Background: Stabilization and increased activity of hypoxia-inducible factor 1-α (HIF-1α) can directly increase cancellous bone formation and play an essential role in bone modeling and remodeling. However, whether an increased HIF-1α expression in adipose-derived stem cells (ADSCs) increases osteogenic capacity and promotes bone regeneration is not known.Results: In this study, ADSCs transfected with small interfering RNA and HIF-1α overexpression plasmid were established to investigate the proliferation, migration, adhesion, and osteogenic capacity of ADSCs and the angiogenic ability of human umbilical vein endothelial cells (HUVECs). Overexpression of HIF-1α could promote the biological functions of ADSCs, and the angiogenic ability of HUVECs. Western blotting showed that the protein levels of osteogenesis-related factors were increased when HIF-1α was overexpressed. Furthermore, the influence of upregulation of HIF-1α in ADSC sheets on osseointegration was evaluated using a Sprague–Dawley (SD) rats implant model, in which the bone mass and osteoid mineralization speed were evaluated by radiological and histological analysis. The overexpression of HIF-1α in ADSCs enhanced bone remodeling and osseointegration around titanium implants. However, transfecting the small interfering RNA (siRNA) of HIF-1α in ADSCs attenuated their osteogenic and angiogenic capacity. Finally, it was confirmed in vitro that HIF-1α promotes osteogenic differentiation and the biological functions in ADSCs via the VEGF/AKT/mTOR pathway.Conclusions: This study demonstrates that HIF-1α has a critical ability to promote osteogenic differentiation in ADSCs by coupling osteogenesis and angiogenesis via the VEGF/AKT/mTOR signaling pathway, which in turn increases osteointegration and bone formation around titanium implants.Graphical

Periodontitis and peri-implantitis are common biofilm-mediated infectious diseases affecting teeth and dental implants and have been considered to be initiated with microbial dysbiosis. To further understand the essence of oral microbiome dysbiosis in terms of bacterial interactions, community structure, and microbial stability, we analyzed 64 plaque samples from 34 participants with teeth or implants under different health conditions using metagenomic sequencing. After taxonomical annotation, we computed the inter-species correlations, analyzed the bacterial community structure, and calculated the microbial stability in supra- and subgingival plaques from hosts with different health conditions. The results showed that when inflammation arose, the subgingival communities became less connective and competitive with fewer hub species. In contrast, the supragingival communities tended to be more connective and competitive with an increased number of hub species. Besides, periodontitis and peri-implantitis were associated with significantly increased microbial stability in subgingival microbiome. These findings indicated that the periodontal and peri-implant dysbiosis is associated with aberrant alterations in the bacterial correlations, community structures, and local stability. The highly connected hub species, as well as the major contributing species of negative correlations, should also be given more concern in future studies.

This study aimed to investigate the accuracy of a robotic computer-assisted implant surgery (r-CAIS) for immediate implant placement. Patients requiring immediate implant placement in the maxillary anterior region were enrolled for r-CAIS. Before surgery, the patients underwent a cone beam computed tomography (CBCT) scan with a positioning marker. Virtual implant placement position and drilling sequences were planned. Following spatial registration and calibration, the implants were placed with the robotic system under supervision. A postoperative CBCT was taken to control the actual implant positions. The DICOM data of the virtually planned and the actually placed implant were superimposed and registered through the accuracy verification software of the robotic system. The accuracy was calculated automatically. The deviation at the mesial-distal, labial-palatal, and apico-coronal directions were recorded. Fifteen patients with 20 implants were included. No adverse surgical events or postoperative complications were reported. The global platform, apex, and angular deviation were 0.75 ± 0.20 mm (95 % CI: 0.65 to 0.84 mm), 0.70 ± 0.27 mm (95 % CI: 0.57 to 0.82 mm), and 1.17 ± 0.73° (95 % CI: 0.83 to 1.51°), respectively. Moreover, the vertical platform and apex deviation were 0.50 ± 0.31 mm, (95 % CI: 0.35 to 0.64 mm) and 0.48 ± 0.32 mm, (95 % CI: 0.33 to 0.63 mm), respectively. All the placed implant positions were further labial and apical than the planned ones, respectively. High accuracy of immediate implant placement was achieved with the robotic system. Our study provided evidence to support the potential of the robotic system in implant placement, even in challenging scenarios.

Background: Age estimation from panoramic radiographs is a fundamental task in forensic sciences. Previous age assessment studies mainly focused on juvenile rather than elderly populations (> 25 years old). Most proposed studies were statistical or scoring-based, requiring wet-lab experiments and professional skills, and suffering from low reliability. Result: Based on Soft Stagewise Regression Network (SSR-Net), we developed DENSEN to estimate the chronological age for both juvenile and older adults, based on their orthopantomograms (OPTs, also known as orthopantomographs, pantomograms, or panoramic radiographs). We collected 1903 clinical panoramic radiographs of individuals between 3 and 85 years old to train and validate the model. We evaluated the model by the mean absolute error (MAE) between the estimated age and ground truth. For different age groups, 3–11 (children), 12–18 (teens), 19–25 (young adults), and 25+ (adults), DENSEN produced MAEs as 0.6885, 0.7615, 1.3502, and 2.8770, respectively. Our results imply that the model works in situations where genders are unknown. Moreover, DENSEN has lower errors for the adult group (> 25 years) than other methods. The proposed model is memory compact, consuming about 1.0 MB of memory overhead. Conclusions: We introduced a novel deep learning approach DENSEN to estimate a subject’s age from a panoramic radiograph for the first time. Our approach required less laboratory work compared with existing methods. The package we developed is an open-source tool and applies to all different age groups.

The study aimed to evaluate the accuracy and safety of a semi-active robotic system for implant placement in atrophic posterior maxilla. Patients underwent robot-assisted implant placement in atrophic posterior maxilla were identified and included. Cone-beam computed tomography (CBCT) was performed before surgery. The virtual implant position and drilling sequences were planned in the robotic planning system. Patients with positioning marker took an intraoral scan. The preoperative CBCT and the intraoral scan were superimposed in the robotic software. After registration, the implant bed was prepared utilizing the robotic arm with 1 mm safety margin below the maxillary sinus floor. The transcrestal sinus floor elevation (TSFE) was performed by the dentist, followed by the implant placement with the robotic arm. A postoperative CBCT was taken and superimposed with the preoperative one to calculate the accuracy of implant placement. Complications and adverse events were recorded. Deviations between the implant platform and apex levels were analyzed using the paired t-test. P < 0.05 was considered statistically significant. Twenty-seven implants of 20 patients were included. No intraoperative and postoperative complications were reported. The global, lateral and vertical platform deviations were 0.73 ± 0.27 mm, 0.35 ± 0.23 mm and 0.35 ± 0.57 mm, respectively. The global, lateral and vertical apex deviations were 0.77 ± 0.23 mm, 0.41 ± 0.20 mm and 0.34 ± 0.57 mm, respectively. There were significant differences between the global, lateral and vertical deviations between the implant platform and apex levels (P < 0.05, respectively). The angular deviation was 1.58 ± 0.76°. High accuracy and safety for implant placement in atrophic posterior maxilla could be achieved using a semi-active robotic system, with the TSFE procedure performed by the dentist. This study provides significant evidence to support the application of semi-active robotic systems for implant placement in atrophic posterior maxilla.