Predicting cerebrovascular age and its clinical relevance: Modeling using 3D morphological features of brain vessels.

Aging manifests as many phenotypes, among which age-related changes in brain vessels are important, but underexplored. Thus, in the present study, we constructed a model to predict age using cerebrovascular morphological features, further assessing their clinical relevance using a novel pipeline. Age prediction models were first developed using data from a normal cohort (n = 1181), after which their relevance was tested in two stroke cohorts (n = 564 and n = 455). Our novel pipeline adapted an existing framework to compute generic vessel features for brain vessels, resulting in 126 morphological features. We further built various machine learning models to predict age using only clinical factors, only brain vessel features, and a combination of both. We further assessed deviation from healthy aging using the age gap and explored its clinical relevance by correlating the predicted age and age gap with various risk factors. The models constructed using only brain vessel features and those combining clinical factors with vessel features were better predictors of age than the clinical factor-only model (r = 0.37, 0.48, and 0.26, respectively). Predicted age was associated with many known clinical factors, and the associations were stronger for the age gap in the normal cohort. The age gap was also associated with important factors in the pooled cohort atherosclerotic cardiovascular disease risk score and white matter hyperintensity measurements. Cerebrovascular age, computed using the morphological features of brain vessels, could serve as a potential individualized marker for the early detection of various cerebrovascular diseases.

Frontoparietal and salience network synchronizations during nonsymbolic magnitude processing predict brain age and mathematical performance in youth.

The development and refinement of functional brain circuits crucial to human cognition is a continuous process that spans from childhood to adulthood. Research increasingly focuses on mapping these evolving configurations, with the aim to identify markers for functional impairments and atypical development. Among human cognitive systems, nonsymbolic magnitude representations serve as a foundational building block for future success in mathematical learning and achievement for individuals. Using task-based frontoparietal (FPN) and salience network (SN) features during nonsymbolic magnitude processing alongside machine learning algorithms, we developed a framework to construct brain age prediction models for participants aged 7-30. Our study revealed differential developmental profiles in the synchronization within and between FPN and SN networks. Specifically, we observed a linear increase in FPN connectivity, concomitant with a decline in SN connectivity across the age span. A nonlinear U-shaped trajectory in the connectivity between the FPN and SN was discerned, revealing reduced FPN-SN synchronization among adolescents compared to both pediatric and adult cohorts. Leveraging the Gradient Boosting machine learning algorithm and nested fivefold stratified cross-validation with independent training datasets, we demonstrated that functional connectivity measures of the FPN and SN nodes predict chronological age, with a correlation coefficient of .727 and a mean absolute error of 2.944 between actual and predicted ages. Notably, connectivity within the FPN emerged as the most contributing feature for age prediction. Critically, a more matured brain age estimate is associated with better arithmetic performance. Our findings shed light on the intricate developmental changes occurring in the neural networks supporting magnitude representations. We emphasize brain age estimation as a potent tool for understanding cognitive development and its relationship to mathematical abilities across the critical developmental period of youth. PRACTITIONER POINTS: This study investigated the prolonged changes in the brain's architecture across childhood, adolescence, and adulthood, with a focus on task-state frontoparietal and salience networks. Distinct developmental pathways were identified: frontoparietal synchronization strengthens consistently throughout development, while salience network connectivity diminishes with age. Furthermore, adolescents show a unique dip in connectivity between these networks. Leveraging advanced machine learning methods, we accurately predicted individuals' ages based on these brain circuits, with a more mature estimated brain age correlating with better math skills.

Age prediction from 12-lead electrocardiograms using deep learning: a comparison of four models on a contemporary, freely available dataset.

Objective The 12-lead electrocardiogram (ECG) is routine in clinical use and deep learning approaches have been shown to have the identify features not immediately apparent to human interpreters including age and sex. Several models have been published but no direct comparisons exist. Approach We implemented three previously published models and one unpublished model to predict age and sex from a 12-lead ECG and then compared their performance on an open-access data set. Main results All models converged and were evaluated on the holdout set. The best preforming age prediction model had a hold-out set mean absolute error of 8.06 years. The best preforming sex prediction model had a hold-out set area under the receiver operating curve of 0.92. Significance We compared performance of four models on an open-access dataset. .

