Pub Date : 2025-02-01DOI: 10.1016/j.ajodo.2024.11.001
Peter M. Greco
{"title":"All eyes are on us","authors":"Peter M. Greco","doi":"10.1016/j.ajodo.2024.11.001","DOIUrl":"10.1016/j.ajodo.2024.11.001","url":null,"abstract":"","PeriodicalId":50806,"journal":{"name":"American Journal of Orthodontics and Dentofacial Orthopedics","volume":"167 2","pages":"Pages 142-143"},"PeriodicalIF":2.7,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143042351","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-01DOI: 10.1016/j.ajodo.2024.09.009
Qianhan Zheng , Lei Ma , Yongjia Wu , Yu Gao , Huimin Li , Jiaqi Lin , Shuhong Qing , Dan Long , Xuepeng Chen , Weifang Zhang
Introduction
Orthodontically induced root resorption (OIRR) is a common and undesirable consequence of orthodontic treatment. Traditionally, studies employ manual methods to conduct 3-dimensional quantitative analysis of OIRR via cone-beam computed tomography (CBCT), which is often subjective and time-consuming. With advancements in computer technology, deep learning-based approaches have gained traction in medical image processing. This study presents a deep learning-based model for the fully automatic extraction of root volume information and the localization of root resorption from CBCT images.
Methods
In this cross-sectional, retrospective study, 4534 teeth from 105 patients were used to train and validate an automatic model for OIRR quantification. The protocol encompassed several steps: preprocessing of CBCT images involving automatic tooth segmentation and conversion into point clouds, followed by segmentation of tooth crowns and roots via the Dynamic Graph Convolutional Neural Network. The root volume was subsequently calculated, and OIRR localization was performed. The intraclass correlation coefficient was employed to validate the consistency between the automatic model and manual measurements.
Results
The proposed method strongly correlated with manual measurements in terms of root volume and OIRR severity assessment. The intraclass correlation coefficient values for average volume measurements at each tooth position exceeded 0.95 (P <0.001), with the accuracy of different OIRR severity classifications surpassing 0.8.
Conclusions
The proposed methodology provides automatic and reliable tools for OIRR assessment, offering potential improvements in orthodontic treatment planning and monitoring.
{"title":"Automatic 3-dimensional quantification of orthodontically induced root resorption in cone-beam computed tomography images based on deep learning","authors":"Qianhan Zheng , Lei Ma , Yongjia Wu , Yu Gao , Huimin Li , Jiaqi Lin , Shuhong Qing , Dan Long , Xuepeng Chen , Weifang Zhang","doi":"10.1016/j.ajodo.2024.09.009","DOIUrl":"10.1016/j.ajodo.2024.09.009","url":null,"abstract":"<div><h3>Introduction</h3><div>Orthodontically induced root resorption (OIRR) is a common and undesirable consequence of orthodontic treatment. Traditionally, studies employ manual methods to conduct 3-dimensional quantitative analysis of OIRR via cone-beam computed tomography (CBCT), which is often subjective and time-consuming. With advancements in computer technology, deep learning-based approaches have gained traction in medical image processing. This study presents a deep learning-based model for the fully automatic extraction of root volume information and the localization of root resorption from CBCT images.</div></div><div><h3>Methods</h3><div>In this cross-sectional, retrospective study, 4534 teeth from 105 patients were used to train and validate an automatic model for OIRR quantification. The protocol encompassed several steps: preprocessing of CBCT images involving automatic tooth segmentation and conversion into point clouds, followed by segmentation of tooth crowns and roots via the Dynamic Graph Convolutional Neural Network. The root volume was subsequently calculated, and OIRR localization was performed. The intraclass correlation coefficient was employed to validate the consistency between the automatic model and manual measurements.</div></div><div><h3>Results</h3><div>The proposed method strongly correlated with manual measurements in terms of root volume and OIRR severity assessment. The intraclass correlation coefficient values for average volume measurements at each tooth position exceeded 0.95 (<em>P</em> <0.001), with the accuracy of different OIRR severity classifications surpassing 0.8.</div></div><div><h3>Conclusions</h3><div>The proposed methodology provides automatic and reliable tools for OIRR assessment, offering potential improvements in orthodontic treatment planning and monitoring.</div></div>","PeriodicalId":50806,"journal":{"name":"American Journal of Orthodontics and Dentofacial Orthopedics","volume":"167 2","pages":"Pages 188-201"},"PeriodicalIF":2.7,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142584908","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-01DOI: 10.1016/j.ajodo.2024.09.012
Huanzhuo Zhao , Baraa Daraqel , Man Jiang , Tianci Zhang , Xiang Li , Jicheng Sun , Leilei Zheng
Introduction
With a shift in orthodontics from a more traditional biomedical model toward a more biopsychosocial model, orthodontists must understand their patients’ psychological condition. This study aimed to investigate treatment motivation, psychosocial impact from malocclusion, and perfectionism in children and adult orthodontic patients and to examine the relationships among these various factors.
