This study presents transatrial stenting as a palliative treatment for central venous obstruction in dogs with cardiac tumors. Two dogs presented with lethargy and respiratory distress. On diagnostic imaging, a large cardiac tumor with central venous obstruction was identified in both dogs. One dog showed pleural effusion, plethoric caudal vena cava (CdVC), hepatic congestion, and ascites. The other dog had cervical swelling and pleural effusion. Transatrial stenting was performed in the cranial vena cava, right atrium, and CdVC for the relief of caval and atrial compression due to cardiac tumors. Clinical improvement was observed post-procedure, despite thrombus formation within the stent in the second case. Endovascular transatrial stenting is a promising palliative treatment for venous return obstruction caused by cardiac tumors, effectively alleviating clinical symptoms and providing a viable management option.

A secondary anterior open bite may develop after a mandibular condyle fracture, particularly when an initial conservative treatment fails to restore vertical ramus height or joint function. Herein, we describe a patient with unstable preexisting occlusion who developed a persistent anterior open bite after bilateral condylar neck fractures were managed conservatively. Bilateral condylar reconstruction was planned through virtual surgical simulation and executed with patient-specific guides to accurately reproduce the intended mandibular position. Partial replacement of the mandibular condyle was selected because the soft tissues within the mandibular fossae, including the discs, remained intact and demonstrated functional mobility over the articulating surfaces. Postoperative evaluation revealed close agreement between the planned and achieved mandibular positions, with less than two millimeters of deviation, and stable occlusion was maintained during long-term follow-up. This case illustrates the potential of digitally assisted planning to achieve predictable outcomes in patients with complex secondary deformities.

Objective: As part of an ongoing study, this study examined the impact of medications, such as heparin, aspirin, and sodium nitroprusside (SNP), on the factors linked to PTE after an intravenous injection of canine mesenchymal stem cell into experimental animals. Methods: Fluorescently labeled canine AdMSCs were administered intravenously into the tail veins of five-week-old male BALB/c hairless mice. This study compared the survival rates, biodistribution, platelet counts, D-dimer levels, and histological examination results among the drug treatment experimental and the control groups. Results: The final survival rates in the SNP, control aspirin, and heparin groups were 25%, 33%, 50%, and 100%, respectively. Ex vivo imaging confirmed fluorescence exclusively in the lungs of all subjects who died during the injection, with no fluorescence detected in the other organs. On the other hand, in the heparin experimental group, the surviving individuals exhibited fluorescence in the lungs and the liver on day one. Histological biopsies revealed PTE in all deceased individuals within the medication experimental groups (p = 0.029). Conclusions and Relevance: Heparin was highly effective, with no PTE-related deaths observed when used alongside cell injections. Aspirin revealed moderate effectiveness, surpassing the control group. On the other hand, the efficacy of SNP was inferior to that of the other two drugs.

Purpose: In this study, we aimed to classify an implant system by comparing the types of implant thread shapes shown on radiographs using various Convolutional Neural Networks (CNNs), particularly Xception, InceptionV3, ResNet50V2, and ResNet101V2. The accuracy of the CNN based on the implant site was compared. Materials and Methods: A total of 1000 radiographic images, consisting of eight types of implants, were preprocessed by resizing and CLAHE filtering, and then augmented. CNNs were trained and validated for implant thread shape prediction. Grad-CAM was used to visualize class activation maps (CAM) on the implant threads shown within the radiographic image. Results: Averaged over 10 validation folds, each model achieved an AUC of over 0.96: AUC of 0.961 (95% CI 0.952–0.970) with Xception, 0.973 (95% CI 0.966-0.980) with InceptionV3, 0.980 (95% CI 0.974-0.988) with ResNet50V2, and 0.983 (95% CI 0.975-0.992) with ResNet101V2. Accuracy was higher in the posterior region than in the anterior area in all four models. Most CAMs highlighted the implant surface where the threads were present; however, some showed responses in other areas. Conclusion The CNN models accurately classified implants in all areas of the oral cavity according to the thread shape, using radiographic images.

Objective: As part of an ongoing study, this study examined the impact of medications, such as heparin, aspirin, and sodium nitroprusside (SNP), on the factors linked to PTE after an intravenous injection of canine mesenchymal stem cell into experimental animals. Methods: Fluorescently labeled canine AdMSCs were administered intravenously into the tail veins of five-week-old male BALB/c hairless mice. This study compared the survival rates, biodistribution, platelet counts, D-dimer levels, and histological examination results among the drug treatment experimental and the control groups. Results: The final survival rates in the SNP, control aspirin, and heparin groups were 25%, 33%, 50%, and 100%, respectively. Ex vivo imaging confirmed fluorescence exclusively in the lungs of all subjects who died during the injection, with no fluorescence detected in the other organs. On the other hand, in the heparin experimental group, the surviving individuals exhibited fluorescence in the lungs and the liver on day one. Histological biopsies revealed PTE in all deceased individuals within the medication experimental groups (p = 0.029). Conclusions and Relevance: Heparin was highly effective, with no PTE-related deaths observed when used alongside cell injections. Aspirin revealed moderate effectiveness, surpassing the control group. On the other hand, the efficacy of SNP was inferior to that of the other two drugs.

