Search Results (12)

Over the last decades, bioprospecting of tropical corals has revealed numerous bioactive compounds with potential for biotechnological applications. However, this search involves sampling in natural reefs, and this is currently hampered by multiple ethical and technological constraints. Living coral displays, research laboratories, and biobanks currently offer an opportunity to continue to unravel coral chemodiversity, acting as “Noah’s Arks” that may continue to support the bioprospecting of molecules of interest. This issue is even more relevant if one considers that tropical coral reefs currently face unprecedent threats and irreversible losses that may impair the biodiscovery of molecules with potential for new products, processes, and services. Living coral displays provide controlled environments for studying corals and producing both known and new metabolites under varied conditions, and they are not prone to common bottlenecks associated with bioprospecting in natural coral reefs, such as loss of the source and replicability. Research laboratories may focus on a particular coral species or bioactive compound using corals that were cultured ex situ, although they may differ from wild conspecifics in metabolite production both in quantitative and qualitative terms. Biobanks collect and preserve coral specimens, tissues, cells, and/or information (e.g., genes, associated microorganisms), which offers a plethora of data to support the study of bioactive compounds’ mode of action without having to cope with issues related to access, standardization, and regulatory compliance. Bioprospecting in these settings faces several challenges and opportunities. On one hand, it is difficult to ensure the complexity of highly biodiverse ecosystems that shape the production and chemodiversity of corals. On the other hand, it is possible to maximize biomass production and fine tune the synthesis of metabolites of interest under highly controlled environments. Collaborative efforts are needed to overcome barriers and foster opportunities to fully harness the chemodiversity of tropical corals before in-depth knowledge of this pool of metabolites is irreversibly lost due to tropical coral reefs’ degradation. Full article

As medical imaging technologies advance, these tools are playing a more and more important role in assisting clinical disease diagnosis. The fusion of biomedical imaging and multi-modal information is profound, as it significantly enhances diagnostic precision and comprehensiveness. Integrating multi-organ imaging with genomic information can significantly enhance the accuracy of disease prediction because many diseases involve both environmental and genetic determinants. In the present study, we focused on the fusion of imaging-derived phenotypes (IDPs) and polygenic risk score (PRS) of diseases from different organs including the brain, heart, lung, liver, spleen, pancreas, and kidney for the prediction of the occurrence of nine common diseases, namely atrial fibrillation, heart failure (HF), hypertension, myocardial infarction, asthma, type 2 diabetes, chronic kidney disease, coronary artery disease (CAD), and chronic obstructive pulmonary disease, in the UK Biobank (UKBB) dataset. For each disease, three prediction models were developed utilizing imaging features, genomic data, and a fusion of both, respectively, and their performances were compared. The results indicated that for seven diseases, the model integrating both imaging and genomic data achieved superior predictive performance compared to models that used only imaging features or only genomic data. For instance, the Area Under Curve (AUC) of HF risk prediction was increased from 0.68 ± 0.15 to 0.79 ± 0.12, and the AUC of CAD diagnosis was increased from 0.76 ± 0.05 to 0.81 ± 0.06. Full article

In recent years, medicine has undergone profound changes, strongly entering a new phase defined as the “era of precision medicine”. In this context, patient clinical management involves various scientific approaches that allow for a comprehensive pathology evaluation: from preventive processes (where applicable) to genetic and diagnostic studies. In this scenario, biobanks play an important role and, over the years, have gained increasing prestige, moving from small deposits to large collections of samples of various natures. Disease-oriented biobanks are rapidly developing as they provide useful information for the management of complex diseases, such as melanoma. Indeed, melanoma, given its highly heterogeneous characteristics, is one of the oncologic diseases with the greatest clinical and therapeutic management complexity. So, the possibility of extrapolating tissue, genetic and imaging data from dedicated biobanks could result in more selective study approaches. In this review, we specifically analyze the several biobank types to evaluate their role in technology development, patient monitoring and research of new biomarkers, especially in the melanoma context. Full article

