Dental age estimation is a crucial aspect and one of the ways to accomplish forensic age estimation, and imaging technology is an important technique for dental age estimation. In recent years, some studies have preliminarily confirmed the feasibility of magnetic resonance imaging （MRI） in evaluating dental development, providing a new perspective and possibility for the evaluation of dental development, suggesting that MRI is expected to be a safer and more accurate tool for dental age estimation. However, further research is essential to verify its accuracy and feasibility. This article reviews the current state, challenges and limitations of MRI in dental development and age estimation, offering reference for the research of dental age assessment based on MRI technology.

In the study of age estimation in living individuals, a lot of data needs to be analyzed by mathematical statistics, and reasonable medical statistical methods play an important role in data design and analysis. The selection of accurate and appropriate statistical methods is one of the key factors affecting the quality of research results. This paper reviews the principles and applicable principles of the commonly used medical statistical methods such as descriptive statistics, difference analysis, consistency test and multivariate statistical analysis, as well as machine learning methods such as shallow learning and deep learning in the age estimation research of living individuals, and summarizes the relevance and application prospects between medical statistical methods and machine learning methods. This paper aims to provide technical guidance for the age estimation research of living individuals to obtain more scientific and accurate results.

Objective To establish age estimation models of northern Chinese Han adults using cranial suture images obtained by CT and multiplanar reformation （MPR）, and to explore the applicability of cranial suture closure rule in age estimation of northern Chinese Han population. Methods The head CT samples of 132 northern Chinese Han adults aged 29-80 years were retrospectively collected. Volume reconstruction （VR） and MPR were performed on the skull, and 160 cranial suture tomography images were generated for each sample. Then the MPR images of cranial sutures were scored according to the closure grading criteria, and the mean closure grades of sagittal suture, coronal sutures （both left and right） and lambdoid sutures （both left and right） were calculated respectively. Finally taking the above grades as independent variables, the linear regression model and four machine learning models for age estimation （gradient boosting regression, support vector regression, decision tree regression and Bayesian ridge regression） were established for northern Chinese Han adults age estimation. The accuracy of each model was evaluated. Results Each cranial suture closure grade was positively correlated with age and the correlation of sagittal suture was the highest. All four machine learning models had higher age estimation accuracy than linear regression model. The support vector regression model had the highest accuracy among the machine learning models with a mean absolute error of 9.542 years. Conclusion The combination of skull CT-MPR and machine learning model can be used for age estimation in northern Chinese Han adults, but it is still necessary to combine with other adult age estimation indicators in forensic practice.

Objective To investigate the application value of combining the Demirjian’s method with machine learning algorithms for dental age estimation in northern Chinese Han children and adolescents. Methods Oral panoramic images of 10 256 Han individuals aged 5 to 24 years in northern China were collected. The development of eight permanent teeth in the left mandibular was classified into different stages using the Demirjian’s method. Various machine learning algorithms, including support vector regression （SVR）, gradient boosting regression （GBR）, linear regression （LR）, random forest regression （RFR）, and decision tree regression （DTR） were employed. Age estimation models were constructed based on total, female, and male samples respectively using these algorithms. The fitting performance of different machine learning algorithms in these three groups was evaluated. Results SVR demonstrated superior estimation efficiency among all machine learning models in both total and female samples, while GBR showed the best performance in male samples. The mean absolute error （MAE） of the optimal age estimation model was 1.246 3, 1.281 8 and 1.153 8 years in the total, female and male samples, respectively. The optimal age estimation model exhibited varying levels of accuracy across different age ranges, which provided relatively accurate age estimations in individuals under 18 years old. Conclusion The machine learning model developed in this study exhibits good age estimation efficiency in northern Chinese Han children and adolescents. However, its performance is not ideal when applied to adult population. To improve the accuracy in age estimation, the other variables can be considered.

