Antihypertensive activities of royal jelly protein hydrolysate and its fractions in spontaneously hypertensive rats

Angiotensin I-converting enzyme (ACE) inhibitory and hypotensive effects of 7 peptide fractions (Frs) of royal jelly protein hydrolysate (RJPH) were studied in comparison with those of RJPH alone. Fr 4 and Fr 5 were the highest in ACE inhibitory activity and yield, respectively. Molecular weights (MWs) of RJPH and Fr 1-Fr 7 were distributed from 100 to 5,000 and those of Fr 1-Fr 7 increased in order from Fr 1 to Fr 7. RJPH, Fr 3 and Fr 4 at doses of 10, 30 and 100mg/kg i.v. and Fr 5 and Fr 6 at doses of 30 and 100mg/kg i.v. caused transiently significant hypotensive effects in spontaneously hypertensive rats (SHR). Fr 3, Fr 4, Fr 5 and Fr 6 at a dose of 1,000mg/kg also caused significant hypotensive effects 3h, 4-5h, 7-8h and 8h after oral administration in SHR, respectively. RJPH caused a long-lasting hypotensive effect in proportion to the magnitude of the MWs of RJPH fractions. The hypotensive pattern of RJPH was similar to the combined pattern of Fr 3-Fr 6. From these results, it can be concluded that the long-lasting hypotensive effect of oral administration of RJPH is dependent on the MWs of its ACE inhibitory peptides and the time required to digest them.</p

Current applications and development of artificial Intelligence for digital dental radiography

In the last few years, artificial intelligence (AI) research has been rapidly developing and emerging in the field of dental and maxillofacial radiology. Dental radiography, which is commonly used in daily practices, provides an incredibly rich resource for AI development and attracted many researchers to develop its application for various purposes. This study reviewed the applicability of AI for dental radiography from the current studies. Online searches on PubMed and IEEE Xplore databases, up to December 2020, and subsequent manual searches were performed. Then, we categorized the application of AI according to similarity of the following purposes: diagnosis of dental caries, periapical pathologies, and periodontal bone loss; cyst and tumor classification; cephalometric analysis; screening of osteoporosis; tooth recognition and forensic odontology; dental implant system recognition; and image quality enhancement. Current development of AI methodology in each aforementioned application were subsequently discussed. Although most of the reviewed studies demonstrated a great potential of AI application for dental radiography, further development is still needed before implementation in clinical routine due to several challenges and limitations, such as lack of datasets size justification and unstandardized reporting format. Considering the current limitations and challenges, future AI research in dental radiography should follow standardized reporting formats in order to align the research designs and enhance the impact of AI development globally