Age estimation from median palatine suture using computed tomography reconstructed 3D images: a comparison of Northern and Southwestern Chinese populations.

To investigate the potential of computed tomography (CT) images of median palatine suture (MP) for adult age estimation in the Northern and Southwestern Chinese populations. A total of 1110 cranial CT scans from individuals aged 10-79 years, including 557 northern Chinese and 553 southwestern Chinese, were collected for analysis. After volume reformation and multiplanar reconstruction, a total of 20 slices of median palatine suture were selected from each individual. The closure of sutures was analyzed into four stages, and the cumulative scores of 20 slices were recorded as the suture closure score (SCS). The correlations between SCS and age were compared among the two Chinese populations residing in diverse geographic regions. Regression models were established for age estimation. The estimation accuracy was evaluated based on the test set. The mean absolute error (MAE) and the correlation between predicted age and chronological age were calculated to evaluate estimation accuracy. The SCS of MP exhibited a significant correlation with age (0.613, northern male; 0.678, southwestern male; 0.730, northern female; 0.704, Southwestern female; 0.662, total). Furthermore, there were statistically significant differences in SCS among different regions and sex groups (p < 0.001). The cubic regression model had the highest R2 value in all subjects, especially among Northern females and Southwestern males, while the power and quadratic regression models showed the highest R2 value in Northern males and Southwestern females, respectively. In the test set, the Northern cohort demonstrated a lower MAE (9.06 ± 7.32 years, males; 9.17 ± 5.28 years, females) compared to the Southwestern cohort (9.19 ± 7.49 years, male; 10.61 ± 6.83 years, female). Additionally, it was observed that males exhibited a lower MAE than females in both regional groups. This study demonstrated the potential utility of CT images of the MP for age estimation in Chinese populations, emphasizing the significance of incorporating regional and sex factors within this context.

Lake-centred sedentary lifestyle of early Tibetan Plateau Indigenous populations at high elevation 4,400 years ago.

The onset of sedentism on the Tibetan Plateau is often presumed to be associated with the dispersal of agriculture or farmers from archaeological sites located in the low elevation margins of the plateau. Previous studies of the plateau assumed that all foragers were probably mobile, but few systematic excavations at forager sites have been conducted to inform us about their settlement patterns. Here we report the world's highest elevation sedentary way of living exhibited by the Mabu Co site at 4,446 metres above sea level, deep in the interior of the Tibetan Plateau 4,400-4,000 years ago. Our interdisciplinary study indicates that the site was occupied by Indigenous inhabitants of the plateau, representing the earliest known DNA evidence of foragers who predominantly harbour the southern plateau ancestry. The evidence shows that they had a sedentary lifestyle primarily supported by fishing at nearby lakes, supplemented by mammal and bird hunting, as well as small-scale exchanges of millet and rice crops.

Forensic age estimation in adults based on multidetector computed tomography analysis of bone density in the medial meta-epiphyseal region of clavicle.

The aim of this study was to investigate the potential of using multidetector computed tomography (MDCT) to measure the bone mineral density (BMD) in the medial meta-epiphyseal region of clavicle (MERC) for adult age estimation. A total of 1064 chest MDCT scans from individuals aged 21 to 102 years were utilized to determine the MERC BMD. The Mimics software was used for the BMD measurements, and the average BMD of both MERC was also calculated. Regression analysis was conducted with chronological age as a dependent variable and MERC BMD as an independent variable to establish a mathematical model for age estimation. The mean absolute error (MAE) was calculated to evaluate the accuracy of the regression model using an independent validation sample. Among all the models, the cubic regression model showed the highest correlation between MERC BMD and chronological age and also provided the most accurate age prediction for both males and females (MAE = 9.41 for males, MAE = 10.38 for females). Our study suggests that BMD measured by MERC can be utilized for age estimation in adults when more reliable indicators are not available.

Age Estimation by Machine Learning and CT-Multiplanar Reformation of Cranial Sutures in Northern Chinese Han Adults.

OBJECTIVES: 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. CONCLUSIONS: 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.

Research Progress on Dental Age Estimation Based on MRI Technology.

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.

Differentiation and immunosuppressive function of CD19+CD24hiCD27+ regulatory B cells are regulated through the miR-29a-3p/NFAT5 pathway.

