Accuracy of artificial intelligence-assisted growth prediction in skeletal Class I preadolescent patients using serial lateral cephalograms for a 2-year growth interval.

OBJECTIVE: To investigate the accuracy of artificial intelligence-assisted growth prediction using a convolutional neural network (CNN) algorithm and longitudinal lateral cephalograms (Lat-cephs). MATERIALS AND METHODS: A total of 198 Japanese preadolescent children, who had skeletal Class I malocclusion and whose Lat-cephs were available at age 8 years (T0) and 10 years (T1), were allocated into the training, validation, and test phases (n = 161, n = 17, n = 20). Orthodontists and the CNN model identified 28 hard-tissue landmarks (HTL) and 19 soft-tissue landmarks (STL). The mean prediction error values were defined as 'excellent,' 'very good,' 'good,' 'acceptable,' and 'unsatisfactory' (criteria: 0.5 mm, 1.0 mm, 1.5 mm, and 2.0 mm, respectively). The degree of accurate prediction percentage (APP) was defined as 'very high,' 'high,' 'medium,' and 'low' (criteria: 90%, 70%, and 50%, respectively) according to the percentage of subjects that showed the error range within 1.5 mm. RESULTS: All HTLs showed acceptable-to-excellent mean PE values, while the STLs Pog', Gn', and Me' showed unsatisfactory values, and the rest showed good-to-acceptable values. Regarding the degree of APP, HTLs Ba, ramus posterior, Pm, Pog, B-point, Me, and mandibular first molar root apex exhibited low APPs. The STLs labrale superius, lower embrasure, lower lip, point of lower profile, B', Pog,' Gn' and Me' also exhibited low APPs. The remainder of HTLs and STLs showed medium-to-very high APPs. CONCLUSION: Despite the possibility of using the CNN model to predict growth, further studies are needed to improve the prediction accuracy in HTLs and STLs of the chin area.

The cGAS-STING signaling pathway is modulated by urolithin A.

During aging, general cellular processes, including autophagic clearance and immunological responses become compromised; therefore, identifying compounds that target these cellular processes is an important approach to improve our health span. The innate immune cGAS-STING pathway has emerged as an important signaling system in the organismal defense against viral and bacterial infections, inflammatory responses to cellular damage, regulation of autophagy, and tumor immunosurveillance. These key functions of the cGAS-STING pathway make it an attractive target for pharmacological intervention in disease treatments and in controlling inflammation and immunity. Here, we show that urolithin A (UA), an ellagic acid metabolite, exerts a profound effect on the expression of STING and enhances cGAS-STING activation and cytosolic DNA clearance in human cell lines. Animal laboratory models and limited human trials have reported no obvious adverse effects of UA administration. Thus, the use of UA alone or in combination with other pharmacological compounds may present a potential therapeutic approach in the treatment of human diseases that involves aberrant activation of the cGAS-STING pathway or accumulation of cytosolic DNA and this warrants further investigation in relevant transgenic animal models.

The GRB221009A gamma-ray burst as revealed by the gamma-ray spectrometer onboard the KPLO (Danuri).

The strongest gamma-ray burst (GRB) of the century, GRB20221009A, has been detected by the Korean Pathfinder Lunar Orbiter Gamma-ray Spectrometer (KGRS) instrument onboard the Korean Pathfinder Lunar Orbiter (KPLO). KGRS uses a LaBr3 detector to measure GRB counts with five energy bins in the energy range from 30 keV to 12 MeV. KGRS detected GRB221009A at a distance of 1.508 million kilometers from the Earth. The full duration of the main burst was recorded between 13:20 and 13:26 on October 9, 2022 with peak counts of over 1000 times background. The dead time of KGRS reached as high as 50%, and the intrinsic gamma-ray spectrum of LaBr3 was significantly altered.