MRI segmentation of tooth tissue in age prediction of sub-adults - a new method for combining data from the 1st, 2nd, and 3rd molars.

PURPOSE: 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. MATERIAL AND METHOD: 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. CONCLUSION: 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.

Age prediction in sub-adults based on MRI segmentation of 3rd molar tissue volumes.

PURPOSE: Our aim was to investigate tissue volumes measured by MRI segmentation of the entire 3rd molar for prediction of a sub-adult being older than 18 years. MATERIAL AND METHOD: We used a 1.5-T MR scanner with a customized high-resolution single T2 sequence acquisition with 0.37 mm iso-voxels. Two dental cotton rolls drawn with water stabilized the bite and delineated teeth from oral air. Segmentation of the different tooth tissue volumes was performed using SliceOmatic (Tomovision©). Linear regression was used to analyze the association between mathematical transformation outcomes of the tissue volumes, age, and sex. Performance of different transformation outcomes and tooth combinations were assessed based on the p value of the age variable, combined or separated for each sex depending on the selected model. The predictive probability of being older than 18 years was obtained by a Bayesian approach. RESULTS: We included 67 volunteers (F/M: 45/22), range 14-24 years, median age 18 years. The transformation outcome (pulp + predentine)/total volume for upper 3rd molars had the strongest association with age (p = 3.4 × 10-9). CONCLUSION: MRI segmentation of tooth tissue volumes might prove useful in the prediction of age older than 18 years in sub-adults.

Prediction of Age Older than 18 Years in Sub-adults by MRI Segmentation of 1st and 2nd Molars.

PURPOSE: To investigate prediction of age older than 18 years in sub-adults using tooth tissue volumes from MRI segmentation of the entire 1st and 2nd molars, and to establish a model for combining information from two different molars. MATERIALS AND METHODS: We acquired T2 weighted MRIs of 99 volunteers with a 1.5-T scanner. Segmentation was performed using SliceOmatic (Tomovision©). Linear regression was used to analyse the association between mathematical transformation outcomes of tissue volumes, age, and sex. Performance of different outcomes and tooth combinations were assessed based on the p-value of the age variable, common, or separate for each sex, depending on the selected model. The predictive probability of being older than 18 years was obtained by a Bayesian approach using information from the 1st and 2nd molars both separately and combined. RESULTS: 1st molars from 87 participants, and 2nd molars from 93 participants were included. The age range was 14-24 years with a median age of 18 years. The transformation outcome (high signal soft tissue + low signal soft tissue)/total had the strongest statistical association with age for the lower right 1st (p= 7.1*10-4 for males) and 2nd molar (p=9.44×10-7 for males and p=7.4×10-10 for females). Combining the lower right 1st and 2nd molar in males did not increase the prediction performance compared to using the best tooth alone. CONCLUSION: MRI segmentation of the lower right 1st and 2nd molar might prove useful in the prediction of age older than 18 years in sub-adults. We provided a statistical framework to combine the information from two molars.

A systematic review of the agreement between chronological age and skeletal age based on the Greulich and Pyle atlas.

OBJECTIVES: This systematic review examines the agreement between assessed skeletal age by the Greulich and Pyle atlas (GP skeletal age) and chronological age. METHODS: We searched electronic databases until January 2017 for studies reporting GP skeletal age and confirmed chronological age in healthy individuals aged 10-25 years. Results are presented as forest plots and meta-analyses (random-effects models). RESULTS: In separate meta-analyses for each age group and sex (14-18 years for girls, 14-19 years for boys), the pooled mean differences between GP skeletal age and chronological age varied from -0.52 years to 0.47 years. In individual studies, age group and sex-specific mean differences between GP skeletal age and chronological age rarely exceeded 1 year, but between-study heterogeneities were large in most age groups. Few studies examined mean chronological age and distribution for each GP skeletal age. One study of good methodological quality indicates that 95% prediction intervals for chronological age from given GP skeletal ages are typically around 4 years. CONCLUSIONS: There is still good correlation between GP skeletal age and mean chronological age in modern populations. However, the individual variation of development within a population and heterogeneities between studies are substantial. KEY POINTS: • The GP atlas still corresponds well with mean chronological age in modern populations. • The substantial variation within a population must be considered. • The heterogeneity between studies is relatively large and of unknown origin.

