OBJECTIVE: To observe the clinical efficacy of acupuncture combined with thunder-fire moxibustion in treating low back pain with cold-damp. METHODS: Seventy-two patients of low back pain with cold-damp were randomly divided into an observation group (36 cases, 1 case was eliminated) and a control group (36 cases, 1 case dropped out). The control group received acupuncture at Jizhong (GV6), Yaoyangguan (GV3), ashi points, bilateral Shenshu (BL23), Dachangshu (BL25), and Weizhong (BL40) for 30 min daily. The observation group was treated with thunder-fire moxibustion in addition to the same acupuncture regimen as the control group, once daily. Both groups were treated for 6 consecutive days followed by one rest day, for a total duration of 4 weeks. The visual analog scale (VAS) score, Oswestry disability index (ODI) score, Japanese Orthopedic Association (JOA) score, present pain intensity (PPI) score, and serum levels of β-endorphin (β-EP), 5-hydroxytryp tamin (5-HT), and substance P (SP) were compared before and after treatment, and the clinical efficacy was also compared between the two groups. RESULTS: Compared before treatment, the VAS scores, ODI scores, PPI scores, and serum levels of 5-HT and SP were decreased (P<0.01), while JOA scores and serum levels of β-EP were increased (P<0.01) in both groups after treatment. The observation group showed lower VAS, ODI, and PPI scores and serum levels of 5-HT and SP than those in the control group (P<0.05), as well as higher JOA score and serum level of β-EP (P<0.05). The total effective rate in the observation group was 94.3% (33/35), higher than 82.9% (29/35) in the control group (P<0.05). CONCLUSION Acupuncture combined with thunder-fire moxibustion could effectively alleviate pain and improve lumbar function in patients of low back pain with cold-damp, possibly by regulating β-EP, 5-HT, and SP levels.
Humans ; Moxibustion ; Low Back Pain/blood* ; Male ; Female ; Adult ; Middle Aged ; Acupuncture Therapy ; Acupuncture Points ; Treatment Outcome ; Combined Modality Therapy ; beta-Endorphin/blood* ; Young Adult ; Aged

Prostate cancer (PCa) ranks as the second most prevalent malignancy among men worldwide. Early diagnosis, personalized treatment, and prognosis prediction of PCa play a crucial role in improving patients' survival rates. The advancement of artificial intelligence (AI), particularly the utilization of deep learning (DL) algorithms, has brought about substantial progress in assisting the diagnosis, treatment, and prognosis prediction of PCa. The introduction of the foundation model has revolutionized the application of AI in medical treatment and facilitated its integration into clinical practice. This review emphasizes the clinical application of AI in PCa by discussing recent advancements from both pathological and imaging perspectives. Furthermore, it explores the current challenges faced by AI in clinical applications while also considering future developments, aiming to provide a valuable point of reference for the integration of AI and clinical applications.
Humans ; Prostatic Neoplasms/diagnosis* ; Male ; Artificial Intelligence ; Deep Learning ; Prognosis

OBJECTIVE: To develop an original-mirror alignment associated deep learning algorithm for intelligent registration of three-dimensional maxillofacial point cloud data, by utilizing a dynamic graph-based registration network model (maxillofacial dynamic graph registration network, MDGR-Net), and to provide a valuable reference for digital design and analysis in clinical dental applications. METHODS: Four hundred clinical patients without significant deformities were recruited from Peking University School of Stomatology from October 2018 to October 2022. Through data augmentation, a total of 2 000 three-dimensional maxillofacial datasets were generated for training and testing the MDGR-Net algorithm. These were divided into a training set (1 400 cases), a validation set (200 cases), and an internal test set (200 cases). The MDGR-Net model constructed feature vectors for key points in both original and mirror point clouds (X, Y), established correspondences between key points in the X and Y point clouds based on these feature vectors, and calculated rotation and translation matrices using singular value decomposition (SVD). Utilizing the MDGR-Net model, intelligent registration of the original and mirror point clouds were achieved, resulting in a combined point cloud. The principal component analysis (PCA) algorithm was applied to this combined point cloud to obtain the symmetry reference plane associated with the MDGR-Net methodology. Model evaluation for the translation and rotation matrices on the test set was performed using the coefficient of determination (R²). Angle error evaluations for the three-dimensional maxillofacial symmetry reference planes were constructed using the MDGR-Net-associated method and the "ground truth" iterative closest point (ICP)-associated method were conducted on 200 cases in the internal test set and 40 cases in an external test set. RESULTS: Based on testing with the three-dimensional maxillofacial data from the 200-case internal test set, the MDGR-Net model achieved an R² value of 0.91 for the rotation matrix and 0.98 for the translation matrix. The average angle error on the internal and external test sets were 0.84°±0.55° and 0.58°±0.43°, respectively. The construction of the three-dimensional maxillofacial symmetry reference plane for 40 clinical cases took only 3 seconds, with the model performing optimally in the patients with skeletal Class Ⅲ malocclusion, high angle cases, and Angle Class Ⅲ orthodontic patients. CONCLUSION This study proposed the MDGR-Net association method based on intelligent point cloud registration as a novel solution for constructing three-dimensional maxillofacial symmetry reference planes in clinical dental applications, which can significantly enhance diagnostic and therapeutic efficiency and outcomes, while reduce expert dependence.
Humans ; Deep Learning ; Algorithms ; Imaging, Three-Dimensional/methods* ; Male ; Female ; Maxilla/diagnostic imaging* ; Adult

