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Oral cancer is the most common malignancy in many developing countries, such as India, due to increased consumption of smokeless tobacco. The trace elemental components in commercially packaged forms of tobacco can play a significant role in the pathogenesis of oral cancer. To qualitatively assess the trace elements in various types of commercially packaged forms of tobacco using laser-induced breakdown spectroscopy (LIBS). Two popular varieties of ‘Paan masala’ that contained a mixture of slaked lime with areca nut, catechu, and other flavouring agents (tobacco was absent) and four types of packaged tobacco were obtained from ‘Paan’ shops. The contents in the packets were made into pellets using a hydraulic press and subjected to elemental analysis using LIBS. A ten-trial experiment was carried out on all six pellets. The National Institute of Standards and Technology (NIST) database was used to assess the emission lines. The elements obtained from commercially packaged tobacco and Paan masala were similar: calcium (Ca), iron (Fe), aluminium (Al), nickel (Ni), and chromium (Cr). Substances that cause DNA damage and carcinogenesis are inorganic elements such as nickel. Our study revealed that carcinogens such as nickel are present in the commercially packaged forms of tobacco and ‘Paan masala’ samples.

Background/Objectives: This study aimed to quantitatively assess site-specific mean autofluorescence intensity across normal oral mucosal subsites and to evaluate the effectiveness of Autofluorescence Imaging (AFI) as an adjunct tool for distinguishing benign lesions, OPMDs, and oral cancers by comparing lesion intensity with anatomically matched healthy subsites. Methods: This observational study employed dual-mode imaging, comprising paired White Light Imaging (WLI) and AFI, captured from different oral cavity subsites using a smartphone-based point-of-care device. The Region of Interest (ROI) was annotated on WLI and automatically mapped to the corresponding AFI for both normal mucosa and lesions. WLI and AFI images were separated into their constituent red, green, and blue (RGB) channels, and AFI intensity was quantified via ImageJ. Results: A total of 1380 dual-mode images were acquired from 86 healthy participants. AFI intensities were comparable across most oral subsites, except for the lateral and ventral tongue. The lateral border showed the lowest fluorescence (Green channel-GC: 68.12 ± 28.27; Blue channel-BC: 25.29 ± 7.93), whereas the ventral tongue showed the highest (GC: 98.89 ± 42.22; BC: 37.08 ± 11.04; both p < 0.001). Among 611 lesions, predominantly from the buccal mucosa, AFI intensity declined progressively with increasing disease severity. Homogeneous leukoplakia (n = 149; GC: 38.62 ± 25.05; BC: 21.60 ± 9.50), non-homogeneous leukoplakia (n = 25; GC: 30.42 ± 18.66; BC: 18.25 ± 7.17) and oral cancer (n = 21; GC: 23.39 ± 15.53; BC: 15.82 ± 7.15; all p < 0.001) showed markedly reduced fluorescence, while benign lesions (n: 44; GC: 66.99 ± 30.88; BC: 32.01 ± 13.62) exhibited intermediate intensities, supporting AFI’s discriminative potential. Conclusions: This phase-1, proof-of-concept study highlights subsite-specific variations in autofluorescence intensity within healthy oral mucosa, providing an essential baseline for objective interpretation of lesion-associated fluorescence changes. AFI has the potential to be used as a non-invasive adjunct for monitoring OPMDs. Further validation in larger and more diverse cohorts is required before clinical implementation.

Objectives Oral cancer is significantly high in India, and screening is an effective approach to downstage the disease. Educating Community Health Workers (CHWs) on early oral cancer detection is an effective step toward reducing the burden and serves as a first step toward facilitating the transfer of knowledge. Therefore, the purpose of this hands-on education was to equip CHWs with insight on the advanced diagnostics, preventive techniques, and innovations for the early detection of oral cancer. Materials and Methods A total of 178 participants were trained in two groups: Group 1 received training for screening and primary prevention, while group 2 received training on updates in recent diagnostic adjuncts and innovations, AI-enabled point-of-care diagnostics, and essential patient care in management of Oral Potentially Malignant Disorders (OPMDs). Pre- and post-assessment questionnaires were used to evaluate the participants. Results The knowledge assessment scores between the pre- and post-tests showed a statistically significant difference ( p  < 0.001), with rise in mean score of 3.99 from baseline. Six months following training, knowledge retention revealed a statistically significant difference ( p  < 0.001) in the participants' ability to recall the information. Conclusion A well-structured training module can create awareness, impart knowledge and upskill the CHWs for early detection of oral cancer. Retraining of CHWs is required for knowledge retention post-training.