Odontogenic keratocyst(OKC)is a common jaw cyst with a high recurrence rate.OKC combined with basal cell carcinoma as well as skeletal and other developmental abnormalities is thought to be associated with Gorlin synd...Odontogenic keratocyst(OKC)is a common jaw cyst with a high recurrence rate.OKC combined with basal cell carcinoma as well as skeletal and other developmental abnormalities is thought to be associated with Gorlin syndrome.Moreover,OKC needs to be differentiated from orthokeratinized odontogenic cyst and other jaw cysts.Because of the different prognosis,differential diagnosis of several cysts can contribute to clinical management.We collected 519 cases,comprising a total of 2157 hematoxylin and eosinstained images,to develop digital pathology-based artificial intelligence(AI)models for the diagnosis and prognosis of OKC.The Inception_v3 neural network was utilized to train and test models developed from patch-level images.Finally,whole slide imagelevel AI models were developed by integrating deep learning-generated pathology features with several machine learning algorithms.The AI models showed great performance in the diagnosis(AUC=0.935,95%CI:0.898–0.973)and prognosis(AUC=0.840,95%CI:0.751–0.930)of OKC.The advantages of multiple slides model for integrating of histopathological information are demonstrated through a comparison with the single slide model.Furthermore,the study investigates the correlation between AI features generated by deep learning and pathological findings,highlighting the interpretative potential of AI models in the pathology.Here,we have developed the robust diagnostic and prognostic models for OKC.The AI model that is based on digital pathology shows promise potential for applications in odontogenic diseases of the jaw.展开更多
Acceleration of tooth movement during orthodontic treatment is challenging, with osteoclast-mediated bone resorption on the compressive side being the rate-limiting step. Recent studies have demonstrated that mechanor...Acceleration of tooth movement during orthodontic treatment is challenging, with osteoclast-mediated bone resorption on the compressive side being the rate-limiting step. Recent studies have demonstrated that mechanoreceptors on the surface of monocytes/macrophages, especially adhesion G protein-coupled receptors (aGPCRs), play important roles in force sensing.However, its role in the regulation of osteoclast differentiation remains unclear. Herein, through single-cell analysis, we revealed that CD97, a novel mechanosensitive aGPCR, was expressed in macrophages. Compression upregulated CD97 expression and inhibited osteoclast differentiation;while knockdown of CD97 partially rescued osteoclast differentiation. It suggests that CD97 may be an important mechanosensitive receptor during osteoclast differentiation. RNA sequencing analysis showed that the Rap1a/ERK signalling pathway mediates the effects of CD97 on osteoclast differentiation under compression. Consistently, we clarified that administration of the Rap1a inhibitor GGTI298 increased osteoclast activity, thereby accelerating tooth movement. In conclusion,our results indicate that CD97 suppresses osteoclast differentiation through the Rap1a/ERK signalling pathway under orthodontic compressive force.展开更多
Oral leukoplakia is a common precursor lesion of oral squamous cell carcinoma,which indicates a high potential of malignancy.The malignant transformation of oral leukoplakia seriously affects patient survival and qual...Oral leukoplakia is a common precursor lesion of oral squamous cell carcinoma,which indicates a high potential of malignancy.The malignant transformation of oral leukoplakia seriously affects patient survival and quality of life;however,it is difficult to identify oral leukoplakia patients who will develop carcinoma because no biomarker exists to predict malignant transformation for effective clinical management.As a major problem in the field of head and neck pathologies,it is imperative to identify biomarkers of malignant transformation in oral leukoplakia.In this review,we discuss the potential biomarkers of malignant transformation reported in the literature and explore the translational probabilities from bench to bedside.Although no single biomarker has yet been applied in the clinical setting,profiling for genomic instability might be a promising adjunct.展开更多
基金supported by the National Nature Science Foundation of China(81671006,81300894)CAMS Innovation Fund for Medical Sciences(2019-I2M-5-038)National Clinical Key Discipline Construction Project(PKUSSNKP202102).
