Oral cancer is one of the most common cancers worldwide. Despite easy access to the oral cavity and significant advances in treatment, the morbidity and mortality rates for oral cancer patients are still very high, mainly due to late-stage diagnosis when treatment is less successful. Oral cancer has also been found to be the most expensive cancer to treat in the United States. Early diagnosis of oral cancer can significantly improve patient survival rate and reduce medical costs. There is an urgent unmet need for an accurate and sensitive molecular-based diagnostic tool for early oral cancer detection. Fourier transform infrared spectroscopy has gained increasing attention in cancer research due to its ability to elucidate qualitative and quantitative information of biochemical content and molecular-level structural changes in complex biological systems. The diagnosis of a disease is based on biochemical changes underlying the disease pathology rather than morphological changes of the tissue. It is a versatile method that can work with tissues, cells, or body fluids. In this review article, we aim to summarize the studies of infrared spectroscopy in oral cancer research and detection. It provides early evidence to support the potential application of infrared spectroscopy as a diagnostic tool for oral potentially malignant and malignant lesions. The challenges and opportunities in clinical translation are also discussed.

Wang, R. and Coderre, J. A. A Bystander Effect in Alpha-Particle Irradiations of Human Prostate Tumor Cells. Radiat. Res. 164, 711–722 (2005).Alpha-particle exposures were used to determine whether cells of the human prostate carcinoma cell line DU-145 can produce and respond to a bystander effect signal. An apparatus for α-particle irradiation of cells growing as a monolayer on a 1.4-μm-thick Mylar membrane directly above an 241Am α-particle source was constructed and calibrated. At the cell irradiation position, the α-particle fluence was 998 counts/mm2 s−1, the average α-particle energy was 3.14 MeV, and the average linear energy transfer was 128 keV/μm. The average dose rate to the cells growing on the Mylar surface was 1.2 Gy/min. A co-culture system was used to examine bystander effects transmitted through the medium from the directly targeted cells to tumor cells growing on an insert well beyond the range of the α particles. Alpha-particle doses from 0.1 to 6.0 Gy to the targeted cells on the Mylar membrane, followed by a 2-h co-incubation of the cells on the insert in the irradiated medium above the irradiated cells, all caused an ∼50% increase in micronucleus formation in the nontargeted co-cultured cells. Addition of the radical scavenger DMSO to the medium during the irradiation and the 2-h postirradiation incubation period completely blocked the bystander effect, whereas addition of a nitric oxide scavenger had no effect. Irradiation of medium containing serum, followed by a 2-h incubation, caused no bystander effect in the co-cultured cells. When the co-cultured cells on the insert were placed into the irradiated medium above the directly targeted cells immediately (∼1 min) after the irradiation and co-incubated for 2 h, there was no bystander effect. These data indicate that the observed bystander effect requires that the co-cultured cells be present in the medium during the irradiation of the directly targeted cells and suggest the involvement of a short-lived radical species.

Clinical Relevance: New dual-cure bulk-fill composites show promise for uniform degree of conversion and microhardness throughout the entire depth of direct restorations. SUMMARY: Objective: The aim of this study was to evaluate the degree of conversion (DC) through micro-Raman spectroscopy and surface microhardness in Vickers hardness (VHN) of three new dual-cure bulk-fill resin-based composites (RBCs) compared with light-cure bulk-fill and incremental RBCs at two clinically relevant depths and for two light irradiation times. Methods: Three commercially available restorative dual-cure bulk-fill RBCs (BulkEZ, HyperFIL, and Injectafil) were evaluated and compared with three light-cure RBCs (Filtek Bulk Fill Flowable, Filtek One Bulk Fill, and incremental Filtek Z250) as controls. Specimens were prepared in two different depths (0.5 mm and 5 mm) and were light irradiated for 20 seconds or 40 seconds. Self-cure was also evaluated for the three dual-cure bulk-fill RBCs. Micro-Raman spectroscopic measurements and VHN tests (n=5) were made after 24 hours of dry storage in the dark at room temperature for all test conditions. Data were analyzed using one-way and two-way analyses of variance (α=0.05). Results: All tested RBCs showed significantly higher DC and VHN values at 0.5-mm depth than at 5-mm depth, with the exception of BulkEZ, which showed similar DC and VHN values at two depths. The three dual-cure bulk-fill RBCs showed significantly higher DC than the three light-cure RBCs under the same curing condition. The three dual-cure RBCs showed much smaller differences in VHN values between the two depths than the three light-cure RBCs. Twenty seconds and 40 seconds of light irradiation did not generate significant difference in DC and VHN values for the three dual-cure bulk-fill RBCs at either depth or for the three light-cure RBCs at the 0.5-mm depth; however, 40 seconds of light irradiation generated significantly higher DC and VHN values for One Bulk Fill and Z250 at the 5-mm depth compared with 20 seconds of light irradiation.

Oral diseases pose a significant burden on global healthcare. While many oral conditions are preventable and manageable through regular dental office visits, a substantial portion of the population faces obstacles in accessing essential and affordable quality oral healthcare. In this mini review, we describe the issue of inequity and bias in oral healthcare and discuss various strategies to address these challenges, with an emphasis on the application of artificial intelligence (AI). Recent advances in AI technologies have led to significant performance improvements in oral healthcare. AI also holds tremendous potential for advancing equity in oral healthcare, yet its application must be approached with caution to prevent the exacerbation of inequities. The “black box” approaches of some advanced AI models raise uncertainty about their operations and decision-making processes. To this end, we discuss the use of interpretable and explainable AI techniques in enhancing transparency and trustworthiness. Those techniques, aimed at augmenting rather than replacing oral health practitioners’ judgment and skills, have the potential to achieve personalized dental and oral care that is unbiased, equitable, and transparent. Overall, achieving equity in oral healthcare through the responsible use of AI requires collective efforts from all stakeholders involved in the design, implementation, regulation, and utilization of AI systems. We use the United States as an example due to its uniquely diverse population, making it an excellent model for our discussion. However, the general and responsible AI strategies suggested in this article can be applied to address equity in oral healthcare on a global level.