Age assessment by Demirjian's development stages of the third molar: a systematic review.

OBJECTIVES: Radiographic evaluation of the wisdom teeth (third molar) formation is a widely used age assessment method for adolescents and young adults. This systematic review examines evidence on the agreement between Demirjian's development stages of the third molar and chronological age. METHODS: We searched four databases up until May 2016 for studies reporting Demirjian's stages of third molar and confirmed chronological age of healthy individuals aged 10-25 years. Heterogeneity test of the included studies was performed. RESULTS: We included 21 studies from all continents except Australia, all published after 2005. The mean chronological age for Demirjian's stages varied considerably between studies. The results from most studies were affected by age mimicry bias. Only a few of the studies based their results on an unbiased age structure, which we argue as important to provide an adequate description of the method's ability to estimate age. CONCLUSION: Observed study variation in the timing of Demirjian's development stages for third molars has often been interpreted as differences between populations and ethnicities. However, we consider age mimicry to be a dominant bias in these studies. Hence, the scientific evidence is insufficient to conclude whether such differences exist. KEY POINTS: • There is significant heterogeneity between studies evaluating age assessment by Demirjian's third molar development. • Most of the studies were subject to the selection bias age mimicry which can be a source of heterogeneity. • Presence of age mimicry bias makes it impossible to compare and combine results. These biased studies should not be applied as reference studies for age assessment.

Advancing estimation of chronological age by utilizing available evidence based on two radiographical methods.

This paper describes a strategy for estimating chronological age of individuals based on age indicators of X-ray of the hand and the third molar tooth. The great majority of studies in the field provide group-wise data of different formats, which makes them difficult to compare and utilize in a model. In this paper, we have provided a framework to utilize different types of data formats to build a common model for estimating chronological age. We used transition analysis to describe the relationship between the age indicators and chronological age. Further, likelihood ratio weight of evidence and posterior distribution of chronological age were used to model the distribution of chronological age given the observed age indicators. Being able to utilize such a large amount of data, with different data formats, from different studies, as presented in this paper improves previous age estimation methods.

BioAlder: a tool for assessing chronological age based on two radiological methods.

We have created the tool BioAlder as an age prediction model based on the systems Greulich and Pyle (hand) and the Demirjian's grading of the third molar tooth. The model compiles information from studies representing a total of 17,151 individuals from several parts of the world. The model offers a solution where issues as group-wise data format and age mimicry bias are bypassed. The model also provides a solution for combining the two grading systems, hand and tooth, to one combined age prediction result assuming independency. We have tested our model of age prediction and the independency assumption on a separate data set from Lebanon with 254 young individuals. The prediction intervals of BioAlder covered most of the data points; however, we observed some outliers. Our analyses indicate at least a weak dependency between the two methods.

An overview of autosomal STRs and identity SNPs in a Norwegian population using massively parallel sequencing.

In recent years, probabilistic genotyping software has been adapted for the analysis of massively parallel sequencing (MPS) forensic data. Likelihood ratios (LR) are based on allele frequencies selected from populations of interest. This study provides an outline of sequence-based (SB) allele frequencies for autosomal short tandem repeats (aSTRs) and identity single nucleotide polymorphisms (iSNPs) in 371 individuals from Southern Norway. 27 aSTRs and 94 iSNPs were previously analysed with the ForenSeq™ DNA Signature Prep Kit (Verogen). The number of alleles with frequencies less than 0.05 for sequenced-based alleles was 4.6 times higher than for length-based alleles. Consistent with previous studies, it was observed that sequence-based data (both with and without flanks) exhibited higher allele diversity compared to length-based (LB) data; random match probabilities were lower for SB alleles confirming their advantage to discriminate between individuals. Two alleles in markers D22S1045 and Penta D were observed with SNPs in the 3´ flanking region, which have not been reported before. Also, a novel SNP with a minor allele frequency (MAF) of 0.001, was found in marker TH01. The impact of the sample size on minor allele frequency (MAF) values was studied in 88 iSNPs from Southern Norway (n = 371). The findings were then compared to a larger Norwegian population dataset (n = 15,769). The results showed that the smaller Southern Norway dataset provided similar results, and it was a representative sample. Population structure was analyzed for regions within Southern Norway; FST estimates for aSTR and iSNPs did not indicate any genetic structure. Finally, we investigated the genetic differences between Southern Norway and two other populations: Northern Norway and Denmark. Allele frequencies between these populations were compared, and we found no significant frequency differences (p-values > 0.0001). We also calculated the pairwise FST values per marker and comparisons between Southern and Northern Norway showed small differences. In contrast, the comparisons between Southern Norway and Denmark showed higher FST values for some markers, possibly driven by distinct alleles that were present in only one of the populations. In summary, we propose that allele frequencies from each population considered in this study could be used interchangeably to calculate genotype probabilities.

