The rol genes on pRiA4 plasmid of Agrobacterium rhizogenes are potent genes that promote secondary metabolism. Molecular breeding of Atropa belladonna can be conducted by introducing rol genes to increase tropane alkaloids (TAs) content in A. belladonna. In this study, the rolB gene was overexpressed in A. belladonna plants to study the effect of rolB gene on the biosynthesis of TAs. The phenotype, TAs content and expression levels of key enzyme genes in the pathway of TAs biosynthesis of transgenic A. belladonna were analyzed. The results showed that transgenic A. belladonna had developed root system, enlarged leaves, increased leaf fresh weight, deepened leaf color, enlarged flowers, changed flower shape, reduced pistil height and decreased pollen vitality. The content of TAs in the stems of transgenic A. belladonna was significantly higher than that of the control, and the contents of scopolamine, anisodamine, hyoscyamine can reach 2.11-2.91, 1.23-2.37 and 4.88-5.20 times of the control, respectively. Compared with the control group, the expressions of key enzymes putrescine N-methyltransferase (PMT), type III polyketide synthase (PYKS), tropinone reductase I (TRI), aromatic amino acid aminotransferase 4 (ArAT4), UDP-glycosyltransferase 1 (UGT1) and hyoscyamine 6-β-hydroxylase (H6H) in the TAs biosynthesis pathway were up-regulated, and the expression of tropinone reductase II (TRII) as a metabolic shunting gene was down-regulated. The results indicated that rolB gene enhanced TAs synthesis ability in roots and accumulation in stems of A. belladonna by enhancing metabolic flow of TAs synthesis pathway and weakening the metabolic shunt of competing pathway. This study laid a foundation for molecular breeding of A. belladonna with high-yield TAs content using rolB gene.

OBJECTIVE: To establish a novel method based on three-dimensional (3D) shape analysis and weighted Procrustes analysis (WPA) algorithm to construct a 3D facial symmetry reference plane (SRP), automatically assigning weight to facial anatomical landmarks. The WPA algorithm suitability for commonly observed clinical cases of mandibular deviation were analysed and evaluated. METHODS: Thirty patients with mandibular deviation were recruited for this study. The 3D facial SRPs were extracted independently based on original-mirror alignment method. Thirty-two anatomical landmarks were selected from the overall region by three times to obtain the mean coordinate. The SRP of experimental groups 1 and 2 were using the standard Procrustes analysis (PA) algorithm and WPA algorithm, respectively. A reference plane defined by experts based on regional iterative closest point (ICP) algorithm, served as the ground truth. Three experts manually selecting facial regions with good symmetry for original model, and common region was included in the study. The angle error values between the SRP of WPA algorithm in the experimental group 1 and the truth plane were evaluated in this study, and the SRP of PA algorithm of experimental group 2 was calculated in the same way. Statistics and measurement analysis were used to comprehensively evaluate the clinical suitability of the WPA algorithm to calculate the SRP. A paired t-test analysis (two-tailed) was conducted to compare the angles. RESULTS: The average angle error between the SRP of WPA algorithm and the ground truth was 1.53°±0.84°, which was smaller than that between the SRP of PA and the ground truth (2.06°±0.86°). There were significant differences in the angle errors among the groups (P < 0.05). For the patients with severe mandibular deviation that the distance between pogonion and facial midline greater than 12 mm, the average angle error of the WPA algorithm was 0.86° smaller than that of the PA algorithm. CONCLUSION The WPA algorithm, based on weighted shape analysis, can provide a more adaptable SRP than the standard PA algorithm when applied to mandibular deviation patients and preliminarily simulate the diagnosis strategies of clinical experts.
