Explore the vast range of titles available.

Translational cancer research is highly dependent of large series of cases including high quality samples and their associated data. Comprehensive Cancer Centers should be involved in networks to enable large-scale multi-center research projects between the centers [Ringborg, U., de Valeriola, D., van Harten, W., Llombart-Bosch, A., Lombardo, C., Nilsson, K., Philip, T., Pierotti, M.A., Riegman, P., Saghatchian, M., Storme, G., Tursz, T., Verellen, D, 2008. Improvement of European translational cancer research. Collaboration between comprehensive cancer centers. Tumori 94, 143–146.]. Combating cancer knows many frontiers. Research is needed for prevention as well as better care for those who have acquired the disease. This implies that human samples for cancer research need to be sourced from distinct forms of biobanking. An easier access to these samples for the scientific community is considered as the main bottleneck for research for health, and biobanks are the most adequate site to try to resolve this issue [Ozols, R.F., Herbst, R.S., Colson, Y.L., Gralow, J., Bonner, J., Curran Jr., W.J., Eisenberg, B.L., Ganz, P.A., Kramer, B.S., Kris, M.G., Markman, M., Mayer, R.J., Raghavan, D., Reaman, G.H., Sawaya, R., Schilsky, R.L., Schuchter, L.M., Sweetenham, J.W., Vahdat, L.T., Winn, R.J., and the American Society of Clinical Oncology, 2007. Clinical cancer advances 2006: major research advances in cancer treatment, prevention, and screening: a report from the American Society of Clinical Oncology. J. Clin. Oncol. 25, 146–162.]. However, biobanks should not be considered a static activity. On the contrary, biobanking is a young discipline [Morente, M.M., Fernandez, P.L., de Alava, E. Biobanking: old activity or young discipline? Semin. Diagn. Pathol., in press.], which need continuously evolve according to the permanent development of new techniques and new scientific goals. To accomplish current requirements of the scientific community biobanks need to face some essential challenges including an appropriate design, harmonized and more suitable procedures, and sustainability, all of them in the framework of their ethic, legal and social dimensions. This review therefore presents an overview on these issues, based on the works and discussions of the Marble Arch International Working Group on Biobanking for Biomedical Research, integrated by experts in biobanking from five continents.

A control group is defined as a group of people used for comparison. Depending on the type of study, it can be a group of healthy people or a group not exposed to risk factors. It is important to allow researchers to select the appropriate control participants. The Korea Biobank Project-sponsored biobanks are affiliated with the Korea Biobank Network (KBN), for which the National Biobank of Korea plays a central coordinating role among KBN biobanks. KBN organized several working groups to address new challenges and needs in biobanking. The “Normal Healthy Control Working Group” developed standardized criteria for three defined control groups, namely, normal, normal-plus, and disease-specific controls. Based on the consensus on the definition of a normal control, we applied the criteria for normal control participants to retrospective data. The main reason for exclusion from the “Normal-plus” group was blood test results beyond 5% of the reference range, including hypercholesterolemia. Subclassification of samples of normal controls by detailed criteria will help researchers select optimal normal controls for their studies.

Although a lack of diversity in genetic studies is an acknowledged obstacle for personalized medicine and precision public health, Latin American populations remain particularly understudied despite their heterogeneity and mixed ancestry. This gap extends to COVID-19 despite its variability in susceptibility and clinical course, where ethnic background appears to influence disease severity, with non-Europeans facing higher hospitalization rates. In addition, access to high-quality samples and data is a critical issue for personalized and precision medicine, and it has become clear that the solution lies in biobanks. The creation of the Chilean COVID-19 Biorepository reported here addresses these gaps, representing the first nationwide multicentric Chilean initiative. It operates under rigorous biobanking standards and serves as one of South America’s largest COVID cohorts. A centralized harmonization strategy was chosen and included unified standard operating procedures, a sampling coding system, and biobanking staff training. Adults with confirmed SARS-CoV-2 infection provided broad informed consent. Samples were collected to preserve blood, plasma, buffy coat, and DNA. Quality controls included adherence to the standard preanalytical code, incident reporting, and DNA concentration and absorbance ratio 260/280 assessments. Detailed sociodemographic, health, medication, and preexisting condition data were gathered. In five months, 2262 participants were enrolled, pseudonymized, and sorted by disease severity. The average Amerindian ancestry considering all participant was 44.0% [SD 15.5%], and this value increased to 61.2% [SD 19.5%] among those who self-identified as Native South Americans. Notably, 279 participants self-identified with one of 12 ethnic groups. High compliance (>90%) in all assessed quality controls was achieved. Looking ahead, our team founded the COVID-19 Genomics Network (C19-GenoNet) focused on identifying genetic factors influencing SARS-CoV-2 outcomes. In conclusion, this bottom-up collaborative effort aims to promote the integration of Latin American populations into global genetic research and welcomes collaborations supporting this endeavor. Interested parties are invited to explore collaboration opportunities through our catalog, accessible online.