Methods
A total of 193 participants (114 children and 79 adults) were included. All participants completed a treatment motivation questionnaire, Psychosocial Impact of Dental Aesthetics Questionnaire (PIDAQ), Frost Multidimensional Perfectionism Scale (FMPS), and self-perceived Aesthetic Component of the Index of Orthodontic Treatment Need. Malocclusion severity was assessed using the Dental Health Component of the Index of Orthodontic Treatment Need by 1 orthodontist.
Results
Statistically significant differences were found between children and adults regarding motivation concerning function, PIDAQ total, as well as subscores except for dental self-confidence (P <0.01) and subscores of FMPS, such as concerns over mistakes and parental expectations (P <0.05). The multiple regression analysis demonstrated a direct relationship between PIDAQ and age, gender, total FMPS, self-perceived Aesthetic Component of the Index of Orthodontic Treatment Need, and motivation (P <0.01).
Conclusions
Adults anticipate greater improvement in oral function and experience greater negative psychosocial impact from malocclusion compared with children. Orthodontic patients with higher age, being female, high level of perfectionism, or negative self-perception of dental esthetics are associated with greater negative psychosocial impact from malocclusion. In addition, patients experiencing a greater negative psychosocial impact tend to have a strong motivation for orthodontic treatment.
{"title":"Treatment motivation, psychosocial impact, and perfectionism in children and adult orthodontic patients: A cross-sectional study","authors":"Huanzhuo Zhao , Baraa Daraqel , Man Jiang , Tianci Zhang , Xiang Li , Jicheng Sun , Leilei Zheng","doi":"10.1016/j.ajodo.2024.09.012","DOIUrl":"10.1016/j.ajodo.2024.09.012","url":null,"abstract":"<div><h3>Introduction</h3><div>With a shift in orthodontics from a more traditional biomedical model toward a more biopsychosocial model, orthodontists must understand their patients’ psychological condition. This study aimed to investigate treatment motivation, psychosocial impact from malocclusion, and perfectionism in children and adult orthodontic patients and to examine the relationships among these various factors.</div></div><div><h3>Methods</h3><div>A total of 193 participants (114 children and 79 adults) were included. All participants completed a treatment motivation questionnaire, Psychosocial Impact of Dental Aesthetics Questionnaire (PIDAQ), Frost Multidimensional Perfectionism Scale (FMPS), and self-perceived Aesthetic Component of the Index of Orthodontic Treatment Need. Malocclusion severity was assessed using the Dental Health Component of the Index of Orthodontic Treatment Need by 1 orthodontist.</div></div><div><h3>Results</h3><div>Statistically significant differences were found between children and adults regarding motivation concerning function, PIDAQ total, as well as subscores except for dental self-confidence (<em>P</em> <0.01) and subscores of FMPS, such as concerns over mistakes and parental expectations (<em>P</em> <0.05). The multiple regression analysis demonstrated a direct relationship between PIDAQ and age, gender, total FMPS, self-perceived Aesthetic Component of the Index of Orthodontic Treatment Need, and motivation (<em>P</em> <0.01).</div></div><div><h3>Conclusions</h3><div>Adults anticipate greater improvement in oral function and experience greater negative psychosocial impact from malocclusion compared with children. Orthodontic patients with higher age, being female, high level of perfectionism, or negative self-perception of dental esthetics are associated with greater negative psychosocial impact from malocclusion. In addition, patients experiencing a greater negative psychosocial impact tend to have a strong motivation for orthodontic treatment.</div></div>","PeriodicalId":50806,"journal":{"name":"American Journal of Orthodontics and Dentofacial Orthopedics","volume":"167 2","pages":"Pages 210-220.e2"},"PeriodicalIF":2.7,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142683323","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-01DOI: 10.1016/j.ajodo.2024.12.003
