Minimum Information Models Research Articles

Tolerogenic dendritic cell reporting: Has a minimum information model made a difference?

Minimum information models are reporting frameworks that describe the essential information that needs to be provided in a publication, so that the work can be repeated or compared to other work. In 2016, Minimum Information about Tolerogenic Antigen-Presenting cells (MITAP) was created to standardize the reporting on tolerogenic antigen-presenting cells, including tolerogenic dendritic cells (tolDCs). tolDCs is a generic term for dendritic cells that have the ability to (re-)establish immune tolerance; they have been developed as a cell therapy for autoimmune diseases or for the prevention of transplant rejection. Because protocols to generate these therapeutic cells vary widely, MITAP was deemed to be a pivotal reporting tool by and for the tolDC community. In this paper, we explored the impact that MITAP has had on the tolDC field. We did this by examining a subset of the available literature on tolDCs. Our analysis shows that MITAP is used in only the minority of relevant papers (14%), but where it is used the amount of metadata available is slightly increased over where it is not. From this, we conclude that MITAP has been a partial success, but that much more needs to be done if standardized reporting is to become common within the discipline.

Reliability of Drug-Drug Interaction Measurement on Real-Word Data: The ReMIAMes Project.

The ReMIAMes project proposes a methodological framework to provide a reliable and reproducible measurement of the frequency of drug-drug interactions (DDI) when performed on real-world data. This framework relies on (i) a fine-grained and contextualized definition of DDIs, (ii) a shared minimum information model to select the appropriate data for the correct interpretation of potential DDIs, (iii) an ontology-based inference module able to handle missing data to classify prescription lines with potential DDIs, (iv) a report generator giving the value of the measurement and explanations when potential false positive are detected due to a lack of available data. All the tools developed are intended to be publicly shared under open license.

Nursing’s Role in Translating Safe Communication Practices to Clinical Trial Management

There is great emphasis on safety-related communication best practices in healthcare. However, little is known about safety-related communication within clinical trial management. Clinical trial participants are at greater risk for adverse events, injury, and trial withdrawal when seeking care outside of the trial when external providers are not aware of protocol constraints with care management. Nurses are traditionally the first providers to gather or denote importance on pertinent information when a participant seeks care. The World Health Organization (WHO) 2014 Minimum Information Model for Patient Safety and the SACCIA Safe Communication framework support identification of communication errors that place participants at risk. Implementation of these frameworks by providers can promote accuracy, clarity, context, and actionable decision-making that is in alignment to protocol mandated requirements for care. This paper reviews participant-nurse communication and provides recommendations for effective communication with clinical trial participants who seek care external to the trial.

Minimum information for dielectric measurements of biological tissues (MINDER): A framework for repeatable and reusable data

The dielectric properties of biological tissues characterise the interaction of human tissues with electromagnetic (EM) fields. Accurate knowledge of the dielectric properties of tissues are vital in EM-based therapeutic and diagnostic techniques, and for assessing the safety of wireless devices. Despite the importance of these properties, the field has suffered from inconsistencies in reported data. The dielectric measurement process for tissues is known to be affected by both measurement confounders and clinical confounders; however, adequate metadata is often lacking in the literature. For this reason, this work proposes a standard, called Minimum Information for Dielectric Measurements of Biological Tissues (MINDER). In the MINDER model, the minimum types of raw data and metadata needed to interpret or replicate a dielectric study are identified and described. Alongside the minimum information model, a controlled vocabulary for metadata parameters is proposed. We also provide an example of this model applied to a dielectric measurement scenario on a biological tissue sample. The MINDER model enables reproducibility of measurements, ease of interpreting and re-using data, and comparison of data across studies. Further, this standard framework will support dielectric databases, with data searchable through metadata parameters such as temperature, frequency range, tissue type, and tissue state.

Minimum information about tolerogenic antigen-presenting cells (MITAP): a first step towards reproducibility and standardisation of cellular therapies

Cellular therapies with tolerogenic antigen-presenting cells (tolAPC) show great promise for the treatment of autoimmune diseases and for the prevention of destructive immune responses after transplantation. The methodologies for generating tolAPC vary greatly between different laboratories, making it difficult to compare data from different studies; thus constituting a major hurdle for the development of standardised tolAPC therapeutic products. Here we describe an initiative by members of the tolAPC field to generate a minimum information model for tolAPC (MITAP), providing a reporting framework that will make differences and similarities between tolAPC products transparent. In this way, MITAP constitutes a first but important step towards the production of standardised and reproducible tolAPC for clinical application.

