Identification of a person by general group characteristics does not lose its relevance over a long period. An analysis of publications (2000-2023) devoted to the possibilities of using the sternum to determine gender and age showed a fairly large amount of work on this topic, with very promising results. The trend in the development of this area is the use of modern methods of medical imaging. This becomes the starting point for conducting such studies on the territory of the Russian Federation and developing a methodology that includes the Russian population, taking into account their population characteristics.
Отождествление личности по общегрупповым признакам не теряет актуальности на протяжении длительного периода. Анализ публикаций (2000—2023), посвященных возможностям использования грудины для установления пола и возраста, показал достаточно большой объем наработок по данной тематике с перспективными итогами. Тренд развития этого направления — использование современных методов медицинской визуализации — становится отправной точкой для проведения подобных исследований на территории Российской Федерации с целью разработки методики опознания неустановленного лица с учетом популяционных особенностей российского населения.

The occurrence of enhanced concentration of the radium triplet 226Ra, 228Ra and 224Ra is a frequently observed property of highly saline anoxic deep water as used e.g. in geothermal plants. In the present study we develop a model to explain the observed activity levels in the brines. The model considers processes at the rock-fluid interface of the aquifer like alpha recoil, sorption and surface precipitation and is implemented by means of a Monte Carlo simulation. The outcomes of the simulations indicate the dominating role of fine-grained constituents of the reservoir rock, e.g. claystone with enhanced specific activities of the natural decay chains. Mass fractions of such material in the order of a few percent are sufficient to result in radium fluid concentrations >1 Bq l-1. Also a generally valid relation between the Th/U ratio in the aquifer rock and the 228Ra/226Ra activity ratio in the fluid was found. This link improves the agreement between radium fluid data and the mean Th/U ratio of the Earth\'s crust. The 224Ra/228Ra fluid ratios reflect the transport time from the location of last radium release to the sampling point. The model findings were applied to a well investigated aquifer used in a geothermal plant in the North German Basin. An eight component system of the aquifer rock was established as the basis for the simulation of the radium concentrations in the deep fluid. The comparison between simulation and fluid analyses revealed a degree of radium sorption of about 50 %, which is necessary to match the model\'s results with the measurements. On the other hand, the 228Ra/226Ra fluid ratio of the brine was well reproduced by the simulation, showing the suitability of the model even in complex heterogeneous reservoirs. From the 224Ra/228Ra fluid ratios a transition from pore-to fracture-guided transport < 10 m distance from the production well is suggested. Precipitates from such deep fluids occurring after changes of the thermodynamic conditions are able to accumulate radium isotopes in Ba/Sr-sulphate phases. The time dependence of the radioactive disequilibrium between 226Ra, 228Ra and its child 228Th in such scales is described by a mathematical model and is applied to two different uptake models. Based on this approach, age determinations on precipitates found in different components of a geothermal plant are conducted. They reveal the triggering of scale formation due to modifications in the plant. The results are suitable for drawing conclusions about the operation of the system, which result in a reduction in the amount of scale and a reduction in downtimes.

UNASSIGNED: Age estimation for sporting activities, legal age determination and migration purpose is oftentimes essential, which makes the need for a scientifically proven method of age estimation an important aspect of medical practice. Forensic odontology is an essential aspect of forensic practice and may be a veritable tool in age estimation.
UNASSIGNED: The objective of this study was to evaluate the usefulness of orthopantomograms (OPG) in age estimation among selected Nigerians.
UNASSIGNED: A retrospective assessment of 202 OPGs was done to review the visibility of the periodontal ligament of third molars with completed root formation according to the methods described by Olze et al (2010). Individual ages were then calculated by deducting date of exposure from the date of birth recorded in years. Mean age with standard deviation were calculated for each group.
UNASSIGNED: Assessing the minimum and maximum age at which each of the stages of the radiographic visibility of the periodontal ligament as seen on the OPGs revealed that there was a gradual increase in the age at which the stage increment occurred, with a few exceptions. Comparison of the radiographic visibility of the periodontal ligament revealed that males tend to achieve early periodontal visibility stages compared to females, but the reverse was observed in later stages. Age 17 is more than likely attained in any person found within stages 1 to 3 as the minimum age found in these stages were above 17 years.
UNASSIGNED: With a few exceptions, the assessment of periodontal ligament visibility on OPGs can adequately predict the age of individuals. Thus, the use of this method may need to be combined with other methods in accurate determination of dental age in instances of forensic age estimation.