Dr Allen H. Moffitt (CE Editor)
{"title":"February 2025","authors":"Dr Allen H. Moffitt (CE Editor)","doi":"10.1016/j.ajodo.2024.12.003","DOIUrl":"10.1016/j.ajodo.2024.12.003","url":null,"abstract":"","PeriodicalId":50806,"journal":{"name":"American Journal of Orthodontics and Dentofacial Orthopedics","volume":"167 2","pages":"Pages 253.e1-253.e2"},"PeriodicalIF":2.7,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143156432","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-01DOI: 10.1016/S0889-5406(24)00540-7
{"title":"Directory: AAO Officers and Organizations","authors":"","doi":"10.1016/S0889-5406(24)00540-7","DOIUrl":"10.1016/S0889-5406(24)00540-7","url":null,"abstract":"","PeriodicalId":50806,"journal":{"name":"American Journal of Orthodontics and Dentofacial Orthopedics","volume":"167 2","pages":"Page 254"},"PeriodicalIF":2.7,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143156433","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-01DOI: 10.1016/j.ajodo.2024.09.005
Huanhuan Chen , Guangying Song , Yi Fan , Jiuhui Jiang , Ruoping Jiang , Xiaoyun Zhang , Gui Chen , Hong Su , Tianyi Wang , Bing Han , Tianmin Xu
Introduction
The objective of this study was to compare the differences in anchorage and torque control among the Tweed edgewise, Roth, and physiological anchorage Spee-wire systems (PASS) appliances (Zhejiang Xinya Technology Co, Ltd, Hangzhou, China).
Methods
A sample of 90 adolescent patients with Angle Class II Division 1 malocclusion (30 Tweed edgewise appliances, 30 Roth appliances, and 30 PASS appliances) with maximum anchorage requirements in the maxilla were collected for this study. The pretreatment baseline levels of the 3 groups were compared initially, and then the differences between the 3 appliances in anchorage and torque control were analyzed after superimposing the pretreatment and posttreatment lateral cephalograms and maxillary 3-dimensional (3D) digital models, respectively.
Results
There was no statistical difference in the pretreatment baseline levels of 3 groups, including gender, age, sagittal skeletal types (ANB), vertical skeletal types (SN-GoGn), anchorage requirements, and occlusal plane inclination (SN-OP). After superimposing the pretreatment and posttreatment lateral cephalograms and 3D digital models, respectively, no statistical differences were observed between the measurement results obtained from lateral cephalograms and 3D digital models. Among the measurement variables assessed in this study, statistical differences were observed in the mesial displacement of maxillary first molars, the incisor retraction, and the torque variation of maxillary central incisors among the 3 groups. Specifically, the Tweed group exhibited lower mesial displacement of maxillary first molars compared with the PASS and Roth groups. Furthermore, the Tweed group exhibited the greatest amount of incisor retraction and torque variation of maxillary central incisors, followed by the Roth group and then the PASS group. The remaining measurement variables for the 3 groups showed no statistical differences, including vertical variation of maxillary first molars and central incisors, torque variation of maxillary first molars and canines, mesiodistal inclination variation of maxillary first molars and canines, width variation between maxillary first molars, and width variation between maxillary canines.
Conclusions
Compared with contemporary preadjusted straight wire appliances, the Tweed edgewise appliance has superiority in molar anchorage control. In contrast, compared with the Roth appliances, the PASS appliances without any auxiliary anchorage devices could make full use of physiological anchorage to achieve adequate control of molar anchorage. Clinical orthodontists may need to pay extra attention to physiological anchorage. The difference in torque control varies depending on the respective characteristics of bracket designs.
