Understanding the contagiousness of Covid-19 strains: A geometric approach.

Protein-protein interaction occurs on surface patches with some degree of complementary geometric and chemical features. Building on this understanding, this study endeavors to characterize the spike protein of the SARS-CoV-2 virus at the morphological and geometrical levels in its Alpha, Delta, and Omicron variants. In particular, the affinity between different SARS-CoV-2 spike proteins and the ACE2 receptor present on the membrane of the human respiratory system cells is investigated. To achieve an adequate degree of geometrical accuracy, the 3D depth maps of the proteins in exam are filtered by developing an ad-hoc convolutional filter with a kernel implemented as a sphere of varying radius, simulating a ball rolling on the surface (similar to the 'rolling ball' filter). This ball ideally models a hypothetical molecule that could interface with the protein and is inspired by the geometric approach to macromolecule-ligand interactions proposed by Kuntz et al. in 1982. The aim is to mitigate the imperfections and to obtain a smoother surface that could be studied from a geometrical perspective for binding purposes. A set of geometric descriptors, borrowed from the 3D face analysis context is then mapped point-by-point onto protein depth maps. Following a feature extraction phase inspired by Histogram of Oriented Gradients and Local Binary Patterns, the final histogram features are used as input for a Support Vector Machine classifier to automatically classify the proteins according to their surface affinity, where a similarity in shape is observed between ACE2 and the spike protein of the SARS-CoV-2 Omicron variant. Finally, Root Mean Square Error analysis is used to quantify the geometrical affinity between the ACE2 receptor and the respective Receptor Binding Domains of the three SARS-CoV-2 variants, culminating in a geometrical explanation for the higher contagiousness of Omicron relative to the other variants under study.

Do facial soft tissue thicknesses change after surgeries correcting dental malocclusions? An intra- and inter-patient statistical analysis on soft-tissue thicknesses in BSSO + LFI surgeries.

OBJECTIVES: The aim of this study was to analyse changes in facial soft tissue thickness (FSTT) after corrective surgeries for dental malocclusion. The correlation between body mass index (BMI) and sex of patients and their FSTT before undergoing surgery was analysed. MATERIALS AND METHODS: Cone beam computed tomography of seventeen patients that underwent Le Fort I osteotomy in combination with bilateral sagittal split osteotomy were collected. Hard and soft tissue landmarks were selected basing on the interventions. FSTT were computed, and measurements from pre- to post-operative were compared. The relationship between FSTT, sex, and BMI was investigated. RESULTS: Considering the comparison between pre- and post-operative measurements, any significant difference emerged (p > .05). The Pearson's correlation coefficient computed between BMI and the FSTT (pre-operative) showed a correlation in normal-weight patients; the region-specific analysis highlighted a stronger correlation for specific landmarks. Higher median values emerged for women than for men; the subset-based analysis showed that women presented higher values in the malar region, while men presented higher values in the nasal region. CONCLUSIONS: The considered surgeries did not affect the FSTT of the patients; differences related to BMI and sex were found. A collection of FSTT mean values was provided for twenty landmarks of pre- and post-operative of female and male subjects. CLINICAL RELEVANCE: This exploratory analysis gave insights on the behaviour of STT after maxillofacial surgeries that can be applied in the development of predictive methodologies for soft tissue displacements and to study modifications in the facial aspect of the patients.

Three-Dimensional Evaluation of Soft Tissue Malar Modifications after Zygomatic Valgization Osteotomy via Geometrical Descriptors.

Patients with severe facial deformities present serious dysfunctionalities along with an unsatisfactory aesthetic facial appearance. Several methods have been proposed to specifically plan the interventions on the patient's needs, but none of these seem to achieve a sufficient level of accuracy in predicting the resulting facial appearance. In this context, a deep knowledge of what occurs in the face after bony movements in specific surgeries would give the possibility to develop more reliable systems. This study aims to propose a novel 3D approach for the evaluation of soft tissue zygomatic modifications after zygomatic osteotomy; geometrical descriptors usually involved in face analysis tasks, i.e., face recognition and facial expression recognition, are here applied to soft tissue malar region to detect changes in surface shape. As ground truth for zygomatic changes, a zygomatic openness angular measure is adopted. The results show a high sensibility of geometrical descriptors in detecting shape modification of the facial surface, outperforming the results obtained from the angular evaluation.