Current Overview of Metal Nanoparticles' Synthesis, Characterization, and Biomedical Applications, with a Focus on Silver and Gold Nanoparticles.

Metal nanoparticles (NPs) have garnered considerable attention, due to their unique physicochemical properties, that render them promising candidates for various applications in medicine and industry. This article offers a comprehensive overview of the most recent advancements in the manufacturing, characterization, and biomedical utilization of metal NPs, with a primary focus on silver and gold NPs. Their potential as effective anticancer, anti-inflammatory, and antimicrobial agents, drug delivery systems, and imaging agents in the diagnosis and treatment of a variety of disorders is reviewed.

Neural gradient boosting in federated learning for hemodynamic instability prediction: towards a distributed and scalable deep learning-based solution.

Federated learning (FL) is a privacy preserving approach to learning that overcome issues related to data access, privacy, and security, which represent key challenges in the healthcare sector. FL enables hospitals to collaboratively learn a shared prediction model without moving the data outside their secure infrastructure. To do so, after having sent model updates to a central server, an update aggregation is performed, and the model is sent back to the sites for further training.

Privacy Enhancing and Scalable Federated Learning to Accelerate AI Implementation in Cross-Silo and IoMT Environments.

Federated Learning (FL) is a machine learning technique that enables to collaboratively learn valuable information across devices or sites without moving the data. In FL, the model is trained and shared across decentralized locations where data are privately owned. After local training, model updates are sent back to a central server, thus enabling access to distributed data on a large scale while maintaining privacy, security, and data access rights.

Patient empowerment for cancer patients through a novel ICT infrastructure.

As a result of recent advances in cancer research and "precision medicine" approaches, i.e. the idea of treating each patient with the right drug at the right time, more and more cancer patients are being cured, or might have to cope with a life with cancer.

iManageMyHealth and iSupportMyPatients: mobile decision support and health management apps for cancer patients and their doctors.

Clinical decision support systems can play a crucial role in healthcare delivery as they promise to improve health outcomes and patient safety, reduce medical errors and costs and contribute to patient satisfaction. Used in an optimal way, they increase the quality of healthcare by proposing the right information and intervention to the right person at the right time in the healthcare delivery process. This paper reports on a specific approach to integrated clinical decision support and patient guidance in the cancer domain as proposed by the H2020 iManageCancer project.

Evaluating the benefits of digital pathology implementation: time savings in laboratory logistics.

Background: The benefits of digital pathology for workflow improvement and thereby cost savings in pathology, at least partly outweighing investment costs, are being increasingly recognised. Successful implementations in a variety of scenarios have started to demonstrate the cost benefits of digital pathology for both research and routine diagnosis, contributing to a sound business case encouraging further adoption. To further support new adopters, there is still a need for detailed assessment of the impact that this technology has on the relevant pathology workflows, with an emphasis on time-saving.

Cohort Selection and Management Application Leveraging Standards-based Semantic Interoperability and a Groovy DSL.

This paper describes a new Cohort Selection application implemented to support streamlining the definition phase of multi-centric clinical research in oncology. Our approach aims at both ease of use and precision in defining the selection filters expressing the characteristics of the desired population. The application leverages our standards-based Semantic Interoperability Solution and a Groovy DSL to provide high expressiveness in the definition of filters and flexibility in their composition into complex selection graphs including splits and merges.

Workflow-driven clinical decision support for personalized oncology.

Background: The adoption in oncology of Clinical Decision Support (CDS) may help clinical users to efficiently deal with the high complexity of the domain, lead to improved patient outcomes, and reduce the current knowledge gap between clinical research and practice. While significant effort has been invested in the implementation of CDS, the uptake in the clinic has been limited. The barriers to adoption have been extensively discussed in the literature.

The INTEGRATE project: Delivering solutions for efficient multi-centric clinical research and trials.

The objective of the INTEGRATE project (http://www.fp7-integrate.eu/) that has recently concluded successfully was the development of innovative biomedical applications focused on streamlining the execution of clinical research, on enabling multidisciplinary collaboration, on management and large-scale sharing of multi-level heterogeneous datasets, and on the development of new methodologies and of predictive multi-scale models in cancer.

Case Study for Integration of an Oncology Clinical Site in a Semantic Interoperability Solution based on HL7 v3 and SNOMED-CT: Data Transformation Needs.