Algorithms ; Cephalometry ; Face ; Facial Asymmetry ; Humans ; Imaging, Three-Dimensional

OBJECTIVE: To quantitatively evaluate the trueness of five chairside three-dimensional facial scanning techniques, and to provide reference for the application of oral clinical diagnosis and treatment. METHODS: The three-dimensional facial data of the subjects were collected by the traditional professional three-dimensional facial scanner Face Scan, which was used as the reference data of this study. Four kinds of portable three-dimensional facial scanners (including Space Spider, LEO, EVA and DS-FScan) and iPhone Ⅹ mobile phone (Bellus3D facial scanning APP) were used to collect three-dimensional facial data from the subjects. In Geomagic Studio 2013 software, through data registration, deviation analysis and other functions, the overall three-dimensional deviation and facial partition three-dimensional deviation of the above five chairside three-dimensional facial scanning technologies were calculated, and their trueness performance evaluated. Scanning time was recorded during the scanning process, and the subject's comfort was scored by visual analogue scale(VAS). The scanning efficiency and patient acceptance of the five three-dimensional facial scanning techniques were evaluated. RESULTS: DS-FScan had the smallest mean overall and mean partition three-dimensional deviation between the test data and the reference data, which were 0.334 mm and 0.329 mm, respectively. The iPhone Ⅹ mobile phone had the largest mean overall and mean partition three-dimensional deviation between the test data and the reference data, which were 0.483 mm and 0.497 mm, respectively. The detailed features of the three-dimensional facial data obtained by Space Spider were the best. The iPhone Ⅹ mobile phone had the highest scanning efficiency and the highest acceptance by the subject. The average scanning time of the iPhone Ⅹ mobile phone was 14 s, and the VAS score of the subjects' scanning comfort was 9 points. CONCLUSION Among the five chairside three-dimensional face scanning technologies, the trueness of the scan data of the four portable devices had no significant difference, and they were all better than the iPhone Ⅹ mobile phone scan. The subject with the iPhone Ⅹ scanning technology had the best expe-rience.
Imaging, Three-Dimensional ; Software ; Models, Dental

OBJECTIVE: To explore an efficient and automatic method for determining the anatomical landmarks of three-dimensional(3D) mandibular data, and to preliminarily evaluate the performance of the method. METHODS: The CT data of 40 patients with normal craniofacial morphology were collected (among them, 30 cases were used to establish the 3D mandibular average model, and 10 cases were used as test datasets to validate the performance of this method in determining the mandibular landmarks), and the 3D mandibular data were reconstructed in Mimics software. Among the 40 cases of mandibular data after the 3D reconstruction, 30 cases that were more similar to the mean value of Chinese mandibular features were selected, and the size of the mandibular data of 30 cases was normalized based on the Procrustes analysis algorithm in MATLAB software. Then, in the Geomagic Wrap software, the 3D mandibular average shape model of the above 30 mandibular data was constructed. Through symmetry processing, curvature sampling, index marking and other processing procedures, a 3D mandible structured template with 18 996 semi-landmarks and 19 indexed mandibular anatomical landmarks were constructed. The open source non-rigid registration algorithm program Meshmonk was used to match the 3D mandible template constructed above with the tested patient's 3D mandible data through non-rigid deformation, and 19 anatomical landmark positions of the patient's 3D mandible data were obtained. The accuracy of the research method was evaluated by comparing the distance error of the landmarks manually marked by stomatological experts with the landmarks marked by the method of this research. RESULTS: The method of this study was applied to the data of 10 patients with normal mandibular morphology. The average distance error of 19 landmarks was 1.42 mm, of which the minimum errors were the apex of the coracoid process [right: (1.01±0.44) mm; left: (0.56±0.14) mm] and maximum errors were the anterior edge of the lowest point of anterior ramus [right: (2.52±0.95) mm; left: (2.57±1.10) mm], the average distance error of the midline landmarks was (1.15±0.60) mm, and the average distance error of the bilateral landmarks was (1.51±0.67) mm. CONCLUSION The automatic determination method of 3D mandibular anatomical landmarks based on 3D mandibular average shape model and non-rigid registration algorithm established in this study can effectively improve the efficiency of automatic labeling of 3D mandibular data features. The automatic determination of anatomical landmarks can basically meet the needs of oral clinical applications, and the labeling effect of deformed mandible data needs to be further tested.