OBJECTIVE: Age estimation plays a critical role in personal identification, especially when determining compliance with the age of consent for adolescents. The age of consent refers to the minimum age at which an individual is legally considered capable of providing informed consent for sexual activities. The purpose of this study is to determine whether adolescents meet the age of 14 or 18 by using dental development combined with machine learning.
METHODS: This study combines dental assessment and machine learning techniques to predict whether adolescents have reached the consent age of 14 or 18. Factors such as the staging of the third molar, the third molar index, and the visibility of the periodontal ligament of the second molar are evaluated.
RESULTS: Differences in performance metrics indicate that the posterior probabilities achieved by machine learning exceed 93% for the age of 14 and slightly lower for the age of 18.
CONCLUSIONS: This study provides valuable insights for forensic identification for adolescents in personal identification, emphasizing the potential to improve the accuracy of age determination within this population by combining traditional methods with machine learning. It underscores the importance of protecting and respecting the dignity of all individuals involved.

This retrospective study was conducted to evaluate different methods for dental age estimation in children and to examine the feasibility of using cone beam computed tomography (CBCT) data for age estimation. A total of 200 radiographic records (both digital panoramic radiographs and CBCTs) were acquired from 100 children aged 9 to 16 years, all taken on the same dates. Radiographic data was acquired from archived records and included both panoramic radiography and CBCT data belonging to the same individual. CBCT was used when panoramic radiographic data was insufficient. The pulp volume and pulp/tooth volume ratio of the left first molar teeth in the mandible were calculated from the CBCT data using MIMICS software. In addition, age was estimated by the Demirjian and Willems methods from data obtained from panoramic radiography images. Statistical analyses and linear regression analysis were performed as necessary. There was a statistically significant difference between the mean difference between the Demirjian method and chronological age, and between the Willems method and chronological age (p < 0.001). Statistically significance was achieved in a linear regression model created from pulp volume (R2 = 0.098) and pulp/tooth volume ratio (R2 = 0.395) data for the estimated dental age analysis (p < 0.001) and a negative correlation was observed with chronological age. When compared estimated dental age from CBCT data with chronological age, the pulp/tooth volume ratio method yielded results closer to chronological age than using only pulp volume data. When considering both panoramic radiographic age estimation methods and age estimation methods using CBCT data, we found that the results obtained with the Willems method, a panoramic radiographic age estimation technique, provided the closest results to the chronological age. More contributions should be made to the literature regarding the feasibility of age estimation using pulp and tooth volume as an alternative method.

The aim of this systematic review is to analyze the literature to determine whether the methods of artificial intelligence are effective in determining age in panoramic radiographs. Searches without language and year limits were conducted in PubMed/Medline, Embase, Web of Science, and Scopus databases. Hand searches were also performed, and unpublished manuscripts were searched in specialized journals. Thirty-six articles were included in the analysis. Significant differences in terms of root mean square error and mean absolute error were found between manual methods and artificial intelligence techniques, favoring the use of artificial intelligence (p < 0.00001). Few articles compared deep learning methods with machine learning models or manual models. Although there are advantages of machine learning in data processing and deep learning in data collection and analysis, non-comparable data was a limitation of this study. More information is needed on the comparison of these techniques, with particular emphasis on time as a variable.

OBJECTIVE: We aimed to establish a model combining MRI volume measurements from the 1st, 2nd and 3rd molars for age prediction in sub-adults and compare the age prediction performance of different combinations of all three molars, internally in the study cohort.
METHODS: We examined 99 volunteers using a 1.5 T MR scanner with a customized high-resolution single T2 sequence. Segmentation was performed using SliceOmatic (Tomovision©). Age prediction was based on the tooth tissue ratio (high signal soft tissue + low signal soft tissue)/total. The model included three correlation parameters to account for statistical dependence between the molars. Age prediction performance of different combinations of teeth for the three molars was assessed using interquartile range (IQR).
RESULTS: We included data from the 1st molars from 87 participants (F/M 59/28), 2nd molars from 93 (F/M 60/33) and 3rd molars from 67 (F/M 45/22). The age range was 14-24 years with a median age of 18 years. The model with the best age prediction performance (smallest IQR) was 46-47-18 (lower right 1st and 2nd and upper right 3rd molar) in males. The estimated correlation between the different molars was 0.620 (46 vs. 47), 0.430 (46 vs. 18), and 0.598 (47 vs. 18). IQR was the smallest in tooth combinations including a 3rd molar.
CONCLUSIONS: We have established a model for combining tissue volume measurements from the 1st, 2nd and 3rd molars for age prediction in sub-adults. The prediction performance was mostly driven by the 3rd molars. All combinations involving the 3rd molar performed well.