This paper describes the data transformation pipeline defined to support the integration of a new clinical site in a standards-based semantic interoperability environment. The available datasets combined structured and free-text patient data in Dutch, collected in the context of radiation therapy in several cancer types. Our approach aims at both efficiency and data quality.

Enhancing reuse of structured eligibility criteria and supporting their relaxation.

Patient recruitment is one of the most important barriers to successful completion of clinical trials and thus to obtaining evidence about new methods for prevention, diagnostics and treatment. The reason is that recruitment is effort consuming. It requires the identification of candidate patients for the trial (the population under study), and verifying for each patient whether the eligibility criteria are met.

Enabling semantic interoperability in multi-centric clinical trials on breast cancer.

Background And Objectives: Post-genomic clinical trials require the participation of multiple institutions, and collecting data from several hospitals, laboratories and research facilities. This paper presents a standard-based solution to provide a uniform access endpoint to patient data involved in current clinical research.

Methods: The proposed approach exploits well-established standards such as HL7 v3 or SPARQL and medical vocabularies such as SNOMED CT, LOINC and HGNC.

Computational Horizons In Cancer (CHIC): Developing Meta- and Hyper-Multiscale Models and Repositories for Oncology - a Brief Technical Outline of the Project.

This paper briefly outlines the aim, the objectives, the architecture and the main building blocks of the ongoing large scale integrating transatlantic research project CHIC (http://chic-vph.eu/).

Supporting patient screening to identify suitable clinical trials.

To support the efficient execution of post-genomic multi-centric clinical trials in breast cancer we propose a solution that streamlines the assessment of the eligibility of patients for available trials. The assessment of the eligibility of a patient for a trial requires evaluating whether each eligibility criterion is satisfied and is often a time consuming and manual task. The main focus in the literature has been on proposing different methods for modelling and formalizing the eligibility criteria.

Improving the implementation of clinical decision support systems.

Clinical decision support (CDS) systems promise to improve the quality of clinical care by helping physicians to make better, more informed decisions efficiently. However, the design and testing of CDS systems for practical medical use is cumbersome. It has been recognized that this may easily lead to a problematic mismatch between the developers' idea of the system and requirements from clinical practice.

Analyzing SNOMED CT and HL7 terminology binding for semantic interoperability on post-genomic clinical trials.

Current post-genomic clinical trials in cancer involve the collaboration of several institutions. Multi-centric retrospective analysis requires advanced methods to ensure semantic interoperability. In this scenario, the objective of the EU funded INTEGRATE project, is to provide an infrastructure to share knowledge and data in post-genomic breast cancer clinical trials.

ACGT: advancing clinico-genomic trials on cancer - four years of experience.

The challenges regarding seamless integration of distributed, heterogeneous and multilevel data arising in the context of contemporary, post-genomic clinical trials cannot be effectively addressed with current methodologies. An urgent need exists to access data in a uniform manner, to share information among different clinical and research centers, and to store data in secure repositories assuring the privacy of patients. Advancing Clinico-Genomic Trials (ACGT) was a European Commission funded Integrated Project that aimed at providing tools and methods to enhance the efficiency of clinical trials in the -omics era.

Alignment method for spectrograms of DNA sequences.

DNA spectrograms express the periodicities of each of the four nucleotides A, T, C, and G in one or several genomic sequences to be analyzed. DNA spectral analysis can be applied to systematically investigate DNA patterns, which may correspond to relevant biological features. As opposed to looking at nucleotide sequences, spectrogram analysis may detect structural characteristics in very long sequences that are not identifiable by sequence alignment.

Service-oriented architecture for grid-enabling medical applications.

Grid technologies have the potential to enable healthcare organizations to efficiently use powerful tools, applications and resources, many of which were so far inaccessible to them. This paper introduces a service-oriented architecture meant to Grid-enable several classes of computationally intensive medical applications for improved performance and cost-effective access to resources. We apply this architecture to fiber tracking [1,2], a computationally intensive medical application suited for parallelization through decomposition, and carry out experiments with various sets of parameters, in realistic environments and with standard network solutions.

A grid architecture for medical applications.

Grid technology can provide medical organisations with powerful tools through which they can gain coordinated access to computational resources that hitherto where inaccessible to them. This paper discusses how several classes of medical applications could benefit from the use of Grid technology. We concentrate on applications that were put forward by partners in the Dutch VL-e project.