BACKGROUND: Regulatory B cells (Bregs) is an indispensable element in inducing immune tolerance after liver transplantation. As one of the microRNAs (miRNAs), miR-29a-3p also inhibits translation by degrading the target mRNA, and yet the relationship between Bregs and miR-29a-3p has not yet been fully explored. This study aimed to investigate the impact of miR-29a-3p on the regulation of differentiation and immunosuppressive functions of memory Bregs (mBregs) and ultimately provide potentially effective therapies in inducing immune tolerance after liver transplantation. METHODS: Flow cytometry was employed to determine the levels of Bregs in peripheral blood mononuclear cells. TaqMan low-density array miRNA assays were used to identify the expression of different miRNAs, electroporation transfection was used to induce miR-29a-3p overexpression and knockdown, and dual luciferase reporter assay was used to verify the target gene of miR-29a-3p. RESULTS: In patients experiencing acute rejection after liver transplantation, the proportions and immunosuppressive function of mBregs in the circulating blood were significantly impaired. miR-29a-3p was found to be a regulator of mBregs differentiation. Inhibition of miR-29a-3p, which targeted nuclear factor of activated T cells 5 (NFAT5), resulted in a conspicuous boost in the differentiation and immunosuppressive function of mBregs. The inhibition of miR-29a-3p in CD19+ B cells was capable of raising the expression levels of NFAT5, thereby promoting B cells to differentiate into mBregs. In addition, the observed enhancement of differentiation and immunosuppressive function of mBregs upon miR-29a-3p inhibition was abolished by the knockdown of NFAT5 in B cells. CONCLUSIONS: miR-29a-3p was found to be a crucial regulator for mBregs differentiation and immunosuppressive function. Silencing miR-29a-3p could be a potentially effective therapeutic strategy for inducing immune tolerance after liver transplantation.

Soil Application of Bacillus subtilis Regulates Flavonoid and Alkaloids Biosynthesis in Mulberry Leaves.

Flavonoids and alkaloids are the major active ingredients in mulberry leaves that have outstanding medicinal value. Bacillus subtilis can effectively activate the plants defense response and regulate the plant secondary metabolism. In this study, we explored the effects of soil application of B. subtilis on the content of flavonoids and the most important alkaloids (1-deoxynojirimycin, DNJ) in mulberry leaves. Significant decreases in flavonoid content were observed in tender leaves and mature leaves after treatment with B. subtilis; at the same time, significant increases in DNJ content were observed in tender leaves. Based on widely targeted LC-MS/MS and high-throughput approaches, we screened out 904 differentially synthesized metabolites (DSMs) and 9715 differentially expressed genes (DEGs). KEGG analyses showed that these DSMs and DEGs were both significantly enriched in the biosynthesis of secondary metabolites, flavonoid synthesis and plant hormone signal transduction. Further correlation analysis of DEMs and DEGs showed that 40 key genes were involved in flavonoid biosynthesis, with 6 key genes involved in DNJ biosynthesis. The expression of CHS, CHI, F3H, F3'H, FLS, UGT and AOC significantly responded to B. subtilis soil application. This study broadens our understanding of the molecular mechanisms underlying the accumulation of flavonoids and alkaloids in mulberry leaves.

Progranulin promotes regulatory T cells plasticity by mitochondrial metabolism through AMPK/PGC-1α pathway in ARDS.

As the aging population increases, the focus on elderly patients with acute respiratory distress syndrome (ARDS) is also increasing. In this article, we found progranulin (PGRN) differential expression in ARDS patients and healthy controls, even in young and old ARDS patients. Its expression strongly correlates with several cytokines in both young and elderly ARDS patients. PGRN has comparable therapeutic effects in young and elderly mice with lipopolysaccharide-induced acute lung injury, manifesting as lung injury, apoptosis, inflammation, and regulatory T cells (Tregs) differentiation. Considering that Tregs differentiation relies on metabolic reprogramming, we discovered that Tregs differentiation was mediated by mitochondrial function, especially in the aged population. Furthermore, we demonstrated that PGRN alleviated the mitochondrial damage during Tregs differentiation through the AMPK/PGC-1α pathway in T cells. Collectively, PGRN may regulate mitochondria function to promote Tregs differentiation through the AMPK/PGC-1α pathway to improve ARDS.

Machine Learning Supported the Modified Gustafson's Criteria for Dental Age Estimation in Southwest China.

