The mixing process is a critical link in the formation of oral solid preparations of traditional Chinese medicine. This paper took the extract powder of Guizhi Fuling Capsules and Paeonol powder as research objects. The angle of repose, loose packing density, and particle size of the two powders were measured to calibrate discrete element simulation parameters for the mixing process. The discrete element method was used to calibrate the simulated solid density of Paeonol powder and extract powder of Guizhi Fuling Capsules based on the Hertz-Mindlin with JKR V2 contact model and particle scaling. The Plackett-Burman experimental design was used to screen out the critical contact parameters that had a significant effect on the simulation of the angle of repose. The regression model between the critical contact parameters and the simulated angle of repose was established by the Box-Behnken experimental design, and the critical contact parameters of each powder were optimized based on the regression model. The best combination of critical contact parameters of the extract powder of Guizhi Fuling Capsules was found to be 0.51 for particle-particle static friction coefficient, 0.31 for particle-particle rolling friction coefficient, and 0.64 for particle-stainless steel static friction coefficient. For Paeonol powder, the best combination of critical contact parameters was 0.4 for particle-particle static friction coefficient and 0.19 for particle-particle rolling friction coefficient. The best combination of contact parameters between Paeonol powder and extract powder of Guizhi Fuling Capsules was 0.27 for collision recovery coefficient, 0.49 for static friction coefficient, and 0.38 for rolling friction coefficient. The verification results show that the relative error between the simulated value and the measured value of the angle of repose of the two single powders is less than 1%, while the relative error between the simulated value and the measured value of the angle of repose of the mixed powder with a mass ratio of 1∶1 is less than 4%. These research results provide reliable physical property simulation data for the mixed simulation experiment of extract powder of Guizhi Fuling Capsules and Paeonol powder.
Wolfiporia ; Calibration ; Powders ; Medicine, Chinese Traditional ; Capsules

Keloids are benign skin tumors resulting from the excessive proliferation of connective tissue in wound skin. Precise prediction of keloid risk in trauma patients and timely early diagnosis are of paramount importance for in-depth keloid management and control of its progression. This study analyzed four keloid datasets in the high-throughput gene expression omnibus (GEO) database, identified diagnostic markers for keloids, and established a nomogram prediction model. Initially, 37 core protein-encoding genes were selected through weighted gene co-expression network analysis (WGCNA), differential expression analysis, and the centrality algorithm of the protein-protein interaction network. Subsequently, two machine learning algorithms including the least absolute shrinkage and selection operator (LASSO) and the support vector machine-recursive feature elimination (SVM-RFE) were used to further screen out four diagnostic markers with the highest predictive power for keloids, which included hepatocyte growth factor (HGF), syndecan-4 (SDC4), ectonucleotide pyrophosphatase/phosphodiesterase 2 (ENPP2), and Rho family guanosine triphophatase 3 (RND3). Potential biological pathways involved were explored through gene set enrichment analysis (GSEA) of single-gene. Finally, univariate and multivariate logistic regression analyses of diagnostic markers were performed, and a nomogram prediction model was constructed. Internal and external validations revealed that the calibration curve of this model closely approximates the ideal curve, the decision curve is superior to other strategies, and the area under the receiver operating characteristic curve is higher than the control model (with optimal cutoff value of 0.588). This indicates that the model possesses high calibration, clinical benefit rate, and predictive power, and is promising to provide effective early means for clinical diagnosis.
Humans ; Keloid/genetics* ; Nomograms ; Algorithms ; Calibration ; Machine Learning

In this paper, the differences between air probe and filled probe for measuring high-frequency dielectric properties of biological tissues are investigated based on the equivalent circuit model to provide a reference for the methodology of high-frequency measurement of biological tissue dielectric properties. Two types of probes were used to measure different concentrations of NaCl solution in the frequency band of 100 MHz-2 GHz. The results showed that the accuracy and reliability of the calculated results of the air probe were lower than that of the filled probe, especially the dielectric coefficient of the measured material, and the higher the concentration of NaCl solution, the higher the error. By laminating the probe terminal, liquid intrusion could be prevented, to a certain extent, to improve the accuracy of measurement. However, as the frequency decreased, the influence of the film on the measurement increased and the measurement accuracy decreased. The results of the study show that the air probe, despite its simple dimensional design and easy calibration, differs from the conventional equivalent circuit model in actual measurements, and the model needs to be re-corrected for actual use. The filled probe matches the equivalent circuit model better, and therefore has better measurement accuracy and reliability.
Reproducibility of Results ; Sodium Chloride ; Calibration