Objective To estimate adolescents and children age using stepwise regression and machine learning methods based on the pulp and tooth volumes of the left maxillary central incisor and cuspid on cone beam computed tomography （CBCT） images, and to compare and analyze the estimation results. Methods A total of 498 Shanghai Han adolescents and children CBCT images of the oral and maxillofacial regions were collected. The pulp and tooth volumes of the left maxillary central incisor and cuspid were measured and calculated. Three machine learning algorithms （K-nearest neighbor, ridge regression, and decision tree） and stepwise regression were used to establish four age estimation models. The coefficient of determination, mean error, root mean square error, mean square error and mean absolute error were computed and compared. A correlation heatmap was drawn to visualize and the monotonic relationship between parameters was visually analyzed. Results The K-nearest neighbor model （R²=0.779） and the ridge regression model （R²=0.729） outperformed stepwise regression （R²=0.617）, while the decision tree model （R²=0.494） showed poor fitting. The correlation heatmap demonstrated a monotonically negative correlation between age and the parameters including pulp volume, the ratio of pulp volume to hard tissue volume, and the ratio of pulp volume to tooth volume. Conclusion Pulp volume and pulp volume proportion are closely related to age. The application of CBCT-based machine learning methods can provide more accurate age estimation results, which lays a foundation for further CBCT-based deep learning dental age estimation research.

Objective To investigate the age-related changes of the mandibular third molar root pulp visibility in individuals in East China, and to explore the feasibility of applying this method to determine whether an individual is 18 years or older. Methods A total of 1 280 oral panoramic images were collected from the 15-30 years old East China population, and the mandibular third molar root pulp visibility in all oral panoramic images was evaluated using OLZE 0-3 four-stage method, and the age distribution of the samples at each stage was analyzed using descriptive statistics. Results Stages 0, 1, 2 and 3 first appeared in 16.88, 19.18, 21.91 and 25.44 years for males and in 17.47, 20.91, 22.01 and 26.01 years for females. In all samples, individuals at stages 1 to 3 were over 18 years old. Conclusion It is feasible to determine whether an individual in East China is 18 years or older based on the mandibular third molar root pulp visibility on oral panoramic images.

Objective To develop a deep learning model for automated age estimation based on 3D CT reconstructed images of Han population in western China, and evaluate its feasibility and reliability. Methods The retrospective pelvic CT imaging data of 1 200 samples （600 males and 600 females） aged 20.0 to 80.0 years in western China were collected and reconstructed into 3D virtual bone models. The images of the ischial tuberosity feature region were extracted to create sex-specific and left/right site-specific sample libraries. Using the ResNet34 model, 500 samples of different sexes were randomly selected as training and verification set, the remaining samples were used as testing set. Initialization and transfer learning were used to train images that distinguish sex and left/right site. Mean absolute error （MAE） and root mean square error （RMSE） were used as primary indicators to evaluate the model. Results Prediction results varied between sexes, with bilateral models outperformed left/right unilateral ones, and transfer learning models showed superior performance over initial models. In the prediction results of bilateral transfer learning models, the male MAE was 7.74 years and RMSE was 9.73 years, the female MAE was 6.27 years and RMSE was 7.82 years, and the mixed sexes MAE was 6.64 years and RMSE was 8.43 years. Conclusion The skeletal age estimation model, utilizing ischial tuberosity images of Han population in western China and employing the ResNet34 combined with transfer learning, can effectively estimate adult ischium age.

Objective To explore the biomarkers and potential mechanisms of chronic restraint stress-induced myocardial injury in hyperlipidemia ApoE^-/- mice. Methods The hyperlipidemia combined with the chronic stress model was established by restraining the ApoE^-/- mice. Proteomics and bioinformatics techniques were used to describe the characteristic molecular changes and related regulatory mechanisms of chronic stress-induced myocardial injury in hyperlipidemia mice and to explore potential diagnostic biomarkers. Results Proteomic analysis showed that there were 43 significantly up-regulated and 58 significantly down-regulated differentially expressed proteins in hyperlipidemia combined with the restraint stress group compared with the hyperlipidemia group. Among them, GBP2, TAOK3, TFR1 and UCP1 were biomarkers with great diagnostic potential. KEGG pathway enrichment analysis indicated that ferroptosis was a significant pathway that accelerated the myocardial injury in hyperlipidemia combined with restraint stress-induced model. The mmu_circ_0001567/miR-7a/Tfr-1 and mmu_circ_0001042/miR-7a/Tfr-1 might be important circRNA-miRNA-mRNA regulatory networks related to ferroptosis in this model. Conclusion Chronic restraint stress may aggravate myocardial injury in hyperlipidemia mice via ferroptosis. Four potential biomarkers are selected for myocardial injury diagnosis, providing a new direction for sudden cardiac death （SCD） caused by hyperlipidemia combined with the restraint stress.