Humans ; Imaging, Three-Dimensional/methods* ; Mandible/diagnostic imaging* ; Software ; Algorithms ; Anatomic Landmarks/anatomy & histology*

Objective: To explore the establishment of an efficient and automatic method to determine anatomical landmarks in three-dimensional (3D) facial data, and to evaluate the effectiveness of this method in determining landmarks. Methods: A total of 30 male patients with tooth defect or dentition defect (with good facial symmetry) who visited the Department of Prosthodontics, Peking University School and Hospital of Stomatology from June to August 2021 were selected, and these participants' age was between 18-45 years. 3D facial data of patients was collected and the size normalization and overlap alignment were performed based on the Procrustes analysis algorithm. A 3D face average model was built in Geomagic Studio 2013 software, and a 3D face template was built through parametric processing. MeshLab 2020 software was used to determine the serial number information of 32 facial anatomical landmarks (10 midline landmarks and 22 bilateral landmarks). Five male patients with no mandibular deviation and 5 with mild mandibular deviation were selected from the Department of Orthodontics or Oral and Maxillofacial Surgery, Peking University School and Hospital of Stomatology from June to August 2021. 3D facial data of patients was collected as test data. Based on the 3D face template and the serial number information of the facial anatomical landmarks, the coordinates of 32 facial anatomical landmarks on the test data were automatically determined with the help of the MeshMonk non-rigid registration algorithm program, as the data for the template method to determine the landmarks. The positions of 32 facial anatomical landmarks on the test data were manually determined by the same attending physician, and the coordinates of the landmarks were recorded as the data for determining landmarks by the expert method. Calculated the distance value of the coordinates of facial anatomical landmarks between the template method and the expert method, as the landmark localization error, and evaluated the effect of the template method in determining the landmarks. Results: For 5 patients with no mandibular deviation, the landmark localization error of all facial anatomical landmarks by template method was (1.65±1.19) mm, the landmark localization error of the midline facial anatomical landmarks was (1.19±0.45) mm, the landmark localization error of bilateral facial anatomical landmarks was (1.85±1.33) mm. For 5 patients with mild mandibular deviation, the landmark localization error of all facial anatomical landmarks by template method was (2.55±2.22) mm, the landmark localization error of the midline facial anatomical landmarks was (1.85±1.13) mm, the landmark localization error of bilateral facial anatomical landmarks was (2.87±2.45) mm. Conclusions: The automatic determination method of facial anatomical landmarks proposed in this study has certain feasibility, and the determination effect of midline facial anatomical landmarks is better than that of bilateral facial anatomical landmarks. The effect of determining facial anatomical landmarks in patients without mandibular deviation is better than that in patients with mild mandibular deviation.
Adolescent ; Adult ; Algorithms ; Anatomic Landmarks ; Cephalometry/methods* ; Face/anatomy & histology* ; Female ; Humans ; Imaging, Three-Dimensional/methods* ; Male ; Malocclusion ; Middle Aged ; Orthodontics ; Software ; Young Adult

Objective: To provide a new solution for the digital design of nasal prostheses, this study explores the three-dimensional (3D) facial morphology completion method for external nasal defects based on the non-rigid registration process of 3D face template. Methods: A total of 20 male patients with tooth defect and dentition defect who visited the Department of Prosthodontics, Peking University School and Hospital of Stomatology from June to December 2022 were selected, age 18-45 years old. The original 3D facial data of patients were collected, and the 3D facial data of the external nose defect was constructed in Geomagic Wrap 2021 software. Using the structured 3D face template data constructed in the previous research of the research group, the 3D face template was deformed and registered to the 3D facial data of external nose defect (based on the morphology of non-defective