Paracoccidioidomycosis screening diagnosis by FTIR spectroscopy and multivariate analysis

Brito, Eliana C.A. de; França, Thiago; Canassa, Thalita; Weber, Simone Schneider; Paniago, Anamaria Mello Miranda; Cena, Cícero

doi:10.1016/j.pdpdt.2022.102921

Cited by 10 publications

(6 citation statements)

References 31 publications

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“…The vibrational bands assigned to amides I (N-H and C]O) and II (N-H and C-N) have shown signicant contributions to biological sample classication in the literature. 56,57 The second band, around 1400 cm −1 , can be assigned to C-H and C]O vibrational modes from lipids and proteins, while the third band, around 1080 cm −1 , is usually assigned to nucleic acid and phospholipids. Also, weak bands assigned to C-H and C-H-O vibrational modes from carbohydrate below 1000 cm −1 was identied in the spectra.…”

Section: Resultsmentioning

confidence: 99%

Multi-resistant diarrheagenicEscherichia coliidentified by FTIR and machine learning: a feasible strategy to improve the group classification

Marangoni-Ghoreyshi,

Franca,

Esteves

et al. 2023

RSC Adv.

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Section: Resultsmentioning

confidence: 99%

Multi-resistant diarrheagenicEscherichia coliidentified by FTIR and machine learning: a feasible strategy to improve the group classification

Marangoni-Ghoreyshi,

Franca,

Esteves

et al. 2023

RSC Adv.

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“…The normalization process typically involves dividing each data point in the spectrum by a normalization factor method to reduce possible random fluctuations in experimental data . Then, the data were processed by the PCA, an unsupervised analysis used for dimensionality reduction, which aims to separate the data according to the data variance with the aid of Python (version 3.9.12) software using the Scikit-learn package (version 1.1.2).…”

Section: Methodsmentioning

confidence: 99%

“…Our group has demonstrated the potential use of FTIR spectroscopy with ML to diagnose canine visceral leishmaniasis (VL) by using blood serum with accuracy above 85%, assigned to changes in the vibrational bands related to protein molecules . ML algorithms also provided an overall accuracy of 91.67% for paracoccidioidomycosis diagnosis using human blood serum FTIR spectra, where the main vibrational changes were assigned to proteins and lipid molecules . In the present study, we explored the potential application of FTIR spectroscopy in combination with ML for diagnosing CL in a murine experimental model.…”

mentioning

confidence: 99%

Diagnosis of Cutaneous Leishmaniasis Using FTIR Spectroscopy and Machine Learning: An Animal Model Study

Pacher,

Franca,

Lacerda

et al. 2024

ACS Infect. Dis.

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Cutaneous leishmaniasis (CL) is a polymorphic and spectral skin disease caused by Leishmania spp. protozoan parasites. CL is difficult to diagnose because conventional methods are time-consuming, expensive, and low-sensitive. Fourier transform infrared spectroscopy (FTIR) with machine learning (ML) algorithms has been explored as an alternative to achieve fast and accurate results for many disease diagnoses. Besides the high accuracy demonstrated in numerous studies, the spectral variations between infected and noninfected groups are too subtle to be noticed. Since variability in sample set characteristics (such as sex, age, and diet) often leads to significant data variance and limits the comprehensive understanding of spectral characteristics and immune responses, we investigate a novel methodology for diagnosing CL in an animal model study. Blood serum, skin lesions, and draining popliteal lymph node samples were collected from Leishmania (Leishmania) amazonensis-infected BALB/C mice under experimental conditions. The FTIR method and ML algorithms accurately differentiated between infected (CL group) and noninfected (control group) samples. The best overall accuracy (∼72%) was obtained in an external validation test using principal component analysis and support vector machine algorithms in the 1800−700 cm −1 range for blood serum samples. The accuracy achieved in analyzing skin lesions and popliteal lymph node samples was satisfactory; however, notable disparities emerged in the validation tests compared to results obtained from blood samples. This discrepancy is likely attributed to the elevated sample variability resulting from molecular compositional differences. According to the findings, the successful functioning of prediction models is mainly related to data analysis rather than the differences in the molecular composition of the samples.

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“…To diagnose P. lutzii infections using the DID test, it is recommended to use cell-free antigens obtained from this particular species [ 62 ]. Besides providing a diagnosis, serological tests may also be used to monitor the host response to antifungal therapy [ 148 ]. The main characteristics of the serological tests are summarized in Table 1 .…”

Section: Paracoccidiodomycosismentioning

confidence: 99%

“…In recent decades, advances have been made in the development of new molecular techniques for the detection of Paracoccidioides DNA in clinical samples, including amplification of ribosomal DNA region (rDNA), gene-expressing GP43 glycoprotein, and quantitative Chain Reaction Polymerase (qPCR) platform duplex PCR assay [ 63 , 138 , 151 , 152 ]. The implementation of new methodologies based on photonic techniques and machine learning algorithms has been suggested, such as Fourier Transform Infrared (FTIR) and Raman spectroscopy [ 148 , 153 , 154 ].…”

Section: Paracoccidiodomycosismentioning

confidence: 99%

Diagnosis and Treatment of Pulmonary Coccidioidomycosis and Paracoccidioidomycosis

Peçanha-Pietrobom

Tirado‐Sánchez

Gonçalves

et al. 2023

JoF

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Coccidioidomycosis (CM) and paracoccidioidomycosis (PCM) are systemic mycoses that are highly endemic in Latin America and have recently been included on the World Health Organization (WHO) Fungal Priority Pathogens List. Coccidioides immitis and Coccidioides posadasii are recognized as etiological agents of CM, with peculiarities in their geographic distribution. The genus Paracoccidioides now includes Paracoccidioides lutzii and the Paracoccidioides brasiliensis complex, which encompasses four phylogenetic species. In both diseases, pulmonary signs and symptoms are the main reasons for patients to seek medical assistance, and they are frequently misdiagnosed as tuberculosis. In this paper, we present a critical view of the strategies for diagnosis and clinical management of CM and PCM. Over the past few decades, there has been an increase in the number of reports of endemic fungal infections in areas previously thought to be “non-endemic” due to climate change and increased travel, among other factors. Learning to recognize their main epidemiological aspects and clinical manifestations is crucial so that clinicians can include them in the differential diagnosis of lung disease and avoid late diagnosis.

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Paracoccidioidomycosis screening diagnosis by FTIR spectroscopy and multivariate analysis

Cited by 10 publications

References 31 publications

Multi-resistant diarrheagenicEscherichia coliidentified by FTIR and machine learning: a feasible strategy to improve the group classification

Multi-resistant diarrheagenicEscherichia coliidentified by FTIR and machine learning: a feasible strategy to improve the group classification

Diagnosis of Cutaneous Leishmaniasis Using FTIR Spectroscopy and Machine Learning: An Animal Model Study

Diagnosis and Treatment of Pulmonary Coccidioidomycosis and Paracoccidioidomycosis

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