Understanding the Temporal Dynamics of Accelerated Brain Aging and Resilient Brain Aging: Insights from Discriminative Event-Based Analysis of UK Biobank Data.

The intricate dynamics of brain aging, especially the neurodegenerative mechanisms driving accelerated (ABA) and resilient brain aging (RBA), are pivotal in neuroscience. Understanding the temporal dynamics of these phenotypes is crucial for identifying vulnerabilities to cognitive decline and neurodegenerative diseases. Currently, there is a lack of comprehensive understanding of the temporal dynamics and neuroimaging biomarkers linked to ABA and RBA. This study addressed this gap by utilizing a large-scale UK Biobank (UKB) cohort, with the aim to elucidate brain aging heterogeneity and establish the foundation for targeted interventions. Employing Lasso regression on multimodal neuroimaging data, structural MRI (sMRI), diffusion MRI (dMRI), and resting-state functional MRI (rsfMRI), we predicted the brain age and classified individuals into ABA and RBA cohorts. Our findings identified 1949 subjects (6.2%) as representative of the ABA subpopulation and 3203 subjects (10.1%) as representative of the RBA subpopulation. Additionally, the Discriminative Event-Based Model (DEBM) was applied to estimate the sequence of biomarker changes across aging trajectories. Our analysis unveiled distinct central ordering patterns between the ABA and RBA cohorts, with profound implications for understanding cognitive decline and vulnerability to neurodegenerative disorders. Specifically, the ABA cohort exhibited early degeneration in four functional networks and two cognitive domains, with cortical thinning initially observed in the right hemisphere, followed by the temporal lobe. In contrast, the RBA cohort demonstrated initial degeneration in the three functional networks, with cortical thinning predominantly in the left hemisphere and white matter microstructural degeneration occurring at more advanced stages. The detailed aging progression timeline constructed through our DEBM analysis positioned subjects according to their estimated stage of aging, offering a nuanced view of the aging brain's alterations. This study holds promise for the development of targeted interventions aimed at mitigating age-related cognitive decline.

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To accurately estimate the age of individual tree and to achieve full-cycle sustainable management of natural Larix gmelinii forest in Great Xing'an Mountains of northeastern China, we constructed individual tree age prediction model using stepwise regression and random forest algorithms based on 44 fixed plots data and 280 stan-dard tree cores obtained from the Pangu Forest Farm. We analyzed the influence of stand structure, site conditions, and competition index on the accuracy of model prediction. The model was evaluated by the coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE). The results showed that the random forest model had the highest prediction accuracy when number of decision trees was 1500 and number of node con-tention variables was 8. The random forest model had better accuracy and prediction ability than the stepwise regression model, with R2, RMSE and MAE of 0.5882, 9.9259 a, 8.1155 a. Diameter at breast height was the most important factor affecting age prediction (83.8%), followed by tree height (34.4%), elevation (17.9%), and basal area per hectare (17.5%). The random forest algorithm exhibited better adaptability and modeling effect on constructing a predictive model for individual tree age. This research contributed to improving the accuracy of growth and harvest estimation for L. gmelinii, and could provide a reference for other scientific studies related to tree age estimation in forests.

Male-specific age prediction based on Y-chromosome DNA methylation with blood using pyrosequencing.

