Age estimation using carpals: study of a slovenian

Age estimation using carpals: study of a slovenian

The Slovenian territory played a crucial role in the past serving as gateway for several human migrations. Previous studies used Slovenians as a source population to interpret different demographic events happened in Europe but not much is known about the genetic background and the demographic history of this population.

Here, we analyzed genome-wide data from 96 individuals to shed light on the genetic role and history of the Slovenian population. Y chromosome diversity splits into two major haplogroups R1b and R1a with the latter suggesting a genetic contribution from the steppe.

Slovenian individuals are more closely related to Northern and Eastern European populations than Southern European populations even though they are geographically closer. This pattern is confirmed by an admixture and clustering analysis. Finally, we looked for signals of selection in candidate variants and we found significant hits in HERC2 and FADS responsible for blue eye color and synthesis of long-chain unsaturated fatty acids, respectively, when Slovenians were compared to Southern Europeans.

While the comparison was done with Eastern Europeans, we identified significant signals in PKD2L1 and IL6R which are genes associated with taste and coronary artery disease, respectively. The Slovenian territory is geographically located between the Alps, the Adriatic Sea and the Pannonian basin and as such it could have been used as a gateway for different populations over several periods of time.

However, the presence of geographical and possibly cultural barriers could have led to a more puzzling role for this region. The territory of modern day Slovenia was settled during the 6th and 7th centuries AD by different Slavic tribes from at least two different directions: one from the north and one from the east Grafenauer, However, this background gives us only little clues regarding the genetic features of the founder pool.

Previous genetic studies were only based on Y chromosome variation Zupan et al. However, a fine characterization of the Slovenia population using genome wide data is missing even though this population was used in previous studies Esko et al.

Therefore, a gap is left on what we know about the history and the role of the Slovenian population in the past. Furthermore, the availability of ancient genomes during the last years empowered our ability to better understand the history of modern and past populations Haber et al. According to the authors, the steppe ancestry persisted in all sampled central Europeans until at least 3, years ago, and it is equally distributed in present-day Europeans, providing also evidences for a steppe-origin of the Indo-European languages Haak et al.

All these elements highlight the necessity of a broader and more comprehensive genetic study on Slovenian population. Outcomes of this kind of study could be useful to several disciplines and not only for population genetics and linguistics.

age estimation using carpals: study of a slovenian

For example, in the context of genetic epidemiology, describing the genetic landscape of this population would be beneficial to understand the genetic background of disease-related loci and their distribution; in fact Slovenians show evidence of familial hypercholesterolemia 3.

For these purposes, we used genotype data from 96 Slovenian individuals and we analyzed them together with previously published modern and ancient samples. Overall, 96 samples ranging from Slovenian littoral to Lower Styria were genotyped formarkers using the OmniExpress V1 BeadChips Figure 1genetic data were obtained from Esko et al.

After removing related individuals, 92 samples were left. The Slovenian dataset has been subsequently merged with the Human Origin dataset Lazaridis et al. We also generated a dataset containing ancient genomes form Haak et al. Additional details can be found in Supplementary Table 1. Geographical location of the samples included in this study.The aim of this study is to develop a new formula for age estimation in a longitudinal study of a sample from the radiological collection of wrist bones of growing infants, children, and adolescents recorded at the Burlington Growth Centre.

Hierarchical Bayesian calibration has been adopted to exceed the bias deriving from the classical regression approach used for age estimation in forensic disciplines, since it tends to overestimate or underestimate the age of the individuals.

Calibration distributions of the dataset obtained by the evaluation of BoCa the ratio of Bo and Ca suggested mean absolute errors MAE of 1. The mean interquartile range MIQR was 1. Furthermore, a correspondence between different BoCa values and estimated age with its standard deviation SD was calculated for boys and girls, respectively. In conclusion, the Bayesian calibration method appears to be suitable for assessing both age and its distribution in subadults, according to hand-wrist maturity.

