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Simple Summary: Oral cancers are associated with high mortality in advanced stages. Early diagnosis is associated with better patient outcomes, but this is challenging to achieve as benign lesions look similar to lesions of concern, and multiple biopsies may be required to ensure the most pathologic tissue is sampled. This work leverages a previously developed endoscopic imaging system and deep learning segmentation tool to provide measurements of subsurface changes in the first few millimeters of oral tissue. We present seven quantitative features that allow for rapid examination of tissue, which we propose may be useful for biopsy site or treatment margin selection. Optical coherence tomography is a noninvasive imaging technique that provides three-dimensional visualization of subsurface tissue structures. OCT has been proposed and explored in the literature as a tool to assess oral cancer status, select biopsy sites, or identify surgical margins. Our endoscopic OCT device can generate widefield (centimeters long) imaging of lesions at any location in the oral cavity—but it is challenging for raters to quantitatively assess and score large volumes of data. Leveraging a previously developed epithelial segmentation network, this work develops quantifiable biomarkers that provide direct measurements of tissue properties in three dimensions. We hypothesize that features related to morphology, tissue attenuation, and contrast between tissue layers will be able to provide a quantitative assessment of disease status (dysplasia through carcinoma). This work retrospectively assesses seven biomarkers on a lesion-contralateral matched OCT dataset of the lateral and ventral tongue (40 patients, 70 sites). Epithelial depth and loss of epithelial–stromal boundary visualization provide the strongest discrimination between disease states. The stroma optical attenuation coefficient provides a distinction between benign lesions from dysplasia and carcinoma. The stratification biomarkers visualize subsurface changes, which provides potential for future utility in biopsy site selection or treatment margin delineation. [ABSTRACT FROM AUTHOR]

Simple Summary: The diagnosis of oral cancer can require multiple biopsies to increase the likelihood of sampling the most pathologic site within a lesion. Optical coherence tomography (OCT) enables the examination of subsurface morphology and has shown potential in biopsy guidance. OCT captures changes in tissue stratification related to depth, topology, and presence of the epithelial-stromal boundary, which are structural biomarkers for pre-invasive and invasive oral cancer. This study presents a neural network pipeline to simplify OCT interpretation by providing information about epithelial depth and stratification through simple en face maps. U-net models were employed to segment the boundaries of the epithelial layer, and supporting convolutional neural networks were used for identification of the imaging field and artifacts. Non-cancerous, precancerous, and cancerous pathologies across the oral cavity were evaluated. The predictions demonstrate as-good-as or better agreement than inter-rater agreement, suggesting strong predictive power. This paper aims to simplify the application of optical coherence tomography (OCT) for the examination of subsurface morphology in the oral cavity and reduce barriers towards the adoption of OCT as a biopsy guidance device. The aim of this work was to develop automated software tools for the simplified analysis of the large volume of data collected during OCT. Imaging and corresponding histopathology were acquired in-clinic using a wide-field endoscopic OCT system. An annotated dataset (n = 294 images) from 60 patients (34 male and 26 female) was assembled to train four unique neural networks. A deep learning pipeline was built using convolutional and modified u-net models to detect the imaging field of view (network 1), detect artifacts (network 2), identify the tissue surface (network 3), and identify the presence and location of the epithelial–stromal boundary (network 4). The area under the curve of the image and artifact detection networks was 1.00 and 0.94, respectively. The Dice similarity score for the surface and epithelial–stromal boundary segmentation networks was 0.98 and 0.83, respectively. Deep learning (DL) techniques can identify the location and variations in the epithelial surface and epithelial–stromal boundary in OCT images of the oral mucosa. Segmentation results can be synthesized into accessible en face maps to allow easier visualization of changes. [ABSTRACT FROM AUTHOR]