Age prediction is an important aspect of forensic science that offers valuable insight into identification. In recent years, extensive studies have been conducted on age prediction based on DNA methylation, and numerous studies have demonstrated that DNA methylation is a reliable biomarker for age prediction. However, almost all studies on age prediction based on DNA methylation have focused on age-related CpG sites in autosomes, which are concentrated on single-source DNA samples. Mixed samples, especially male-female mixed samples, are common in forensic casework. The application of Y-STRs and Y-SNPs can provide clues for the genetic typing of male individuals in male-female mixtures, but they cannot provide the age information of male individuals. Studies on Y-chromosome DNA methylation can address this issue. In this study, we identified five age-related CpG sites on the Y chromosome (Y-CpGs) and developed a male-specific age prediction model using pyrosequencing combined with a support vector machine algorithm. The mean absolute deviation of the model was 5.50 years in the training set and 6.74 years in the testing set. When we used a male blood sample to predict age, the deviation between the predicted and chronological age was 1.18 years. Then, we mixed the genomic DNA of the male and a female at ratios of 1:1, 1:5, 1:10, and 1:50, the range of deviation between the predicted and chronological age of the male in the mixture was 1.16-1.74 years. In addition, there was no significant difference between the methylation values of bloodstains and blood in the same sample, which indicates that our model is also suitable for bloodstain samples. Overall, our results show that age prediction using DNA methylation of the Y chromosome has potential applications in forensic science and can be of great help in predicting the age of males in male-female mixtures. Furthermore, this work lays the foundation for future research on age-related applications of Y-CpGs.

The effect of head motion on brain age prediction using deep convolutional neural networks.

Deep learning can be used effectively to predict participants' age from brain magnetic resonance imaging (MRI) data, and a growing body of evidence suggests that the difference between predicted and chronological age-referred to as brain-predicted age difference (brain-PAD)-is related to various neurological and neuropsychiatric disease states. A crucial aspect of the applicability of brain-PAD as a biomarker of individual brain health is whether and how brain-predicted age is affected by MR image artifacts commonly encountered in clinical settings. To investigate this issue, we trained and validated two different 3D convolutional neural network architectures (CNNs) from scratch and tested the models on a separate dataset consisting of motion-free and motion-corrupted T1-weighted MRI scans from the same participants, the quality of which were rated by neuroradiologists from a clinical diagnostic point of view. Our results revealed a systematic increase in brain-PAD with worsening image quality for both models. This effect was also observed for images that were deemed usable from a clinical perspective, with brains appearing older in medium than in good quality images. These findings were also supported by significant associations found between the brain-PAD and standard image quality metrics indicating larger brain-PAD for lower-quality images. Our results demonstrate a spurious effect of advanced brain aging as a result of head motion and underline the importance of controlling for image quality when using brain-predicted age based on structural neuroimaging data as a proxy measure for brain health.

A multi-organization epigenetic age prediction based on a channel attention perceptron networks.

DNA methylation indicates the individual's aging, so-called Epigenetic clocks, which will improve the research and diagnosis of aging diseases by investigating the correlation between methylation loci and human aging. Although this discovery has inspired many researchers to develop traditional computational methods to quantify the correlation and predict the chronological age, the performance bottleneck delayed access to the practical application. Since artificial intelligence technology brought great opportunities in research, we proposed a perceptron model integrating a channel attention mechanism named PerSEClock. The model was trained on 24,516 CpG loci that can utilize the samples from all types of methylation identification platforms and tested on 15 independent datasets against seven methylation-based age prediction methods. PerSEClock demonstrated the ability to assign varying weights to different CpG loci. This feature allows the model to enhance the weight of age-related loci while reducing the weight of irrelevant loci. The method is free to use for academics at www.dnamclock.com/#/original.

A ResNet mini architecture for brain age prediction.

The brain presents age-related structural and functional changes in the human life, with different extends between subjects and groups. Brain age prediction can be used to evaluate the development and aging of human brain, as well as providing valuable information for neurodevelopment and disease diagnosis. Many contributions have been made for this purpose, resorting to different machine learning methods. To solve this task and reduce memory resource consumption, we develop a mini architecture of only 10 layers by modifying the deep residual neural network (ResNet), named ResNet mini architecture. To support the ResNet mini architecture in brain age prediction, the brain age dataset (OpenNeuro #ds000228) that consists of 155 study participants (three classes) and the Alzheimer MRI preprocessed dataset that consists of 6400 images (four classes) are employed. We compared the performance of the ResNet mini architecture with other popular networks using the two considered datasets. Experimental results show that the proposed architecture exhibits generality and robustness with high accuracy and less parameter number.

Predicting Brain Age and Gender from Brain Volume Data Using Variational Quantum Circuits.