OBJECTIVES: This study aimed to evaluate the repeatability and reliability of a self-developed domestic wireless surface electromyography (sEMG) system (Oralmetry) in assessing the activity of the temporalis and masseter muscles to provide theoretical support for its clinical application. METHODS: Twenty-two volunteers were recruited. Through multiple repeated measurements, the sEMG signals of bilateral anterior temporalis and masseter muscles during maximum voluntary clenching were collected using the self-developed sEMG device, Oralmetry, and two commercial sEMG devices (Zebris and Teethan), filtered, screened, and standardized. Seven sEMG indicators for assessing masticatory muscle function were calculated. The intraclass correlation coefficient (ICC) was used to evaluate the repeatability of the measurements from the three sEMG devices, and statistical analysis was conducted to compare the consistency of the seven sEMG indicators obtained from the devices. RESULTS: Among the 22 participants, the ICC values of the repeated measurements from the three sEMG devices ranged from 0.88 to 0.99. The measurements of three sEMG indicators (antero-posterior coeffificient, percentage overlapping coeffificient_MM, and percentage overlapping coeffificient_TA) obtained by Zebris were significantly different from those obtained by Oralmetry and Teethan (P<0.05). No significant differences in the measurements of the seven sEMG indicators were found between Oralmetry and Teethan. CONCLUSIONS Oralmetry and the two commercial sEMG devices demonstrated good repeatability in capturing sEMG indicators for evaluating masticatory muscle function. In particular, Oralmetry showed the highest ICC values. All three devices also exhibited good consistency in measuring sEMG indicators, and a high agreement was observed between the two wireless sEMG devices (Oralmetry and Teethan). These findings provide theoretical support for the clinical application of Oralmetry.
Humans ; Electromyography/methods* ; Masseter Muscle/physiology* ; Masticatory Muscles/physiology* ; Wireless Technology ; Reproducibility of Results ; Temporal Muscle/physiology* ; Male ; Adult ; Female ; Young Adult

Objective This study proposes a chairside digital design and manufacturing method for band and loop space maintainers and preliminarily validates its clinical feasibility.Methods Clinical cases of 10 children requiring space maintenance caused by premature loss of primary teeth were collected.Intraoral scan data of the affected children were also collected to establish digital models of the missing teeth.Using a pediatric band and loop space maintainer de-sign software developed by our research team,a rapid personalized design of band and loop structures was achieved,and a digital model of an integrated band and loop space maintainer was ultimately generated.A chairside space maintainer was manufactured through metal computer numerical control machining for the experimental group,whereas metal 3D printing in the dental laboratory was used for the control group.A model fitting assessment was conducted for the space maintainers of both groups,and senior pediatric dental experts were invited to evaluate the clinical feasibility of the space maintainers with regard to fit and stability using the visual analogue scale scoring system.Statistical analysis was also performed.Results The time spent in designing and manufacturing the 10 space maintainers of the experimental group was all less than 1 h.Statistical analysis of expert ratings showed that the experimental group outperformed the control group with regard to fit and stability.Both types of space maintainers met clinical requirements.Conclusion The chairside digital design and manufacturing method for pediatric band and loop space maintainers proposed in this study can achieve same-day fitting of space maintainers at the first appointment,demonstrating good clinical feasibility and significant potential for clinical application.