Accurate age estimation is vital for clinical and forensic purposes. With the rapid advancement of artificial intelligence(AI) technologies, traditional methods relying on tooth development, while reliable, can be enhanced by leveraging deep learning, particularly neural networks. This study evaluated the efficiency of an AI model by applying the entire panoramic image for age estimation. The outcome performances were analyzed through supervised learning (SL) models.
Total of 27,877 dental panorama images from 5 to 90 years of age were classified by 2 types of grouping. In type 1 they were classified by each age and in type 2, applying heuristic grouping, the age over 20 years were classified by every 5 years. Wide ResNet (WRN) and DenseNet (DN) were used for supervised learning. In addition, the analysis with ± 3 years of deviation in both types were performed.
For the DN model, while the type 1 grouping achieved an accuracy of 0.1016 and F1 score of 0.058, the type 2 achieved an accuracy of 0.3146 and F1 score of 0.2027. Incorporating ± 3years of deviation, the accuracy of type 1 and 2 were 0.281, 0.7323 respectively; and the F1 score were 0.1768, 0.6583 respectively. For the WRN model, while the type 1 grouping achieved an accuracy of 0.1041 and F1 score of 0.0599, the type 2 achieved an accuracy of 0.3182 and F1 score of 0.2071. Incorporating ± 3years of deviation, the accuracy of type 1 and 2 were 0.2716, 0.7323 respectively; and the F1 score were 0.1709, 0.6437 respectively.
The application of entire panorama image data for supervised with classification by heuristics grouping with ± 3years of deviation for supervised learning models and demonstrated satisfactory outcome for the age estimation.

Telomeres are short repetitive DNA sequences capping the ends of chromosomes. Telomere shortening occurs during cell division and may be accelerated by oxidative damage or ameliorated by telomere maintenance mechanisms. Consequently, telomere length changes with age, which was recently confirmed in a large meta-analysis across vertebrates. However, based on the correlation between telomere length and age, it was concluded that telomere length can be used as a tool for chronological age estimation in animals. Correlation should not be confused with predictability, and the current data and studies suggest that telomeres cannot be used to reliably predict individual chronological age. There are biological reasons for why there is large individual variation in telomere dynamics, which is mainly due to high susceptibility to a wide range of environmental, but also genetic factors, rendering telomeres unfeasible as a tool for age estimation. The use of telomeres for chronological age estimation is largely a misguided effort, but its occasional reappearance in the literature raises concerns that it will mislead resources in wildlife conservation.

The American burying beetle, Nicrophorus americanus Olivier, is a federally protected insect that once occupied most of eastern North America. Adult beetles feature distinct, recognizable markings on the pronotum and elytra, and color changes with age have been observed. Among the challenges faced by research scientists and conservation practitioners is the ability to determine beetle age in the field between and including teneral (young) and senescent (old) adult stages. Using 20 (10 male and 10 female) captive-bred beetles, we characterized the change in greyscale and red, green, and blue (RGB) color channels over the lifespan of each beetle for field-aging applications. Individual beetles were photographed at set intervals from eclosion to death, and color data were extracted using open-source ImageJ Version 1.54f software. A series of linear mixed-effects models determined that red color showed the steepest decrease among all color channels in the pronotum and elytral markings, with a more significant decrease in the pronotum. The change in greyscale between the pronotum and elytral markings was visibly different, with more rapid darkening in the pronotum. The resulting pronotum color chart was tested under field conditions in Oklahoma, aging 299 adult N. americanus, and six age categories (day range) were discernable by eye: teneral (0-15), late teneral (15-31), early mature (31-45), mature (45-59), early senescent (59-76), and senescent (76-90). The ability to more precisely estimate age will improve population structure estimates, laboratory breeding programs, and potential reintroduction efforts.