The morphology of the brain undergoes changes throughout the aging process, and accurately predicting a person's brain age and gender using brain morphology features can aid in detecting atypical brain patterns. Neuroimaging-based estimation of brain age is commonly used to assess an individual's brain health relative to a typical aging trajectory, while accurately classifying gender from neuroimaging data offers valuable insights into the inherent neurological differences between males and females. In this study, we aimed to compare the efficacy of classical machine learning models with that of a quantum machine learning method called a variational quantum circuit in estimating brain age and predicting gender based on structural magnetic resonance imaging data. We evaluated six classical machine learning models alongside a quantum machine learning model using both combined and sub-datasets, which included data from both in-house collections and public sources. The total number of participants was 1157, ranging from ages 14 to 89, with a gender distribution of 607 males and 550 females. Performance evaluation was conducted within each dataset using training and testing sets. The variational quantum circuit model generally demonstrated superior performance in estimating brain age and gender classification compared to classical machine learning algorithms when using the combined dataset. Additionally, in benchmark sub-datasets, our approach exhibited better performance compared to previous studies that utilized the same dataset for brain age prediction. Thus, our results suggest that variational quantum algorithms demonstrate comparable effectiveness to classical machine learning algorithms for both brain age and gender prediction, potentially offering reduced error and improved accuracy.

Predicting human chronological age via AI analysis of dorsal hand versus facial images: A study in a cohort of Indian females.

Predicting a person's chronological age (CA) from visible skin features using artificial intelligence (AI) is now commonplace. Often, convolutional neural network (CNN) models are built using images of the face as biometric data. However, hands hold telltale signs of a person's age. To determine the utility of using only hand images in predicting CA, we developed two deep CNNs based on 1) dorsal hand images (H) and 2) frontal face images (F). Subjects (n = 1454) were Indian women, 20-80 years, across three geographic cohorts (Mumbai, New Delhi and Bangalore) and having a broad variation in skin tones. Images were randomised: 70% of F and 70% of H were used to train CNNs. The remaining 30% of F and H were retained for validation. CNN validation showed mean absolute error for predicting CA using F and H of 4.1 and 4.7 years, respectively. In both cases correlations of predicted and actual age were statistically significant (r(F) = 0.93, r(H) = 0.90). The CNNs for F and H were validated for dark and light skin tones. Finally, by blurring or accentuating visible features on specific regions of the hand and face, we identified those features that contributed to the CNN models. For the face, areas of the inner eye corner and around the mouth were most important for age prediction. For the hands, knuckle texture was a key driver for age prediction. Collectively, for AI estimates of CA, CNNs based solely on hand images are a viable alternative and comparable to CNNs based on facial images.

Methods and Annotated Data Sets Used to Predict the Gender and Age of Twitter Users: Scoping Review.

BACKGROUND: Patient health data collected from a variety of nontraditional resources, commonly referred to as real-world data, can be a key information source for health and social science research. Social media platforms, such as Twitter (Twitter, Inc), offer vast amounts of real-world data. An important aspect of incorporating social media data in scientific research is identifying the demographic characteristics of the users who posted those data. Age and gender are considered key demographics for assessing the representativeness of the sample and enable researchers to study subgroups and disparities effectively. However, deciphering the age and gender of social media users poses challenges. OBJECTIVE: This scoping review aims to summarize the existing literature on the prediction of the age and gender of Twitter users and provide an overview of the methods used. METHODS: We searched 15 electronic databases and carried out reference checking to identify relevant studies that met our inclusion criteria: studies that predicted the age or gender of Twitter users using computational methods. The screening process was performed independently by 2 researchers to ensure the accuracy and reliability of the included studies. RESULTS: Of the initial 684 studies retrieved, 74 (10.8%) studies met our inclusion criteria. Among these 74 studies, 42 (57%) focused on predicting gender, 8 (11%) focused on predicting age, and 24 (32%) predicted a combination of both age and gender. Gender prediction was predominantly approached as a binary classification task, with the reported performance of the methods ranging from 0.58 to 0.96 F1-score or 0.51 to 0.97 accuracy. Age prediction approaches varied in terms of classification groups, with a higher range of reported performance, ranging from 0.31 to 0.94 F1-score or 0.43 to 0.86 accuracy. The heterogeneous nature of the studies and the reporting of dissimilar performance metrics made it challenging to quantitatively synthesize results and draw definitive conclusions. CONCLUSIONS: Our review found that although automated methods for predicting the age and gender of Twitter users have evolved to incorporate techniques such as deep neural networks, a significant proportion of the attempts rely on traditional machine learning methods, suggesting that there is potential to improve the performance of these tasks by using more advanced methods. Gender prediction has generally achieved a higher reported performance than age prediction. However, the lack of standardized reporting of performance metrics or standard annotated corpora to evaluate the methods used hinders any meaningful comparison of the approaches. Potential biases stemming from the collection and labeling of data used in the studies was identified as a problem, emphasizing the need for careful consideration and mitigation of biases in future studies. This scoping review provides valuable insights into the methods used for predicting the age and gender of Twitter users, along with the challenges and considerations associated with these methods.

