Autofluorescence Detection Method for Dental Plaque Bacteria Detection and Classification: Example of Porphyromonas gingivalis, Aggregatibacter actinomycetemcomitans, and Streptococcus mutans

The use of fluorescence spectroscopy for plaque detection is a fast and effective way to monitor oral health. At present, there is no uniform specification for the design of the excitation light source of related products for generating fluorescence. To carry out experiments on dental plaque, the fluorescence spectra of three different bacterial species (Porphyromonas gingivalis, Aggregatibacter actinomycetemcomitans, and Streptococcus mutans) were measured by hyperspectral imaging microscopy (HIM). Three critical issues were found in the experiments. One issue was the unwanted spectrum generated from a mercury line source; two four-order low-pass filters were evaluated for eliminating the unwanted spectrum and meet the experimental requirements. The second issue was the red fluorescence generated from the microscope slide made of borosilicate glass; this could affect the observation of the red fluorescence from the bacteria; quartz microscope slides were found to reduce the fluorescence intensity by about 2 dB compared with the borosilicate slide. The third issue of photobleaching in the fluorescence of the Porphyromonas gingivalis was studied. This study proposes a method of classifying three bacteria based on the spectral intensity ratios (510/635 and 500/635 nm) under the 405 nm excitation light was proposed in this study. The sensitivity and specificity of the classification were approximately 99% and 99%, respectively.

Band-Selection of a Portal LED-Induced Autofluorescence Multispectral Imager to Improve Oral Cancer Detection

This aim of this study was to find effective spectral bands for the early detection of oral cancer. The spectral images in different bands were acquired using a self-made portable light-emitting diode (LED)-induced autofluorescence multispectral imager equipped with 365 and 405 nm excitation LEDs, emission filters with center wavelengths of 470, 505, 525, 532, 550, 595, 632, 635, and 695 nm, and a color image sensor. The spectral images of 218 healthy points in 62 healthy participants and 218 tumor points in 62 patients were collected in the ex vivo trials at China Medical University Hospital. These ex vivo trials were similar to in vivo because the spectral images of anatomical specimens were immediately acquired after the on-site tumor resection. The spectral images associated with red, blue, and green filters correlated with and without nine emission filters were quantized by four computing method, including summated intensity, the highest number of the intensity level, entropy, and fractional dimension. The combination of four computing methods, two excitation light sources with two intensities, and 30 spectral bands in three experiments formed 264 classifiers. The quantized data in each classifier was divided into two groups: one was the training group optimizing the threshold of the quantized data, and the other was validating group tested under this optimized threshold. The sensitivity, specificity, and accuracy of each classifier were derived from these tests. To identify the influential spectral bands based on the area under the region and the testing results, a single-layer network learning process was used. This was compared to conventional rules-based approaches to show its superior and faster performance. Consequently, four emission filters with the center wavelengths of 470, 505, 532, and 550 nm were selected by an AI-based method and verified using a rule-based approach. The sensitivities of six classifiers using these emission filters were more significant than 90%. The average sensitivity of these was about 96.15%, the average specificity was approximately 69.55%, and the average accuracy was about 82.85%.

Betel quid-associated cancer: Prevention strategies and targeted treatment

Betel quid (BQ) and areca nut use are at risk of cancer. This review includes the latest evidence of carcinogenesis caused by BQ exposure, suggests possible prevention strategies. We conducted a systematic literature search in the PubMed and Web of Science databases to identify relevant articles published in the past 10 years according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses criteria. Arecoline N-oxide, a metabolite of areca nut, is likely an initiator in carcinogenesis and is detoxified by N-acetylcysteine. Oral potentially malignant disorder and reactive oxygen species involved in carcinogenesis pathways may be treatable using antioxidants. Screening programs conducted by trained physicians are useful for identifying patients with early stages of oral cancer in high-risk groups. Anti-inflammatory medications may be used as chemopreventive agents in the disease-free stage after surgery. The association between survival and tumor somatic mutations in patients who chew BQ should be addressed in cancer studies. Current evidence on the natural course from BQ exposure to cancer occurrence and development provides information for developing primary, secondary, and tertiary prevention strategies against BQ-associated cancer at clinical or translational levels.

Portable LED-induced autofluorescence spectroscopy for oral cancer diagnosis

Oral cancer is a serious and growing problem in many developing and developed countries. To improve the cancer screening procedure, we developed a portable light-emitting-diode (LED)-induced autofluorescence (LIAF) imager that contains two wavelength LED excitation light sources and multiple filters to capture ex vivo oral tissue autofluorescence images. Compared with conventional means of oral cancer diagnosis, the LIAF imager is a handier, faster, and more highly reliable solution. The compact design with a tiny probe allows clinicians to easily observe autofluorescence images of hidden areas located in concave deep oral cavities. The ex vivo trials conducted in Taiwan present the design and prototype of the portable LIAF imager used for analyzing 31 patients with 221 measurement points. Using the normalized factor of normal tissues under the excitation source with 365 nm of the central wavelength and without the bandpass filter, the results revealed that the sensitivity was larger than 84%, the specificity was not smaller than over 76%, the accuracy was about 80%, and the area under curve of the receiver operating characteristic (ROC) was achieved at about 87%, respectively. The fact shows the LIAF spectroscopy has the possibilities of ex vivo diagnosis and noninvasive examinations for oral cancer.