Adult age estimation is one of the most challenging problems in forensic science and physical anthropology. In this study, we aimed to develop and evaluate machine learning (ML) methods based on the modified Gustafson's criteria for dental age estimation. In this retrospective study, a total of 851 orthopantomograms were collected from patients aged 15 to 40 years old. The secondary dentin formation (SE), periodontal recession (PE), and attrition (AT) of four mandibular premolars were analyzed according to the modified Gustafson's criteria. Ten ML models were generated and compared for age estimation. The partial least squares regressor outperformed other models in males with a mean absolute error (MAE) of 4.151 years. The support vector regressor (MAE = 3.806 years) showed good performance in females. The accuracy of ML models is better than the single-tooth model provided in the previous studies (MAE = 4.747 years in males and MAE = 4.957 years in females). The Shapley additive explanations method was used to reveal the importance of the 12 features in ML models and found that AT and PE are the most influential in age estimation. The findings suggest that the modified Gustafson method can be effectively employed for adult age estimation in the southwest Chinese population. Furthermore, this study highlights the potential of machine learning models to assist experts in achieving accurate and interpretable age estimation.

Bone age assessment based on different MRI modalities of the proximal humerus epiphysis: the comparisons of T1WI, T2WI, and PDWI.

Bone age assessment (BAA) is crucial in various fields, including legal proceedings, athletic competitions, and clinical medicine. However, the use of X-ray methods for age estimation without medical indication is subject to ethical debate, especially in forensic and athletic fields. The application of magnetic resonance imaging (MRI) with non-ionizing radiation can overcome this limitation in BAA. This study aimed to compare the application value of several MRI modalities of proximal humeral in BAA. A total of 468 patients with shoulder MRIs were retrospectively collected from a Chinese Han population aged 12-30 years (259 males and 209 females) for training and testing, including T1 weighted MRI (T1WI), T2 weighted MRI (T2WI), and Proton density weighted MRI (PDWI). Optimal regression models were established for age estimation, yielding mean absolute error (MAE) values below 2.0 years. The MAE values of T1WI were the lowest, with 1.700 years in males and 1.798 years in females. The area under the curve (AUC) and accuracy values of different MRI modalities of 16-year and 18-year thresholds were all around 0.9. For the 18-year threshold, T1WI outperformed T2WI and PDWI. In conclusion, the three MRI modalities of the proximal humerus can serve as reliable indicators for age assessment, while the T1WI performed better in age assessment and classification.

Adult age estimation from the sternum using maximum intensity projection images of CT and data mining in a Chinese population.

This study aimed to explore and develop data mining models for adult age estimation based on CT reconstruction images from the sternum. Maximum intensity projection (MIP) images of chest CT were retrospectively collected from a modern Chinese population, and data from 2700 patients (1349 males and 1351 females) aged 20 to 70 years were obtained. A staging technique within four indicators was applied. Several data mining models were established, and mean absolute error (MAE) was the primary comparison parameter. The intraobserver and interobserver agreement levels were good. Within internal validation, the optimal data mining model obtained the lowest MAE of 9.08 in males and 10.41 in females. For the external validation (N = 200), MAEs were 7.09 in males and 7.15 in females. In conclusion, the accuracy of our model for adult age estimation was among similar studies. MIP images of the sternum could be a potential age indicator. However, it should be combined with other indicators since the accuracy level is still unsatisfactory.

Machine learning and deep learning enabled age estimation on medial clavicle CT images.

The medial clavicle epiphysis is a crucial indicator for bone age estimation (BAE) after hand maturation. This study aimed to develop machine learning (ML) and deep learning (DL) models for BAE based on medial clavicle CT images and evaluate the performance on normal and variant clavicles. This study retrospectively collected 1049 patients (mean± SD: 22.50±4.34 years) and split them into normal training and test sets, and variant training and test sets. An additional 53 variant clavicles were incorporated into the variant test set. The development stages of normal MCE were used to build a linear model and support vector machine (SVM) for BAE. The CT slices of MCE were automatically segmented and used to train DL models for automated BAE. Comparisons were performed by linear versus ML versus DL, and normal versus variant clavicles. Mean absolute error (MAE) and classification accuracy was the primary parameter of comparison. For BAE, the SVM had the best MAE of 1.73 years, followed by the commonly-used CNNs (1.77-1.93 years), the linear model (1.94 years), and the hybrid neural network CoAt Net (2.01 years). In DL models, SE Net 18 was the best-performing DL model with similar results to SVM in the normal test set and achieved an MAE of 2.08 years in the external variant test. For age classification, all the models exhibit superior performance in the classification of 18-, 20-, 21-, and 22-year thresholds with limited value in the 16-year threshold. Both ML and DL models produce desirable performance in BAE based on medial clavicle CT.

Automated bone age assessment from knee joint by integrating deep learning and MRI-based radiomics.

