Introduction The induced membrane technique for the treatment of large bone defects is a two-step procedure. In the first operation, a foreign body membrane is induced around a spacer, then, in the second step, several weeks or months later, the spacer is removed and the Membrane pocket is filled with autologous bone material. Induction of a functional biological membrane might be avoided by initially using a biological membrane. In this study, the effect of a human acellular dermis (hADM, Epiflex, DIZG gGmbH) was evaluated for the treatment of a large (5 mm), plate-stabilised femoral bone defect. Material and MethodsIn an established rat model, hADM was compared to the two-stage induced membrane technique and a bone defect without membrane cover. Syngeneous spongiosa from donor animals was used for defect filling in all groups. The group size in each case was n = 5, the induction time of the membrane was 3-4 weeks and the healing time after filling of the defect was 8 weeks. Results The ultimate loads were increased to levels comparable with native bone in both membrane groups (hADM: 63.2% ± 29.6% of the reference bone, p < 0.05 vs. no membrane, induced membrane: 52.1% ± 25.8% of the reference bone, p < 0.05 vs. no membrane) and were significantly higher than the control group without membrane (21.5%). The membrane groups were radiologically and histologically almost completely bridged by new bone formation, in contrast to the control Group where no closed osseous bridging could be observed. Conclusion The use of the human acellular dermis leads to equivalent healing results in comparison to the two-stage induced membrane technique. This could lead to a shortened therapy duration of large bone defects.
The Masquelet technique is used to treat large bone defects; it is a two-stage procedure based on an induced membrane. To improve the induced membrane process, demineralized bone matrix in granular (GDBM) and fibrous form (f-DBM) was tested with and without bone marrow mononuclear cells (BMC) as filling of the membrane against the gold standard filling with syngeneic cancellous bone (SCB). A total of 65 male Sprague–Dawley rats obtained a 5 mm femoral defect. These defects were treated with the induced membrane technique and filled with SCB, GDBM, or f-DBM, with or without BMC. After a healing period of eight weeks, the femurs were harvested and submitted for histological, radiological, and biomechanical analyses. The fracture load in the defect zone was lower compared to SCB in all groups. However, histological analysis showed comparable new bone formation, bone mineral density, and cartilage proportions and vascularization. The results suggest that f-DBM in combination with BMC and the induced membrane technique cannot reproduce the very good results of this material in large, non-membrane coated bone defects, nevertheless it supports the maturation of new bone tissue locally. It can be concluded that BMC should be applied in lower doses and inflammatory cells should be removed from the cell preparation before implantation.
Introduction Cell-based therapy by bone marrow mononuclear cells (BMC) in a large-sized bone defect has already shown improved vascularization and new bone formation. First clinical trials are already being conducted. BMC were isolated from bone marrow aspirate and given back to patients in combination with a scaffold within some hours. However, the optimal concentration of BMC has not yet been determined for bone healing. With this study, we want to determine the optimal dosage of the BMC in the bone defect to support bone healing. Material and methods Scaffolds with increasing BMC concentrations were inserted into a 5 mm femoral defect, cell concentrations of 2 × 10 6 BMC/mL, 1 × 10 7 BMC/mL and 2 × 10 7 BMC/mL were used. Based on the initial cell number used to colonize the scaffolds, the groups are designated 1 × 10 6 , 5 × 10 6 and 1 × 10 7 group. Bone healing was assessed biomechanically, radiologically (µCT), and histologically after 8 weeks healing time. Results Improved bone healing parameters were noted in the 1 × 10 6 and 5 × 10 6 BMC groups. A significantly higher BMD was observed in the 1 × 10 6 BMC group compared to the other groups. Histologically, a significantly increased bone growth in the defect area was observed in group 5 × 10 6 BMC. This finding could be supported radiologically. Conclusion It was shown that the effective dose of BMC for bone defect healing ranges from 2 × 10 6 BMC/mL to 1 × 10 7 BMC/mL. This concentration range seems to be the therapeutic window for BMC-supported therapy of large bone defects. However, further studies are necessary to clarify the exact BMC-dose dependent mechanisms of bone defect healing and to determine the therapeutically effective range more precisely.
Background and study aims: Artificial intelligence (AI) is increasingly being used to detect neoplasia and interpret endoscopic images. The T stage of Barrett’s carcinoma is a major criterion for deciding on subsequent treatment. Although endoscopic ultrasound is still the standard for preoperative staging, its value is debatable. Novel tools are required to assist with staging, to optimize results. This study aimed to investigate correctly identified T stages of Barrett’s carcinoma using an artificial intelligence system, on the basis of endoscopic images. Patients and methods: 1020 images (minimum one per patient, maximum three) from 577 patients with Barrett’s adenocarcinoma were used for training and internal validation of a convolutional neuronal network. In all, 821 images were selected to train the model and 199 images were ultimately used for validation. Results: AI recognized Barrett’s mucosa without neoplasia with an accuracy of 85% (95% CI, 82.7–87.1). Mucosal cancer was identified with a sensitivity of 72% (95% CI, 67.5–76.4), specificity of 64% (95% CI, 60.0–68.4), and accuracy of 68% (95% CI, 64.6–70.7). The sensitivity, specificity, and accuracy for early Barrett’s neoplasia < T1b (sm2) were 57% (95% CI, 51.8–61.0), 77% (95% CI, 72.3–80.2), and 67% (95% CI, 63.4–69.5), respectively. More advanced stages (T3/T4) were diagnosed correctly with a sensitivity of 71% (95% CI, 65.1–76.7) and specificity of 73% (95% CI, 69.7–76.5). The overall accuracy was 73% (95% CI, 69.6–75.5). Conclusions: Clinically important decision-making on endoscopic therapy can be achieved with a high degree of certainty using artificial intelligence.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.