RESUMO
In our previous studies, geraniin was reported to have a preventive effect in the rat model of tretinoin-induced osteoporosis. However, whether geraniin exhibits an inhibitory effect on bone resorption or on MMP-9 expression is not yet known. We present here our novel findings from in vitro experiments that geraniin (a) decreases the number of mature osteoclasts and pre-osteoclast in cultures, (b) reduces the osteoclastic fusion index, and (c) inhibits the resorption areas and resorption pits. We also report that geraniin suppresses the mRNA and protein expression levels of MMP-9. These results demonstrate that geraniin has an inhibitory effect on the bone-absorption ability of osteoclasts in vitro, and the mechanisms may be closely associated with the downregulation of mRNA and protein expression of MMP-9.
Assuntos
Reabsorção Óssea , Regulação Enzimológica da Expressão Gênica/efeitos dos fármacos , Glucosídeos/farmacologia , Taninos Hidrolisáveis/farmacologia , Metaloproteinase 9 da Matriz/metabolismo , Osteoclastos/efeitos dos fármacos , Animais , Conservadores da Densidade Óssea/farmacologia , Linhagem Celular , Inibidores Enzimáticos/farmacologia , Glucosídeos/química , Taninos Hidrolisáveis/química , Metaloproteinase 9 da Matriz/genética , Estrutura Molecular , RNA Mensageiro/genética , RNA Mensageiro/metabolismo , RatosRESUMO
Background: Semantic segmentation is crucial in medical image diagnosis. Traditional deep convolutional neural networks excel in image classification and object detection but fall short in segmentation tasks. Enhancing the accuracy and efficiency of detecting high-level cervical lesions and invasive cancer poses a primary challenge in segmentation model development. Methods: Between 2018 and 2022, we retrospectively studied a total of 777 patients, comprising 339 patients with high-level cervical lesions and 313 patients with microinvasive or invasive cervical cancer. Overall, 1554 colposcopic images were put into the DeepLabv3+ model for learning. Accuracy, Precision, Specificity, and mIoU were employed to evaluate the performance of the model in the prediction of cervical high-level lesions and cancer. Results: Experiments showed that our segmentation model had better diagnosis efficiency than colposcopic experts and other artificial intelligence models, and reached Accuracy of 93.29 %, Precision of 87.2 %, Specificity of 90.1 %, and mIoU of 80.27 %, respectively. Conclution: The DeepLabv3+ model had good performance in the segmentation of cervical lesions in colposcopic post-acetic-acid images and can better assist colposcopists in improving the diagnosis.