Huge advances in peer-to-peer systems and attempts to develop the semantic web have revealed a critical issue in information systems across multiple domains: the absence of semantic interoperability. Today, businesses operating in a digital environment require increased supply-chain automation, interoperability, and data governance. While research on the semantic web and interoperability has recently received much attention, a dearth of studies investigates the relationship between these two concepts in depth. To address this knowledge gap, the objective of this study is to conduct a review and bibliometric analysis of 3511 Scopus-registered papers on the semantic web and interoperability published over the past two decades. In addition, the publications were analyzed using a variety of bibliometric indicators, such as publication year, journal, authors, countries, and institutions. Keyword co-occurrence and co-citation networks were utilized to identify the primary research hotspots and group the relevant literature. The findings of the review and bibliometric analysis indicate the dominance of conference papers as a means of disseminating knowledge and the substantial contribution of developed nations to the semantic web field. In addition, the keyword co-occurrence network analysis reveals a significant emphasis on semantic web languages, sensors and computing, graphs and models, and linking and integration techniques. Based on the co-citation clustering, the Internet of Things, semantic web services, ontology mapping, building information modeling, bioinformatics, education and e-learning, and semantic web languages were identified as the primary themes contributing to the flow of knowledge and the growth of the semantic web and interoperability field. Overall, this review substantially contributes to the literature and increases scholars' and practitioners' awareness of the current knowledge composition and future research directions of the semantic web field. [ABSTRACT FROM AUTHOR]

Early identification and management of precancerous lesions at high risk of developing cancers is the most effective and economical way to reduce the incidence, mortality, and morbidity of cancers as well as minimizing treatment-related complications, including pain, impaired functions, and disfiguration. Reliable cancer-risk-predictive markers play an important role in enabling evidence-based decision making as well as providing mechanistic insight into the malignant conversion of precancerous lesions. The focus of this article is to review updates on markers that may predict the risk of oral premalignant lesions (OPLs) in developing into oral squamous cell carcinomas (OSCCs), which can logically be discovered only by prospective or retrospective longitudinal studies that analyze pre-progression OPL samples with long-term follow-up outcomes. These risk-predictive markers are different from those that prognosticate the survival outcome of cancers after they have been diagnosed and treated, or those that differentiate between different lesion types and stages. Up-to-date knowledge on cancer-risk-predictive markers discovered by longitudinally followed studies will be reviewed. The goal of this endeavor is to use this information as a starting point to address some key challenges limiting our progress in this area in the hope of achieving effective translation of research discoveries into new clinical interventions. [ABSTRACT FROM AUTHOR]

Oral cancer is a global health burden with significantly poor survival, especially when the diagnosis is at its late stage. Despite advances in current treatment modalities, there has been minimal improvement in survival rates over the last five decades. The development of local recurrence, regional failure, and the formation of second primary tumors accounts for this poor outcome. For survivors, cosmetic and functional compromises resulting from treatment are often devastating. These statistics underscore the need for novel approaches in the management of this deadly disease. Photodynamic therapy (PDT) is a treatment modality that involves administration of a light-sensitive drug, known as a photosensitizer, followed by light irradiation of an appropriate wavelength that corresponds to an absorbance band of the sensitizer. In the presence of tissue oxygen, cytotoxic free radicals that are produced cause direct tumor cell death, damage to the microvasculature, and induction of inflammatory reactions at the target sites. PDT offers a prospective new approach in controlling this disease at its various stages either as a stand-alone therapy for early lesions or as an adjuvant therapy for advanced cases. In this review, we aim to explore the applications of PDT in oral cancer therapy and to present an overview of the recent advances in PDT that can potentially reposition its utility for oral cancer treatment. [ABSTRACT FROM AUTHOR]

Pentaherb formula (PHF) has been proven to improve the quality of life of children with atopic dermatitis without side effects. The aim of this study was to elucidate the potential anti-inflammatory and anti-allergic activities of PHF, Moutan Cortex (Danpi/DP) and gallic acid (GA) using human basophils (KU812 cells), which are crucial effector cells in allergic inflammation. PHF, DP and GA could significantly suppress the expression of allergic inflammatory cytokine IL-33-upregulated intercellular adhesion molecule (ICAM)-1, and the release of chemokines CCL2, CCL5, CXCL8 and inflammatory cytokine IL-6 from KU812 cells (all p < 0.05). With the combined use of dexamethasone (0.01 μg/mL) and GA (10 μg/mL), the suppression of ICAM-1 expression and CCL5 and IL-6 release of IL-33-activated KU812 cells were significantly greater than the use of GA alone (all p < 0.05). The suppression of the IL-33-induced activation of intracellular signalling molecules p38 mitogen activated protein kinase, nuclear factor-kB and c-Jun amino-terminal kinase in GA-treated KU812 cells could be the underlying mechanism for the suppression on ICAM-1, chemokines and cytokines. The combined use of dexamethasone with the natural products PHF or DP or GA might therefore enhance the development of a novel therapeutic modality for allergic inflammatory diseases with high potency and fewer side effects.