Computer Simulation ; Models, Statistical

The active ingredients in traditional Chinese medicine(TCM)are the foundation for the efficiency of TCM and the key to the formation of Dao-di herbs. It is of great significance to study the biosynthesis and regulation mechanisms of these active ingredients for analyzing the formation mechanism of Daodi herbs and providing components for the production of active ingredients in TCM by synthetic biology. With the advancements in omics technology, molecular biology, synthetic biology, artificial intelligence, etc., the analysis of biosynthetic pathways for active ingredients in TCM is rapidly progressing. New methods and technologies have promoted the analysis of the synthetic pathways of active ingredients in TCM and have also made this area a hot topic in molecular pharmacognosy. Many researchers have made significant progress in analyzing the biosynthetic pathways of active ingredients in TCM such as Panax ginseng, Salvia miltiorrhiza, Glycyrrhiza uralensis, and Tripterygium wilfordii. This paper systematically reviewed current research me-thods for analyzing the biosynthetic functional genes of active ingredients in TCM, elaborated the mining of gene elements based on multiomics technology and the verification of gene functions in plants in vitro and in vivo with candidate genes as objects. Additionally, the paper summarized new technologies and methods that have emerged in recent years, such as high-throughput screening, molecular probes, genome-wide association studies, cell-free systems, and computer simulation screening to provide a comprehensive reference for the analysis of the biosynthetic pathways of active ingredients in TCM.
Medicine, Chinese Traditional ; Drugs, Chinese Herbal ; Artificial Intelligence ; Biosynthetic Pathways ; Computer Simulation ; Genome-Wide Association Study

In order to address the issues of spatial induction bias and lack of effective representation of global contextual information in colon polyp image segmentation, which lead to the loss of edge details and mis-segmentation of lesion areas, a colon polyp segmentation method that combines Transformer and cross-level phase-awareness is proposed. The method started from the perspective of global feature transformation, and used a hierarchical Transformer encoder to extract semantic information and spatial details of lesion areas layer by layer. Secondly, a phase-aware fusion module (PAFM) was designed to capture cross-level interaction information and effectively aggregate multi-scale contextual information. Thirdly, a position oriented functional module (POF) was designed to effectively integrate global and local feature information, fill in semantic gaps, and suppress background noise. Fourthly, a residual axis reverse attention module (RA-IA) was used to improve the network's ability to recognize edge pixels. The proposed method was experimentally tested on public datasets CVC-ClinicDB, Kvasir, CVC-ColonDB, and EITS, with Dice similarity coefficients of 94.04%, 92.04%, 80.78%, and 76.80%, respectively, and mean intersection over union of 89.31%, 86.81%, 73.55%, and 69.10%, respectively. The simulation experimental results show that the proposed method can effectively segment colon polyp images, providing a new window for the diagnosis of colon polyps.
Humans ; Colonic Polyps/diagnostic imaging* ; Computer Simulation ; Electric Power Supplies ; Semantics ; Image Processing, Computer-Assisted

OBJECTIVES: To compare the application value of the likelihood ratio (LR) method and identity by state (IBS) method in the identification involving half sibling relationships, and to provide a reference for the setting of relevant standards for identification of half sibling relationship. METHODS: (1) Based on the same genetic marker combinations, the reliability of computer simulation method was verified by comparing the distributions of cumulated identity by state score (CIBS) and combined full sibling index in actual cases with the distributions in simulated cases. (2) In different numbers of three genetic marker combinations, the simulation of full sibling, half sibling and unrelated individual pairs, each 1 million pairs, was obtained; the CIBS, as well as the corresponding types of cumulative LR parameters, were calculated. (3) The application value of LR method was compared with that of IBS method, by comparing the best system efficiency provided by LR method and IBS method when genetic markers in different amounts and of different types and accuracy were applied to distinguish the above three relational individual pairs. (4) According to the existing simulation data, the minimum number of genetic markers required to distinguish half siblings from the other two relationships using different types of genetic markers was estimated by curve fitting. RESULTS: (1) After the rank sum test, under the premise that the real relationship and the genetic marker combination tested were the same, there was no significant difference between the simulation method and the results obtained in the actual case. (2) In most cases, under the same conditions, the system effectiveness obtained by LR method was greater than that by IBS method. (3) According to the existing data, the number of genetic markers required for full-half siblings and half sibling identification could be obtained by curve fitting when the system effectiveness reached 0.95 or 0.99. CONCLUSIONS When distinguishing half sibling from full sibling pairs or unrelated pairs, it is recommended to give preference to the LR method, and estimate the required number of markers according to the identification types and the population data, to ensure the identification effect.
Humans ; Siblings ; Genetic Markers ; Computer Simulation ; Irritable Bowel Syndrome/genetics* ; Reproducibility of Results ; Genotype