Objective To detect the expression changes of interleukin-10 （IL-10） and transforming growth factor-β1 （TGF-β1） during the development of deep vein thrombosis in mice, and to explore the application value of them in thrombus age estimation. Methods The mice in the experimental group were subjected to ligation of inferior vena cava. The mice were sacrificed by excessive anesthesia at 1 d, 3 d, 5 d, 7 d, 10 d, 14 d and 21 d after ligation, respectively. The inferior vena cava segment with thrombosis was extracted below the ligation point. The mice in the control group were not ligated, and the inferior vena cava segment at the same position as the experimental group was extracted. The expression changes of IL-10 and TGF-β1 were detected by immunohistochemistry （IHC）, Western blotting and real-time qPCR. Results IHC results revealed that IL-10 was mainly expressed in monocytes in thrombosis and TGF-β1 was mainly expressed in monocytes and fibroblast-like cells in thrombosis. Western blotting and real-time qPCR showed that the relative expression levels of IL-10 and TGF-β1 in each experimental group were higher than those in the control group. The mRNA and protein levels of IL-10 reached the peak at 7 d and 10 d after ligation, respectively. The mRNA expression level at 7 d after ligation was 4.72±0.15 times that of the control group, and the protein expression level at 10 d after ligation was 7.15±0.28 times that of the control group. The mRNA and protein levels of TGF-β1 reached the peak at 10 d and 14 d after ligation, respectively. The mRNA expression level at 10 d after ligation was 2.58±0.14 times that of the control group, and the protein expression level at 14 d after ligation was 4.34±0.19 times that of the control group. Conclusion The expressions of IL-10 and TGF-β1 during the evolution of deep vein thrombosis present time-dependent sequential changes, and the expression levels of IL-10 and TGF-β1 can provide a reference basis for thrombus age estimation.

Objective To explore the postmortem diffusion rule of Aconitum alkaloids and their metabolites in poisoned rabbits, and to provide a reference for identifying the antemortem poisoning or postmortem poisoning of Aconitum alkaloids. Methods Twenty-four rabbits were sacrificed by tracheal clamps. After 1 hour, the rabbits were administered with aconitine LD₅₀ in decocting aconite root powder by intragastric administration. Then, they were placed supine and stored at 25 ℃. The biological samples from 3 randomly selected rabbits were collected including heart blood, peripheral blood, urine, heart, liver, spleen, lung and kidney tissues at 0 h, 4 h, 8 h, 12 h, 24 h, 48 h, 72 h and 96 h after intragastric administration, respectively. Aconitum alkaloids and their metabolites in the biological samples were analyzed by high performance liquid chromatography-tandem mass spectrometry （HPLC-MS/MS）. Results At 4 h after intragastric administration, Aconitum alkaloids and their metabolites could be detected in heart blood, peripheral blood and major organs, and the contents of them changed dynamically with the preservation time. The contents of Aconitum alkaloids and their metabolites were higher in the spleen, liver and lung, especially in the spleen which was closer to the stomach. The average mass fraction of benzoylmesaconine metabolized in rabbit spleen was the highest at 48 h after intragastric administration. In contrast, the contents of Aconitum alkaloids and their metabolites in kidney were all lower. Aconitum alkaloids and their metabolites were not detected in urine. Conclusion Aconitum alkaloids and their metabolites have postmortem diffusion in poisoned rabbits, diffusing from high-content organs （stomach） to other major organs and tissues as well as the heart blood. The main mechanism is the dispersion along the concentration gradient, while urine is not affected by postmortem diffusion, which can be used as the basis for the identification of antemortem and postmortem Aconitum alkaloids poisoning.