Background: Both substance P and hypoxia-inducible factor 1 alpha (HIF-1α) are involved in inflammation and angiogenesis. However, the relationship between substance P and HIF-1α in rat periodontitis is still unknown. Methods: Ligation-induced rat periodontitis was established to observe the distribution and expression of substance P and HIF-1α by immunohistochemistry. Rat gingival fibroblasts were cultured and stimulated with Porphyromonas gingivalis lipopolysaccharide (LPS). Recombinant substance P was applied to elaborate the relationship between substance P and HIF-1α in gingival fibroblasts in vitro. Primary mouse bone marrow-derived macrophages (BMMs) were isolated and cultured to observe the effect of substance P on receptor activator of NF-κB ligand (RANKL)-induced osteoclastogenesis by TRAP staining. Western blotting was used to investigate the expression of HIF-1α, osteoprotegerin (OPG) and RANKL. Results: Rat experimental periodontitis was successfully established 6 weeks after ligation. Gingival inflammatory infiltration and alveolar bone loss were observed. Positive expression of substance P was found in the infiltrating cells. Higher HIF-1α levels were observed in periodontitis compared to that of normal tissues. Substance P upregulated the level of HIF-1α in gingival fibroblasts with or without 1 μg/ml LPS in vitro (*P < 0.05). Substance P upregulated the expression of HIF-1α in RANKL-stimulated BMMs in vitro. Substance P also increased the RANKL/OPG ratio in gingival fibroblasts (*P < 0.05). Both 10 nM and 50 nM substance P promoted RANKL-induced osteoclast differentiation (*P < 0.05). Conclusion: Substance P participates in periodontitis by upregulating HIF-1α and the RANKL/OPG ratio.

To overcome the limitation of a single classification model’s inability to simultaneously identify multiple lesion targets within periapical radiographs, This study proposes YoCNET (Yolov5 + ConvNeXt), a novel deep learning integrated model. YoCNET leverages the target detection capability of Yolov5 and the image classification capability of ConvNeXt to achieve automatic segmentation of individual teeth and concurrent detection of periapical lesions across multiple teeth. A dataset of 1,305 periapical radiographs was used to train and validate the ConvNeXt and ResNet34 models, with an 8:2 split for training and validation. Deciduous teeth were excluded from the dataset. Furthermore, 717 individual teeth images were extracted from 200 previously unused periapical radiographs for integrated model validation. Evaluation metrics included accuracy, precision, sensitivity, F1 score, AUC (Area Under Curve), and a confusion matrix.The YoCNET integrated model demonstrated values of 90.93%, 98.88%, 85.30%, 0.9159, and 0.9757 for accuracy, precision, sensitivity, F1 score, and AUC, respectively. These metrics were superior to those achieved by the YoRNET (Yolov5 + ResNet34) integrated model, which recorded 80.47%, 83.78%, 82.16%, 0.8296, and 0.8822. The integrated model achieved high accuracy and efficiency in automatic teeh segmentation by Yolov5 and in automatically detecting multiple periapical lesions by ConvNeXt. YoCNET exhibited superior overall data performance, making it a more suitable deep learning integrated model for clinical applications.

Purpose: Numerous studies have investigated the use of convolutional neural network (CNN) models for detecting periapical lesions(PLs). However, limited research has focused on evaluating their potential in assisting clinicians with diagnosis. This study aims to utilize two deep learning(DL) models, ConvNeXt and ResNet34, to aid novice dentists in the detection of PLs on periapical radiographs (PRs). By assessing the diagnostic support provided by these models, this research seeks to promote the clinical application of DL in dentistry. Materials and methods: In this study, 1,305 PRs were gathered and then split into a training set of 1,044 images and a validation set of 261 images, following an 80/20 ratio. The model’s effectiveness was assessed using various measures, including precision, sensitivity, F1 score, specificity, accuracy, and the area under the curve (AUC). To evaluate the impact of the model on diagnostic performance by novice dentists, we used an additional set of 800 individual teeth PRs, which were not included in the training or validation sets. The diagnostic performance and time of three novice dentists were measured both with and without model assistance. Results: The precision of ConvNeXt was 85.93%, with an F1 score of 0.92, accuracy of 91.25%, sensitivity of 98.49%, specificity of 84.11%, and an AUC of 0.9693, outperforming ResNet34 across all metrics. In comparison, ResNet34 achieved a precision of 83.08%, an F1 score of 0.84, accuracy of 81.63%, sensitivity of 84.38%, specificity of 78.13%, and an AUC of 0.8988. In the model-assisted diagnosis phase, both ConvNeXt and ResNet34 improved the diagnostic performance of novice dentists. With the help of ConvNeXt, the average AUC of three dentists increased from 0.88 to 0.94, while with ResNet34, the average AUC of the three dentists improved from 0.88 to 0.91. ConvNeXt performed better than ResNet34 (p < 0.05). Additionally, ConvNeXt reduced the average diagnostic time of the three dentists from 178.8 min to 141.9 min, while ResNet34 reduced the average diagnostic time from 178.8 min to 153.6 min. Conclusion: ConvNeXt significantly improved the diagnostic performance of novice dentists and reduced the time required for diagnosis, thereby enhancing clinical efficiency in both diagnosis and treatment. This model shows potential for application in dental clinics or educational institutions where experienced specialists are limited, but there is a large presence of novice, less-experienced dentists.

Tendinopathy is a prevalent musculoskeletal disorder, accounting for over 30% of musculoskeletal lesions. However, current therapeutic strategies for tendinopathy are limited and often yield unsatisfactory clinical outcomes. Therefore, there is a critical need to explore novel therapeutic approaches with minimal side effects for tendinopathy and its early lesions. In this study, we successfully designed and synthesized a copper(I)-based nanocluster, named Cu₁₁, which possesses remarkable enzyme-like and ROS-scavenging activities. This unique combination of properties qualifies Cu₁₁ as an attractive anti-inflammatory and anti-ROS agent. The Cu₁₁ clusterzymes specifically target mitochondria, effectively scavenging excessive reactive oxygen species (ROS) and reducing oxidative stress. Furthermore, Cu₁₁ clusterzymes inhibit the activation of key signaling pathways involved in inflammation, namely tumor necrosis factor (TNF), mitogen-activated protein kinase (MAPK), and nuclear factor (NF)-κB, leading to a decrease in the release of proinflammatory cytokines. Excitingly, our in vivo experiments using a collagenase-induced acute Achilles tendinopathy model demonstrated that Cu₁₁ clusterzymes effectively alleviate inflammation and oxidative stress, without causing systemic toxicity. This study highlights the potential of copper-based clusterzymes as therapeutic agents for the treatment of Achilles tendinopathy and other inflammatory diseases. The unique enzyme-like and ROS-scavenging activities of Cu₁₁ make it a promising candidate for further development and clinical translation.