Objective: based on summarizing the simulation and prediction of tobacco control measures across the globe and sorting out the various scenarios of tobacco control measures, the potential short-term effects of seven tobacco control measures in different scenarios were systematically analyzed. Methods: Until April 2022, PubMed, Embase, EconLit, PsychINFO, and CINAHL databases were used to retrieve literature about tobacco control measures simulation and prediction models across the globe. Inclusion and exclusion criteria were strictly followed. Meta-analysis for the potential short-term effects of seven tobacco control measures in different scenarios was performed using the R software. Results: A total of 22 papers covering 16 countries were selected. Five studies were conducted in the United States, three in Mexico, and two in Italy. There were all papers with the measures to tax increases, smoke-free air laws, and mass media campaigns, 21 papers with youth access restrictions, 20 with marketing restrictions, and 19 with cessation treatment programs and health warnings. The tax increases had diverse influences on the price elasticity of different age groups. The price elasticity in the age group 15-17 years was the highest, which was 0.044 (95%CI: 0.038-0.051). The potential short-term effects of smoke-free air laws in workplaces were higher than in restaurants and other indoor public places. The effects of youth access restrictions were greater in the age group <16 years than in the age group 16-17. The stronger the implementation of other measures, the greater the potential short-term effects. A comparison of seven tobacco control measures showed that the cessation treatment programs increase in cessation rate was the highest, 0.404 (95%CI: 0.357-0.456). The reduction in smoking rate and reduction in initiation rate of youth access restrictions strongly enforced and publicized was the highest in the age group <16 years, 0.292 (95%CI: 0.269-0.315), and 0.292 (95%CI: 0.270-0.316). Conclusions: The potential short-term effects of seven tobacco control measures in different scenarios were evaluated more accurately and objectively through Meta-analysis. In the short term, cessation treatment programs will substantially increase smoking cessation rates, and strong youth access enforcement will sharply reduce smoking and initiation rates among adolescents under 16. These results also offer strong data-related support for the simulation and prediction of tobacco control measures in China and other countries.
Adolescent ; Humans ; United States ; Tobacco Control ; Prevalence ; Computer Simulation ; Smoking Cessation ; Health Behavior ; Smoking Prevention

OBJECTIVE: To propose a framework that combines sinogram interpolation with unsupervised image-to-image translation (UNIT) network to correct metal artifacts in CT images. METHODS: The initially corrected CT image and the prior image without artifacts, which were considered as different elements in two different domains, were input into the image transformation network to obtain the corrected image. Verification experiments were carried out to assess the effectiveness of the proposed method using the simulation data, and PSNR and SSIM were calculated for quantitative evaluation of the performance of the method. RESULTS: The experiment using the simulation data showed that the proposed method achieved better results for improving image quality as compared with other methods, and the corrected images preserved more details and structures. Compared with ADN algorithm, the proposed algorithm improved the PSNR and SSIM by 2.4449 and 0.0023 when the metal was small, by 5.9942 and 8.8388 for images with large metals, and by 8.8388 and 0.0130 when both small and large metals were present, respectively. CONCLUSION The proposed method for metal artifact correction can effectively remove metal artifacts, improve image quality, and preserve more details and structures on CT images.
Artifacts ; Algorithms ; Computer Simulation ; Tomography, X-Ray Computed

