Evaluation of Facial Aesthetic Changes in Growing Class II Patients Treated with Herbst or Elastodontics: A Retrospective Study.

The objective of this study was to evaluate the facial profile changes of patients treated for class II skeletal malocclusions with an elastodontic appliance compared to those treated with the Herbst appliance and a control group. Forty class II patients were treated using an elastodontic appliance (Group EA) and were compared to 40 patients treated with the Herbst appliance (Group H) and to 40 untreated class II children (Group C). Aesthetic profile variables were analysed using Arnett's analysis.

A machine learning-based analysis for the definition of an optimal renal biopsy for kidney cancer.

Objective: Renal Tumor biopsy (RTB) can assist clinicians in determining the most suitable approach for treatment of renal cancer. However, RTB's limitations in accurately determining histology and grading have hindered its broader adoption and data on the concordance rate between RTB results and final pathology after surgery are unavailable. Therefore, we aimed to develop a machine learning algorithm to optimize RTB technique and to investigate the degree of concordance between RTB and surgical pathology reports.

Gold nanorod-assisted theranostic solution for nonvisible residual disease in bladder cancer.

Residual nonvisible bladder cancer after proper treatment caused by technological and therapeutic limitations is responsible for tumor relapse and progression. This study aimed to demonstrate the feasibility of a solution for simultaneous detection and treatment of bladder cancer lesions smaller than one millimeter. The α5β1 integrin was identified as a specific marker in 81% of human high-grade nonmuscle invasive bladder cancers and used as a target for the delivery of targeted gold nanorods (GNRs).

Effects of interfacial debonding on the stability of finitely strained periodic microstructured elastic composites.

Predicting failure initiation in nonlinear composite materials, often referred to as metamaterials, is a fundamental challenge in nonlinear solid mechanics. Microstructural failure mechanisms encompass fracture, decohesion, cavitation, compression-induced contact and instabilities, affecting their unconventional static and dynamic performances. To fully take advantage of these materials, especially in extreme applications, it is imperative to predict their nonlinear behaviour using reliable, accurate and computationally efficient numerical methodologies.