In order to solve the problems of quality control and traceability of medical test lung for meeting the calibration conditions of JJF 1234-2018 Calibration Specification for Ventilators, the calibration device and method are researched for compliance and airway resistance of medical test lung in this paper. A calibration device for medical test lung is designed using constant volume active piston technology to simulate human breathing. Through comparison experiment, the deviation between this device and the similar foreign device can be found. The deviation is lower than 0.4% for lung compliance and lower than 0.7% for airway resistance. The calibration of lung compliance and airway resistance can be completed by this device. This device has a clear and complete traceability path to ensure quality control from the source. The calibration of ventilator is improved. This paper provides a reference for related metrology departments and medical institutions to study on quality inspection of respiratory medical instruments.
Humans ; Calibration ; Ventilators, Mechanical ; Respiration ; Quality Control ; Lung

Robotic puncture system has been widely used in modern minimally invasive surgery, which usually uses hand-eye calibration to calculate the spatial relationship between the robot and the optical tracking system. However, the hand-eye calibration process is time-consuming and sensitive to environmental changes, which makes it difficult to guarantee the puncture accuracy of the robot. This study proposes an uncalibrated positioning method for puncture robot based on optical navigation. The method divides the target path positioning into two stages, angle positioning and position positioning, and designs angle image features and position image features respectively. The corresponding image Jacobian matrix is constructed based on the image features and updated by online estimation with a cubature Kalman filter to drive the robot to perform target path localization. The target path positioning results show that the method is more accurate than the traditional hand-eye calibration method and saves significant preoperative preparation time by eliminating the need for calibration.
Robotics ; Robotic Surgical Procedures ; Calibration ; Minimally Invasive Surgical Procedures ; Optical Devices

OBJECTIVE: To propose a sensitivity test method for geometric correction position deviation of cone-beam CT systems. METHODS: We proposed the definition of center deviation and its derivation. We analyzed the influence of the variation of the three-dimensional spatial center of the steel ball point, the projection center and the size of the steel ball point on the deviation of geometric parameters and the reconstructed image results by calculating the geometric correction parameters based on the Noo analytical method using the FDK reconstruction algorithm for image reconstruction. RESULTS: The radius of the steel ball point was within 3 mm. The deviation of the center of the calibration parameter was within the order of magnitude and negligible. A 10% Gaussian perturbation of a single pixel in the 3D spatial coordinates of the steel ball point produced a deviation of about 3 pixel sizes, while the same Gaussian perturbation of the 2D projection coordinates of the steel ball point produced a deviation of about 2 pixel sizes. CONCLUSION The geometric correction is more sensitive to the deviation generated by the three-dimensional spatial coordinates of the steel ball point with limited sensitivity to the deviation generated by the two-dimensional projection coordinates of the steel ball point. The deviation sensitivity of a small diameter steel ball point can be ignored.
Algorithms ; Calibration ; Cone-Beam Computed Tomography ; Steel

OBJECTIVE: To construct an inherent interpretability machine learning model as an explainable boosting machine model (EBM) for predicting one-year risk of death in patients with severe ischemic stroke. METHODS: We randomly divided the data of 2369 eligible patients with severe ischemic stroke in the MIMIC-Ⅳ(2.0) database, who were admitted in ICU in 2008 to 2019, into a training dataset (80%) and a test dataset (20%), and assessed the prognosis of the patients using the EBM model. The prediction performance of the model was evaluated by calculating the area under the receiver operating characteristic (AUC) curve. The calibration curve and Brier score were used to evaluate the degree of calibration of the model, and a decision curve was generated to assess the net clinical benefit. RESULTS: The EBM model constructed in this study had good discrimination power, calibration and net benefit, with an AUC of 0.857 (95% CI: 0.831-0.887) for predicting prognosis of severe ischemic stroke. Calibration curve analysis showed that the standard curve of the EBM model was the closest to the ideal curve. Decision curve analysis showed that the model had the greatest net benefit rate at the prediction probability threshold of 0.10 to 0.80. The top 5 independent predictive variables based on the EBM model were age, SOFA score, mean heart rate, mechanical ventilation, and mean respiratory rate, whose significance scores ranged from 0.179 to 0.370. CONCLUSION This EBM model has a good performance for predicting the risk of death within one year in patients with severe ischemic stroke and allows clinicians to better understand the contributing factors of the patients' outcomes through the model interpretability.
Humans ; Ischemic Stroke ; Calibration ; Databases, Factual ; Intensive Care Units ; Machine Learning

