A comparison of carotid atherosclerosis in symptomatic patients between 2002-2005 and 2012-2015 cohorts using multi-contrast magnetic resonance vessel wall imaging

OBJECTIVE To compare the morphological and compositional characteristics of carotid plaques in two cohorts(2002−2005 and 2012−2015)of Chinese patients using magnetic resonance vessel wall imaging.METHODS Symptomatic patients with carotid atherosclerotic plaques who underwent carotid vessel wall magnetic resonance imaging between 2002−2005 and 2012−2015 were retrospectively recruited.Plaque morphology[including mean wall area,wall thickness,and maximum normalized wall index(NWI)]and composition[including calcification,intraplaque hemorrhage,and lipid-rich necrotic core(LRNC)]in symptomatic carotid arteries were evaluated and compared between patients in these two time periods.RESULTS A total of 258 patients,including 129 patients in the 2002−2005 cohort and 129 patients in the 2012−2015 cohort,were recruited.Statin use(49.6%vs.32.6%,P=0.004)and hypertension(76.0%vs.62.8%,P=0.015)were significantly more common in the 2012-2015 cohort than in the 2002−2005 cohort.Patients in the 2012−2015 cohort also exhibited significantly low plaque burden parameters(all P<0.05),as well as a lower prevalence(68.2%vs.89.9%,P<0.001)and volume percentages of LRNC(11.2%±14.2%vs.25.7%±17.7%,P<0.001).These differences remained significant after adjustment for clinical factors.The differences in the volume percentages of LRNC also remained significant after an additional adjustment for maximum NWI(P<0.001).CONCLUSIONS Patients in the 2012−2015 cohort had a lower plaque burden and volume percentages of LRNC in symptomatic carotid arteries than those in the 2002−2005 cohort.These findings indicate that carotid plaques in the recent cohort had a lower severity and vulnerability.

Integrating machine learning and CALPHAD method for exploring low-modulus near-β-Ti alloys

Traditional theoretical and empirical calculation methods can guide the design of β-and metastable β-alloys for bio-titanium. However, it is still difficult to obtain novel near-β-Ti alloys with low modulus. This study developed a method that combines machine learning with calculation of phase diagrams(CALPHAD) to facilitate the design of near-β-Ti alloys. An elastic modulus database of Ti–Nb–Zr–Mo–Ta–Sn system was constructed first, and then three features(the electron to atom ratio, mean absolute deviation of atom mass, and mean electronegativity) were selected as the key factors of modulus by performing a three-step feature selection. With these features, a highly accurate model was built for predicting the modulus of near-β-Ti alloys. To further ensure the accuracy of modulus prediction, machine learning with the elastic constants calculated was leveraged by CALPHAD database. The root mean square error of the well-trained model can be as low as 6.75 GPa. Guided by the prediction of machine learning and CALPHAD, three novel near-β-Ti alloys with elastic modulus below 50 GPa were successfully designed in this study. The best candidate alloy(Ti–26Nb–4Zr–4Sn–1Mo–Ta) exhibits an ultra-low modulus(36.6 GPa) after cold rolling with a thickness reduction of 20%. Our method can greatly save time and resources in the development of novel Ti alloys, and experimental verifications have demonstrated the reliability of this method.