MATERIALS AND METHODS: Saliva was collected from 4- to 6-year-old kindergarten students. Salivary neutrophils were obtained by instructing the subjects to rinse their mouth with 1 mL of sterile 1.5% NaCl for 30 seconds before expectorating it into a sterile glass. The expression of CFSE+CD35+ and CFSE+CD89+was measured and analyzed using flow cytometry.
RESULTS: The expression of CFSE+CD89+ in the caries-free group (2.46 ± 0.39) was significantly lower than that in the S-ECC group (3.41 ± 1.11), with a p-value of 0.0001, while the expression of CFSE+CD35+ in the caries-free group was (2.35 ± 0.56) compared with (1.54 ± 0.35) (p = 0.0001) in the S-ECC group.
CONCLUSIONS: The expression ratio of CFSE+CD89+ and CFSE+CD35+constitutes a marker for S-ECC.
RESULTS: Regardless of the season, we have observed a significant (p
METHOD: To overcome the limitation, the use of artificial intelligence along with technical tools has been extensively investigated for AD diagnosis. For developing a promising artificial intelligence strategy that can diagnose AD early, it is critical to supervise neuropsychological outcomes and imaging-based readouts with a proper clinical review.
CONCLUSION: Profound knowledge, a large data pool, and detailed investigations are required for the successful implementation of this tool. This review will enlighten various aspects of early diagnosis of AD using artificial intelligence.