文摘Odontogenic keratocyst(OKC)is a common jaw cyst with a high recurrence rate.OKC combined with basal cell carcinoma as well as skeletal and other developmental abnormalities is thought to be associated with Gorlin syndrome.Moreover,OKC needs to be differentiated from orthokeratinized odontogenic cyst and other jaw cysts.Because of the different prognosis,differential diagnosis of several cysts can contribute to clinical management.We collected 519 cases,comprising a total of 2157 hematoxylin and eosinstained images,to develop digital pathology-based artificial intelligence(AI)models for the diagnosis and prognosis of OKC.The Inception_v3 neural network was utilized to train and test models developed from patch-level images.Finally,whole slide imagelevel AI models were developed by integrating deep learning-generated pathology features with several machine learning algorithms.The AI models showed great performance in the diagnosis(AUC=0.935,95%CI:0.898–0.973)and prognosis(AUC=0.840,95%CI:0.751–0.930)of OKC.The advantages of multiple slides model for integrating of histopathological information are demonstrated through a comparison with the single slide model.Furthermore,the study investigates the correlation between AI features generated by deep learning and pathological findings,highlighting the interpretative potential of AI models in the pathology.Here,we have developed the robust diagnostic and prognostic models for OKC.The AI model that is based on digital pathology shows promise potential for applications in odontogenic diseases of the jaw.
基金supported by the Natural Science Foundation of Hebei Province (H2020206226)Hebei Province Science and Technology Support Program (18277756D)+1 种基金the Science and Technology Research Project of Hebei Higher Education Institutions (ZD2022010)High-level Talent Funding Project of Hebei (C20231141) to W.W。
文摘Acceleration of tooth movement during orthodontic treatment is challenging, with osteoclast-mediated bone resorption on the compressive side being the rate-limiting step. Recent studies have demonstrated that mechanoreceptors on the surface of monocytes/macrophages, especially adhesion G protein-coupled receptors (aGPCRs), play important roles in force sensing.However, its role in the regulation of osteoclast differentiation remains unclear. Herein, through single-cell analysis, we revealed that CD97, a novel mechanosensitive aGPCR, was expressed in macrophages. Compression upregulated CD97 expression and inhibited osteoclast differentiation;while knockdown of CD97 partially rescued osteoclast differentiation. It suggests that CD97 may be an important mechanosensitive receptor during osteoclast differentiation. RNA sequencing analysis showed that the Rap1a/ERK signalling pathway mediates the effects of CD97 on osteoclast differentiation under compression. Consistently, we clarified that administration of the Rap1a inhibitor GGTI298 increased osteoclast activity, thereby accelerating tooth movement. In conclusion,our results indicate that CD97 suppresses osteoclast differentiation through the Rap1a/ERK signalling pathway under orthodontic compressive force.
基金supported by the National Natural Science Foundation of China(Nos.81671006 and 81300894)the CAMS Innovation Fund for Medical Sciences(No.2019-I2M-5-038)+2 种基金the National Clinical Key Discipline Construction Project(No.PKUSSNKP-202102)the Program for New Clinical Techniques and Therapies of Peking University Hospital of Stomatology(No.PKUSSNCT-22A14)the Innovation Fund for Outstanding Doctoral Candidates of Peking University Health Science Center(No.BMU2022BSS001),China.
文摘Oral leukoplakia is a common precursor lesion of oral squamous cell carcinoma,which indicates a high potential of malignancy.The malignant transformation of oral leukoplakia seriously affects patient survival and quality of life;however,it is difficult to identify oral leukoplakia patients who will develop carcinoma because no biomarker exists to predict malignant transformation for effective clinical management.As a major problem in the field of head and neck pathologies,it is imperative to identify biomarkers of malignant transformation in oral leukoplakia.In this review,we discuss the potential biomarkers of malignant transformation reported in the literature and explore the translational probabilities from bench to bedside.Although no single biomarker has yet been applied in the clinical setting,profiling for genomic instability might be a promising adjunct.