OBJECTIVE: To compare the predictive ability of two extended Cox models in nonlinear survival data analysis. METHODS: Through Monte Carlo simulation and empirical study and with the conventional Cox Proportional Hazards model and Random Survival Forests as the reference models, we compared restricted cubic spline Cox model (Cox_RCS) and DeepSurv neural network Cox model (Cox_DNN) for their prediction ability in nonlinear survival data analysis. Concordance index was used to evaluate the differentiation of the prediction results (a larger concordance index indicates a better prediction ability of the model). Integrated Brier Score was used to evaluate the calibration degree of the prediction (a smaller index indicates a better prediction ability). RESULTS: For data that met requirement of the proportion risk, the Cox_RCS model had the best prediction ability regardless of the sample size or deletion rate. For data that failed to meet the proportion risk, the prediction ability of Cox_DNN was optimal for a large sample size (≥500) with a low deletion (< 40%); the prediction ability of Cox_RCS was superior to those of other models in all other scenarios. For example data, the Cox_RCS model showed the best performance. CONCLUSION In analysis of nonlinear low maintenance data, Cox_RCS and Cox_DNN have their respective advantages and disadvantages in prediction. The conventional survival analysis methods are not inferior to machine learning or deep learning methods under certain conditions.
Proportional Hazards Models ; Survival Analysis ; Calibration ; Computer Simulation ; Data Analysis

Objective. Second-generation force-controlled periodontal probes were developed to address examiner-related factors in the application of standard forces while probing. However, previous researches comparing the accuracy of first and second-generation probes have failed to yield unequivocal results. This study aimed to determine the accuracy and reproducibility of a first-generation UNC-15 probe and force-controlled Gram probe #2, used by senior and junior dental students in measuring probing pocket depths (PPD).

Methods. Eight senior and eight junior dental students participated in the study. A periodontal model was positioned on a digital balance. Each participant performed probing four times, twice using a UNC-15 and twice employing a Gram probe #2. The order of probe usage was randomly assigned. The PPD and probing forces employed were recorded initially and after a 15-minute interval. Data were analyzed using a t-test at a 0.05 significance level.

Results. Both junior and senior students used significantly higher probing forces (35.3 ± 1.4 and 29.9 ± 2.1 g, respectively) than the standard 25 g when using the UNC-15 probe. The junior students employed accurate forces using the Gram probe while probing both anterior (25.6 ± 1.2 g) and posterior teeth (25.1 ± 0.7 g). In comparison, the seniors used accurate forces (26.0 ± 1.3 g) only while examining anterior teeth. The PPD values obtained by both student groups were comparable to the actual PPD, except for significantly higher measurements (P=0.0003) obtained by juniors when examining posterior teeth using the Gram probe. When assessing the reproducibility of PPD measurements obtained from two examinations, values were reproducible for both juniors and seniors when using the Gram probe and during an inspection of posterior teeth using the UNC-15 probe.

Conclusions. The Gram probe #2 had better reproducibility and yielded forces closer to the standard 25 g. However, more accurate PPD values were obtained with the UNC-15 probe.

Spatial distribution uniformity is the critical quality attribute(CQA) of Ginkgo Leaves Tablets, a variety of big brand traditional Chinese medicine. The evaluation of the spatial distribution uniformity of active pharmaceutical ingredients(APIs) in Ginkgo Leaves Tablets is important in ensuring their stable and controllable quality. In this study, hyperspectral imaging technology was used to construct the spatial distribution map of API concentration based on three prediction models, further to realize the visualization research on the spatial distribution uniformity of Ginkgo Leaves Tablets. The region of interest(ROI) was selected from each Ginkgo Leaves Tablet, with length and width of 50 pixels, and a total of 2 500 pixels. Each pixel had 288 spectral channels, and the number of content prediction data could reach 1×10~5 for a single sample. The results of the three models showed that the Partial Least Squares(PLS) model had the highest prediction accuracy, with calibration set determination coefficient R_(pre)~2 of 0.987, prediction set determination coefficient R_(pre)~2 of 0.942, root mean square error of calibration(RMSEC) of 0.160%, and root mean square error of prediction(RMSEP) of 0.588%. The classical least-squares(CLS) model had a greater prediction error, with the RMSEP of 0.867%. Multivariate Curve Resolution-Alternating Least Square(MCR-ALS) model showed the worst predictive ability among the three models, and it couldn't realize content prediction. Based on the prediction results of PLS and CLS models, the spatial distribution map of APIs concentration was obtained through three-dimensional data reconstruction. Furthermore, histogram method was used to evaluate the spatial distribution uniformity of API. The data showed that the spatial distribution of APIs in Ginkgo Leaves Tablets was relatively uniform. The study explored the feasibility of visualization of spatial distribution of Ginkgo Leaves Tablets based on three models. The results showed that PLS model had the highest prediction accuracy, and MCR-ALS model had the lowest prediction accuracy. The research results could provide a new strategy for the visualization method of quality control of Ginkgo Leaves Tablets.
Calibration ; Ginkgo biloba ; Least-Squares Analysis ; Medicine, Chinese Traditional ; Plant Leaves ; Quality Control ; Spectroscopy, Near-Infrared ; Tablets