Bone age assessment (BAA) is a crucial task in clinical, forensic, and athletic fields. Since traditional age estimation methods are suffered from potential radiation damage, this study aimed to develop and evaluate a deep learning radiomics method based on multiparametric knee MRI for noninvasive and automatic BAA. This retrospective study enrolled 598 patients (age range,10.00-29.99 years) who underwent MR examinations of the knee joint (T1/T2*/PD-weighted imaging). Three-dimensional convolutional neural networks (3D CNNs) were trained to extract and fuse multimodal and multiscale MRI radiomic features for age estimation and compared to traditional machine learning models based on hand-crafted features. The age estimation error was greater in individuals aged 25-30 years; thus, this method may not be suitable for individuals over 25 years old. In the test set aged 10-25 years (n = 95), the 3D CNN (a fusion of T1WI, T2*WI, and PDWI) demonstrated the lowest mean absolute error of 1.32 ± 1.01 years, which is higher than that of other MRI modalities and the hand-crafted models. In the classification for 12-, 14-, 16-, and 18- year thresholds, accuracies and the areas under the ROC curves were all over 0.91 and 0.96, which is similar to the manual methods. Visualization of important features showed that 3D CNN estimated age by focusing on the epiphyseal plates. The deep learning radiomics method enables non-invasive and automated BAA from multimodal knee MR images. The use of 3D CNN and MRI-based radiomics has the potential to assist radiologists or medicolegists in age estimation.

Application Progress of Objective Audiological Detection Techniques in Forensic Clinical Medicine.

The qualitative, quantitative, and localization analysis of hearing loss is one of the important contents of forensic clinical research and identification. Pure-tone audiometry is the "gold standard" for hearing loss assessment, but it is affected by the subjective cooperation of the assessed person. Due to the complexity of the auditory pathway and the diversity of hearing loss, the assessment of hearing loss requires the combination of various subjective and objective audiometric techniques, along with comprehensive evaluation based on the case situation, clinical symptoms, and other examinations to ensure the scientificity, accuracy and reliability of forensic hearing impairment assessment. Objective audiometry includes acoustic impedance, otoacoustic emission, and various auditory evoked potentials. The frequency-specific auditory brainstem response (ABR), 40 Hz auditory event related potential, and auditory steady-state response are commonly used for objective hearing threshold assessment. The combined application of acoustic impedance, otoacoustic emission and ABR can be used to locate hearing loss and determine whether it is located in the middle ear, cochlea, or posterior cochlea. This article reviews the application value of objective audiometry techniques in hearing threshold assessment and hearing loss localization, aiming to provide reference for forensic identification of hearing loss.

The effect of cytokines on osteoblasts and osteoclasts in bone remodeling in osteoporosis: a review.

The complicated connections and cross talk between the skeletal system and the immune system are attracting more attention, which is developing into the field of Osteoimmunology. In this field, cytokines that are among osteoblasts and osteoclasts play a critical role in bone remodeling, which is a pathological process in the pathogenesis and development of osteoporosis. Those cytokines include the tumor necrosis factor (TNF) family, the interleukin (IL) family, interferon (IFN), chemokines, and so on, most of which influence the bone microenvironment, osteoblasts, and osteoclasts. This review summarizes the effect of cytokines on osteoblasts and osteoclasts in bone remodeling in osteoporosis, aiming to providing the latest reference to the role of immunology in osteoporosis.

Forensic age estimation in adults by pubic bone mineral density using multidetector computed tomography.

Radiology plays a crucial role in forensic anthropology for age estimation. However, most studies rely on morphological methods. This study aims to investigate the feasibility of using pubic bone mineral density (BMD) as a new age estimation method in the Chinese population. 468 pubic bone CT scans from living individuals in a Chinese hospital aged 18 to 87 years old were used to measure pubic BMD. The BMD of the bilateral pubic bone was measured using the Mimics software on cross-sectional CT images and the mean BMD of the bilateral pubic bone was also calculated. Regression analysis was performed to assess the correlation between pubic BMD and chronological age and to develop mathematical models for age estimation. We evaluated the accuracy of the best regression model using an independent validation sample by calculating the mean absolute error (MAE). Among all established models, the cubic regression model had the highest R2 value in both genders, with R2 = 0.550 for males and R2 = 0.634 for females. The results of the best model test showed that the MAE for predicting age using pubic BMD was 8.66 years in males and 7.69 years in females. This study highlights the potential of pubic BMD as a useful objective indicator for adult age estimation and could be used as an alternative in forensic practice when other better indicators are lacking.

Distributed Online Learning Algorithm for Noncooperative Games Over Unbalanced Digraphs.

