Introduction: Oral squamous cell carcinoma (OSCC) and oral potentially malignant disorders (OPMD) are a significant health burden globally. Smoking, alcohol and betel quid are the main risk factors. Lack of screening methods has been highlighted as a significant challenge in management. Salivary biomarkers are proposed as non-invasive diagnostic tools. The aim of this systematic review was to study salivary biomarkers reported in OSCC and OPMD. Specific objectives were to select a salivary biomarker panel suitable for early detection of OSCC and OPMD and to assess relationships between salivary biomarkers and risk factors. Methods: A literature search was conducted in academic databases (Scopus, Medline, Embase and Web of Science) without any restrictions. Following calibration, two blinded reviewers screened the studies and extracted data. A risk of bias assessment was conducted using Newcastle Ottawa scale. 295 studies were included with descriptive data analysis. Expert opinion: A salivary biomarker panel including Interleukin 1β, IL6 and IL8 was selected for OSCC and OPMD. Reported relationships between salivary biomarkers and risk factors are discussed and research gaps are highlighted. Future research should be directed to assess potential salivary biomarkers and their relationships to risk factors in order to understand the biomarker’s role in disease initiation.

Oral Squamous Cell Carcinoma (OSCC) poses a significant health challenge, with early detection being crucial for effective treatment and improved survival rates. While previous studies have examined the use of standard photographs, such as those from smartphones, for oral lesion classification, they typically rely solely on images, overlooking the potential benefits of incorporating multiple modalities. This study addresses this gap by proposing a multimodal deep-learning pipeline incorporating diverse data sources, including patient metadata, which mimics the diagnostic approach of clinicians in early detection of oral cancer. The study leverages state-of-the-art image encoders to classify oral lesions into benign and potentially malignant categories. A performance comparison of six pre-trained deep-learning models (MobileNetV3-Large, MixNet-S, ResNet-50, HRNet-W18-C, DenseNet-121, and Inception_v3) is presented. The performance of the proposed pipeline achieved an overall accuracy of 81%, precision of 79%, recall of 79%, F1-score of 78%, and a Matthews Correlation Coefficient (MCC) of 0.57 using the MobileNetV3-Large image encoder. The findings highlight the efficacy of integrating multiple data modalities for more accurate early detection of potential malignancies compared to using only image data. The outcomes could pave the way for improved clinical decision-making and patient outcomes.

This study aims to address the critical gap of unavailability of publicly accessible oral cavity image datasets for developing machine learning (ML) and artificial intelligence (AI) technologies for the diagnosis and prognosis of oral cancer (OCA) and oral potentially malignant disorders (OPMD), with a particular focus on the high prevalence and delayed diagnosis in Asia. Following ethical approval and informed written consent, images of the oral cavity were obtained from mobile phone cameras and clinical data was extracted from hospital records from patients attending to the Dental Teaching Hospital, Peradeniya, Sri Lanka. After data management and hosting, image categorization and annotations were done by clinicians using a custom-made software tool developed by the research team. A dataset comprising 3000 high-quality, anonymized images obtained from 714 patients were classified into four distinct categories: healthy, benign, OPMD, and OCA. Images were annotated with polygonal shaped oral cavity and lesion boundaries. Each image is accompanied by patient metadata, including age, sex, diagnosis, and risk factor profiles such as smoking, alcohol, and betel chewing habits. Researchers can utilize the annotated images in the COCO format, along with the patients' metadata, to enhance ML and AI algorithm development.

The incidence of oral squamous cell carcinoma (OSCC), and its precursor, oral epithelial dysplasia (OED), is on the rise, especially in South Asia. OSCC is the leading cancer in males in Sri Lanka, with >80% diagnosed at advanced clinical stages. Early detection is paramount to improve patient outcome, and saliva testing is a promising non-invasive tool. The aim of this study was to assess salivary interleukins (lL1β, IL6, and IL8) in OSCC, OED and disease-free controls in a Sri Lankan study cohort. A case-control study with OSCC (n = 37), OED (n = 30) patients and disease-free controls (n = 30) was conducted. Salivary lL1β, IL6, and IL8 were quantified using enzyme-linked immuno-sorbent assay. Comparisons between different diagnostic groups and potential correlations to risk factors were assessed. Salivary levels for the three tested interleukins increased from disease-free controls through OED, and were highest in OSCC samples. Furthermore, the levels of IL1β, IL6, and IL8 increased progressively with OED grade. The discrimination between patients (OSCC and OED) and controls, as assessed by AUC of receiver operating characteristic curves, was 0.9 for IL8 (p = 0.0001) and 0.8 for IL6 (p = 0.0001), while IL1β differentiated OSCC from controls (AUC 0.7, p = 0.006). No significant associations were found between salivary interleukin levels and smoking, alcohol, and betel quid risk factors. Our findings suggest that salivary IL1β, IL6, and IL8 are associated with disease severity of OED, and are potential biomarkers for predicting disease progression in OED, and the screening of OSCC.

