypes: Linear Discriminant Analysis, Partial Least Squares Discriminant Analysis, K-Nearest Neighbors, Support Vector Machine, Random Forest and Extreme Gradient Boosting. Best mean accuracy of 0.995 was achieved by Extreme Gradient Boosting model. It was found that all models achieved similar high accuracies with groups b256_064 (256 background and 064 scans), b256_128 and b128_128. Besides assessing the performance of different models, the b256_064 was established as the optimal group due to the minimum acquisition time. Therefore, this work indicates b256_064 for breast cancer subtype classification and also as a basis for other studies using machine learning for cancer evaluation.
Breast cancer estrogen and progesterone receptors evaluation using FTlR spectroscopy imaging
2020 - DEL-VALLE, M.; SANTOS, M.O.; SANTOS, S.N. dos; BERNARDES, E.S.; ZEZELL, D.M.
INTRODUCTION. Breast cancer is the second leading cause of cancer death in woman worldwide with an incidence of 2.09 million and 627 thousand deaths in 2018. Histopathology is the gold standard method for cancer diagnosis and identification of therapeutic targets, however it still presents interpretation difficulties, especially when comparing different cancer subtypes. OBJETICVE: The aim of this study was to evaluate Fourier transform infrared (FTIR) spectroscopy in the diagnose and differentiation of molecular differences between two different breast cancer subtypes: positive and negative for estrogen (ER) and progesterone (PR) receptors. METHODS: Two human breast cell lines, BT474 (ER and PR positive) and SKBR3 (ER and PR negative), were inoculated in Balb/c nude mice. Tumors were collected when reached 0.5 cm3, processed by formalin fixation and paraffin embedding. 5μm thick tissue cuts were fixed in low-e slides (MirrIR, Kevley Technologies). Spectral images were performed in a micro-FTIR (Cary 660, Agilent Technologies) with 32 x 32 FPA of 5.5 μm pixel size. Scattering correction (RMieS-EMSC) was performed using MATLAB and remaining processing using Python. Groups differentiation were evaluated by PCA from 1350 to 1000 cm-1 second derivatives. RESULTS AND DISCUSSION: Groups were split in two clusters, separated by PC-1 with a 99 % accuracy in both groups and 45 % of explained variance. The absorptions in the selected region for the PCA were mainly related to DNA, RNA and protein content. The main contribution was presented by the 1238 cm-1 peak, which was correlated with nucleic acids symmetrical stretching. Hyperspectral image built from this peak presented a spatial correlation with the microscope white light imaging, indicating that possible region for histopathological correlation might be present. CONCLUSAO: Our pilot study shows that FTIR spectroscopy imaging can distinguish ER/PR positive from negative breast cancer subtypes.
Evaluation of machine learning models for the classification of breast cancer hormone receptors using micro-FTIR images
2021 - VALLE, MATHEUS del; SANTOS, MOISES O. dos; SANTOS, SOFIA N. dos; BERNARDES, EMERSON S.; ZEZELL, DENISE M.
The breast cancer is the most incident cancer in women. Evaluation of hormone receptors expression plays an important role to outline treatment strategies. FTIR spectroscopy imaging may be employed as an additional technique, providing extra information to help physicians. In this work, estrogen and progesterone receptors expression were evaluated using tumors biopsies from human cell lines inoculated in mice. FTIR images were collect from histological sections, and six machine learning models were applied and assessed. Xtreme gradient boost and Linear Discriminant Analysis presented the best accuracies results, indicating to be potential models for breast cancer classification tasks.
IPEN e a nanotecnologia
2020 - LINARDI, MARCELO; LUGAO, ADEMAR B.; OLIVEIRA NETO, ALMIR; NETTO, ANA P.F.A.; FREITAS, ANDERSON Z. de; CARBONARI, ARTUR W.; RODRIGUES, CLAUDIO; VIEIRA, DANIEL P.; ANDRADE, DELVONEI A. de; ZEZELL, DENISE M.; PARRA, DUCLERC F.; FONSECA, EDVALDO R.P. da; GALEGO, EGUIBERTO; MUCCILLO, ELIANA N. dos S.; SANTIAGO, ELISABETE I.; CARVALHO, ELITA F.U. de; BERNARDES, EMERSON S.; MOURA, ESPERIDIANA A.B. de; SPINACE, ESTEVAM V.; FONSECA, FABIO C.; SILVA, FLAVIA R. de O.; COSENTINO, IVANA C.; MENGATTI, JAIR; PERROTTA, JOSE A.; BRESSIANI, JOSE C.; ROGERO, JOSE R.; RAMANATHAN, LALGUDI V.; ROCHA, MARCELO da S.; PIRES, MARIA A.F.; ROSTELATO, MARIA E.C.M.; RIBEIRO, MARTHA S.; COTRIM, MARYCEL E.B.; IGAMI, MERY P.Z.; WETTER, NIKLAUS U.; VIEIRA JUNIOR, NILSON D.; RODRIGUES JUNIOR, ORLANDO; FARIA JUNIOR, RUBENS N. de; SAKATA, SOLANGE K.; BALDOCHI, SONIA L.; LOPES, THIAGO; ROSSI, WAGNER de; CALVO, WILSON A.P.