This article investigates constrained online noncooperative games (NGs) of multiagent systems over unbalanced digraphs, where the cost functions of players are time-varying and are gradually revealed to corresponding players only after decisions are made. Moreover, in the problem, the players are subject to local convex set constraints and time-varying coupling nonlinear inequality constraints. To the best of our knowledge, no result about online games with unbalanced digraphs has been reported, let alone constrained online games. To seek the variational generalized Nash equilibrium (GNE) of the game online, a distributed learning algorithm is proposed based on gradient descent, projection, and primal-dual methods. Under the algorithm, sublinear dynamic regrets and constraint violations are established. Finally, online electricity market games illustrate the algorithm.

Deep learning enables automatic adult age estimation based on CT reconstruction images of the costal cartilage.

OBJECTIVE: Adult age estimation (AAE) is a challenging task. Deep learning (DL) could be a supportive tool. This study aimed to develop DL models for AAE based on CT images and compare their performance to the manual visual scoring method. METHODS: Chest CT were reconstructed using volume rendering (VR) and maximum intensity projection (MIP) separately. Retrospective data of 2500 patients aged 20.00-69.99 years were obtained. The cohort was split into training (80%) and validation (20%) sets. Additional independent data from 200 patients were used as the test set and external validation set. Different modality DL models were developed accordingly. Comparisons were hierarchically performed by VR versus MIP, single-modality versus multi-modality, and DL versus manual method. Mean absolute error (MAE) was the primary parameter of comparison. RESULTS: A total of 2700 patients (mean age = 45.24 years ± 14.03 [SD]) were evaluated. Of single-modality models, MAEs yielded by VR were lower than MIP. Multi-modality models generally yielded lower MAEs than the optimal single-modality model. The best-performing multi-modality model obtained the lowest MAEs of 3.78 in males and 3.40 in females. On the test set, DL achieved MAEs of 3.78 in males and 3.92 in females, which were far better than the MAEs of 8.90 and 6.42 respectively, for the manual method. For the external validation, MAEs were 6.05 in males and 6.68 in females for DL, and 6.93 and 8.28 for the manual method. CONCLUSIONS: DL demonstrated better performance than the manual method in AAE based on CT reconstruction of the costal cartilage. CLINICAL RELEVANCE STATEMENT: Aging leads to diseases, functional performance deterioration, and both physical and physiological damage over time. Accurate AAE may aid in diagnosing the personalization of aging processes. KEY POINTS: • VR-based DL models outperformed MIP-based models with lower MAEs and higher R2 values. • All multi-modality DL models showed better performance than single-modality models in adult age estimation. • DL models achieved a better performance than expert assessments.

Semi-supervised automatic dental age and sex estimation using a hybrid transformer model.

Teeth-based age and sex estimation is an important task in mass disasters, criminal scenes, and archeology. Although various methods have been proposed, most of them are subjective and influenced by observers' experiences. In this study, we aimed to develop a deep learning model for automatic dental age and sex estimation from orthopantomograms (OPGs) and compare to manual methods. A large dataset of 15,195 OPGs (age range, 16 ~ 50 years; mean age, 29.65 years ± 9.36 [SD]; 10,218 females) was used to train and test a hybrid deep learning model which is a combination of convolutional neural network and transformer model. The final performance of this model was evaluated on additional independent 100 OPGs and compared to the manual method for external validation. In the test of 1413 OPGs, the mean absolute error (MAE) of age estimation was 2.61 years by this model. The accuracy and the area under the receiver operating characteristic curve (AUC) of sex estimation were 95.54% and 0.984. The heatmap indicated that the crown and pulp chamber of premolars and molars contain the most age-related information. In the additional independent 100 OPGs, this model achieved an MAE of 3.28 years for males and 3.79 years for females. The accuracy of this model was much higher than that of the manual models. Therefore, this model has the potential to assist radiologists in automated age and sex estimation.

Nash Equilibrium Seeking Algorithm Design for Distributed Nonsmooth Multicluster Games Over Weight-Balanced Digraphs.

In this article, we study the multicluster games over weight-balanced digraphs, where the cost functions of all players are nonsmooth. Besides, in the problem, not only are the decisions of all players constrained by heterogeneous local constraints but also the decisions of players in the same cluster are constrained by coupling constraints. Due to the nonsmooth cost functions, the coupling constraints, the general local convex constraints, and the weight-balanced digraphs, existing Nash equilibrium seeking algorithms cannot solve our problem. In order to seek the Nash equilibrium of the game, we design a distributed algorithm based on subgradient descent, differential inclusions, and projection operations. In the algorithm, a distributed learning strategy is embedded for the players to estimate the decisions of other players. Moreover, we analyze the asymptotical convergence of the algorithm via set-valued LaSalle invariance principle. Finally, a numerical simulation about electricity market games is presented to illustrate the effectiveness of our result.