Driving under the influence of zopiclone: Elimination between two consecutive blood samples.

AIM: Zopiclone is a widely used hypnotic drug which is frequently detected in apprehended drivers. For assessments in forensic cases, the elimination half-life (t1/2) of a drug is sometimes important. A t1/2 of 3.5-6.5 h for zopiclone is previously reported in healthy individuals, but different factors like age and drug-interactions can affect the t1/2 of zopiclone. The aim of this study was to describe concentrations of zopiclone and co-ingestion of additional drugs in apprehended drivers, and to investigate the t1/2 of zopiclone based on two consecutive blood samples. METHODS: Data was collected from apprehended drivers in Norway between 2003 and 2021. All cases where zopiclone was detected were included. In a subset of the material, two consecutive whole blood samples were collected ≥ 20 and < 60 min apart. Concentrations of zopiclone in blood were determined by LC-MS or UHPLC-MS/MS. The elimination and t1/2 of zopiclone was estimated from the concentration change of zopiclone and the time interval between the two consecutive blood samples, under the assumption of first order kinetics. RESULTS: The median concentration among all zopiclone positive cases was 0.044 mg/L (IQR 0.070 mg/L) (n = 2401). The most frequent additional drugs detected were ethanol (36%), diazepam (22%), amphetamine (14%) and THC (14%). In zopiclone-only cases (n = 364), the median concentration of zopiclone was 0.066 mg/L (IQR 0.115 mg/L). In 112 cases, two consecutive blood samples were collected. Of these, 28 cases showed increasing concentrations of zopiclone between the two sampling time points. Among the cases in which the concentration decreased (n = 84), the median C1 was 0.048 mg/L (IQR 0.062 mg/L) and the median C2 was 0.043 mg/L (IQR 0.056 mg/L). A Bayesian statistical model was used to obtain the posterior distribution of t1/2. The posterior median of t1/2 was estimated to 3.1 h (IQR=0.39 h) when including only the cases showing decreasing concentrations, and this increased to 3.8 h (IQR=0.52 h) when also including samples showing non outlying increase in concentrations. There was no statistically significant gender difference in the calculated half-lives (two-sided Mann-Whitney U test, p = .525). CONCLUSIONS: This study showed that zopiclone is frequently detected in apprehended drivers in supra therapeutic concentrations and poly drug cases. The elimination of zopiclone in blood from two consecutive blood samples indicated an apparent t1/2 of between 3.1 and 3.8 h, which is within the lower range of what previous experimental studies on healthy individuals have reported.

Quantitative PCR analysis of bloodstains of different ages.

An accurate method to estimate the age of a stain or the time since deposition (TsD) would represent an important tool in police investigations for evaluating the true relevance of a stain. In this study, two laboratories reproduced an mRNA-based method for TsD estimation published by another group. The qPCR-based assay includes four transcripts (B2M, LGALS2, CLC, and S100A12) and showed preferential degradation of the 5' end over the 3' end. In this study, the blood-specific marker ALAS2 was added to examine whether it would show the same degradation pattern. Based on our qPCR data several elastic net models with different penalty combinations were created, using training data from the two laboratories separately and combined. Each model was then used to estimate the age of bloodstains from two independent test sets each laboratory had prepared. The elastic net model built on both datasets with training samples up to 320 days old displayed the best prediction performance across all test samples (MAD=18.9 days). There was a substantial difference in the prediction performance for the two laboratories: Restricting TsD to up to 100 days for test data, one laboratory obtained an MAD of 2.0 days when trained on its own data, whereas the other laboratory obtained an MAD of 15 days.