Objective:To explore the method of obtaining the key parameters of the average value articulator in healthy people based on mandibular movement trajectory data, with a view to providing a reference for the clinical application of the average value articulator.Methods:One hundred healthy volunteers (42 males and 58 females) with individual normal occlusion, aged 18-55 years old, who met the inclusion criteria were recruited from Beijing, and their mandibular movement trajectory data were collected. The left and right sagittal condylar inclination(SCI) and transversal condylar inclination(TCI) were obtained from the values of the articulator parameters which were generated in the mandibular movement analysis system.The SCI and TCI were grouped by gender and calculated separately for the two groups and the overall sample; the gender differences in the two parameters and the differences between the mean values of the two parameters and the average value articulator empirical values (35° for SCI and 15° for TCI) for the overall sample were compared.Results:The differences between SCI (35.8°±7.4°) and TCI [11.2° (11.3°)] in males and the corresponding parameters [35.6°±8.3° and 10.8° (9.5°), respectively] in females were not statistically significant ( t=0.10, P=0.922; Z=-0.60, P=0.552); the overall sample SCI (35.7°±7.9°) did not differ statistically from the average value articulator empirical value ( t=1.23, P=0.221), and the overall sample TCI [10.9° (10.3°)] was significantly smaller than the average value articulator empirical value ( W=5 825.00, P<0.001). Conclusions:The mandibular movement trajectory data of 100 adults with individual normal occlusion in this study shows that the gender factor does not affect the setting of the key parameters of the average value articulator, the SCI of the average value articulator empirical values is appropriate, and the TCI has the possibility of being on the large side. In the clinical use of the articulator to assist in the design of restorations, the parameter values should be rationally adjusted according to the actual situation of the patient′s dentition and mandibular movement.

Objective:To explore the impact of digital technology in constructing a three-dimensional (3D) symmetry reference plane (SRP) for esthetic restoration of anterior teeth and to evaluate its clinical applicability.Methods:A cross-sectional study was conducted from February to May 2024, involving 20 patients [11 males and 9 females, aged (36.8±11.4) years] who underwent anterior esthetic restorations at the Department of Stomatology, People′s Hospital of Ningxia Hui Autonomous Region. Symmetrical reference planes of patients′ 3D facial models were constructed using three different algorithms: weighted Procrustes analysis (WPA), Procrustes analysis (PA) based on the ontology-mirror correlation method, and iterative closest point (ICP). The SRP defined by an associate chief physician served as the control (true-value group). The angular errors between each algorithm group and the true-value group were compared. The optimal algorithm was selected and combined with a three-dimensional digital smile design (DSD) to create virtual patients, followed by designing anterior restorations. The visual analogue scale (VAS) was used by patients to score the aesthetic restoration results of the conventional design (control group) and the algorithm-based design (algorithm group).Results:The angular errors of the WPA, PA, and ICP groups were 1.43°±0.66°, 1.82°±0.88°, and 4.74°±2.03° respectively, with statistically significant differences among the groups ( F=41.10, P<0.001). Pairwise comparisons showed that the WPA group had significantly smaller angular errors compared to the PA and ICP groups ( P<0.05). The VAS scores for aesthetic restoration were significantly higher in the algorithm group (8.09±0.74) compared to the control group (6.30±1.38) ( t=-5.49, P<0.001). Conclusions:The SRP constructed using the WPA algorithm demonstrated minimal angular error when compared to the expert-defined SRP and is considered the optimal choice in clinical practice, yielding high patient satisfaction.