Furthermore, it can easily incorporate other age predictors, obtaining a distribution of the subjects with multivariate predictors.

age estimation using carpals: study of a slovenian

This is a preview of subscription content, log in to check access. Rent this article via DeepDyve. Todd WT Atlas of skeletal maturity. The C. Mosby Company, St Louis. Google Scholar. Physical growth and development. Monogr Soc Res Child Dev 9:i. Am J Phys Anthr — Chin Med J — Comput Med Imaging Graph — Gilsanz V, Ratib O Hand bone age: a digital atlas of skeletal maturity. Springer-Verlag, Berlin Heidelberg. Pietka E Computer assisted bone age assessment based on features automatically extracted from a hand radiograph.

Stanford University Press, Stanford. Arch Dis Child — Pediatr Radiol 26 3 — Clin Radiol 37 2 — Academic Press, London. WD Saunders, London. Charles C. Thomas, Springfield. Am J Hum Biol 1 2 — Int J Legal Med — J Forensic Legal Med — Stat Med 34 10 — Am J Phys Anthropol 3 — J Forensic Sci 46 4 —Background: Protein energy malnutrition, a major health and nutritional problem in India, leads to retardation in bone maturity and eventually physical growth.

Many studies have been done on ossification centers of hand bones for estimation of skeletal maturation and to diagnose malnutrition but very few studies have been done so far to see effect of protein energy malnutrition on surface area of carpal bones.

Aims and Objectives: The surface areas of carpal bones on radiographs on both sides were calculated, analysed and compared in protein energy malnutrition PEM cases and healthy controls upto five years of age to see the variability among study groups. Radiography of wrist done and surface areas of carpal bones were calculated by using millimeter Graph Transparency paper by Hit method and data were evaluated for significance by using t-test. Result: The surface areas of capitate, hamate and triquetral bones of both hands were reduced in PEM children of all grades as compared to healthy controls.

The surface areas of these carpal bones were much reduced in grade 2 and grade 3 PEM cases as compared to healthy controls and grade 1 PEM cases. Asymmetry in surface areas of capitate and hamate bones were ascertained in healthy controls and grade 1 PEM cases but due to delay in appearance of ossification centers and reduced surface areas of these bones, asymmetry could not be ascertained in grade 2 and grade 3 malnutrition children.

Conclusion: The surface areas of capitate, hamate and triquetral bones on both sides were reduced in grade 2 and grade 3 PEM cases as compared to controls. Asymmetry in surface area of carpal bones was ascertained in healthy controls and grade 1 PEM cases. Protein energy malnutrition PEM is still an important health problem that is responsible for childhood morbidity and mortality leading to permanent impairment of physical and possibly mental growth who survive.

Fourty six percent of preschool children in India suffer from moderate to severe protein energy malnutrition ICMR [ 1 ].

Nearly 80 percent of PEM cases are of mild and moderate degree malnutrition in India. It is of great concern that malnutrition is like an iceberg leading to undiagnosed retardation of bone maturity and eventually physical growth [ 2 ]. Skeletal development is a useful and reliable indicator of growth and development. No other organ is as technically amenable to measurement throughout the growth period as skeletal system [ 3 ].

Bones serve as a mirror of nutritional status of child and skeletal maturation is consistent in the age at which they occur in the individual and can be used as a guideline to measure degree of maturation [ 4 ].

Hand and wrist are good index of maturity of entire skeleton of a healthy child [ 5 ]. Previously many studies have been done on ossification centres of hand bones for estimation of skeletal maturation and to diagnose malnutrition but very few studies have been done so far to see effect of malnutrition on surface area of carpal bones. Hsieh [ 7 ] developed a computerized bone age estimation system based on geometric analysis of carpal bones They included shape and bony surface area in their study and concluded that bone surface area had a higher discriminating power to judge bone age.

Considering all these facts, this study was done to see the effects of Protein energy malnutrition on surface areas of carpal bones of children upto five years of age. The present study was conducted at Department of Paediatrics and Department of Radiodiagnosis of SN Medical college, Agra comprising cases of Protein energy malnutrition PEM of various grades diagnosed by consultant paediatrician using IAP criteria [ 8 ] and comparable numbers of children as controls upto 5 years of age of Agra region.

