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Purpose: Facial recognition software may serve as a more objective tool for measuring aesthetic improvements following facial plastic surgery. This study utilized a novel facial recognition and artificial intelligence software in order to assess perceived changes in age and attractiveness amongst patients receiving rhinoplasty. Methods: This study was a retrospective evaluation of 3D photographs of patients who underwent aesthetic rhinoplasty alone by the senior author (DS). Both pre- and post-operative (>12-month follow-up) Vectra 3D images (Canfield Scientific, Parsippany, NJ) were assessed using Haystack Artificial Intelligence Software (Haystack AI, New York, NY). Facial attractiveness (score 1-10) and apparent age were predicted. Paired t-tests were used to compare age and attractiveness scores before and after surgery. Multivariate linear regression was performed to identify factors associated with age and attractiveness scores. Results: Forty patients receiving rhinoplasty met the study criteria (average age 48.3). Overall, rhinoplasty was associated with increases in AI-rated attractiveness (+0.58, p = 0.004) and decreases in perceived age relative to the patient’s true age (-1.62 years, p = 0.021). Greater decreases in post-operative perceived age were achieved in patients who appeared older than their actual age pre-operatively (p = 0.04). AI age predictions were also closer to true patient age post-operatively (+1.71 years) than they were pre-operatively (+3.33 years). Conclusion: Facial recognition software was successfully implemented to evaluate improvements in perceived age and attractiveness in patients undergoing aesthetic rhinoplasty. Using this technology, patients were found to appear more attractive and younger after rhinoplasty. Additionally, age reversal was greatest among patients who appeared older than their actual age at the time of surgery.

Summary: Mechanisms coordinating pancreatic β cell metabolism with insulin secretion are essential for glucose homeostasis. One key mechanism of β cell nutrient sensing uses the mitochondrial GTP (mtGTP) cycle. In this cycle, mtGTP synthesized by succinyl-CoA synthetase (SCS) is hydrolyzed via mitochondrial PEPCK (PEPCK-M) to make phosphoenolpyruvate, a high-energy metabolite that integrates TCA cycling and anaplerosis with glucose-stimulated insulin secretion (GSIS). Several strategies, including xenotopic overexpression of yeast mitochondrial GTP/GDP exchanger (GGC1) and human ATP and GTP-specific SCS isoforms, demonstrated the importance of the mtGTP cycle. These studies confirmed that mtGTP triggers and amplifies normal GSIS and rescues defects in GSIS both in vitro and in vivo. Increased mtGTP synthesis enhanced calcium oscillations during GSIS. mtGTP also augmented mitochondrial mass, increased insulin granule number, and membrane proximity without triggering de-differentiation or metabolic fragility. These data highlight the importance of the mtGTP signal in nutrient sensing, insulin secretion, mitochondrial maintenance, and β cell health. : Jesinkey et al. report that mitochondrial GTP (mtGTP) is an integrative nutrient sentinel regulating β cell function. Signaling from mtGTP raises calcium independent of oxidative phosphorylation to promote insulin secretion. Without overworking the β cell, mtGTP cycling potentiates insulin secretion, nutrient sensing, and mitochondrial expansion alongside promoting health and increasing insulin reserves. Keywords: mitochondrial GTP, PEPCK-M, insulin secretion, oxidative phosphorylation, phosphoenolpyruvate, anaplerosis, succinyl-CoA synthetase, stable isotope, metabolic flux, MIMOSA