The invisible witness: air and dust as DNA evidence of human occupancy in indoor premises.

Humans constantly shed deoxyribonucleic acid (DNA) into the surrounding environment. This DNA may either remain suspended in the air or it settles onto surfaces as indoor dust. In this study, we explored the potential use of human DNA recovered from air and dust to investigate crimes where there are no visible traces available-for example, from a recently vacated drugs factory where multiple workers had been present. Samples were collected from three indoor locations (offices, meeting rooms and laboratories) characterized by different occupancy types and cleaning regimes. The resultant DNA profiles were compared with the reference profiles of 55 occupants of the premises. Our findings showed that indoor dust samples are rich sources of DNA and provide an historical record of occupants within the specific locality of collection. Detectable levels of DNA were also observed in air and dust samples from ultra-clean forensic laboratories which can potentially contaminate casework samples. We provide a Bayesian statistical model to estimate the minimum number of dust samples needed to detect all inhabitants of a location. The results of this study suggest that air and dust could become novel sources of DNA evidence to identify current and past occupants of a crime scene.

A new blood based epigenetic age predictor for adolescents and young adults.

Children have special rights for protection compared to adults in our society. However, more than 1/4 of children globally have no documentation of their date of birth. Hence, there is a pressing need to develop biological methods for chronological age prediction, robust to differences in genetics, psychosocial events and physical living conditions. At present, DNA methylation is the most promising biological biomarker applied for age assessment. The human genome contains around 28 million DNA methylation sites, many of which change with age. Several epigenetic clocks accurately predict chronological age using methylation levels at age associated GpG-sites. However, variation in DNA methylation increases with age, and there is no epigenetic clock specifically designed for adolescents and young adults. Here we present a novel age Predictor for Adolescents and Young Adults (PAYA), using 267 CpG methylation sites to assess the chronological age of adolescents and young adults. We compared different preprocessing approaches and investigated the effect on prediction performance of the epigenetic clock. We evaluated performance using an independent validation data set consisting of 18-year-old individuals, where we obtained a median absolute deviation of just below 0.7 years. This tool may be helpful in age assessment of adolescents and young adults. However, there is a need to investigate the robustness of the age predictor across geographical and disease populations as well as environmental effects.

Source level interpretation of mixed biological stains using coding region SNPs.

The association of body fluids/cell types and donors in mixed biological traces is an important, but challenging task required to evaluate the value of evidence given forensic propositions concerning the source of the DNA. The linking of a DNA profile with evidence from presumptive tests or RNA analysis is not straightforward. Coding region SNPs (cSNPs) are a novel type of evidential markers that are both cell type specific and individual specific. They thereby provide a direct link between a donor and a body fluid in mixed biological stains. In this proof-of-concept paper we consider the evaluation of cSNP profiles given source level propositions and explore the use of the open-source software EuroForMix to compute likelihood ratios. The discrimination power of the cSNPs for various body fluids is investigated with simulations. We provide case examples where the type of biological material is questioned and where cSNP profiles can be used to assign a donor to a body fluid, and discuss how the results can be reported in court.

Limitations of qPCR to estimate DNA quantity: An RFU method to facilitate inter-laboratory comparisons for activity level, and general applicability.

The application of qPCR to estimate the quantity of DNA present is usually based upon a short amplicon (typically c.80bp) and a longer amplicon (typically c.200-300bp) where the latter is used to determine the amount of degradation present in a sample. The data are used to make decisions about a) whether there is sufficient template to amplify? b) how much of the elution volume to forward to PCR? A typical multiplex amplifies template in the region of 100-500bp. Consequently, the results from an 80bp amplicon will tend to overestimate the actual amplifiable quantity that is present in a degraded sample. To compensate, a method is presented that relates the quantity of amplifiable DNA to the average RFU of the amplified fragments. This provides greatly improved accuracy of the estimated quantity of DNA present, which may differ by more than an order of magnitude compared to qPCR. The relative DNA quantities can be apportioned per contributor once mixture proportions are ascertained with probabilistic genotyping software (EuroForMix). The motivation for this work was to provide an improved method to generate data to prepare distributions that are used to inform activity level propositions. However, other applications will benefit, particularly those where extraction and quantification are bypassed: For example direct PCR and Rapid DNA technology. The overall aim of this work was to provide a method of quantification that is standardised and can be used to compare results between different laboratories that use different multiplexes. A software solution "ShinyRFU" is provided to aid calculations.

