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 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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Human identification performed with skull's sphenoid sinus based on deep learning.

Human identification plays a significant role in the investigations of disasters and criminal cases. Human identification could be achieved quickly and efficiently via 3D sphenoid sinus models by customized convolutional neural networks. In this retrospective study, a deep learning neural network was proposed to achieve human identification of 1475 noncontrast thin-slice CT scans. A total of 732 patients were retrieved and studied (82% for model training and 18% for testing). By establishing an individual recognition framework, the anonymous sphenoid sinus model was matched and cross-tested, and the performance of the framework also was evaluated on the test set using the recognition rate, ROC curve and identification speed. Finally, manual matching was performed based on the framework results in the test set. Out of a total of 732 subjects (mean age 46.45 years ± 14.92 (SD); 349 women), 600 subjects were trained, and 132 subjects were tested. The present automatic human identification has achieved Rank 1 and Rank 5 accuracy values of 93.94% and 99.24%, respectively, in the test set. In addition, all the identifications were completed within 55 s, which manifested the inference speed of the test set. We used the comparison results of the MVSS-Net to exclude sphenoid sinus models with low similarity and carried out traditional visual comparisons of the CT anatomical aspects of the sphenoid sinus of 132 individuals with an accuracy of 100%. The customized deep learning framework achieves reliable and fast human identification based on a 3D sphenoid sinus and can assist forensic radiologists in human identification accuracy.

Forensic age prediction and age classification for critical age thresholds via 3.0T magnetic resonance imaging of the knee in the Chinese Han population.

OBJECTIVES: To assess the performance of knee MRI for forensic age prediction and classification for 12-, 14-, 16-, and 18-year thresholds. METHODS: The ossification stages of distal femoral epiphyses and proximal tibial epiphyses were assessed using an integrated staging system by Schmeling et al. and Kellinghaus et al. for knee 3.0T MRI with T1-weighted turbo spin-echo (T1-TSE) in sagittal orientation among 852 Chinese Han individuals (483 males and 369 females) aged 7-30 years. Regression models for age prediction were constructed and their performances were evaluated based on mean absolute deviation (MAD) values. In addition, the performances of age classification were assessed using receiver operating characteristic (ROC) analyses. RESULTS: The intra- and inter-observer agreement levels were very good (κ > 0.80). The complete fusion of those two types of epiphyses took place before 18.0 years in our study participants. The minimum MAD values were 2.51 years (distal femur) and 2.69 years (proximal tibia) in males, and 2.75 years (distal femur) and 2.87 years (proximal tibia) in females. The specificity values of constructed prediction models were all above 90% for the 12-, 14-, and 16-year thresholds, compared to the 74.8-84.6% for the 18-year threshold. Better performances of age prediction and classification were observed in males by distal femoral epiphyses. CONCLUSIONS: Ossification stages via 3.0T MRI of the knee with T1-TSE sequence using an integrated staging system could be a reliable noninvasive method for age prediction or for age classification for 12-, 14-, and 16-year thresholds, especially in males by distal femoral epiphyses. However, assessments based on the full bony fusion of the distal femoral epiphysis and proximal tibial epiphysis seemed not reliable for age classification for the 18-year threshold in the Chinese Han population.

Forensic Identification from Three-Dimensional Sphenoid Sinus Images Using the Iterative Closest Point Algorithm.

