Real time FTIR spectroscopy of tissue smears from gynecologic cancer tissues: A preliminary study.

e17089 Background: Women with suspected Gynecologic cancer undergo surgical procedures during which tissue from suspected areas is excised. Fast histopathology analysis is performed intra-operatively using frozen section (FS) analysis, results of which are available within less than an hour. However, the accuracy of the FS test ranges between 75% and 100% when compared to final histopathology diagnoses. Fourier transform Infrared (FTIR) spectroscopy, utilized for classification of tissue samples into malignant and benign tumors, has shown comparable results to those of FS histopathology analysis. However, the sample preparation time and the effects of tissue preparation on the measured spectra have been a concern for the utilization of this technique in clinical practice. In this study we used attenuated total reflection (ATR) FTIR spectroscopy to examine fresh tissue impression smears as an alternative to the FS technique for rapid classification of tissue samples obtained during surgery. Methods: The study was approved by relevant ethics committees and was conducted in accordance with the Declaration of Helsinki. All patients provided written, informed consent. In total, 23 biopsies (ovarian and uterine) were extracted from suspected tumor sites during surgical procedures and sent to the histopathology laboratory for both pathological and FTIR analyses. Results of the histopathology analysis classified 15 samples as benign and 8 samples as malignant. Prior to the histopathologic analysis, tissue samples from these tumors were lightly pressed against the surface of an ATR crystal, leaving on its' surface impression smears. These smears were air dried for ~5 minutes. Mid-IR absorbance spectra were collected using an ATR-FTIR spectrometer. Machine learning techniques (PCA-LDA and SVM) were utilized to build discrimination models from the absorbance data of the measured smears. Sensitivity and specificity were calculated. Results: IR absorbance spectra of malignant smears were consistently higher from spectra of benign smears in the 850cm-1 to 1450 cm-1 range and they were consistently lower in the 3200cm-1 to 3600cm-1 range. The PCA-LDA discrimination model correctly classified the samples with a sensitivity and specificity of 100%, and the SVM showed a training accuracy of 100% and a cross validation accuracy of 91.3%. Conclusions: These preliminary results suggest that ATR-FTIR spectroscopy of tissue smears may have an important role in the development of next-generation techniques for intra-operative tumor classification.

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Attenuated Total Reflection-Fourier Transform Infrared (ATR-FTIR) Spectroscopy Analysis of Saliva for Breast Cancer Diagnosis

Journal of Oncology ◽

10.1155/2020/4343590 ◽

2020 ◽

Vol 2020 ◽

pp. 1-11 ◽

Cited By ~ 1

Author(s):

Izabella C. C. Ferreira ◽

Emília M. G. Aguiar ◽

Alinne T. F. Silva ◽

Letícia L. D. Santos ◽

Léia Cardoso-Sousa ◽

...

Keyword(s):

Breast Cancer ◽

Fourier Transform ◽

Ftir Spectroscopy ◽

Cancer Patients ◽

Sensitivity And Specificity ◽

Fourier Transform Infrared ◽

Total Reflection ◽

Attenuated Total Reflection ◽

Breast Cancer Patients ◽

Potential Use

Saliva biomarkers using reagent-free biophotonic technology have not been investigated as a strategy for early detection of breast cancer (BC). The attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopy has been proposed as a promising tool for disease diagnosis. However, its utilization in cancer is still incipient, and currently saliva has not been used for BC screening. We have applied ATR-FTIR onto saliva from patients with breast cancer, benign breast disease, and healthy matched controls to investigate its potential use in BC diagnosis. Several salivary vibrational modes have been identified in original and second-derivative spectra. The absorbance levels at wavenumber 1041 cm−1 were significantly higher (p<0.05) in saliva of breast cancer patients compared with those of benign patients, and the ROC curve analysis of this peak showed a reasonable accuracy to discriminate breast cancer from benign and control patients. The 1433–1302.9 cm−1 band area was significantly higher (p<0.05) in saliva of breast cancer patients than in control and benign patients. This salivary ATR-FTIR spectral area was prevalidated as a potential diagnostic biomarker of BC. This spectral biomarker was able to discriminate human BC from controls with sensitivity and specificity of 90% and 80%, respectively. Besides, it was able to differentiate BC from benign disease with sensitivity and specificity of 90% and 70%, respectively. Briefly, for the first time, saliva analysis by ATR-FTIR spectroscopy has demonstrated the potential use of salivary spectral biomarkers (1041 cm−1 and 1433–1302.9 cm−1) as a novel alternative for noninvasive BC diagnosis, which could be used for screening purposes.