Background. Biobanks are vital research infrastructures aiming to collect, process, store, and distribute biological specimens along with associated data in an organized and governed manner. Exploiting diverse datasets produced by the biobanks and the downstream research from various sources and integrating bioinformatics and “omics” data has proven instrumental in advancing research such as cancer research. Biobanks offer different types of biological samples matched with rich datasets comprising clinicopathologic information. As digital pathology and artificial intelligence (AI) have entered the precision medicine arena, biobanks are progressively transitioning from mere biorepositories to integrated computational databanks. Consequently, the application of AI and machine learning on these biobank datasets holds huge potential to profoundly impact cancer research. Methods. In this paper, we explore how AI and machine learning can respond to the digital evolution of biobanks with flexibility, solutions, and effective services. We look at the different data that ranges from specimen-related data, including digital images, patient health records and downstream genetic/genomic data and resulting “Big Data” and the analytic approaches used for analysis. Results. These cutting-edge technologies can address the challenges faced by translational and clinical research, enhancing their capabilities in data management, analysis, and interpretation. By leveraging AI, biobanks can unlock valuable insights from their vast repositories, enabling the identification of novel biomarkers, prediction of treatment responses, and ultimately facilitating the development of personalized cancer therapies. Conclusions. The integration of biobanking with AI has the potential not only to expand the current understanding of cancer biology but also to pave the way for more precise, patient-centric healthcare strategies. Full article

Infertility, although not a life-threatening condition, affects around 15% of couples trying for a pregnancy. The increasing availability of large datasets from various sources, together with advances in machine learning (ML) and artificial intelligence (AI), are enabling a transformational change in infertility care. However, real-world applications of data-driven medicine in infertility care are still relatively limited. At present, very little can prevent infertility from arising; more work is required to learn about ways to improve natural conception and the detection and diagnosis of infertility, improve assisted reproduction treatments (ART) and ultimately develop useful clinical-decision support systems to assure the successful outcome of either fertility preservation or infertility treatment. In this opinion article, we discuss recent influential work on the application of big data and AI in the prevention, diagnosis and treatment of infertility. We evaluate the challenges of the sector and present an interpretation of the different innovation forces that are driving the emergence of a systems approach to infertility care. Efforts including the integration of multi-omics information, collection of well-curated biological samples in specialised biobanks, and stimulation of the active participation of patients are considered. In the era of Big Data and AI, there is now an exciting opportunity to leverage the progress in genomics and digital technologies and develop more sophisticated approaches to diagnose and treat infertility disorders. Full article

Archival formalin-fixed, paraffin-embedded (FFPE) tissues and their related diagnostic records are an invaluable source of biological information. The archival samples can be used for retrospective investigation of molecular fingerprints and biomarkers of diseases and susceptibility. Radiobiological archives were set up not only following clinical performance such as cancer diagnosis and therapy but also after accidental and occupational radiation exposure events where autopsies or cancer biopsies were sampled. These biobanks provide unique and often irreplaceable materials for the understanding of molecular mechanisms underlying radiation-related biological effects. In recent years, the application of rapidly evolving “omics” platforms, including transcriptomics, genomics, proteomics, metabolomics and sequencing, to FFPE tissues has gained increasing interest as an alternative to fresh/frozen tissue. However, omics profiling of FFPE samples remains a challenge mainly due to the condition and duration of tissue fixation and storage, and the extraction methods of biomolecules. Although biobanking has a long history in radiation research, the application of omics to profile FFPE samples available in radiobiological archives is still young. Application of the advanced omics technologies on archival materials provides a new opportunity to understand and quantify the biological effects of radiation exposure. These newly generated omics data can be well integrated into results obtained from earlier experimental and epidemiological analyses to shape a powerful strategy for modelling and evaluating radiation effects on health outcomes. This review aims to give an overview of the unique properties of radiation biobanks and their potential impact on radiation biology studies. Studies recently performed on FFPE samples from radiobiology archives using advanced omics are summarized. Furthermore, the compatibility of archived FFPE tissues for omics analysis and the major challenges that lie ahead are discussed. Full article

Organoids hold great promises for numerous applications in biomedicine and biotechnology. Despite its potential in science, organoid technology poses complex ethical challenges that may hinder any future benefits for patients and society. This study aims to analyze the multifaceted ethical issues raised by organoids and recommend measures that must be taken at various levels to ensure the ethical use and application of this technology. Organoid technology raises several serious ethics issues related to the source of stem cells for organoid creation, informed consent and privacy of cell donors, the moral and legal status of organoids, the potential acquisition of human “characteristics or qualities”, use of gene editing, creation of chimeras, organoid transplantation, commercialization and patentability, issues of equity in the resulting treatments, potential misuse and dual use issues and long-term storage in biobanks. Existing guidelines and regulatory frameworks that are applicable to organoids are also discussed. It is concluded that despite the serious ethical challenges posed by organoid use and biobanking, we have a moral obligation to support organoid research and ensure that we do not lose any of the potential benefits that organoids offer. In this direction, a four-step approach is recommended, which includes existing regulations and guidelines, special regulatory provisions that may be needed, public engagement and continuous monitoring of the rapid advancements in the field. This approach may help maximize the biomedical and social benefits of organoid technology and contribute to future governance models in organoid technology. Full article

