Universitätsmedizin der Johannes Gutenberg-Universität Mainz

Radicular concavities, particularly in the maxillary premolar region, represent anatomical features that may significantly affect periodontal health. Therefore, the aim of this study was to evaluate the prevalence, dimensions and distribution patterns of radicular grooves in the maxillary premolar region using CBCT.

In total, 3492 CBCT image datasets from patients treated in all dentistry departments of the University Hospital of Mainz were screened. After applying strict inclusion criteria, a cohort of 836 patients with 1678 maxillary premolars could be included in this study. Horizontal depth, vertical extent, angulation, dentine wall thickness, and the distance from the cemento-enamel junction to the groove origin were analyzed.

Patients with concavities in at least one premolar were identified in 637 of 836 cases (76.2 %). The analyzed first premolars exhibited one or more concavities in 83.7 % (right) and 80.7 % (left) of teeth; second premolars were affected in 37.4 % (right) and 30.0 % (left). Mesial concavities were the most prevalent (≈ 60 %), followed by distal (≈ 35 %) and buccal (≈ 4 %). Mean mesial depth reached 1.09 ± 0.39 mm and vertical extension frequently exceeded 6 mm. Three concavities in one premolar increased the risk ratio for the contralateral homologue up to 33.33 (p < 0.001).

The present study provides a detailed morphological characterization of radicular concavities in maxillary premolars using three‑dimensional imaging. These anatomical structures should be considered in clinical decision‑making, especially in patients with a predisposition to periodontal disease.

Social play behaviour is a fundamental aspect of animal interaction, shaping social bonds and enhancing cognitive capacity. While studies on human-animal playful interactions have primarily focused on a few selected species, research on rodents beyond rats remains scarce. We, therefore, addressed the dynamics of social play in tame mice, selectively bred to approach human hands, and golden hamsters, comparing their responses during interactions with humans and conspecifics. Tame mice exhibited heightened playfulness with humans, marked by increased vocalisations and hand chasing behaviour, in addition to more frequent interactions and playful conspecific interactions accompanied by elevated ultrasonic vocalisations, whereas unselected control mice showed more aggressive behaviours. Hamsters showed minimal interaction with human hands, and their vocalisations were not associated with heterospecific contact. In contrast, they engaged in playful conspecific behaviours such as boxing and pinning, which were accompanied by frequent vocalisations. Notably, vocalisation patterns varied between heterospecific and conspecific engagement in both species, suggesting context-dependent vocal expression. These findings offer insights into the evolutionary basis influencing social play across species with differing social structures. Understanding these mechanisms enriches our comprehension of the diverse pathways through which animals form social bonds.

Robot-assisted minimally invasive esophagectomy (RAMIE) is a complex procedure that may benefit from workflow analysis for context-aware assistance and surgical data science. This study aimed to model the RAMIE workflow, validate the applicability of the obtained workflow model in the operating room (OR) and retrospectively assess its generalizability across three academic centers using video data and automated workflow analysis with machine learning (ML).

A RAMIE workflow model was developed based on currently available literature, participatory OR observation, and expert interviews. This model was formalized to be included into a checklist tool to document the workflow live in the OR. To investigate generalizability of the workflow model, the surgical phases of 36 RAMIE videos from three different academic hospitals were retrospectively annotated. Based on this data set, a ML model was trained and tested within a six-fold cross validation.

Ten surgical phases with 60 underlying steps were identified for RAMIE. The applicability of the workflow model was validated with live documentation in the OR. Multicenter video annotations revealed significant inter-institutional differences in the duration of all ten RAMIE phases. The ML model for automatic phase recognition showed an accuracy of 0.872 ± 0.091 and an f1-score of 0.872 ± 0.082 over all videos. The center with the best performing videos achieved a mean accuracy of 0.919 ± 0.036.

The RAMIE workflow was successfully modeled and validated in a retrospective multicenter setting. Despite high variability in phase duration between surgical centers, ML-based phase recognition achieved highly promising results.