Frequency distribution of various age groups was Sixty-eight healthy children of either sex were taken as controls having frequency distribution of Medical College, Agra. Various ethical issues related to this study were discussed under Institutional Review Board and ethical clearance was obtained.Approximately food samples from the Slovenian market and drinking tap water samples in the period - were evaluated for the presence of toxic elements lead Pb and cadmium Cd and the overall chronic dietary exposure was assessed for each element.

The highest mean concentrations were found for Pb in products for special nutritional use - dietary supplements 0. The consumption data were obtained from the national study of the household budget and from the European Food Safety Authority EFSA summary statistics. Chronic dietary exposure was estimated deterministically. The average Pb and Cd intakes from food and drinking tap water ranged for children from 0.

The high consumption of cereal-based foods and vegetables contributed most to overall Pb and Cd intake in the majority of the age groups. The exposure to Pb was generally lower and that to Cd was higher compared to the average EU values.

The most exposed were children, as the health-based guidance values were exceeded for both elements, which indicates a health concern. An important contribution to Cd exposure can be attributed to grains and potatoes grown in Slovenia, where the soil contains more Cd than that in other EU countries. Further efforts to reduce Pb and Cd intake are required to minimize potential health risks.

Keywords: Lead; Slovenia; cadmium; dietary exposure; drinking water; food. Abstract Approximately food samples from the Slovenian market and drinking tap water samples in the period - were evaluated for the presence of toxic elements lead Pb and cadmium Cd and the overall chronic dietary exposure was assessed for each element.

Substances Drinking Water Cadmium Lead.Skip to search form Skip to main content You are currently offline. Some features of the site may not work correctly. DOI: Not only does the Bayesian approach offer a rational and logical environment for evidence evaluation in a forensic framework, but it also allows scientists to coherently deal with uncertainty related to a collection of multiple items of evidence, due to its flexible nature.

Such flexibility might come at the expense of elevated computational complexity, which can be handled by using specific probabilistic graphical tools, namely Bayesian networks.

age estimation using carpals: study of a slovenian

View on PubMed. Save to Library. Create Alert. Launch Research Feed. Share This Paper. Topics from this paper. Physiologic calcification. Citations Publications citing this paper. Lyle W KonigsbergSusan R. FrankenbergHelen Liversidge Medicine Journal of forensic sciences Age estimation by multidetector computed tomography of cranial sutures in Chinese male adults.

Carpal bones

References Publications referenced by this paper. Probabilistic age classification with Bayesian networks: A study on the ossification status of the medial clavicular epiphysis.

Probabilistic classification of age by third molar development: the use of soft evidence. Human dental age estimation using third molar developmental stages: does a Bayesian approach outperform regression models to discriminate between juveniles and adults? Bayesian calibration for forensic age estimation.Search Reset filters.

Genetic Landscape of Slovenians: Past Admixture and Natural Selection Pattern

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Log in. Our policy towards the use of cookies All DeepDyve websites use cookies to improve your online experience.Carpals are often used as age indicators. In a recent study, Cameriere et al. The present study, of a sample of Slovenian children and adolescents aged between 6 and 16 years, focused on analysing the best regression for age estimation. The median of the absolute values of residuals observed age minus predicted age was 0.

Comparisons between the previous equation referring to Slovenian children and the equivalent linear equation proposed by Cameriere et al. These results suggested a common regression model for both Italian and Slovenian samples. I dati possono differire da quelli visualizzati in reportistica.

Automatic bone age assessment for young children from newborn to 7-year-old using carpal bones

Si consiglia il caricamento di immagini con una proporzione tra larghezza e altezza. Scheda prodotto non validato Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo. File in questo prodotto:. I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione. Annulla Invia. La simulazione si basa sui dati IRIS e sugli indicatori bibliometrici alla data indicata e non tiene conto di eventuali periodi di congedo obbligatorio, che in sede di domanda ASN danno diritto a incrementi percentuali dei valori.

Si consideri che Anvur calcola i valori degli indicatori all'ultima data utile per la presentazione delle domande. Si specifica inoltre che la simulazione contiene calcoli effettuati con dati e algoritmi di pubblico dominio e deve quindi essere considerata come un mero ausilio al calcolo svolgibile manualmente o con strumenti equivalenti.

Annulla procedi. Documento in post-print versione successiva alla peer review e accettata per la pubblicazione.


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