The advancement of the pediatric traumatic brain injury (TBI) knowledge base requires biospecimens and data from large samples. This study seeks to describe a novel clinical research modality to establish best practices for enrolling a diverse pediatric TBI population and quantifying key information on enrollment into biobanks. Screening form responses were standardized and cleaned through Google Sheets. Data were used to analyze total individuals at each enrollment stage. R was utilized for final analysis, including logistic model and proportion statistical tests, to determine further significance and relationships. Issues throughout data cleaning shed light on limitations of the consent modality. The results suggest that through a diverse research team, the recruited sample exceeds traditional measures of representation (e.g., sex, race, ethnicity). Sex demographics of the study are representative of the local population. Screening for candidates is critical to the success of the consent modality. The consent modality may be modified to increase the diversity of the study population and accept bilingual candidates. Researchers must implement best practices, including increasing inclusivity of bilingual populations, utilizing technology, and improving participant follow-up, to improve health disparities for understudied clinical populations. Full article

Partners HealthCare Personalized Medicine (PPM) is a center within the Partners HealthCare system (founded by Massachusetts General Hospital and Brigham and Women’s Hospital) whose mission is to utilize genetics and genomics to improve the care of patients in a cost effective manner. PPM consists of five interconnected components: (1) Laboratory for Molecular Medicine (LMM), a CLIA laboratory performing genetic testing for patients world-wide; (2) Translational Genomics Core (TGC), a core laboratory providing genomic platforms for Partners investigators; (3) Partners Biobank, a biobank of samples (DNA, plasma and serum) for 50,000 Consented Partners patients; (4) Biobank Portal, an IT infrastructure and viewer to bring together genotypes, samples, phenotypes (validated diagnoses, radiology, and clinical chemistry) from the electronic medical record to Partners investigators. These components are united by (5) a common IT system that brings researchers, clinicians, and patients together for optimal research and patient care. Full article

The Translational Genomics Core (TGC) at Partners Personalized Medicine (PPM) serves as a fee-for-service core laboratory for Partners Healthcare researchers, providing access to technology platforms and analysis pipelines for genomic, transcriptomic, and epigenomic research projects. The interaction of the TGC with various components of PPM provides it with a unique infrastructure that allows for greater IT and bioinformatics opportunities, such as sample tracking and data analysis. The following article describes some of the unique opportunities available to an academic research core operating within PPM, such the ability to develop analysis pipelines with a dedicated bioinformatics team and maintain a flexible Laboratory Information Management System (LIMS) with the support of an internal IT team, as well as the operational challenges encountered to respond to emerging technologies, diverse investigator needs, and high staff turnover. In addition, the implementation and operational role of the TGC in the Partners Biobank genotyping project of over 25,000 samples is presented as an example of core activities working with other components of PPM. Full article

We have designed a Biobank Portal that lets researchers request Biobank samples and genotypic data, query associated electronic health records, and design and download datasets containing de-identified attributes about consented Biobank subjects. This do-it-yourself functionality puts a wide variety and volume of data at the fingertips of investigators, allowing them to create custom datasets for their clinical and genomic research from complex phenotypic data and quickly obtain corresponding samples and genomic data. The Biobank Portal is built upon the i2b2 infrastructure [1] and uses an open-source web client that is available to faculty members and other investigators behind an institutional firewall. Built-in privacy measures [2] ensure that the data in the Portal are utilized only according to the processes to which the patients have given consent. Full article

The Biobank and Translational Genomics core at Partners Personalized Medicine requires robust software and hardware. This Information Technology (IT) infrastructure enables the storage and transfer of large amounts of data, drives efficiencies in the laboratory, maintains data integrity from the time of consent to the time that genomic data is distributed for research, and enables the management of complex genetic data. Here, we describe the functional components of the research IT infrastructure at Partners Personalized Medicine and how they integrate with existing clinical and research systems, review some of the ways in which this IT infrastructure maintains data integrity and security, and discuss some of the challenges inherent to building and maintaining such infrastructure. Full article