Brain Age Prediction Using Multi-Hop Graph Attention Combined with Convolutional Neural Network.

Convolutional neural networks (CNNs) have been used widely to predict biological brain age based on brain magnetic resonance (MR) images. However, CNNs focus mainly on spatially local features and their aggregates and barely on the connective information between distant regions. To overcome this issue, we propose a novel multi-hop graph attention (MGA) module that exploits both the local and global connections of image features when combined with CNNs. After insertion between convolutional layers, MGA first converts the convolution-derived feature map into graph-structured data by using patch embedding and embedding-distance-based scoring. Multi-hop connections between the graph nodes are modeled by using the Markov chain process. After performing multi-hop graph attention, MGA re-converts the graph into an updated feature map and transfers it to the next convolutional layer. We combined the MGA module with sSE (spatial squeeze and excitation)-ResNet18 for our final prediction model (MGA-sSE-ResNet18) and performed various hyperparameter evaluations to identify the optimal parameter combinations. With 2788 three-dimensional T1-weighted MR images of healthy subjects, we verified the effectiveness of MGA-sSE-ResNet18 with comparisons to four established, general-purpose CNNs and two representative brain age prediction models. The proposed model yielded an optimal performance with a mean absolute error of 2.822 years and Pearson's correlation coefficient (PCC) of 0.968, demonstrating the potential of the MGA module to improve the accuracy of brain age prediction.

Strategies to deal with genetic analyzer-specific DNA methylation measurements.

Targeted bisulfite sequencing using single-base extension (SBE) can be used to measure DNA methylation via capillary electrophoresis on genetic analyzers in forensic labs. Several accurate age prediction models have been reported using this method. However, using different genetic analyzers with different software settings can generate different methylation values, leading to significant errors in age prediction. To address this issue, the study proposes and compares four methods as follows: (1) adjusting methylation values using numerous actual body fluid DNA samples, (2) adjusting methylation values using control DNAs with varying methylation ratios, (3) constructing new age prediction models for each genetic analyzer type, and (4) constructing new age prediction models that could be applied to all types of genetic analyzers. To test the methods for adjusting values using actual body fluid DNA samples, previously reported adjusting equations were used for blood/saliva DNA age prediction markers (ELOVL2, FHL2, KLF14, MIR29B2CHG/C1orf132, and TRIM59). New equations were generated for semen DNA age prediction markers (TTC7B, LOC401324/cg12837463, and LOC729960/NOX4) by drawing polynomial regression lines between the results of the three types of genetic analyzers (3130, 3500, and SeqStudio). The same method was applied to obtain adjustment equations using 11 control DNA samples. To develop new age prediction models for each genetic analyzer type, linear regression analysis was conducted using DNA methylation data from 150 blood, 150 saliva, and 62 semen samples. For the genetic analyzer-independent models, control DNAs were used to formulate equations for calibrating the bias of the data from each genetic analyzer, and linear regression analysis was performed using calibrated body fluid DNA data. In the comparison results, the genetic analyzer-specific models showed the highest accuracy. However, genetic analyzer-independent models through bias adjustment also provided accurate age prediction results, suggesting its use as an alternative in situations with multiple constraints.