OBJECTIVE: Current mainstream PET scattering correction methods are introduced and evaluated horizontally, and finally, the existing problems and development direction of scattering correction are discussed. METHODS: Based on NeuWise Pro PET/CT products of Neusoft Medical System Co. Ltd. , the simulation experiment is carried out to evaluate the influence of radionuclide distribution out of FOV (field of view) on the scattering estimation accuracy of each method. RESULTS: The scattering events produced by radionuclide out of FOV have an obvious impact on the spatial distribution of scattering, which should be considered in the model. The scattering estimation accuracy of Monte Carlo method is higher than single scatter simulation (SSS). CONCLUSIONS Clinically, if the activity of the adjacent parts out of the FOV is high, such as brain, liver, kidney and bladder, it is likely to lead to the deviation of scattering estimation. Considering the Monte Carlo scattering estimation of the distribution of radionuclide out of FOV, it's helpful to improve the accuracy of scattering distribution estimation.
Positron Emission Tomography Computed Tomography ; Scattering, Radiation ; Computer Simulation ; Brain ; Monte Carlo Method ; Phantoms, Imaging ; Image Processing, Computer-Assisted

OBJECTIVE: To investigate whether Suxiao Jiuxin Pills (SJP), a Chinese herbal remedy, is an anti-ventricular fibrillation (VF) agent. METHODS: VF was induced by isoproterenolol (ISO) intraperitoneal injection followed by electrical pacing in mice and rabbits. The effects of SJP on the L-type calcium channel current (CaV1.2), voltage-dependent sodium channel current (I_Na), rapid and slow delayed rectifier potassium channel current (I_Kr and I_Ks, respectively) were studied by whole-cell patch-clamp method. Computer simulation was implemented to incorporate the experimental data of SJP effects on the CaV1.2 current into the action potential (AP) and pseudo-electrocardiography (pseudo-ECG) models. RESULTS: SJP prevented VF induction and reduced VF durations significantly in mice and rabbits. Patch-clamp experiments revealed that SJP decreased the peak amplitude of the CaV1.2 current with a half maximal concentration (IC₅₀) value of 16.9 mg/L (SJP-30 mg/L, -32.8 ± 6.1 pA; Verapamil, -16.2 ±1.8 pA; vs. control, -234.5 ±16.7 pA, P<0.01, respectively). The steady-state activation curve, inactivation curve, and the recovery from inactivation of the CaV1.2 current were not shifted significantly. Specifically, SJP did not altered I_Na, I_Kr, and I_Ks currents significantly (SJP vs. control, P>0.05). Computer simulation showed that SJP-reduced CaV1.2 current shortened the AP duration, transiting VF into sinus rhythm in pseudo-ECG. CONCLUSION SJP reduced VF via inhibiting the CaV1.2 current with in vivo, in vitro, and in silico studies, which provide experimental basis for SJP anti-VF clinical application.
Animals ; Rabbits ; Mice ; Calcium ; Computer Simulation ; Arrhythmias, Cardiac ; Electrocardiography

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: To compare different methods for calculating sample size based on confidence interval estimation for a single proportion with different event incidences and precisions. METHODS: We compared 7 methods, namely Wald, AgrestiCoull add z2, Agresti-Coull add 4, Wilson Score, Clopper-Pearson, Mid-p, and Jefferys, for confidence interval estimation for a single proportion. The sample size was calculated using the search method with different parameter settings (proportion of specified events and half width of the confidence interval [ω=0.05, 0.1]). With Monte Carlo simulation, the estimated sample size was used to simulate and compare the width of the confidence interval, the coverage of the confidence interval and the ratio of the noncoverage probability. RESULTS: For a high accuracy requirement (ω =0.05), the Mid-p method and Clopper Pearson method performed better when the incidence of events was low (P < 0.15). In other settings, the performance of the 7 methods did not differ significantly except for a poor symmetry of the Wald method. In the setting of ω=0.1 with a very low p (0.01-0.05), failure of iteration occurred with nearly all the methods except for the Clopper-Pearson method. CONCLUSION Different sample size determination methods based on confidence interval estimation should be selected for single proportions with different parameter settings.
Confidence Intervals ; Sample Size ; Computer Simulation ; Monte Carlo Method ; Probability