{"title":"Evaluating anchorage and torque control in adolescent patients with Class II Division 1 malocclusion among 3 appliances","authors":"Huanhuan Chen , Guangying Song , Yi Fan , Jiuhui Jiang , Ruoping Jiang , Xiaoyun Zhang , Gui Chen , Hong Su , Tianyi Wang , Bing Han , Tianmin Xu","doi":"10.1016/j.ajodo.2024.09.005","DOIUrl":"10.1016/j.ajodo.2024.09.005","url":null,"abstract":"<div><h3>Introduction</h3><div>The objective of this study was to compare the differences in anchorage and torque control among the Tweed edgewise, Roth, and physiological anchorage Spee-wire systems (PASS) appliances (Zhejiang Xinya Technology Co, Ltd, Hangzhou, China).</div></div><div><h3>Methods</h3><div>A sample of 90 adolescent patients with Angle Class II Division 1 malocclusion (30 Tweed edgewise appliances, 30 Roth appliances, and 30 PASS appliances) with maximum anchorage requirements in the maxilla were collected for this study. The pretreatment baseline levels of the 3 groups were compared initially, and then the differences between the 3 appliances in anchorage and torque control were analyzed after superimposing the pretreatment and posttreatment lateral cephalograms and maxillary 3-dimensional (3D) digital models, respectively.</div></div><div><h3>Results</h3><div>There was no statistical difference in the pretreatment baseline levels of 3 groups, including gender, age, sagittal skeletal types (ANB), vertical skeletal types (SN-GoGn), anchorage requirements, and occlusal plane inclination (SN-OP). After superimposing the pretreatment and posttreatment lateral cephalograms and 3D digital models, respectively, no statistical differences were observed between the measurement results obtained from lateral cephalograms and 3D digital models. Among the measurement variables assessed in this study, statistical differences were observed in the mesial displacement of maxillary first molars, the incisor retraction, and the torque variation of maxillary central incisors among the 3 groups. Specifically, the Tweed group exhibited lower mesial displacement of maxillary first molars compared with the PASS and Roth groups. Furthermore, the Tweed group exhibited the greatest amount of incisor retraction and torque variation of maxillary central incisors, followed by the Roth group and then the PASS group. The remaining measurement variables for the 3 groups showed no statistical differences, including vertical variation of maxillary first molars and central incisors, torque variation of maxillary first molars and canines, mesiodistal inclination variation of maxillary first molars and canines, width variation between maxillary first molars, and width variation between maxillary canines.</div></div><div><h3>Conclusions</h3><div>Compared with contemporary preadjusted straight wire appliances, the Tweed edgewise appliance has superiority in molar anchorage control. In contrast, compared with the Roth appliances, the PASS appliances without any auxiliary anchorage devices could make full use of physiological anchorage to achieve adequate control of molar anchorage. Clinical orthodontists may need to pay extra attention to physiological anchorage. The difference in torque control varies depending on the respective characteristics of bracket designs.</div></div>","PeriodicalId":50806,"journal":{"name":"American Journal of Orthodontics and Dentofacial Orthopedics","volume":"167 2","pages":"Pages 166-176"},"PeriodicalIF":2.7,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142479950","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-01DOI: 10.1016/j.ajodo.2024.08.016
Federica Pellitteri, Paolo Albertini, Luca Brucculeri, Francesca Cremonini, Daniela Guiducci, Virginia Falconi, Luca Lombardo
Introduction
The aim was to compare the soft tissue changes in pretreatment and posttreatment facial scans of patients who had undergone various orthopedic treatments vs a control group of untreated growing patients.
Methods
Facial scans were performed before (T0) and after (T1) orthopedic treatment in 15 patients prescribed rapid palatal expander (RPE), 15 cervical headgear (HG), and 15 facemasks (FM), as well as 6 months apart in 15 untreated growing patients. After best-fit scan alignment using Geometric Control X software (3D Systems Inc, Rock Hill, SC), a 3-dimensional (3D) analysis of soft tissue changes was performed, comparing 3D reference points (total 22) and 8 areas on T0 and T1 scans. Kruskal-Wallis nonparametric tests and pairwise comparison with Bonferroni’s correction were applied to identify any statistically significant differences among groups (P <0.05). All analyses were conducted with SPSS software (version 28; IBM, Armonk, NY).
Results
At T1, reduced soft tissue projection was found at the nose and upper lip in the HG group, the lower lip in the HG and RPE groups, and the chin in the FM and RPE groups. The RPE group displayed a statistically significant increase in facial divergence, confirmed by gnathion position (RPE vs FM [P = 0.018] and RPE vs control [P = 0.046]), as well as an increase in the soft tissue projection of both cheeks (left cheek in range of 1-2 mm [P = 0.030] and range of 0 to −1 mm [P = 0.022]; right cheek in range of 1-2 mm [P = 0.003] and range −1 to −2 mm [P = 0.001]). There were no clinically significant differences among groups in mandibular right and left body areas.
Conclusions
The 3D facial analysis revealed significant differences in soft tissues among orthopedic treatments, especially at the upper and lower lip and chin areas, as compared with untreated patients.