Forensic identification of human remains is crucial for legal, humanitarian, and civil reasons. Wide heterogeneity in sphenoid sinus morphology can be used for personal identification. This study aimed to propose a new protocol for personal identification based on three-dimensional (3D) reconstruction of sphenoid sinus CT images using Iterative Closest Point (ICP) algorithm. Seven hundred thirty-two patients which consisted of 348 females and 384 males were retrospectively included. The study sample includes 732 previous images as a source point set and 743 later ones as a scene target set. The sphenoid sinus computed tomography (CT) images were processed on a workstation (Dolphin imaging) to obtain 3D images and stored as a file format of Stereo lithography (.STL). Then, a Python library vtkplotter was used to transform the STL format to PLY format, which was adapted to Point Cloud Library (PCL). The ICP algorithm was used for point clouds matching. The metric Rank-N recognition rate was used for evaluation. The scene target set of 743 individuals was compared with the source point set of 732 individual models and achieved Rank-1 accuracy of 96.24%, Rank-2 accuracy of 99.73%, and Rank-3 accuracy of 100%. Our results indicated that the 3D point cloud registration of sphenoid sinuses was useful for assessing personal identification in forensic contexts.

Forensic age estimation based on magnetic resonance imaging of the proximal humeral epiphysis in Chinese living individuals.

Forensic age estimation in living individuals is mainly based on radiological features, but direct radiography and computed tomography lead to a rise in ethical concerns due to radiation exposure. Thus, the contribution of magnetic resonance imaging (MRI) to age estimation of living individuals is a subject of ongoing research. In the current study, MRIs of shoulder were retrospectively collected from a modern Chinese Han population and data from 835 individuals (599 males and 236 females) in the age group 12 to 30 years were obtained. A staging technique based on (Schmidt et al. Int J Legal Med 121(4):321-324, 2007) and (Kellinghaus et al. Int J Legal Med 124(4):321-325, 2010) was used and all images were evaluated with T1-wieghted turbo spin echo (T1-TSE) sequence and T2-weighed fat suppression (T2-FS) sequence. One-sided images were assessed because data from both sides were considered coincidental, as no significant differences were found (P > 0.05). Two MRI sequences were evaluated separately and subsequently compared. Regression models and supportive vector classification (SVC) models were established accordingly. The intraobserver and interobserver agreement levels were good. Compared with T1-TSE sequence, the R2 values of T2-FS sequence were generally higher, while the mean absolute deviation (MAD) values were slightly lower. For T2-FS sequence, the MAD value was 1.49 years in males and 2.19 years in females. With two MRI sequences incorporated, the SVC model obtained with 85.7% correctly classified minors and 96.2% correctly classified adults in males, while 83.3% and 98.0% respectively in females. In conclusion, T2-FS sequence may slightly outperform the T1-TSE sequence in shoulder MRI analysis for age estimation, while shoulder MRIs could be a reliable prediction indicator for the 18-year threshold and two MRI sequences incorporated are encouraged.

Fermented dairy foods intake and risk of cardiovascular diseases: A meta-analysis of cohort studies.

Since the associations of fermented dairy foods intake with risk of cardiovascular diseases (CVD) remained inconsistent, we carried out this meta-analysis on all published cohort studies to estimate the overall effect. We searched the PubMed and CNKI (China National Knowledge Infrastructure) databases for all articles within a range of published years from 1980 to 2018 on the association between fermented dairy foods intake and CVD risk. Finally, 10 studies met the inclusion criteria for this study, with 385,122 participants, 1,392 Myocardial infarction, 4,490 coronary heart disease (CHD), 7,078 stroke, and 51,707 uncategorized CVD cases. Overall, statistical evidence of significantly decreased CVD risk was found to be associated with fermented dairy foods intake (OR = 0.83, 95% CI = 0.76-0.91). In subgroup analysis, cheese and yogurt consumptions were associated with decreased CVD risk (OR = 0.87, 95% CI = 0.80-0.94 for cheese and OR = 0. 78, 95% CI = 0.67-0.89 for yogurt). Our meta-analysis indicated that fermented dairy foods intake was associated with decreased CVD risk.

Age estimation based on magnetic resonance imaging of the ankle joint in a modern Chinese Han population.