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Spectrochemical differentiation of meningioma tumours based on attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy

Analytical and Bioanalytical Chemistry ◽

10.1007/s00216-019-02332-w ◽

2019 ◽

Vol 412 (5) ◽

pp. 1077-1086

Author(s):

Taha Lilo ◽

Camilo L. M. Morais ◽

Katherine M. Ashton ◽

Ana Pardilho ◽

Charles Davis ◽

...

Keyword(s):

Fourier Transform ◽

Discriminant Analysis ◽

Ftir Spectroscopy ◽

Principal Component ◽

Fourier Transform Infrared ◽

Total Reflection ◽

Attenuated Total Reflection ◽

Tissue Samples ◽

Linear Discriminant ◽

Grade Ii

AbstractMeningiomas are the commonest types of tumours in the central nervous system (CNS). It is a benign type of tumour divided into three WHO grades (I, II and III) associated with tumour growth rate and likelihood of recurrence, where surgical outcomes and patient treatments are dependent on the meningioma grade and histological subtype. The development of alternative approaches based on attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy could aid meningioma grade determination and its biospectrochemical profiling in an automated fashion. Herein, ATR-FTIR in combination with chemometric techniques is employed to distinguish grade I, grade II and grade I meningiomas that re-occurred. Ninety-nine patients were investigated in this study where their formalin-fixed paraffin-embedded (FFPE) brain tissue samples were analysed by ATR-FTIR spectroscopy. Subsequent classification was performed via principal component analysis plus linear discriminant analysis (PCA-LDA) and partial least squares plus discriminant analysis (PLS-DA). PLS-DA gave the best results where grade I and grade II meningiomas were discriminated with 79% accuracy, 80% sensitivity and 73% specificity, while grade I versus grade I recurrence and grade II versus grade I recurrence were discriminated with 94% accuracy (94% sensitivity and specificity) and 97% accuracy (97% sensitivity and 100% specificity), respectively. Several wavenumbers were identified as possible biomarkers towards tumour differentiation. The majority of these were associated with lipids, protein, DNA/RNA and carbohydrate alterations. These findings demonstrate the potential of ATR-FTIR spectroscopy towards meningioma grade discrimination as a fast, low-cost, non-destructive and sensitive tool for clinical settings.

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Estimation of The Age of Bloodstains on Soil Matrices By ATR-FTIR Spectroscopy and Chemometrics

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.a4454.119119 ◽

2019 ◽

Vol 9 (1) ◽

pp. 4750-4755

Keyword(s):

Ftir Spectroscopy ◽

Mean Squared Error ◽

Crime Scene ◽

Storage Conditions ◽

Attenuated Total Reflection ◽

Least Squares Regression ◽

Set Up ◽

Non Destructive ◽

Indoor And Outdoor

Estimating the accurate time of a crime occurred is one of the priceless information in forensics practice and for the investigation purposes. There are profuse of evidence can be found at the crime scene and each of the evidence will give an important information for the investigation purposes. In this study, the Attenuated Total Reflection (ATR)- Fourier Transform Infrared (FTIR) technique combined with advanced chemometrics method was deployed. For the purpose of determining the age of the bloodstain, two storage conditions; indoor and outdoor were set up to simulate real crime scene scenario and bloodstains on soil matrices were exposed and analyzed for selected time intervals for up to 63 days. Six partial least squares regression-discriminant analysis (PLSR-DA) models were constructed-indoor and outdoor models with 1-63 days-exhibited good performance with acceptable values of predictive root mean squared error (7.04-16.0) and r2 values (0.45-0.89), respectively. Using these models, correct classification of the aged bloodstains was calculated up to 70%. In conclusion, the multivariate analysis based on PLS-DA models indicates that ATR-FTIR spectroscopy, coupled with chemometrics provides acceptable discrimination for rapid and non-destructive determination the age of bloodstains on soil matrices in particularly for outdoor and very aged bloodstains.

