The development of reliable and robust diagnostic tests is one of the most efficient methods to limit the spread of coronavirus disease 2019 (COVID-19), which is caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). However, most laboratory diagnostics for COVID-19, such as enzyme-linked immunosorbent assay (ELISA) and reverse transcriptase-polymerase chain reaction (RT-PCR), are expensive, time-consuming, and require highly trained professional operators. On the other hand, the lateral flow immunoassay (LFIA) is a simpler, cheaper device that can be operated by unskilled personnel easily. Unfortunately, the current technique has some limitations, mainly inaccuracy in detection. This review article aims to highlight recent advances in novel lateral flow technologies for detecting SARS-CoV-2 as well as innovative approaches to achieve highly sensitive and specific point-of-care testing. Lastly, we discuss future perspectives on how smartphones and Artificial Intelligence (AI) can be integrated to revolutionize disease detection as well as disease control and surveillance.

Hypermethylated ZNF582 and PAX1 genes in oral epithelial cells collected by mouth rinse are effective biomarkers for the detection of oral dysplasia and oral cancer.

AKR1B10 overexpression is an independent poor prognostic biomarker for OSCC. AKR1B10 inhibitors may be promising in clinical trials against OSCC. © 2017 Wiley Periodicals, Inc. Head Neck 39: 1327-1332, 2017.

More than 90% of head and neck infections are caused by pathological changes originating in the teeth. When odontogenic infections are not properly treated, infections may spread to distant spaces and cause more serious infections in fascial spaces, ultimately leading to deep neck infections. Clinical experience has indicated that patients with diabetes mellitus (DM) may be more susceptible to facial cellulitis and deep neck infections caused by odontogenic infections. This study used the Taiwan National Health Insurance Database (NHIRD) to analyze and examine the correlation between DM and odontogenic infections in patients. To this end, this study analyzed 1 million NHIRD individual datasets from 2005, of which 964,182 individuals had medical treatment records. The insurance database also recorded related factors such as age, sex, duration of hospital stays, season, and whether patients were low income. We also analyzed the correlation between urbanization and the studied diseases. The results indicated that the correlation between facial cellulitis and DM patients was confirmed; facial cellulitis was most likely to occur 2 years after the initial DM diagnosis, with a risk occurrence 1.409 times greater than that of the control group. Facial cellulitis is more likely to occur in patients originating from poorer socioeconomic backgrounds, and female DM patients are more likely to experience this condition. These conclusions may facilitate the establishment of clinical guidelines for preventative education and treatment. Oral prevention and health education for high-risk patients, as well as early-stage surgical intervention and antibiotic usage in early-stage odontogenic infections, can prevent disease progression, improve patient recovery rates, and reduce the use and waste of medical resources.