Fluorescence spectroscopic study on malignant and premalignant oral mucosa of patients undergoing treatment: An observational prospective study

Kanchwala, Nosheen; Kumar, Naveen; Gupta, Shobhit; Lokhandwala, Hozefa

doi:10.1016/j.ijsu.2018.05.029

Cited by 7 publications

(5 citation statements)

References 16 publications

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“…The serum fluorescence spectra of E. granulosus s.s .‐infected and uninfected E. granulosus s.s . group consisted of different large and small peaks, each fluorophore both has a particular fluorescence peak, the analysis of serum fluorescence spectra was based on the differences in intensity and location of fluorescence peaks between normal and abnormal serum samples [17, 43]. Figure 1A,B showed the normalized average fluorescence spectra of serum with E. granulosus s.s .‐infected and uninfected E. granulosus s.s .…”

Section: Resultsmentioning

confidence: 99%

“…Fluorescence spectra of E. granulosus s.s.-infected and uninfected E. granulosus s.s. group at the excitation wavelength of 405 nm were showed in Figure 1. The serum fluorescence spectra of E. granulosus s.s.-infected and uninfected E. granulosus s.s. group consisted of different large and small peaks, each fluorophore both has a particular fluorescence peak, the analysis of serum fluorescence spectra was based on the differences in intensity and location of fluorescence peaks between normal and abnormal serum samples [17,43]. Figure 1A,B -infected group was different from that of the uninfected E. granulosus s. s. group.…”

Section: Analysis Of Fluorescence Spectrummentioning

confidence: 99%

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Rapid and non‐invasive detection of cystic echinococcosis in sheep based on serum fluorescence spectrum combined with machine learning algorithms

Xu,

Dawuti,

Maimaitiaili

et al. 2024

Journal of Biophotonics

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Cystic echinococcosis (CE) is a grievous zoonotic parasitic disease. Currently, the traditional technology of screening CE is laborious and expensive, developing an innovative technology is urgent. In this study, we combined serum fluorescence spectroscopy with machine learning algorithms to develop an innovative screening technique to diagnose CE in sheep. Serum fluorescence spectra of Echinococcus granulosus sensu stricto‐infected group (n = 63) and uninfected E. granulosus s.s. group (n = 60) under excitation at 405 nm were recorded. The linear support vector machine (Linear SVM), Quadratic SVM, medium radial basis function (RBF) SVM, K‐nearest neighbor (KNN), and principal component analysis‐linear discriminant analysis (PCA‐LDA) were used to analyze the spectra data. The results showed that Quadratic SVM had the great classification capacity, its sensitivity, specificity, and accuracy were 85.0%, 93.8%, and 88.9%, respectively. In short, serum fluorescence spectroscopy combined with Quadratic SVM algorithm has great potential in the innovative diagnosis of CE in sheep.

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Section: Resultsmentioning

confidence: 99%

Section: Analysis Of Fluorescence Spectrummentioning

confidence: 99%

Rapid and non‐invasive detection of cystic echinococcosis in sheep based on serum fluorescence spectrum combined with machine learning algorithms

Xu,

Dawuti,

Maimaitiaili

et al. 2024

Journal of Biophotonics

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“…In oral medicine and dentistry, AF-based devices are commercially available as adjunctive tools to detect (pre)malignant lesions, dentin characteristics, or oral bacteria abundance. Kanchwala et al characterized the AF spectra of the oral mucosa, using a spectroscopic system consisting of a nitrogen laser and a fiber optic probe, in 52 patients with squamous cell carcinoma or premalignant lesions and different cycles of post-operative radiotherapy [76]. The AF spectra of healthy squamous tissues showed an intense signal at the 460 nm band, while the intensity of the 635 nm band was highest in the spectra from oral squamous cell carcinoma lesions.…”

Section: Af In Oral Medicinementioning

confidence: 99%

Recent Advances and the Potential for Clinical Use of Autofluorescence Detection of Extra-Ophthalmic Tissues

et al. 2020

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The autofluorescence (AF) characteristics of endogenous fluorophores allow the label-free assessment and visualization of cells and tissues of the human body. While AF imaging (AFI) is well-established in ophthalmology, its clinical applications are steadily expanding to other disciplines. This review summarizes clinical advances of AF techniques published during the past decade. A systematic search of the MEDLINE database and Cochrane Library databases was performed to identify clinical AF studies in extra-ophthalmic tissues. In total, 1097 articles were identified, of which 113 from internal medicine, surgery, oral medicine, and dermatology were reviewed. While comparable technological standards exist in diabetology and cardiology, in all other disciplines, comparability between studies is limited due to the number of differing AF techniques and non-standardized imaging and data analysis. Clear evidence was found for skin AF as a surrogate for blood glucose homeostasis or cardiovascular risk grading. In thyroid surgery, foremost, less experienced surgeons may benefit from the AF-guided intraoperative separation of parathyroid from thyroid tissue. There is a growing interest in AF techniques in clinical disciplines, and promising advances have been made during the past decade. However, further research and development are mandatory to overcome the existing limitations and to maximize the clinical benefits.

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“…These molecules are excited by and emit over a broad range of wavelengths and often overlap the spectra of commonly used fluorescent probes 19 . This interesting phenomenon, together with the technological advancements, opened a large field of investigation and application of autofluorescence in biological research 14 , 20 , 21 and biomedical diagnosis 22 , 23 . On the other hand, it must be noted as an obstacle and a potential pitfall of fluorescence-based techniques 12 , 24 .…”

Section: Introductionmentioning

confidence: 99%

Variability of fluorescence intensity distribution measured by flow cytometry is influenced by cell size and cell cycle progression

Fedr

Kahounová

Remšík

et al. 2023

Sci Rep

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The distribution of fluorescence signals measured with flow cytometry can be influenced by several factors, including qualitative and quantitative properties of the used fluorochromes, optical properties of the detection system, as well as the variability within the analyzed cell population itself. Most of the single cell samples prepared from in vitrocultures or clinical specimens contain a variable cell cycle component. Cell cycle, together with changes in the cell size, are two of the factors that alter the functional properties of analyzed cells and thus affect the interpretation of obtained results. Here, we describe the association between cell cycle status and cell size, and the variability in the distribution of fluorescence intensity as determined with flow cytometry, at population scale. We show that variability in the distribution of background and specific fluorescence signals is related to the cell cycle state of the selected population, with the 10% low fluorescence signal fraction enriched mainly in cells in their G0/G1 cell cycle phase, and the 10% high fraction containing cells mostly in the G2/M phase. Therefore we advise using caution and additional experimental validation when comparing populations defined by fractions at both ends of fluorescence signal distribution to avoid biases caused by the effect of cell cycle and cell size.

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Fluorescence spectroscopic study on malignant and premalignant oral mucosa of patients undergoing treatment: An observational prospective study

Cited by 7 publications

References 16 publications

Rapid and non‐invasive detection of cystic echinococcosis in sheep based on serum fluorescence spectrum combined with machine learning algorithms

Rapid and non‐invasive detection of cystic echinococcosis in sheep based on serum fluorescence spectrum combined with machine learning algorithms

Recent Advances and the Potential for Clinical Use of Autofluorescence Detection of Extra-Ophthalmic Tissues

Variability of fluorescence intensity distribution measured by flow cytometry is influenced by cell size and cell cycle progression

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