area) by non-rigid registration algorithm (MeshMonk program), and the personalized deformed data of the 3D face template was obtained, as the complemented facial 3D data. Based on the defect boundary of the 3D facial data of the external nose defect, the complemented external nose 3D data can be cut out from the complemented facial 3D data. Then the nasofacial angle and nasolabial angle of the complemented facial 3D data and the original 3D facial data was compared and analyzed, the ratio between the nose length and mid-face height, nose width and medial canthal distance of the complemented facial 3D data was measured, the edge fit between the edge curve of the complemented external nose 3D data and the defect edge curve of the 3D facial data of external nose defect was evaluated, and the morphological difference of the nose between the complemented external nose 3D data and the original 3D facial data was analyzed. Results: There was no significant statistically difference (t=-0.23, P=0.823; Z=-1.72, P=0.086) in the nasofacial angle (28.2°±2.9°, 28.4°±3.5° respectively) and nasolabial angle [95.4°(19.2°), 99.9°(9.5°) respectively] between the 20 original 3D facial data and the complemented facial 3D data. The value of the ratio of nose length to mid-face height in the complemented facial 3D data was 0.63±0.03, and the value of the ratio of nose width to medial canthal distance was 1.07±0.08. The curve deviation (root mean square value) between the edge curve of the complemented external nose 3D data and the defect edge curve of the 3D facial data of external nose defect was (0.37±0.09) mm, the maximum deviation was (1.14±0.32) mm, and the proportion of the curve deviation value within±1 mm was (97±3)%. The distance of corresponding nose landmarks between the complemented facial 3D data and the original 3D facial data were respectively, Nasion: [1.52(1.92)] mm; Pronasale: (3.27±1.21) mm; Subnasale: (1.99±1.09) mm; Right Alare: (2.64±1.34) mm; Left Alare: (2.42± 1.38) mm. Conclusions: The method of 3D facial morphology completion of external nose defect proposed in this study has good feasibility. The constructed complemented external nose 3D data has good facial coordination and edge fit, and the morphology is close to the nose morphology of the original 3D facial data.

Objective: To explore a method for digitally designing and fabricating a sequential tooth-sectioning guide that can assist in the extraction of mandibular horizontal impacted third molars, preliminarily evaluate its feasibility and provide a reference for clinical application. Methods: Twenty patients with mandibular low level impacted third molars who visited the Department of General Dentistry, Peking University School and Hospital of Stomatology from March 2021 to January 2022 were selected. Cone-beam CT showed direct contact between the roots and mandibular canal, and full range impressions of the patients' intraoral teeth were taken and optical scans of the dental model were performed. The patients' cone-beam CT data and optical scan data were reconstructed in three dimensions, anatomical structure extraction, registration fusion, and the design of the structure of the guide (including crown-sectioning guide and root-sectioning guide) by Mimics 24.0, Geomagic Wrap 2021, and Magics 21.0 software, and then the titanium guide was three dimension printed, and the guide was tried on the dental model. After confirmation, the guide was used to assist the dentist in the operation. We observed whether the guide was in place, the number of tooth splitting, the matching of tooth splitting with the preoperative design, the operation time, and whether there were any complications. Results: In this study, 20 sectioning guides were successfully printed, all of them were well fitted in the patients' mouth, the average number of section was 3.4 times, the tooth parts was better matched with the preoperative design, and the average operative time of the guides was (29.2±9.8) minutes without complications such as perforation of the bone cortex. Conclusions: The use of sequential sectioning guides to assist in the extraction of mandibular impacted third molars was initially validated to accurately replicate the preoperative sectioning design, and is expected to provide a digital solution to improve surgical precision and ensure safety. Further studies with larger sample sizes are needed to evaluate its accuracy and safety.

