Kolyasnikova Anna Sergeevna

Introduction. Hyperostosis frontalis interna (HFI) is a pathological condition characterized by the growth of the inner surface of the frontal bone. Most researchers describe HFI as a symptom associated with hormonal dysfunctions, which prevails in the modern population. This paper provides an analysis of HFI frequency on the craniological material of four adaptive types.

Materials and methods. We have examined 2211 skulls (59 craniological collections from the Anuchin Research Institute and Museum of Anthropology, Peter the Great Museum of Anthropology and Ethnography, RAS and Federal Research Center of the Tyumen Scientific Center of the Siberian Branch of the Russian Academy of Sciences). We have analyzed the total frequency of HFI, used a comparative intergroup analysis, and evaluated the degree of HFI expression and the distribution of the trait.

Results and discussion. It was found that the frequency of HFI in groups of arctic, continental and temperate adaptive types ranged from 2.3% to 4.3%, which is significantly less than in the modern population (12-37%). In the group of the tropical adaptive type, HFI was not found. As a result of a comparative study of the severity of cases of frontal hyperostosis, type A was the most common, HFI type B was less common, and type C was recorded only for one individual of the Arctic adaptive type. The reasons for the relatively low prevalence of HFI in representatives of various adaptive types are discussed.

Conclusion. According to a low frequency of HFI in studied adaptive types comparing to data in modern population can be considered that adaptation to environment and life style is more important than climate and geographical features. © 2023. This work is licensed under a CC BY 4.0 license

Introduction. Hyperostosis frontalis interna (HFI) is a thickening of the frontal bone associated with metabolic and hormonal disorders. While prevalent among elderly women in modern populations, archaeological studies have documented male-dominated HFI cases in certain groups. This study examines the frequency of HFI in Mesolithic/Neolithic craniological series fr om the Dnieper region.

Materials and methods. Eight craniological series (107 skulls) from the collections of the Research Institute and Museum of Anthropology, Lomonosov Moscow State University, were analyzed. The overall prevalence of HFI and its developmental stages (4-point scale) were assessed.

Results and discussion. A high HFI frequency (20.6%) was identified in Mesolithic/Neolithic groups of the Dnieper region, suggesting a link to chronic metabolic stress during transitional periods. Notably, HFI types A, B, and C were equally represented (1:1:1 ratio), contrasting with typical patterns wh ere type A dominates. Elevated frequencies of types B and C likely indicate metabolic/hormonal imbalances. Although no statistically significant sex or age differences were observed, a trend of increased HFI prevalence in older individuals aligns with prior research. Associations with periodontitis, trauma, and cribra orbitalia point to environmental stress influences.

Conclusion. The identified HFI cases represent the earliest known instances in prehistoric Eastern Europe. Results underscore HFI’s role as a stress marker in transitional historical periods. © 2025. This work is licensed under a CC BY 4.0 license

Introduction. Sex determination of individuals fr om postcranial skeletal bones is a significant task in paleoanthropology and forensic medicine, particularly when skull and pelvic bones are fragmented or absent. The traditional osteometric approach, based on sexual dimorphism in size characteristics, requires consideration of population specificity and is continuously being refined. The aim of this review is to systematize and analyze modern methods for sex determination based on osteometric data from postcranial skeletal bones.

Materials and Methods. This review analyzes contemporary scientific publications dedicated to methods of sex determination from the postcranial skeleton. Three main methodological approaches are considered: methods based on univariate statistics (analysis of individual metric traits), methods of multivariate statistics (discriminant analysis, logistic regression), as well as modern technologies, including the use of computed tomography (CT) data for 3D reconstructions and the application of machine learning algorithms (specifically, deep learning) for bone image analysis.

Results and discussion. Univariate methods retain practical value due to their simplicity of application and suitability for working with fragmented material, although their accuracy is typically lower than that of multivariate methods. Multivariate statistical models, which account for a complex of interrelated traits, ensure higher accuracy in sex determination. A key factor influencing the reliability of any method is its population specificity—applying models developed for one population to another leads to a significant reduction in accuracy. The integration of CT data and artificial intelligence methods opens new prospects for automation, increased objectivity, and the discovery of new diagnostic features.

Conclusion. Modern methods for sex determination from the postcranial skeleton constitute an evolving toolkit, wh ere traditional univariate approaches are effectively complemented by complex multivariate models and innovative technologies. To ensure high result reliability, the development and validation of population-specific standards are necessary. The future of the field is linked to the further integration of 3D visualization and machine learning methods, which will enhance the accuracy, speed, and objectivity of expert identification.

Acknowledgements. The study was conducted under the state assignment of Lomonosov Moscow State University.