Identification of Commercial Antimalarial Herbal Drugs Using Laser-Induced Autofluorescence Technique and Multivariate Algorithms.

In malaria-prone developing countries the integrity of Anti-Malarial Herbal Drugs (AMHDs) which are easily preferred for treatment can be compromised. Currently, existing techniques for identifying AMHDs are destructive. We report on the use of non-destructive and sensitive technique, Laser-Induced-Autofluorescence (LIAF) in combination with multivariate algorithms for identification of AMHDs. The LIAF spectra were recorded from commercially prepared decoction AMHDs purchased from accredited pharmacy shop in Ghana. Deconvolution of the LIAF spectra revealed secondary metabolites belonging to derivatives of alkaloids and classes of phenolic compounds of the AMHDs. Principal Component Analysis (PCA) and Hierarchical Clustering Analysis (HCA) were able to discriminate the AMHDs base on their physicochemical properties. Based on two principal components, the PCA- QDA (Quadratic Discriminant Analysis), PCA-LDA (Linear Discriminant Analysis), PCA-SVM (Support Vector Machine) and PCA-KNN (K-Nearest Neighbour) models were developed with an accuracy performance of 99.0, 99.7, 100.0, and 100%, respectively, in identifying AMHDs. PCA-SVM and PCA-KNN provided the best classification and stability performance. The LIAF technique in combination with multivariate techniques may offer a non-destructive and viable tool for AMHDs identification.

Automatic Classification of Antimalarial Herbal Drugs Exposed to Ultraviolet Radiation from Unexposed Ones Using Laser-Induced Autofluorescence with Chemometric Techniques.

Exposure of antimalarial herbal drugs (AMHDs) to ultraviolet radiation (UVR) affects the potency and integrity of the AMHDs. Instant classification of the AMHDs exposed to UVR (UVR-AMHDs) from unexposed ones (Non-UVR-AMHDs) would be beneficial for public health safety, especially in warm regions. For the first time, this work combined laser-induced autofluorescence (LIAF) with chemometric techniques to classify UVR-AMHDs from Non-UVR-AMHDs. LIAF spectra data were recorded from 200 ml of each of the UVR-AMHDs and Non-UVR-AMHDs. To extract useful data from the spectra fingerprint, principal components (PCs) analysis was used. The performance of five chemometric algorithms: random forest (RF), neural network (NN), support vector machine (SVM), linear discriminant analysis (LDA), and k-nearest neighbour (KNN), were compared after optimization by validation. The chemometric algorithms showed that KNN, SVM, NN, and RF were superior with a classification accuracy of 100% for UVR-AMHDs while LDA had a classification accuracy of 98.8% after standardization of the spectra data and was used as an input variable for the model. Meanwhile, a classification accuracy of 100% was obtained for KNN, LDA, SVM, and NN when the raw spectra data was used as input except for RF for which a classification accuracy of 99.9% was obtained. Classification accuracy above 99.74 ± 0.26% at 3 PCs in both the training and testing sets were obtained from the chemometric models. The results showed that the LIAF, combined with the chemometric techniques, can be used to classify UVR-AMHDs from Non-UVR-AMHDs for consumer confidence in malaria-prone regions. The technique offers a non-destructive, rapid, and viable tool for identifying UVR-AMHDs in resource-poor countries.

Case series about ex vivo identification of squamous cell carcinomas by laser-induced autofluorescence and Fourier transform infrared spectroscopy.

An ex vivo case series aimed at identification of normal laryngeal tissue from laryngeal epidermoid squamous keratinized carcinoma by measuring laser-induced autofluorescence (LIAF) and Fourier transform infrared-attenuated total reflectance (FTIR-ATR) spectra is presented. The case series results were obtained for paired samples extracted from three patients (exclusion: macroscopic changes of normal vocal cord observed during surgery; surgical intervention on vocal cord, treated only with chemotherapy or radiotherapy for carcinoma; inclusion: men, aged 57-68, non-smokers). For LIAF analysis, a 375-nm picosecond pulsed laser diode with 31 MHz pulse repetition rate, 100 ps full-time width at half-maximum, and average power 0.49 µW was used. LIAF and FTIR-ATR spectra show noticeable differences between normal and malignant tissues. LIAF spectra differed in shape of emitted band, peak position, and band relative intensity of the two kinds of samples, evidencing hypsochromic shift and mean fluorescence intensity decrease of (75.42 ± 3)% in malignant tissue with respect to the normal one. The lack of 1745 cm-1 band in FTIR-ATR spectra for malignant tissues could be considered an important indicative of the presence of this kind of tissue; moreover, it resulted a greater contribution of lipids and proteins in normal tissue and of collagen in malignant tissue. Penetration depth of the evanescent wave was about 2 µm at an angle of 42°. The two spectroscopic methods are complementary, are applicable for real-time measurements, and may enhance cancer detection and diagnostics. Results presented in this study evidence the potential of the two methods for future in vivo studies.