Objective: To explore an automatic landmarking method for anatomical landmarks in the three-dimensional (3D) data of the maxillary complex and preliminarily evaluate its reproducibility and accuracy. Methods: From June 2021 to December 2022, spiral CT data of 31 patients with relatively normal craniofacial morphology were selected from those who visited the Department of Oral and Maxillofacial Surgery, Peking University School and Hospital of Stomatology. The sample included 15 males and 16 females, with the age of (33.3±8.3) years. The maxillary complex was reconstructed in 3D using Mimics software, and the resulting 3D data of the maxillary complex was mesh-refined using Geomagic software. Two attending physicians and one associate chief physician manually landmarked the 31 maxillary complex datasets, determining 24 anatomical landmarks. The average values of the three expert landmarking results were used as the expert-defined landmarks. One case that conformed to the average 3D morphological characteristics of healthy individuals' craniofacial bones was selected as the template data, while the remaining 30 cases were used as target data. The open-source MeshMonk program (a non-rigid registration algorithm) was used to perform an initial alignment of the template and target data based on 4 landmarks (nasion, left and right zygomatic arch prominence, and anterior nasal spine). The template data was then deformed to the shape of the target data using a non-rigid registration algorithm, resulting in the deformed template data. Based on the unchanged index property of homonymous landmarks before and after deformation of the template data, the coordinates of each landmark in the deformed template data were automatically retrieved as the automatic landmarking coordinates of the homonymous landmarks in the target data, thus completing the automatic landmarking process. The automatic landmarking process for the 30 target data was repeated three times. The root-mean-square distance (RMSD) of the dense corresponding point pairs (approximately 25 000 pairs) between the deformed template data and the target data was calculated as the deformation error of the non-rigid registration algorithm, and the intra-class correlation coefficient (ICC) of the deformation error in the three repetitions was analyzed. The linear distances between the automatic landmarking results and the expert-defined landmarks for the 24 anatomical landmarks were calculated as the automatic landmarking errors, and the ICC values of the 3D coordinates in the three automatic landmarking repetitions were analyzed. Results: The average three-dimensional deviation (RMSD) between the deformed template data and the corresponding target data for the 30 cases was (0.70±0.09) mm, with an ICC value of 1.00 for the deformation error in the three repetitions of the non-rigid registration algorithm. The average automatic landmarking error for the 24 anatomical landmarks was (1.86±0.30) mm, with the smallest error at the anterior nasal spine (0.65±0.24) mm and the largest error at the left oribital (3.27±2.28) mm. The ICC values for the 3D coordinates in the three automatic landmarking repetitions were all 1.00. Conclusions: This study established an automatic landmarking method for three-dimensional data of the maxillary complex based on a non-rigid registration algorithm. The accuracy and repeatability of this method for landmarking normal maxillary complex 3D data were relatively good.
Male ; Female ; Humans ; Adult ; Imaging, Three-Dimensional/methods* ; Reproducibility of Results ; Algorithms ; Software ; Tomography, Spiral Computed ; Anatomic Landmarks/anatomy & histology*

OBJECTIVE: To establish a deep learning algorithm that can accurately determine three-dimensional facial anatomical landmarks, multi-view stacked hourglass convolutional neural networks (MSH-CNN) and to construct three-dimensional facial midsagittal plane automatically based on MSH-CNN and weighted Procrustes analysis algorithm. METHODS: One hundred subjects with no obvious facial deformity were collected in our oral clinic. Three-dimensional facial data were scanned by three-dimensional facial scanner. Experts annotated twenty-one facial landmarks and midsagittal plane of each data. Eighty three-dimensional facial data were used as training set, to train the MSH-CNN in this study. The overview of MSH-CNN network architecture contained multi-view rendering and training the MSH-CNN network. The three-dimensional facial data were rendered from ninety-six views that were fed to MSH-CNN and the output was one heatmap per landmark. The result of the twenty-one landmarks was accurately placed on the three-dimensional facial data after a three-dimensional view ray voting process. The remaining twenty three-dimensional facial data were used as test set. The trained MSH-CNN automatically determined twenty-one three-dimensional facial anatomical landmarks of each case of data, and calculated the distance between each MSH-CNN landmark and the expert landmark, which was defined as position error. The midsagittal plane of the twenty subjects' could be automatically constructed, using the MSH-CNN and Procrustes analysis algorithm. To evaluate the effect of midsagittal plane by automatic method, the angle between the midsagittal plane constructed by the automatic method and the expert annotated plane was calculated, which was defined as angle error. RESULTS: For twenty subjects with no obvious facial deformity, the average angle error of the midsagittal plane constructed by MSH-CNN and weighted Procrustes analysis algorithm was 0.73°±0.50°, in which the average position error of the twenty-one facial landmarks automatically determined by MSH-CNN was (1.13±0.24) mm, the maximum position error of the orbital area was (1.31±0.54) mm, and the minimum position error of the nasal area was (0.79±0.36) mm. CONCLUSION This research combines deep learning algorithms and Procrustes analysis algorithms to realize the fully automated construction of the three-dimensional midsagittal plane, which initially achieves the construction effect of clinical experts. The obtained results constituted the basis for the independent intellectual property software development.
Algorithms ; Deep Learning ; Face ; Humans ; Neural Networks, Computer ; Software