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Allergologie Select 2023Although used for over 100 years, allergen immunotherapy (AIT) is still an indispensable tool in modern allergy managemen20t due to its potential to cure allergic... (Review)
Review
Although used for over 100 years, allergen immunotherapy (AIT) is still an indispensable tool in modern allergy managemen20t due to its potential to cure allergic diseases. Its current rapid development through the application of personalized and precision medicine approaches is strongly supported by advances in mHealth, component-resolved diagnosis (CRD)-based diagnostics, validation of novel biomarkers, advanced data management, and development of novel preparations. This review summarizes the key advances in the field and shows the perspectives for further development of next-generation AIT treatments.
PubMed: 38143940
DOI: 10.5414/ALX02379E -
Military Psychology : the Official... 2023This paper covers considerations in using criterion measures based on administrative data. We begin with a conceptual framework for understanding and evaluating...
This paper covers considerations in using criterion measures based on administrative data. We begin with a conceptual framework for understanding and evaluating administrative criterion measures as "objective" rather than (ratings-based) assessments of job performance. We then describe the associated advantages (e.g., availability) and disadvantages (e.g., contamination) of using administrative data for criterion-related validation purposes. Best practices in the use of administrative data for validation purposes, including procedures for (a) handling missing data, (b) performing data checks, and (c) reporting detailed decision rules so future researchers can replicate the analyses are also described. Finally, we discuss "modern data management" approaches for improving administrative data for supporting organizational decision-making.
PubMed: 37352447
DOI: 10.1080/08995605.2022.2063614 -
Journal of Personalized Medicine Sep 2023Biobanks are vital research infrastructures aiming to collect, process, store, and distribute biological specimens along with associated data in an organized and... (Review)
Review
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.
PubMed: 37763157
DOI: 10.3390/jpm13091390 -
Genomics, Proteomics & Bioinformatics Oct 2023With the development of artificial intelligence (AI) technologies, biomedical imaging data play an important role in scientific research and clinical application, but...
With the development of artificial intelligence (AI) technologies, biomedical imaging data play an important role in scientific research and clinical application, but the available resources are limited. Here we present Open Biomedical Imaging Archive (OBIA), a repository for archiving biomedical imaging and related clinical data. OBIA adopts five data objects (Collection, Individual, Study, Series, and Image) for data organization, and accepts the submission of biomedical images of multiple modalities, organs, and diseases. In order to protect personal privacy, OBIA has formulated a unified de-identification and quality control process. In addition, OBIA provides friendly and intuitive web interfaces for data submission, browsing, and retrieval, as well as image retrieval. As of September 2023, OBIA has housed data for a total of 937 individuals, 4136 studies, 24,701 series, and 1,938,309 images covering 9 modalities and 30 anatomical sites. Collectively, OBIA provides a reliable platform for biomedical imaging data management and offers free open access to all publicly available data to support research activities throughout the world. OBIA can be accessed at https://ngdc.cncb.ac.cn/obia.
Topics: Humans; Artificial Intelligence
PubMed: 37806555
DOI: 10.1016/j.gpb.2023.09.003 -
Endocrine Oncology (Bristol, England) Jan 2024The field of nuclear theranostic clinical trials is continuously expanding as an increasing number of novel agents and treatment combinations are explored for treating... (Review)
Review
The field of nuclear theranostic clinical trials is continuously expanding as an increasing number of novel agents and treatment combinations are explored for treating advanced and metastatic cancers. Moving from 'bench-to-bedside' is oftentimes a complex and lengthy process. The objective of this overview is to explore the basic elements involved in designing clinical trials with a special focus on theranostics in nuclear medicine. The 'bench-to-bedside' journey involves translating basic scientific research into patient-effective treatments. Preclinical studies, a crucial initial step, are a complex process encompassing experiments, studies, and animal models to explore hypotheses in humans. Clinical trials follow, with predefined phases assessing safety, effectiveness, and comparisons to existing treatments. This process demands investments in data management, statistics, good clinical practice (GCP) accreditations, and collaborative efforts for funding and sustainable pricing. Theranostics, merging diagnostics and personalized treatment, is at the forefront. Continuous efforts to enhance existing agents involve reducing adverse effects, exploring new indications, and incorporating advanced imaging modalities. Radionuclide therapy, unique with non-uniform distribution and complex radiobiology, plays a distinct role. This article explores trends and challenges in each clinical trial phase in light of the emerging field of theranostics in nuclear medicine, emphasizing meticulous trial design, dosimetry optimization, and the necessity of collaborative stakeholder efforts for successful implementation.
PubMed: 38770190
DOI: 10.1530/EO-23-0045 -
JAMIA Open Dec 2023Using agile software development practices, develop and evaluate an architecture and implementation for reliable and user-friendly self-service management of...
OBJECTIVES
Using agile software development practices, develop and evaluate an architecture and implementation for reliable and user-friendly self-service management of bioinformatic data stored in the cloud.
MATERIALS AND METHODS
Comprehensive Oncology Research Environment (CORE) Browser is a new open-source web application for cancer researchers to manage sequencing data organized in a flexible format in Amazon Simple Storage Service (S3) buckets. It has a microservices- and hypermedia-based architecture, which we integrated with Test-Driven Development (TDD), the iterative writing of computable specifications for how software should work prior to development. Relying on repeating patterns found in hypermedia-based architectures, we hypothesized that hypermedia would permit developing test "templates" that can be parameterized and executed for each microservice, maximizing code coverage while minimizing effort.