MPSproto: An extension of EuroForMix to evaluate MPS-STR mixtures.

We have developed MPSproto as an extension of EuroForMix to improve handling of stutter artefacts and other typing errors that commonly occur in MPS-STR data. MPSproto implements two models for read depth: gamma and negative binomial. It differs from EuroForMix in that calibration is required before mixtures are interpreted. In this study a mixture dataset (2-4 persons) was revisited, where EuroForMix interpretation of MPS-STR mixtures using the LUS+ format was first described; the performance of this model was compared to the MPSproto models. Results indicated that, overall, the MPSproto models performed better than the conventional EuroForMix model, and the gamma model implemented in MPSproto performed best. Differences were highlighted and further investigated to establish causality. Goodness of fit tests showed that the MPSproto models were adequate for the sequence reads when a low analytical threshold was applied.

RFU derived LRs for activity level assignments using Bayesian Networks.

A comparative study has been carried out, comparing two different methods to estimate activity level likelihood ratios (LRa) using Bayesian Networks. The first method uses the sub-source likelihood ratio (log10LRϕ) as a 'quality indicator'. However, this has been criticised as introducing potential bias from population differences in allelic proportions. An alternative method has been introduced that is based upon the total RFU of a DNA profile that is adjusted using the mixture proportion (Mx) which is calculated from quantitative probabilistic genotyping software (EuroForMix). Bayesian logistic regressions of direct transfer data showed that the two methods were comparable. Differences were attributed to sampling error, and small sample sizes of secondary transfer data. The Bayesian approach facilitates comparative studies by taking account of sampling error; it can easily be extended to compare different methods.

EFMrep: An extension of EuroForMix for improved combination of STR DNA mixture profiles.

The EuroForMix model has been extended to create a new open-source software called EFMrep which enables the combination of STR DNA mixture samples from different multiplexes. In addition to calculating combined likelihood ratios and carrying out deconvolution, the software also includes the capability to specify related unknown individuals. A graphical user interface has been implemented to ease the analysis for practitioners in real case work. The effect of combining multiple samples based on the PROVEDIt dataset was investigated, either from the same or different multiplexes. The information gain increases when more samples are combined. A head-to-head comparison against EuroForMix shows the benefit of a more general model. Guidelines are provided. A real case example was used to demonstrate how EFMrep could be used to combine multiple samples when a proposition includes kinship.

A comprehensive characterization of MPS-STR stutter artefacts.

Interpretation of DNA evidence involving mixtures is challenging when alleles from minor contributors coincide with stutters from major contributors. To accommodate this, it is important to have a good understanding of stutter sequence formation trends. Here, multiple stutter types were characterized based on massively parallel sequencing (MPS) data from 387 single source samples, using the Verogen ForenSeq™ DNA Signature Prep kit. A beta regression model was used to investigate the relationship between the stutter proportion and candidate explanatory variables. In the final model, stutter proportions were explained by the length of the parental uninterrupted stretch (PTUS), which is comparable to block length of the missing motif (BLMM). Also, different stutter types (n+1, n-1, n+2, n-2, n0) were analyzed separately per locus. The fitted stutter models were then integrated into an extended probabilistic genotyping model based on EuroForMix (MPSproto). An illustrative minor/major mock mixture example is discussed. Evaluation of multiple types of stutters on a per locus basis improved the probabilistic genotyping result compared to the conventional EuroForMix model, using the LUS+ nomenclature.

A Review of Probabilistic Genotyping Systems: EuroForMix, DNAStatistX and STRmix™.

Probabilistic genotyping has become widespread. EuroForMix and DNAStatistX are both based upon maximum likelihood estimation using a γ model, whereas STRmix™ is a Bayesian approach that specifies prior distributions on the unknown model parameters. A general overview is provided of the historical development of probabilistic genotyping. Some general principles of interpretation are described, including: the application to investigative vs. evaluative reporting; detection of contamination events; inter and intra laboratory studies; numbers of contributors; proposition setting and validation of software and its performance. This is followed by details of the evolution, utility, practice and adoption of the software discussed.