Objective:To establish a prediction method of diopter based on sequence of ocular biological parameters.Methods:A stratified random cluster sampling method was used to extract the dataset. The dataset consisted of data collected from January 2022 to January 2023 by the Eye & ENT Hospital, Fudan University, from children aged 5 to 13 years in 2 key schools and 2 general schools of Yangpu District, Shanghai. Children’s ocular biological parameters, including sex, age, diopter, axial length, corneal curvature, and anterior chamber depth were collected. The slope of the optimally fitted straight line was calculated using the least squares method. The least square-back propagation (BP) neural network model was established by combining baseline data and the pre-processed rate of the change of ocular biological parameters. The dataset was divided into the training set and the validation set according to the ratio of 8:2 for five-fold cross-validation. The model performance was evaluated by using the mean absolute error (MAE), mean squared error (MSE), root mean square error (RMSE), correlation coefficient R, and coefficient of determination R2. Results:The optimal performances of R2, R, RMSE, MAE, and MSE of the least square-BP neural network model were 0.96, 0.981 9, 0.214 2, 0.139 9 D, 0.045 9, respectively. The regression equation between the predicted value and the true value of the diopter was y=0.97 x+ 0.014 8, R2=0.97, with good correlation. In the internal verification, MAE values of the diopter at three, six, nine, and twelve months of follow-up were 0.110 1, 0.136 0, 0.153 7, and 0.184 8 D, respectively, which achieved clinically acceptable performance (less than 0.25 D). In the external validation, the errors were less than 0.25 D at all ages. Conclusions:A prediction method of diopter based on sequence of ocular biological parameters was successfully developed.

Corona virus disease 2019 (COVID-19) is an acute respiratory infectious disease with strong contagiousness, strong variability, and long incubation period. The probability of misdiagnosis and missed diagnosis can be significantly decreased with the use of automatic segmentation of COVID-19 lesions based on computed tomography images, which helps doctors in rapid diagnosis and precise treatment. This paper introduced the level set generalized Dice loss function (LGDL) in conjunction with the level set segmentation method based on COVID-19 lesion segmentation network and proposed a dual-path COVID-19 lesion segmentation network (Dual-SAUNet++) to address the pain points such as the complex symptoms of COVID-19 and the blurred boundaries that are challenging to segment. LGDL is an adaptive weight joint loss obtained by combining the generalized Dice loss of the mask path and the mean square error of the level set path. On the test set, the model achieved Dice similarity coefficient of (87.81 ± 10.86)%, intersection over union of (79.20 ± 14.58)%, sensitivity of (94.18 ± 13.56)%, specificity of (99.83 ± 0.43)% and Hausdorff distance of 18.29 ± 31.48 mm. Studies indicated that Dual-SAUNet++ has a great anti-noise capability and it can segment multi-scale lesions while simultaneously focusing on their area and border information. The method proposed in this paper assists doctors in judging the severity of COVID-19 infection by accurately segmenting the lesion, and provides a reliable basis for subsequent clinical treatment.
Humans ; COVID-19/diagnostic imaging* ; Respiratory Rate ; Tomography, X-Ray Computed

Objective:To understand the status quo of emotional intelligence, emotional labor, and humanistic caring ability of medical staff, and to clarify their internal relationship.Methods:Convenience sampling was used to select 713 medical staff from a grade A tertiary hospital in Chengdu, Sichuan Province, China. Emotional Intelligence Scale, Humanistic Caring Scale, and Emotional Labor Scale were used to measure the emotional intelligence, humanistic caring ability, and emotional labor of medical staff. SPSS 22.0 software was used to establish a database for statistical description and analysis. Process 3.2 software was used to analyze the mediating effect.Results:In humanistic caring ability, the average score of comprehension dimension was the highest (75.62±8.20) and the average score of patience dimension was the lowest (58.53±5.01). In emotional labor, the average score of the deep action dimension was the highest (23.39±3.85) and the average score of the surface action dimension was the lowest (17.73±3.18). In emotional intelligence, the average score of self-emotion evaluation dimension was the highest (21.76±3.30) and the average score of other-emotion evaluation dimension was the lowest (20.07±3.71). Positive correlations were found between humanistic caring ability and emotional intelligence, between humanistic caring ability and emotional labor, and between emotional intelligence and emotional labor ( P<0.01). Humanistic caring ability had a partial mediating effect between emotional intelligence and emotional labor. Humanistic caring ability had direct and indirect effects on emotional labor, and the effect sizes were 0.279 and 0.029, respectively. Conclusion:Emotional intelligence has a direct positive predictive effect on emotional labor, humanistic caring ability as an intermediary variable indirectly and positively predicts emotional labor. Humanistic caring ability plays a partial mediating role between emotional intelligence and emotional labor. Attention should be paid to the emotional labor of medical staff, and the emotional labor of medical staff should be improved through targeted training on emotional intelligence and humanistic caring ability. These efforts will improve the current situation and establish a harmonious doctor-patient relationship.