Currently, each biobank in Latin America operates with its own set of standards for database creation and management, resulting in a lack of regional and international interoperability. Furthermore, regulations concerning data protection, curation, and the transfer of biological samples and associated data vary significantly from country to country, by complicating efforts to create a unified data-sharing platform. To address these challenges, Latin America should promote the development of an integrated regional network of biobanks to generate high-quality evidence within the global research ecosystem. This initiative will combine regulatory science—focused on interoperability standards across semantic, technical, legal, and organizational dimensions—and meta-science, which assesses the quality of scientific practice. Evidence indicates that harmonized standards in biobanks lead to higher-quality, more reliable data, thereby facilitating the reproducibility of scientific studies. This paper aims to identify and address existing regulatory, policy, and infrastructure gaps in Latin America to establish harmonized interoperability criteria essential for reproducing biomedical studies. Additionally, it seeks to propose minimum standards for regulating biobank networks, which will promote the development of medical products on a global scale, thereby engendering high quality evidence for the global research ecosystem and enhancing Latin America’s integration into it.

The importance of clinical information related to specimens is increasing due to the research on human biological specifications being conducted worldwide. In order to utilize data, it is necessary to define the range of data and develop a standardized system for collected resources. The purpose of this study is to establish clinical information standardization and to allow clinical information management systems to improve the utilization of biological specifications. The KBN CDM, consisting of 18 tables and 177 variables, was developed. The clinical information codes were mapped in standard terms. The 27 diseases in the group were collected from 17 biobanks, and all disorders not belonging to the group were standardized and loaded. We also developed a system that provides statistical visualization screens and data retrieval tools for data collection. This study developed a unified management system to model KBN CDM that collects standardized data, manages clinical information, and shares the information systematically. Through this system, all participating biobanks can be integrated into one system for integrated management and research.

Purpose - Biobanks are collections of biological samples (e.g. tissue samples and body fluids) and their associated data intended for various approaches in medical research. The field of biobanking evolves rapidly as an interdisciplinary branch of research and requires educational efforts to provide skilled experts in Europe and beyond. New ways in research and research education play a pivotal role in the future of biobanking. Design/methodology/approach - The increasing of requests and potential uses of biospecimens from biobanks necessitates an international and national intensified transfer of forward looking knowledge and know-how. In Austria, this could be realized by special trainings as well as a postgraduate education. Furthermore, the forward looking research and further development of infrastructure will play a pivotal role in biobanks in the future. Findings - Few opportunities are available for specific education on biobanking in Europe. This could be remedied by the creation networks of ISO-certified biobanks and co-operation with interested parties. Research limitations/implications - The current research focuses on the situation of information transfer in the field of biobanking in Europe. A wider investigation in better harmonization and standardization of methods in other parts of the world would be beneficial. Originality/value - The value of biomolecular resources such as biobanks has previously been discussed in detail, e.g. by the Time magazine. The paper focuses on demonstrating the importance for education in the future of biobanking in general.