OBJECTIVES: To expand the database on magnetic resonance imaging (MRI) analysis of distal tibial and calcaneal epiphyses as proposed by Saint-Martin et al. and investigate a more elaborate staging technique to establish regression models for age estimation in a modern Chinese Han population. MATERIALS AND METHODS: T1-weighted ankle MRIs were retrospectively collected from April 2008 to July 2019, and data from 590 individuals (372 males and 218 females; aged from 8 to 25 years old) were obtained. One-sided sagittal images were assessed because data from both sides were considered coincidental, as no significant differences were found (P > 0.05). Three-stage and six-stage staging techniques were applied separately and subsequently compared. A subset was re-assessed a second time and by a different observer. Regression models were established accordingly. RESULTS: Our results showed very good repeatability and consistency of two staging techniques (all Cohen's kappa values were more than 0.8). By comparison, the values of the coefficient of determination (R2) of the six-stage technique were generally higher than those of the three-stage technique. Compared with the distal tibia and two ankle bones combined, the calcaneus decreased the mean absolute deviation (MAD) with the six-stage technique. In males, incorporating only the calcaneus resulted in a MAD of 2.15 years, with correct classification rates of 87.5% adults and 50.0% among minors. In females, the corresponding results were 1.67 years, 100.0%, and 44.4%, respectively. CONCLUSIONS: The six-stage technique may outperform the three-stage technique in MRI analysis of ankle bones for age estimation, while age estimation based on the calcaneus may perform better than that based on the distal tibia or both ankle bones in a modern Chinese Han population.

Age estimation by multidetector computed tomography of cranial sutures in Chinese male adults.

OBJECTIVES: This study aimed to explore whether computed tomography (CT) images of cranial sutures can contribute to adult age estimation in Chinese Han individuals. MATERIALS AND METHODS: This study was based on cranial CT scans of 230 Chinese Han males aged 23.33-76.93 years. A total of 160 images from 16 suture segments were scored after volume reformation and multiplanar reconstruction in each individual. Decision tree regression, linear support vector regression, Bayesian ridge regression, and gradient boosting regression were developed for adult age estimation by a training set using leave-one-out cross-validation and further evaluated by the test set. The inaccuracy and bias were calculated to evaluate the four models and the previously used models from the literature. RESULTS: The degree of suture closure was associated with adult age. The minimum inaccuracy of the test set was 7.73 years obtained by linear support vector regression, while the inaccuracy of previous simple linear regression models was 13.09 and 10.97 years. The accuracy was higher in the age group from 40.00 to 59.99 years compared to the other age groups. DISCUSSION: The accuracy of our models for adult age estimation was superior to those in previous studies based on cranial sutures. Hence, the application of novel statistical data mining tools helps to improve aging issues. Nevertheless, age estimation of adults should be combined with other methods, since the accuracy level is still not satisfactory.

Fermented dairy foods intake and risk of cancer.

Fermented dairy foods are known to be nutrient-rich and probiotic content, which gather optimism due to their potential in prevention and management of cancer. We searched the PubMed, Embase and CNKI databases for all available studies through July 2018 on the association between fermented dairy foods intake and cancer risk. The odds ratio (OR) corresponding to the 95% confidence interval (95% CI) was used to assess the association using a random-effect meta-analysis. Finally, 61 studies met the inclusion criteria for our study, with 1,962,774 participants and 38,358 cancer cases. Overall, statistical evidence of significantly decreased cancer risk was found to be associated with fermented dairy foods intake (OR = 0.86, 95% CI = 0.80-0.92) in cohort studies. Yogurt consumption was significantly with decreased cancer risk in the overall comparison (OR = 0.87, 95% CI = 0.80-0.95) and in the cohort studies (OR = 0.81, 95% CI = 0.74-0.88). In terms of subgroup analyses by cancer type, fermented dairy foods intake significantly decreased bladder cancer, colorectal cancer and esophageal cancer risk. In stratified analyses, significantly decreased colorectal cancer risk was found to be associated with cheese intake. Yogurt consumption was significantly decreased bladder cancer and colorectal cancer risk. Our meta-analysis indicated that fermented dairy foods intake was associated with an overall decrease in cancer risk.