Background: The Sri Lankan traditional betel quid (TBQ) which had been extensively used in the country before its colonization is claimed to have antiperiodontopathic effects in the Sri Lankan folklore. However, there is no reported scientific evidence to support the claimed antiperiodontopathic effects mediated by this TBQ. The present study was carried out to investigate the protective effect of the Sri Lankan TBQ in the pathogenesis of periodontitis.Methods: We investigate the ethyl acetate extract of the Sri Lankan TBQ for its antibacterial effects against the keystone periodontopathic bacterium,P. gingivalisand also its antioxidant potential, which is important to protect the periodontium from oxidative stress. Further, its safety was analyzed using the cytokinesis block micronucleus assay on human peripheral blood lymphocytes (PBLs).Results: Ethyl acetate extract of this TBQ inhibited the growth ofP. gingivaliswith a minimum bactericidal concentration (MBC) of 125 μg/ml. It was found to be a rich source of polyphenols and displayed considerable DPPH and ABTS radical scavenging activities and a strong ferric reducing antioxidant power. This extract could protect the cultured human gingival fibroblasts from H₂O₂induced oxidative stress. In addition, this TBQ extract was not genotoxic to human PBLs even at a concentration of 2.5 mg/ml. Moreover, it exhibited protective effects against bleomycin induced genotoxicity in PBLs.Conclusion: Ethyl acetate extract of the Sri Lankan TBQ is a source of natural antibacterial compounds againstP. gingivalis. It is also a source of natural antioxidants which can protect human gingival fibroblasts from H₂O₂induced oxidative stress. These properties of the TBQ may have contributed to its claimed antiperiodontopathic effects. Besides, it was found to be relatively non-toxic to human cells. Thus this TBQ extract has a huge potential to be developed as a novel adjunctive therapeutic lead against periodontitis.

Objectives: Antibiotic resistance (ABR) constitutes a significant burden to economies in developing countries. In the ‘One‐Health’ concept, ABR in human, animals, and environment is interconnected. The aim of this study was to critically appraise literature on ABR in all three domains in One Health, within the Sri Lankan geographical context.Methods: The protocol was registered with PROSPERO and followed PRISMA 2020 guidelines. A comprehensive electronic literature search was conducted in PubMed, Scopus, Web of Science databases and grey literature via Google Scholar. Out of 298 abstracts, 37 articles were selected following screening. A risk of bias assessment was conducted using Joanna Briggs Institute tools. Following blinded data extraction, descriptive data analysis and narrative synthesis were performed.Results: This review included studies published between 2016‐2023. Of the included studies, 17 (45.9%) reported data on samples obtained from humans, 9 (24.3%) from animals, and 6 (16.2%) from environmental sources, two studies (5.4%) from humans and animals, one study on animal and environment; whereas two studies including all three domains. ABR of 32 different bacteria (Gram negative⸺17, Gram positive⸺14) was retrieved; E. coli was the most frequently studied bacteria followed by MRSA and ESBL. For E. coli, a median resistance over 50% was reported for sulfamethoxazole (88.8%), trimethoprim (79.1%), ampicillin (60%) and tetracycline (50.3%) with the highest resistance for erythromycin (98%). Of a total of 21 antibiotic‐resistance genes in E. coli, the highest genotypic resistance was for tet‐A (48.5%).Conclusions: A comprehensive description of ABR for a total of 32 bacteria, 62 antibiotics and 46 ABR genes is presented. This review discusses the contemporary ABR landscape in Sri Lanka through the One Health lens, highlighting key methodological and empirical research gaps.