Computation of a probabilistic and anisotropic failure metric on the aortic wall using a machine learning-based surrogate model.

Scalar-valued failure metrics are commonly used to assess the risk of aortic aneurysm rupture and dissection, which occurs under hypertensive blood pressures brought on by extreme emotional or physical stress. To compute failure metrics under an elevated blood pressure, a classical patient-specific computer model consists of multiple computation steps involving inverse and forward analyses. These classical procedures may be impractical for time-sensitive clinical applications that require prompt feedback to clinicians. In this study, we developed a machine learning-based surrogate model to directly predict a probabilistic and anisotropic failure metric, namely failure probability (FP), on the aortic wall using aorta geometries at the systolic and diastolic phases. Ascending thoracic aortic aneurysm (ATAA) geometries of 60 patients were obtained from their CT scans, and biaxial mechanical testing data of ATAA tissues from 79 patients were collected. Finite element simulations were used to generate datasets for training, validation, and testing of the ML-surrogate model. The testing results demonstrated that the ML-surrogate can compute the maximum FP failure metric, with 0.42% normalized mean absolute error, in 1 s. To compare the performance of the ML-predicted probabilistic FP metric with other isotropic or deterministic metrics, a numerical case study was performed using synthetic "baseline" data. Our results showed that the probabilistic FP metric had more discriminative power than the deterministic Tsai-Hill metric, isotropic maximum principal stress, and aortic diameter criterion.

Isotretinoin Induced Hyperlipidemia and Impact of Leptin Gene rs 7799039 Polymorphism in Safety of Acne Patients.

BACKGROUND: Acne vulgaris (AV) is a chronic inflammatory disease that affects the pilosebaceous unit. Leptin (LEP) gene polymorphisms is associated with higher risk of multiple disorders. Insulin-like growth factor-1 (IGF-1) exerts comedogenic effect by stimulating the sebaceous glands. Isotretinoin is an effective oral therapy for AV with many side effects including hyperlipidemia and increased serum levels of liver enzymes. PURPOSE: To evaluate the impact of LEP gene rs7799039 polymorphism in acne patients' clinical response lipid profile and liver enzymes following 6 months oral isotretinoin therapy in Egyptian AV patients. METHODS: One hundred eligible AV patients received 0.5 mg/kg oral isotretinoin for 6 months. Patients' demographics and clinical data were obtained. Body mass index (BMI), lipid profile, liver enzymes and IGF-1 were measured at baseline and after 6 months of therapy. Genotyping was done for LEP gene rs 7799039. RESULTS: Six month administration of oral isotretinoin in Egyptian AV patients is associated with significantly elevated aspartate transaminase (AST) in CC and AC genotypes (P<0.001). Significant alanine aminotransferase (ALT) elevation was observed in CC, AC and AA genotypes (P <0.001, 0.004, 0.002, respectively). Total cholesterol (TC), triglycerides (TG) and low density lipoprotein (LDL) were elevated significantly P<0.001) in the three genotypes. IGF-1 was decreased significantly in CC and AC genotypes (P<0.001). CC genotype is associated with highest response (P<0.001). CONCLUSION: LEP rs7799039 gene had an impact on the clinical response, lipid profile and liver enzymes in AV patients treated with oral isotretinoin. LEP rs7799039 CC genotype is predicted to be the treatment candidate for 6 month oral isotretinoin.