Objective: As part of an ongoing study, this study examined the impact of medications, such as heparin, aspirin, and sodium nitroprusside (SNP), on the factors linked to PTE after an intravenous injection of canine mesenchymal stem cell into experimental animals. Methods: Fluorescently labeled canine AdMSCs were administered intravenously into the tail veins of five-week-old male BALB/c hairless mice. This study compared the survival rates, biodistribution, platelet counts, D-dimer levels, and histological examination results among the drug treatment experimental and the control groups. Results: The final survival rates in the SNP, control aspirin, and heparin groups were 25%, 33%, 50%, and 100%, respectively. Ex vivo imaging confirmed fluorescence exclusively in the lungs of all subjects who died during the injection, with no fluorescence detected in the other organs. On the other hand, in the heparin experimental group, the surviving individuals exhibited fluorescence in the lungs and the liver on day one. Histological biopsies revealed PTE in all deceased individuals within the medication experimental groups (p = 0.029). Conclusions and Relevance: Heparin was highly effective, with no PTE-related deaths observed when used alongside cell injections. Aspirin revealed moderate effectiveness, surpassing the control group. On the other hand, the efficacy of SNP was inferior to that of the other two drugs.

Objective: The present study assessed the occurrence of PTE after the intravenous infusion of canine AdMSCs (cAdMSCs) into experimental animals. Methods: Five-week-old male BALB/c hairless mice were categorized into groups labeled A to G. In the control group (A), fluorescently stained 2 × 106 cAdMSCs were diluted in 200 μL of suspension and injected into the tail vein as a single bolus. The remaining groups included the following: group B with 5 × 106 cells, group C with 3 × 106 cells, group D with 1 × 106 cells, group E with 1 × 106 cells injected twice with a one-day interval, group F with 2 × 106 cells in 100 μL of suspension, and group G with 2 × 106 cells in 300 μL of suspension. Results: Group D achieved a 100% survival rate, while none of the subjects in groups B and C survived (p = 0.002). Blood tests revealed a tendency for the D-dimer levels to increase as the cell dose increased (p = 0.006). The platelet count was higher in the low cell concentration groups and lower in the high cell concentration groups (p = 0.028). A histological examination revealed PTE in most deceased subjects (96.30%). Conclusions and Relevance: PTE was verified, and various variables were identified as potential contributing factors, including the cell dose, injection frequency, and suspension volume.

Purpose: In this study, we aimed to classify an implant system by comparing the types of implant thread shapes shown on radiographs using various Convolutional Neural Networks (CNNs), particularly Xception, InceptionV3, ResNet50V2, and ResNet101V2. The accuracy of the CNN based on the implant site was compared. Materials and Methods: A total of 1000 radiographic images, consisting of eight types of implants, were preprocessed by resizing and CLAHE filtering, and then augmented. CNNs were trained and validated for implant thread shape prediction. Grad-CAM was used to visualize class activation maps (CAM) on the implant threads shown within the radiographic image. Results: Averaged over 10 validation folds, each model achieved an AUC of over 0.96: AUC of 0.961 (95% CI 0.952–0.970) with Xception, 0.973 (95% CI 0.966-0.980) with InceptionV3, 0.980 (95% CI 0.974-0.988) with ResNet50V2, and 0.983 (95% CI 0.975-0.992) with ResNet101V2. Accuracy was higher in the posterior region than in the anterior area in all four models. Most CAMs highlighted the implant surface where the threads were present; however, some showed responses in other areas. Conclusion The CNN models accurately classified implants in all areas of the oral cavity according to the thread shape, using radiographic images.

Objective: As part of an ongoing study, this study examined the impact of medications, such as heparin, aspirin, and sodium nitroprusside (SNP), on the factors linked to PTE after an intravenous injection of canine mesenchymal stem cell into experimental animals. Methods: Fluorescently labeled canine AdMSCs were administered intravenously into the tail veins of five-week-old male BALB/c hairless mice. This study compared the survival rates, biodistribution, platelet counts, D-dimer levels, and histological examination results among the drug treatment experimental and the control groups. Results: The final survival rates in the SNP, control aspirin, and heparin groups were 25%, 33%, 50%, and 100%, respectively. Ex vivo imaging confirmed fluorescence exclusively in the lungs of all subjects who died during the injection, with no fluorescence detected in the other organs. On the other hand, in the heparin experimental group, the surviving individuals exhibited fluorescence in the lungs and the liver on day one. Histological biopsies revealed PTE in all deceased individuals within the medication experimental groups (p = 0.029). Conclusions and Relevance: Heparin was highly effective, with no PTE-related deaths observed when used alongside cell injections. Aspirin revealed moderate effectiveness, surpassing the control group. On the other hand, the efficacy of SNP was inferior to that of the other two drugs.

Purpose: In this study, we aimed to classify an implant system by comparing the types of implant thread shapes shown on radiographs using various Convolutional Neural Networks (CNNs), particularly Xception, InceptionV3, ResNet50V2, and ResNet101V2. The accuracy of the CNN based on the implant site was compared. Materials and Methods: A total of 1000 radiographic images, consisting of eight types of implants, were preprocessed by resizing and CLAHE filtering, and then augmented. CNNs were trained and validated for implant thread shape prediction. Grad-CAM was used to visualize class activation maps (CAM) on the implant threads shown within the radiographic image. Results: Averaged over 10 validation folds, each model achieved an AUC of over 0.96: AUC of 0.961 (95% CI 0.952–0.970) with Xception, 0.973 (95% CI 0.966-0.980) with InceptionV3, 0.980 (95% CI 0.974-0.988) with ResNet50V2, and 0.983 (95% CI 0.975-0.992) with ResNet101V2. Accuracy was higher in the posterior region than in the anterior area in all four models. Most CAMs highlighted the implant surface where the threads were present; however, some showed responses in other areas. Conclusion The CNN models accurately classified implants in all areas of the oral cavity according to the thread shape, using radiographic images.