DNA methylation clocks for estimating biological age in Chinese cohorts.

Epigenetic clocks are accurate predictors of human chronological age based on the analysis of DNA methylation (DNAm) at specific CpG sites. However, a systematic comparison between DNA methylation data and other omics datasets has not yet been performed. Moreover, available DNAm age predictors are based on datasets with limited ethnic representation. To address these knowledge gaps, we generated and analyzed DNA methylation datasets from two independent Chinese cohorts, revealing age-related DNAm changes. Additionally, a DNA methylation aging clock (iCAS-DNAmAge) and a group of DNAm-based multi-modal clocks for Chinese individuals were developed, with most of them demonstrating strong predictive capabilities for chronological age. The clocks were further employed to predict factors influencing aging rates. The DNAm aging clock, derived from multi-modal aging features (compositeAge-DNAmAge), exhibited a close association with multi-omics changes, lifestyles, and disease status, underscoring its robust potential for precise biological age assessment. Our findings offer novel insights into the regulatory mechanism of age-related DNAm changes and extend the application of the DNAm clock for measuring biological age and aging pace, providing the basis for evaluating aging intervention strategies.

Predicting age from blood by droplet digital PCR using a set of three DNA methylation markers.

Evaluation of DNA methylation (DNAm) patterns is a promising tool for age estimation. The duplex droplet digital PCR (ddPCR) method has been recently investigated for DNAm evaluation, revealing to be a potential methodology for DNAm evaluation and molecular age estimation. In this study, we evaluated DNAm levels of CpGs located at the three age-associated genes ELOVL2, FHL2 and PDE4C using ddPCR to develop an age prediction model. Blood-derived DNA samples from 58 healthy individuals (42 women and 16 men; aged 1-93 years old) were submitted to bisulfite conversion followed by ddPCR using dual-labeled probes targeting methylated and unmethylated DNA sequences. Simple linear regression statistics revealed a strong correlation between DNAm levels and chronological age for FHL2 (R = 0.948; P = 1.472 × 10-29) and PDE4C (R = 0.819; P = 3.917 × 10-15), addressing only one CpG for each gene. For the ELOVL2 gene, evaluating five CpG sites in simultaneous, revealed a strong age correlation (R = 0.887; P = 2.099 × 10-20) in a simple linear regression statistics and very strong age correlation (R = 0.926; P = 2.202 × 10-25) when using quadratic regression statistics. The multivariable regression analysis, using methylation information captured on ELOVL2 (squared), FHL2 and PDE4C genes, revealed a very strong age correlation (R = 0.970; P = 5.356 ×10-33), explaining 93.7 % of age variance, displaying a mean absolute deviation (MAD) between chronological and predicted age of 4.657 years (RMSE = 6.044). We postulate that the ddPCR method should be further investigated for DNAm-based age prediction, because it is a relatively simple and an accurate method that can be routinely used in forensic laboratories for testing a few numbers of markers.

Accurate estimation of biological age and its application in disease prediction using a multimodal image Transformer system.

Aging in an individual refers to the temporal change, mostly decline, in the body's ability to meet physiological demands. Biological age (BA) is a biomarker of chronological aging and can be used to stratify populations to predict certain age-related chronic diseases. BA can be predicted from biomedical features such as brain MRI, retinal, or facial images, but the inherent heterogeneity in the aging process limits the usefulness of BA predicted from individual body systems. In this paper, we developed a multimodal Transformer-based architecture with cross-attention which was able to combine facial, tongue, and retinal images to estimate BA. We trained our model using facial, tongue, and retinal images from 11,223 healthy subjects and demonstrated that using a fusion of the three image modalities achieved the most accurate BA predictions. We validated our approach on a test population of 2,840 individuals with six chronic diseases and obtained significant difference between chronological age and BA (AgeDiff) than that of healthy subjects. We showed that AgeDiff has the potential to be utilized as a standalone biomarker or conjunctively alongside other known factors for risk stratification and progression prediction of chronic diseases. Our results therefore highlight the feasibility of using multimodal images to estimate and interrogate the aging process.