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A Single Cohort Study Collecting Interval Timed Incisional Epidermal and Dermal Tissue Samples During Surgical Procedures to Profile Temporal Response of Tissue After Noxious Stimuli

Case Medical Research ◽

10.31525/ct1-nct04224870 ◽

2020 ◽

Author(s):

Keyword(s):

Cohort Study ◽

Surgical Procedures ◽

Temporal Response ◽

Tissue Samples ◽

Dermal Tissue ◽

Noxious Stimuli ◽

Single Cohort

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Reclassification of Kidney Clear Cell Carcinoma Based on Immune Cell Gene-Related DNA CpG Pairs

Biomedicines ◽

10.3390/biomedicines9020215 ◽

2021 ◽

Vol 9 (2) ◽

pp. 215

Author(s):

Qizhan Luo ◽

Thomas-Alexander Vögeli

Keyword(s):

Cell Carcinoma ◽

Immune Cell ◽

Clear Cell ◽

Clear Cell Carcinoma ◽

Pairwise Comparison ◽

Clinicopathological Features ◽

Immune Checkpoints ◽

Potential Mechanism ◽

Tissue Samples

Background: A new method was developed based on the relative ranking of gene expression level, overcoming the flaw of the batch effect, and having reliable results in various studies. In the current study, we defined the two methylation sites as a pair. The methylation level in a specific sample was subject to pairwise comparison to calculate a score for each CpGs-pair. The score was defined as a CpGs-pair score. If the first immune-related CpG value was higher than the second one in a specific CpGs-pair, the output score of this immune-related CpGs-pair was 1; otherwise, the output score was 0. This study aimed to construct a new classification of Kidney Clear Cell Carcinoma (KIRC) based on DNA CpGs (methylation sites) pairs. Methods: In this study, the biomarkers of 28 kinds of immune infiltration cells and corresponding methylation sites were acquired. The methylation data were compared between KIRC and normal tissue samples, and differentially methylated sites (DMSs) were obtained. Then, DNA CpGs-pairs were obtained according to the pairs of DMSs. In total, 441 DNA CpGs-pairs were utilized to construct a classification using unsupervised clustering analysis. We also analyzed the potential mechanism and therapy of different subtypes, and validated them in a testing set. Results: The classification of KIRC contained three subgroups. The clinicopathological features were different across three subgroups. The distribution of immune cells, immune checkpoints and immune-related mechanisms were significantly different across the three clusters. The mutation and copy number variation (CNV) were also different. The clinicopathological features and potential mechanism in the testing dataset were consistent with those in the training set. Conclusions: Our findings provide a new accurate and stable classification for developing personalized treatments for the new specific subtypes.

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Quantification and Classification of Diclofenac Sodium Content in Dispersed Commercially Available Tablets by Attenuated Total Reflection Infrared Spectroscopy and Multivariate Data Analysis

Pharmaceuticals ◽

10.3390/ph14050440 ◽

2021 ◽

Vol 14 (5) ◽

pp. 440

Author(s):

Eirini Siozou ◽

Vasilios Sakkas ◽

Nikolaos Kourkoumelis

Keyword(s):

Infrared Spectroscopy ◽

Low Cost ◽

Diclofenac Sodium ◽

Reference Method ◽

Sodium Content ◽

Attenuated Total Reflection ◽

Accuracy And Precision ◽

Significant Difference ◽

Ft Ir

A new methodology, based on Fourier transform infrared spectroscopy equipped with an attenuated total reflectance accessory (ATR FT-IR), was developed for the determination of diclofenac sodium (DS) in dispersed commercially available tablets using chemometric tools such as partial least squares (PLS) coupled with discriminant analysis (PLS-DA). The results of PLS-DA depicted a perfect classification of the tablets into three different groups based on their DS concentrations, while the developed model with PLS had a sufficiently low root mean square error (RMSE) for the prediction of the samples’ concentration (~5%) and therefore can be practically used for any tablet with an unknown concentration of DS. Comparison with ultraviolet/visible (UV/Vis) spectrophotometry as the reference method revealed no significant difference between the two methods. The proposed methodology exhibited satisfactory results in terms of both accuracy and precision while being rapid, simple and of low cost.

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Precise Identification of Prostate Cancer from DWI Using Transfer Learning

Sensors ◽

10.3390/s21113664 ◽

2021 ◽

Vol 21 (11) ◽

pp. 3664

Author(s):

Islam R. Abdelmaksoud ◽

Ahmed Shalaby ◽

Ali Mahmoud ◽

Mohammed Elmogy ◽

Ahmed Aboelfetouh ◽

...

