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•The current manuscript can be considered the first standard in the field.•This first study facilitated an international agreement on different aspects of CFS.•The proposed best practices will help practitioners to make a decision on the applicability of CFS in daily forensic caseworks. Craniofacial superimposition, although existing for one century, is still a controversial technique within the scientific community. Objective and unbiased validation studies over a significant number of cases are required to establish a more solid picture on the reliability. However, there is lack of protocols and standards in the application of the technique leading to contradictory information concerning reliability. Instead of following a uniform methodology, every expert tends to apply his own approach to the problem, based on the available technology and deep knowledge on human craniofacial anatomy, soft tissues, and their relationships. The aim of this study was to assess the reliability of different craniofacial superimposition methodologies and the corresponding technical approaches to this type of identification. With all the data generated, some of the most representative experts in craniofacial identification joined in a discussion intended to identify and agree on the most important issues that have to be considered to properly employ the craniofacial superimposition technique. As a consequence, the consortium has produced the current manuscript, which can be considered the first standard in the field; including good and bad practices, sources of error and uncertainties, technological requirements and desirable features, and finally a common scale for the craniofacial matching evaluation. Such a document is intended to be part of a more complete framework for craniofacial superimposition, to be developed during the FP7-founded project MEPROCS, which will favour and standardize its proper application.

•A multiple-lab study on CFS has been carried out for the first time.•Each participant employed her/his particular methodology and technological means.•Provide important insights to better understand the most convenient characteristics. As part of the scientific tasks coordinated throughout The ‘New Methodologies and Protocols of Forensic Identification by Craniofacial Superimposition (MEPROCS)’ project, the current study aims to analyse the performance of a diverse set of CFS methodologies and the corresponding technical approaches when dealing with a common dataset of real-world cases. Thus, a multiple-lab study on craniofacial superimposition has been carried out for the first time. In particular, 26 participants from 17 different institutions in 13 countries were asked to deal with 14 identification scenarios, some of them involving the comparison of multiple candidates and unknown skulls. In total, 60 craniofacial superimposition problems divided in two set of females and males. Each participant follow her/his own methodology and employed her/his particular technological means. For each single case they were asked to report the final identification decision (either positive or negative) along with the rationale supporting the decision and at least one image illustrating the overlay/superimposition outcome. This study is expected to provide important insights to better understand the most convenient characteristics of every method included in this study.

Dry eye is a multifactorial ocular surface disease (DED) characterized by a loss of tear film homeostasis, which is widely associated with alterations in mental health. Problematic internet use (PIU) is defined as the feeling of concern about using this tool irresistibly, for longer periods than usual, accompanied by anguish that results from not doing so without reaching mania or hypomania behaviors. Both PIU and DED present a theoretical link; however, there are no published studies that report its relationship with problematic internet use. Therefore, this study aims to determine the association between PIU and symptomatic DED in Peruvian medical students. Analytical cross-sectional study that included human medical students from Peru. For the measurement of main variables, the Ocular Surface Disease Index (OSDI) and the Internet Addiction Test (IAT) were used. To study their relationship, the Poisson regression analysis was used, we consider a p-value <0.05 as significant. Data from 844 medical students were analyzed, 35.7% male and 64.3% female, with an average age of 21.8 ± 3.3 years. Likewise, the prevalence of symptomatic DED was 70.9%, and the internet's controlled use was 85.3%. In the analysis adjusted for symptomatic DED, the men showed significant differences in the controlled use of the internet (p <0.003), of which those who had PIU, 50% had severe symptomatic DED, as well as 80% of those addicted to the internet. The adjusted model showed that the PIU increases the prevalence of symptomatic DED in men (PR = 1.17, 95% CI: 1.06-1.29); however, in women, no association was found between both variables. PIU and symptomatic DED showed a significant association in male medical students from Peru.

Kidney abnormality segmentation has important potential to enhance the clinical workflow, especially in settings requiring quantitative assessments. Kidney volume could serve as an important biomarker for renal diseases, with changes in volume correlating directly with kidney function. Currently, clinical practice often relies on subjective visual assessment for evaluating kidney size and abnormalities, including tumors and cysts, which are typically staged based on diameter, volume, and anatomical location. To support a more objective and reproducible approach, this research aims to develop a robust, thoroughly validated kidney abnormality segmentation algorithm, made publicly available for clinical and research use. We employ publicly available training datasets and leverage the state-of-the-art medical image segmentation framework nnU-Net. Validation is conducted using both proprietary and public test datasets, with segmentation performance quantified by Dice coefficient and the 95th percentile Hausdorff distance. Furthermore, we analyze robustness across subgroups based on patient sex, age, CT contrast phases, and tumor histologic subtypes. Our findings demonstrate that our segmentation algorithm, trained exclusively on publicly available data, generalizes effectively to external test sets and outperforms existing state-of-the-art models across all tested datasets. Subgroup analyses reveal consistent high performance, indicating strong robustness and reliability. The developed algorithm and associated code are publicly accessible at https://github.com/DIAGNijmegen/oncology-kidney-abnormality-segmentation.