简介：目的是比较接受过各种矫形治疗的患者与未接受治疗的生长期患者对照组在治疗前和治疗后面部扫描图像中的软组织变化：目的是比较接受过各种矫形治疗的患者与未经治疗的生长期患者对照组在治疗前和治疗后面部扫描中的软组织变化：方法：对15名接受过快速腭扩张器（RPE）、15名接受过颈椎头套（HG）和15名接受过面罩（FM）矫形治疗的患者，以及15名未接受过矫形治疗的生长期患者，在矫形治疗前（T0）和矫形治疗后（T1），分别进行面部扫描。使用 Geometric Control X 软件（3D Systems Inc, Rock Hill, SC）进行最佳拟合扫描对齐后，对软组织变化进行了三维（3D）分析，比较了 T0 和 T1 扫描的三维参考点（共 22 个）和 8 个区域。采用 Kruskal-Wallis 非参数检验和配对比较，并进行 Bonferroni 校正，以确定组间是否存在显著的统计学差异（P 结果）：在 T1 阶段，HG 组的鼻子和上唇、HG 组和 RPE 组的下唇以及 FM 组和 RPE 组的下巴软组织投影减少。RPE 组的面部发散度有显著的统计学意义（RPE vs FM [P = 0.018] 和 RPE vs 对照组 [P = 0.046]），两侧脸颊的软组织突起也有所增加（左侧脸颊的范围为 1-2 mm [P = 0.030]，范围为 0-1 mm [P = 0.022]；右侧脸颊的范围为 1-2 mm [P = 0.003]，范围为 -1-2 mm [P=0.001]）。下颌体左右区域的临床差异不明显：三维面部分析显示，与未接受治疗的患者相比，接受矫形治疗的患者在软组织方面存在明显差异，尤其是上下唇和下巴部位。
{"title":"Soft tissue changes during orthopedic therapy: An in vivo 3-dimensional facial scan study","authors":"Federica Pellitteri, Paolo Albertini, Luca Brucculeri, Francesca Cremonini, Daniela Guiducci, Virginia Falconi, Luca Lombardo","doi":"10.1016/j.ajodo.2024.08.016","DOIUrl":"10.1016/j.ajodo.2024.08.016","url":null,"abstract":"<div><h3>Introduction</h3><div>The aim was to compare the soft tissue changes in pretreatment and posttreatment facial scans of patients who had undergone various orthopedic treatments vs a control group of untreated growing patients.</div></div><div><h3>Methods</h3><div>Facial scans were performed before (T0) and after (T1) orthopedic treatment in 15 patients prescribed rapid palatal expander (RPE), 15 cervical headgear (HG), and 15 facemasks (FM), as well as 6 months apart in 15 untreated growing patients. After best-fit scan alignment using Geometric Control X software (3D Systems Inc, Rock Hill, SC), a 3-dimensional (3D) analysis of soft tissue changes was performed, comparing 3D reference points (total 22) and 8 areas on T0 and T1 scans. Kruskal-Wallis nonparametric tests and pairwise comparison with Bonferroni’s correction were applied to identify any statistically significant differences among groups (<em>P</em> <0.05). All analyses were conducted with SPSS software (version 28; IBM, Armonk, NY).</div></div><div><h3>Results</h3><div>At T1, reduced soft tissue projection was found at the nose and upper lip in the HG group, the lower lip in the HG and RPE groups, and the chin in the FM and RPE groups. The RPE group displayed a statistically significant increase in facial divergence, confirmed by gnathion position (RPE vs FM [<em>P</em> = 0.018] and RPE vs control [<em>P</em> = 0.046]), as well as an increase in the soft tissue projection of both cheeks (left cheek in range of 1-2 mm [<em>P</em> = 0.030] and range of 0 to −1 mm [<em>P</em> = 0.022]; right cheek in range of 1-2 mm [<em>P</em> = 0.003] and range −1 to −2 mm [<em>P</em> = 0.001]). There were no clinically significant differences among groups in mandibular right and left body areas.</div></div><div><h3>Conclusions</h3><div>The 3D facial analysis revealed significant differences in soft tissues among orthopedic treatments, especially at the upper and lower lip and chin areas, as compared with untreated patients.</div></div>","PeriodicalId":50806,"journal":{"name":"American Journal of Orthodontics and Dentofacial Orthopedics","volume":"167 2","pages":"Pages 154-165"},"PeriodicalIF":2.7,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142479953","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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