Spectral characteristics of caries autofluorescence obtained from different locations and caries severities.

Dental caries usually occurs at interproximal and occlusal surfaces. The purpose of the present study was to determine if characteristic spectral factors extracted from autofluorescence (AF) spectra are informative regarding caries detection and the determination of caries stage as compared with DIAGNOdent results. AF spectra were obtained from caries lesions of different severities at two locations using a 405 nm laser. Three spectral factors, that is, spectral slope at 550 to 600 nm, spectral area under the curve at 500 to 590 nm and two-peak ratio between 625 and 667 nm, were extracted. The values of three spectral factors linearly decreased as caries progressed. According to micro-CT images, conventional visual and tactile inspections of lesions under or overestimated (25%-65%) caries states, and brown or thickly stained layer on interproximal or occlusal surfaces, respectively, caused misclassifications of caries stage. Of the spectral factors examined, spectral slope and area under curve for interproximal and occlusal surfaces, respectively, were found to be significantly related to caries stage and showed least data overlap. For interproximal and occlusal surfaces, DIAGNOdent readings of different stages overlapped considerably though their mean values were significantly different regardless of stage.

Probing endogenous collagen by laser-induced autofluorescence in burn wound biopsies: A pilot study.

The focus of the current study was to interrogate the predictive potential of laser-induced autofluorescence (LIAF) by objectively assessing collagen synthesis in burn wound granulation tissues ex vivo. Prior grafting, granulation tissues (20 samples) following burn injury were collected from 17 subjects of age range 18 to 60 years with patient/donor consent and the corresponding autofluorescence spectra were recorded at 325 nm He-Cd laser (≈2 mW) excitations. The resulting endogenous collagen intensity from the above tissue samples was computed by normalizing the nicotinamide adenine dinucleotide levels. In addition, the hydroxyproline content was also estimated biochemically from the same granulation tissues. A comparative assessment of both LIAF and biochemical estimations for endogenous collagen by hydroxyproline resulted in strong positive correlation among them. The above relevant observations suggest that LIAF is equally informative as that of biochemical estimations, in evaluating endogenous collagen content in wound granulation tissues. Thus, it can be concluded that LIAF has the predictive potential, as a noninvasive objective tool to measure the endogenous collagen levels in wound biopsy tissues and provide complementary data conducive for making clinical decisions.

Diagnosis and staging of caries using spectral factors derived from the blue laser-induced autofluorescence spectrum.

OBJECTIVE: The aim of this study was to identify the factors derived from the 405nm laser-induced autofluorescence (AF) spectra that could be used to diagnose and stage caries. MATERIALS AND METHODS: Teeth (20 teeth per stage) were classified as sound, stage II, III, and IV based on a visual and tactile inspection. The specimens were re-examined and reclassified based on micro-CT analysis. From the teeth, the AF was obtained using a 405nm laser. Three spectral factors (spectral slope at 550-600nm, area under the curve at 500-590nm, and two-peak ratio between 625 and 667nm) were derived from the AF spectra. Using these factors, the diagnosis and staging of caries were tested, and the results were compared with those of DIAGNOdent. RESULTS: After micro-CT analysis, only 13, 11, and 13 teeth were reclassified as stages II, III, and IV, respectively. The reclassified groups showed less data overlap between the stages, and the spectral slope was 40.1-74.6, 27.5-39.6, 11.1-27.4, and 1.0-9.7 for sound, stage II, III, and IV, respectively. The differentiation of stages III and IV using DIAGNOdent appeared to be difficult due to the considerable data overlap. CONCLUSION: Among the factors tested, the spectral slope at 550-600nm showed the best match with the caries specimens, in which their stage had been identified precisely. CLINICAL SIGNIFICANCE: The 405nm laser-induced AF spectra can be applied to the diagnosis and staging of caries alone or in conjunction with conventional methods, such as visual, tactile, and X-ray inspection.