•Brain age is estimated using a 3D CNN from 12,802 full T1-weighted images.•Regions used to drive predictions are different for linearly and nonlinearly registered data.•Linear registrations utilise a greater diversity of biologically meaningful areas.•Correlations with IDPs and non-imaging variables are consistent with other publications.•Excluding subjects with various health conditions had minimal impact on main correlations. Both normal ageing and neurodegenerative diseases cause morphological changes to the brain. Age-related brain changes are subtle, nonlinear, and spatially and temporally heterogenous, both within a subject and across a population. Machine learning models are particularly suited to capture these patterns and can produce a model that is sensitive to changes of interest, despite the large variety in healthy brain appearance. In this paper, the power of convolutional neural networks (CNNs) and the rich UK Biobank dataset, the largest database currently available, are harnessed to address the problem of predicting brain age. We developed a 3D CNN architecture to predict chronological age, using a training dataset of 12,802 T1-weighted MRI images and a further 6,885 images for testing. The proposed method shows competitive performance on age prediction, but, most importantly, the CNN prediction errors ΔBrainAge=AgePredicted−AgeTrue correlated significantly with many clinical measurements from the UK Biobank in the female and male groups. In addition, having used images from only one imaging modality in this experiment, we examined the relationship between ΔBrainAge and the image-derived phenotypes (IDPs) from all other imaging modalities in the UK Biobank, showing correlations consistent with known patterns of ageing. Furthermore, we show that the use of nonlinearly registered images to train CNNs can lead to the network being driven by artefacts of the registration process and missing subtle indicators of ageing, limiting the clinical relevance. Due to the longitudinal aspect of the UK Biobank study, in the future it will be possible to explore whether the ΔBrainAge from models such as this network were predictive of any health outcomes.

We mapped functional and structural brain networks for more than 40,000 UK Biobank participants. Structural connectivity was estimated with tractography and diffusion MRI. Resting-state functional MRI was used to infer regional functional connectivity. We provide high-quality structural and functional connectomes for multiple parcellation granularities, several alternative measures of interregional connectivity, and a variety of common data pre-processing techniques, yielding more than one million connectomes in total and requiring more than 200,000 h of compute time. For a single subject, we provide 28 out-of-the-box versions of structural and functional brain networks, allowing users to select, e.g., the parcellation and connectivity measure that best suit their research goals. Furthermore, we provide code and intermediate data for the time-efficient reconstruction of more than 1000 different versions of a subject’s connectome based on an array of methodological choices. All connectomes are available via the UK Biobank data-sharing platform and our connectome mapping pipelines are openly available. In this report, we describe our connectome resource in detail for users, outline key considerations in developing an efficient pipeline to map an unprecedented number of connectomes, and report on the quality control procedures that were completed to ensure connectome reliability and accuracy. We demonstrate that our structural and functional connectivity matrices meet a number of quality control checks and replicate previously established findings in network neuroscience. We envisage that our resource will enable new studies of the human connectome in health, disease, and aging at an unprecedented scale. •We provide a brain network resource for more than 40,000 UK Biobank participants.•Diffusion MRI data was used to compute structural connectivity and resting-state functional MRI was used to infer regional functional connectivity.•For every individual, we provide 28 ready-to-use precomputed structural and functional brain networks for a range of alternative parcellations and connection metrics.•We provide supporting code and data enabling time-efficient reconstruction of more than 1000 different versions of an individual’s connectome.•A battery of quality control procedures was conducted to ensure connectome reliability and accuracy.

Graph representations are often used to model structured data at an individual or population level and have numerous applications in pattern recognition problems. In the field of neuroscience, where such representations are commonly used to model structural or functional connectivity between a set of brain regions, graphs have proven to be of great importance. This is mainly due to the capability of revealing patterns related to brain development and disease, which were previously unknown. Evaluating similarity between these brain connectivity networks in a manner that accounts for the graph structure and is tailored for a particular application is, however, non-trivial. Most existing methods fail to accommodate the graph structure, discarding information that could be beneficial for further classification or regression analyses based on these similarities. We propose to learn a graph similarity metric using a siamese graph convolutional neural network (s-GCN) in a supervised setting. The proposed framework takes into consideration the graph structure for the evaluation of similarity between a pair of graphs, by employing spectral graph convolutions that allow the generalisation of traditional convolutions to irregular graphs and operates in the graph spectral domain. We apply the proposed model on two datasets: the challenging ABIDE database, which comprises functional MRI data of 403 patients with autism spectrum disorder (ASD) and 468 healthy controls aggregated from multiple acquisition sites, and a set of 2500 subjects from UK Biobank. We demonstrate the performance of the method for the tasks of classification between matching and non-matching graphs, as well as individual subject classification and manifold learning, showing that it leads to significantly improved results compared to traditional methods. •Metric learning approach for similarity estimation between brain connectivity graphs.•The method employs spectral graph convolutions to learn localised feature maps.•Quantitative and qualitative evaluation on ABIDE and UK Biobank databases.•Global loss function leads to improved results on heterogeneous datasets.