RESULTS
After one-and-a-half years of development, the CORE Browser backend had 121 test templates and 875 custom tests that were parameterized and executed 3031 times, providing 78% code coverage.
DISCUSSION
Architecting to permit test reuse through a hypermedia approach was a key success factor for our testing efforts. CORE Browser's application of hypermedia and TDD illustrates one way to integrate software engineering methods into data-intensive networked applications. Separating bioinformatic data management from analysis distinguishes this platform from others in bioinformatics and may provide stable data management while permitting analysis methods to advance more rapidly.
CONCLUSION
Software engineering practices are underutilized in informatics. Similar informatics projects will more likely succeed through application of good architecture and automated testing. Our approach is broadly applicable to data management tools involving cloud data storage.
PubMed: 37860604
DOI: 10.1093/jamiaopen/ooad089 -
JAMA Network Open Jul 2023
Topics: Humans; Data Management; International Classification of Diseases
PubMed: 37498604
DOI: 10.1001/jamanetworkopen.2023.27991 -
Journal of Pathology Informatics 2023The Pathology Informatics Bootcamp, held annually at the Pathology Informatics Summit, provides pathology trainees with essential knowledge in the rapidly evolving field... (Review)
Review
The Pathology Informatics Bootcamp, held annually at the Pathology Informatics Summit, provides pathology trainees with essential knowledge in the rapidly evolving field of Pathology Informatics. With a focus on data analytics, data science, and data management in 2022, the bootcamp addressed the growing importance of data analysis in pathology and laboratory medicine practice. The expansion of data-related subjects in Pathology Informatics Essentials for Residents (PIER) and the Clinical Informatics fellowship examinations highlights the increasing significance of these skills in pathology practice in particular and medicine in general. The curriculum included lectures on databases, programming, analytics, machine learning basics, and specialized topics like anatomic pathology data analysis and dashboarding.
PubMed: 37705688
DOI: 10.1016/j.jpi.2023.100331 -
Histochemistry and Cell Biology Sep 2023Bioimaging has now entered the era of big data with faster-than-ever development of complex microscopy technologies leading to increasingly complex datasets. This... (Review)
Review
Bioimaging has now entered the era of big data with faster-than-ever development of complex microscopy technologies leading to increasingly complex datasets. This enormous increase in data size and informational complexity within those datasets has brought with it several difficulties in terms of common and harmonized data handling, analysis, and management practices, which are currently hampering the full potential of image data being realized. Here, we outline a wide range of efforts and solutions currently being developed by the microscopy community to address these challenges on the path towards FAIR bioimaging data. We also highlight how different actors in the microscopy ecosystem are working together, creating synergies that develop new approaches, and how research infrastructures, such as Euro-BioImaging, are fostering these interactions to shape the field.
Topics: Ecosystem; Microscopy
PubMed: 37341795
DOI: 10.1007/s00418-023-02203-7 -
Cureus Aug 2023The integration of artificial intelligence (AI) into healthcare promises groundbreaking advancements in patient care, revolutionizing clinical diagnosis, predictive... (Review)
Review
The integration of artificial intelligence (AI) into healthcare promises groundbreaking advancements in patient care, revolutionizing clinical diagnosis, predictive medicine, and decision-making. This transformative technology uses machine learning, natural language processing, and large language models (LLMs) to process and reason like human intelligence. OpenAI's ChatGPT, a sophisticated LLM, holds immense potential in medical practice, research, and education. However, as AI in healthcare gains momentum, it brings forth profound ethical challenges that demand careful consideration. This comprehensive review explores key ethical concerns in the domain, including privacy, transparency, trust, responsibility, bias, and data quality. Protecting patient privacy in data-driven healthcare is crucial, with potential implications for psychological well-being and data sharing. Strategies like homomorphic encryption (HE) and secure multiparty computation (SMPC) are vital to preserving confidentiality. Transparency and trustworthiness of AI systems are essential, particularly in high-risk decision-making scenarios. Explainable AI (XAI) emerges as a critical aspect, ensuring a clear understanding of AI-generated predictions. Cybersecurity becomes a pressing concern as AI's complexity creates vulnerabilities for potential breaches. Determining responsibility in AI-driven outcomes raises important questions, with debates on AI's moral agency and human accountability. Shifting from data ownership to data stewardship enables responsible data management in compliance with regulations. Addressing bias in healthcare data is crucial to avoid AI-driven inequities. Biases present in data collection and algorithm development can perpetuate healthcare disparities. A public-health approach is advocated to address inequalities and promote diversity in AI research and the workforce. Maintaining data quality is imperative in AI applications, with convolutional neural networks showing promise in multi-input/mixed data models, offering a comprehensive patient perspective. In this ever-evolving landscape, it is imperative to adopt a multidimensional approach involving policymakers, developers, healthcare practitioners, and patients to mitigate ethical concerns. By understanding and addressing these challenges, we can harness the full potential of AI in healthcare while ensuring ethical and equitable outcomes.
PubMed: 37692617
DOI: 10.7759/cureus.43262