Epigenetic age prediction using costal cartilage for the investigation of disaster victims and missing persons.

The DNA intelligence tool, DNA methylation-based age prediction, can help identify disaster victims and suspects in criminal investigations. In this study, we developed a costal cartilage-based age prediction tool that uses massive parallel sequencing (MPS) of age-associated DNA methylation markers. Costal cartilage samples were obtained from 85 deceased Koreans, aged between 26 and 89 years. An MPS library was prepared using two rounds of multiplex polymerase chain reaction of nine genes (TMEM51, MIR29B2CHG, EDARADD, FHL2, TRIM59, ELOVL2, KLF14, ASPA, and PDE4C). The DNA methylation status of 45 CpG sites was determined and used to train an age prediction model via stepwise regression analysis. Nine CpGs in MIR29B2CHG, FHL2, TRIM59, ELOVL2, KLF14, and ASPA were selected for regression model construction. A leave-one-out cross-validation analysis revealed the high performance of the age prediction model, with a mean absolute error (MAE) and root mean square error of 4.97 and 6.43 years, respectively. Additionally, our model showed good performance with a MAE of 6.06 years in the analysis of data of 181 costal cartilage samples collected from Europeans. Our model effectively estimates the age of deceased individuals using costal cartilage samples; therefore, it can be a valuable forensic tool for disaster victim and missing person investigation.

Brain structure ages-A new biomarker for multi-disease classification.

Age is an important variable to describe the expected brain's anatomy status across the normal aging trajectory. The deviation from that normative aging trajectory may provide some insights into neurological diseases. In neuroimaging, predicted brain age is widely used to analyze different diseases. However, using only the brain age gap information (i.e., the difference between the chronological age and the estimated age) can be not enough informative for disease classification problems. In this paper, we propose to extend the notion of global brain age by estimating brain structure ages using structural magnetic resonance imaging. To this end, an ensemble of deep learning models is first used to estimate a 3D aging map (i.e., voxel-wise age estimation). Then, a 3D segmentation mask is used to obtain the final brain structure ages. This biomarker can be used in several situations. First, it enables to accurately estimate the brain age for the purpose of anomaly detection at the population level. In this situation, our approach outperforms several state-of-the-art methods. Second, brain structure ages can be used to compute the deviation from the normal aging process of each brain structure. This feature can be used in a multi-disease classification task for an accurate differential diagnosis at the subject level. Finally, the brain structure age deviations of individuals can be visualized, providing some insights about brain abnormality and helping clinicians in real medical contexts.

A high-throughput droplet digital PCR system aiming eight DNA methylation targets for age prediction.

The droplet digital Polymerase Chain Reaction (ddPCR) has garnered recognition for its distinctive attribute of absolute quantification. And it has found practical utility in age prediction through DNA methylation profiles. However, a prevalent limitation in current ddPCR methodologies is the restricted capacity to detect only two targets concurrently in most instruments, leading to high costs, sample wastage, and labor-intensive procedures. To address the limitations, a novel high-throughput ddPCR system allowing for the simultaneous detection of eight targets was developed. Through the implementation of a new 8-plex ddPCR assay, coupled with comprehensive linear regression analyses involving primers and probes ratios, diverse inputs of single CpG sites with distinct primers and probes, and varying plex assay configurations, stable DNA methylation values for four CpGs and stable measurement precisions for distinct multiplex systems were consistently observed. These findings pave the way for advancing the field of chemistry science by enabling more efficient and cost-effective methods. Furthermore, the comparative validation of ddPCR and SNaPshot demonstrated a remarkable concordance in results, and the system also displayed well in the field of various aspects, including species specificity, DNA input, and aged samples. In this study, the recommended input of bisulfite-converted DNA was determined to be 10-50 ng due to the double-positive droplets. Notably, the Pearson correlation coefficient squared values of four CpGs were 0.4878 (ASPA), 0.4832 (IGSF1), 0.6881 (COL1A1), and 0.6475 (MEIS1-AS3). And the testing set exhibited a mean absolute error of 4.5923 years, indicating the robustness and accuracy of the age-predictive model.