Keyword(s):

Prostate Cancer ◽

Transfer Learning ◽

Sensitivity And Specificity ◽

Benign Tumors ◽

B Values ◽

Cad System ◽

Average Accuracy ◽

Apparent Diffusion ◽

Invasive Techniques ◽

Fine Tune

Background and Objective: The use of computer-aided detection (CAD) systems can help radiologists make objective decisions and reduce the dependence on invasive techniques. In this study, a CAD system that detects and identifies prostate cancer from diffusion-weighted imaging (DWI) is developed. Methods: The proposed system first uses non-negative matrix factorization (NMF) to integrate three different types of features for the accurate segmentation of prostate regions. Then, discriminatory features in the form of apparent diffusion coefficient (ADC) volumes are estimated from the segmented regions. The ADC maps that constitute these volumes are labeled by a radiologist to identify the ADC maps with malignant or benign tumors. Finally, transfer learning is used to fine-tune two different previously-trained convolutional neural network (CNN) models (AlexNet and VGGNet) for detecting and identifying prostate cancer. Results: Multiple experiments were conducted to evaluate the accuracy of different CNN models using DWI datasets acquired at nine distinct b-values that included both high and low b-values. The average accuracy of AlexNet at the nine b-values was 89.2±1.5% with average sensitivity and specificity of 87.5±2.3% and 90.9±1.9%. These results improved with the use of the deeper CNN model (VGGNet). The average accuracy of VGGNet was 91.2±1.3% with sensitivity and specificity of 91.7±1.7% and 90.1±2.8%. Conclusions: The results of the conducted experiments emphasize the feasibility and accuracy of the developed system and the improvement of this accuracy using the deeper CNN.

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Ultrarapid On-Site Detection of SARS-CoV-2 Infection Using Simple ATR-FTIR Spectroscopy and an Analysis Algorithm: High Sensitivity and Specificity

Analytical Chemistry ◽

10.1021/acs.analchem.0c04608 ◽

2021 ◽

Vol 93 (5) ◽

pp. 2950-2958

Author(s):

Valério G. Barauna ◽

Maneesh N. Singh ◽

Leonardo Leal Barbosa ◽

Wena Dantas Marcarini ◽

Paula Frizera Vassallo ◽

...

Keyword(s):

Ftir Spectroscopy ◽

Sensitivity And Specificity ◽

High Sensitivity ◽

Analysis Algorithm

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An Ensemble Feature Selection Approach to Identify Relevant Features from EEG Signals

Applied Sciences ◽

10.3390/app11156983 ◽

2021 ◽

Vol 11 (15) ◽

pp. 6983

Author(s):

Maritza Mera-Gaona ◽

Diego M. López ◽

Rubiel Vargas-Canas

Keyword(s):

Feature Selection ◽

Sensitivity And Specificity ◽

Eeg Signals ◽

Specificity And Sensitivity ◽

Selection Approach ◽

Feature Selection Approach ◽

The Stability ◽

Selection Algorithms ◽

Epileptiform Events

Identifying relevant data to support the automatic analysis of electroencephalograms (EEG) has become a challenge. Although there are many proposals to support the diagnosis of neurological pathologies, the current challenge is to improve the reliability of the tools to classify or detect abnormalities. In this study, we used an ensemble feature selection approach to integrate the advantages of several feature selection algorithms to improve the identification of the characteristics with high power of differentiation in the classification of normal and abnormal EEG signals. Discrimination was evaluated using several classifiers, i.e., decision tree, logistic regression, random forest, and Support Vecctor Machine (SVM); furthermore, performance was assessed by accuracy, specificity, and sensitivity metrics. The evaluation results showed that Ensemble Feature Selection (EFS) is a helpful tool to select relevant features from the EEGs. Thus, the stability calculated for the EFS method proposed was almost perfect in most of the cases evaluated. Moreover, the assessed classifiers evidenced that the models improved in performance when trained with the EFS approach’s features. In addition, the classifier of epileptiform events built using the features selected by the EFS method achieved an accuracy, sensitivity, and specificity of 97.64%, 96.78%, and 97.95%, respectively; finally, the stability of the EFS method evidenced a reliable subset of relevant features. Moreover, the accuracy, sensitivity, and specificity of the EEG detector are equal to or greater than the values reported in the literature.

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