Attenuated total reflection Fourier transform-infrared (ATR-FTIR) spectroscopy as a new technology for discrimination between Cryptococcus neoformans and Cryptococcus gattii

Costa, Fernanda S. L.; Silva, Priscila P.; Morais, Camilo L. M.; Arantes, Thales Domingos; Milan, Eveline Pı́polo; Theodoro, Raquel Cordeiro; Lima, Kássio M. G.

doi:10.1039/c6ay01893a

Cited by 16 publications

(8 citation statements)

References 37 publications

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“…The spectra were pre-processed by cut in the biofingerprint region (900-1800 cm -1 ), followed by automatic weighted least squares baseline correction and normalisation to the Amide I peak (1650 cm -1 ). More information about this dataset can be found in literature [12,13].…”

Section: Datasetsmentioning

confidence: 99%

Uncertainty estimation and misclassification probability for classification models based on discriminant analysis and support vector machines

Morais

Lima

Martin

2019

Analytica Chimica Acta

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Section: Datasetsmentioning

confidence: 99%

Uncertainty estimation and misclassification probability for classification models based on discriminant analysis and support vector machines

Morais

Lima

Martin

2019

Analytica Chimica Acta

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“…In addition, these methodologies are not hindered by operator dependence and intra‐observer and inter‐observer variability, difficult sample preparation and time‐consuming procedures. For example, there are many applications using GA‐based techniques for analyzing FT‐MIR biological datasets, such as characterization and classification of astrocytic glioma tissue in brain tumors; diagnostic of basal cell carcinoma using blood sample analysis; identification of intraepithelial lesion of cervix; identification of low‐grades cases of cervical cancer and possible biomarkers of disease progression; diagnosis of Alzheimer's disease using blood samples; and microbiological investigations, such as virus and fungi detections.…”

Section: Introductionmentioning

confidence: 99%

SVM for FT‐MIR prostate cancer classification: An alternative to the traditional methods

Siqueira

Morais

Júnior

et al. 2018

Journal of Chemometrics

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In this paper, principal component analysis (PCA), successive projections algorithm (SPA), and genetic algorithm (GA) followed by support vector machines (SVM), combined with Fourier-transform mid-infrared (FT-MIR) spectroscopy were presented as complementary or alternatives tools to the traditional methods for prostate cancer screening and classification. These approaches were applied to analyze tissue samples, and their performances were compared within dependent SVM models and with traditional methods of diagnosis, according to class separation interpretability, time consumption, and figures of merit. The results showed that variable reduction and selection methods followed by SVM can reduce drawbacks of independent SVM analysis. The potential biomarkers indicated by PCA-SVM, SPA-SVM, and GA-SVM were amide I, II, and III; as well as protein regions (1400-1585 cm −1 ), followed by DNA/RNA (O-P-O symmetric stretch) (1080 cm −1 ) and DNA (O-P-O asymmetric stretch) (1230 cm −1 ) regions. GA-SVM was the best classification approach, with higher sensitivity (100%) and specificity (80%), particularly in early stages, being better than traditional methods of diagnosis. KEYWORDSFT-MIR, prostate cancer, SVM, tissue 1 | INTRODUCTION Currently, prostate cancer recognition follows some phases. First of all, the proctologist evaluates the prostate by Digital Rectal Examination (DRE), which allows palpation of only 40% to 50% of the prostate, and it is mainly affected by intraobserver and inter-observer variability, resulting in DRE sensitivity and specificity of about 21% to 37% and 71% to 91%, respectively. 1-5 Concomitantly, a measurement of serum prostatic specific antigen levels is performed which have about 21% and 64% sensitivity and specificity in earliest stages, respectively, and about 32% and 93% sensitivity and specificity in high grade prostate cancers, respectively. When combined with DRE, sensitivity and specificity increase from 51% to 68% and from 92% to 94%, respectively 1-8 ; however, this measurement can be affected by other factors beyond prostate cancer, such as ejaculation, bacterial prostatitis, biopsy, and acute urinary retention, which may elevate prostatic specific antigen levels. 3,[5][6][7][8] According to the results of these exams, a biopsy coupled with an anatomopathological examination can be indicated in order to identify cancer stage. Trans-rectal ultrasound (TRUS)-guided biopsy is currently the gold standard, characterized by 39% to 52% sensitivity and 81% to 82% specificity. 9 The anatomopathological examination by Gleason score system classifies biopsy samples according to tumor aggressiveness and provides a prognostic idea. It defines Gleason 1 (best differentiation and most favorable prognosis) to Gleason 5 (least differentiation and poor prognosis). [10][11][12] The sum of the primary (most architectural atypia) and secondary (least architectural atypia) patterns leads to the Gleason score (eg, Gleason 3 + Gleason 4 is equivalent to a Gleason score 3 + 4 = 7). 12 In 2014, a new Gleaso...

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“…In classification applications, samples are assigned to groups based on their IR spectrochemical signature. This includes, for example, differentiation of brain tumour types (3), identification of neurodegenerative diseases (4), cervical cancer screening (5), endometrial and ovarian cancer identification (6), identification of prostate cancer tissue samples (7), differentiation of endometrial tissue regions (8), toxicology screening (9,10), and microbiologic studies involving fungi and virus identification (11)(12)(13). However, before model construction, a fundamental step is to split the spectral dataset into at least two subsets: training and test.…”

Section: Introductionmentioning

confidence: 99%

A computational protocol for sample selection in biological-derived infrared spectroscopy datasets using Morais-Lima-Martin (MLM) algorithm

Morais¹,

Lima²,

Martin³

2018

Protocol Exchange

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Infrared (IR) spectroscopy is a powerful analytical technique that can be applied to investigate a wide range of biological materials (e.g., biofluids, cells, tissues), where a specific biochemical signature is obtained representing the 'fingerprint' signal of the sample being analysed. This chemical information can be used as an input data for classification models in order to distinguish or predict samples groups based on computational algorithms. One fundamental step towards building such computational models is sample selection, where a fraction of the samples measured during an experiment are used for building the classifier, whereas the remaining ones are used for evaluating the model classification performance. This protocol shows how sample selection can be performed in a computational environment (MATLAB) by using a combination of Euclidian-distance calculation and random selection, named Morais-Lima-Martin (MLM) algorithm, as a previous step before building classification models in biological-derived IR datasets.

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Attenuated total reflection Fourier transform-infrared (ATR-FTIR) spectroscopy as a new technology for discrimination between Cryptococcus neoformans and Cryptococcus gattii

Abstract: ATR-FTIR spectroscopy with discriminant analysis was employed to distinguish Cryptococcus neoformans and Cryptococcus gattii.

Cited by 16 publications

References 37 publications

Uncertainty estimation and misclassification probability for classification models based on discriminant analysis and support vector machines

Uncertainty estimation and misclassification probability for classification models based on discriminant analysis and support vector machines

SVM for FT‐MIR prostate cancer classification: An alternative to the traditional methods

A computational protocol for sample selection in biological-derived infrared spectroscopy datasets using Morais-Lima-Martin (MLM) algorithm

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