Imputing trip purposes for long-distance travel

Planning and policy analysis at the national, state and inter-regional corridor levels depends on reliable information and forecasts about long-distance travel. Emerging passive data collection technologies such as GPS, smartphones, and social media provide the opportunity for researchers and practitioners to potentially supplement or replace traditional long-distance travel surveys. However, certain important trip information, such as trip purpose, travel mode, and travelers’ socio-demographic characteristics, is missing from passively collected travel data. One promising solution to this data issue is to impute the missing information based on supplementary data (e.g., land use) and advanced statistical or data mining algorithms. This paper develops machine learning methods, including decision tree and meta-learning, to estimate trip purposes for long-distance passenger travel. A passively collected long-distance trip dataset is simulated from the 1995 American Travel Survey for the development and validation of the machine learning methods. The predictive accuracy of the proposed methods is evaluated for several scenarios varying with trip purposes and the extent of data availability as inputs. This research design will provide not only a practically useful approach for long-distance trip purpose imputation, but also generate valuable insights for future long-distance travel surveys. Results show that the accuracy of the trip purpose imputation methods based on all available data decreases from 95 % with two purposes (business and non-business) to 77 % with four purposes (business, personal business, social visit, and leisure). Based on a two-purpose scheme, the predictive accuracy of the imputation algorithms decreases from 95 % when all input data is used (a full-information model), to 72 % with a minimum information model that only utilizes the passively collected data. If traveler’s socio-demographic characteristics are available (possibly through other imputation models), the predictive accuracy only decreases from 95 to 91 %.

Clinical Use of Tolerogenic Dendritic Cells-Harmonization Approach in European Collaborative Effort.

The number of patients with autoimmune diseases and severe allergies and recipients of transplants increases worldwide. Currently, these patients require lifelong administration of immunomodulatory drugs. Often, these drugs are expensive and show immediate or late-occurring severe side effects. Treatment would be greatly improved by targeting the cause of autoimmunity, that is, loss of tolerance to self-antigens. Accumulating knowledge on immune mechanisms has led to the development of tolerogenic dendritic cells (tolDC), with the specific objective to restrain unwanted immune reactions in the long term. The first clinical trials with tolDC have recently been conducted and more tolDC trials are underway. Although the safety trials have been encouraging, many questions relating to tolDC, for example, cell-manufacturing protocols, administration route, amount and frequency, or mechanism of action, remain to be answered. Aiming to join efforts in translating tolDC and other tolerogenic cellular products (e.g., Tregs and macrophages) to the clinic, a European COST (European Cooperation in Science and Technology) network has been initiated—A FACTT (action to focus and accelerate cell-based tolerance-inducing therapies). A FACTT aims to minimize overlap and maximize comparison of tolDC approaches through establishment of minimum information models and consensus monitoring parameters, ensuring that progress will be in an efficient, safe, and cost-effective way.

An information theoretic approach to the estimation of migration flows.

This study is concerned with the application of different types of models to the study of migration flows. Two traditional approaches to migration modeling, the demographic fixed-rate and the gravity/entropy families of models, are first described, and their adequacy is assessed. The author next discusses the derivation of minimum information models. Consideration is given to destination-weighted models and to net-constrained models. A unified approach that incorporates both a priori micro and estimated macro parameters is then considered. The author concludes that the extended minimum information approach is the most suitable for building dynamic spatial interaction models, given the current state of U.S. internal migration data.

Estimation of place-to-place migration flows from net migration totals: a minimum information approach.

"A truism in demography has been that net migration may be derived from information on gross place-to-place flows, but that gross place-to-place flows cannot be inferred back from information on the net population movements in a system. Some recent work on maximum entropy and minimum information models, however, suggests a possible means for estimating just such as set of place-to-place flows. The net migration constrained model presented here could prove particularly useful for updating detailed migration matrices on the basis of current net migration estimates, and could even provide some clues as to the nature of the still poorly understood relationship between gross and net migration. Performance of the model is demonstrated using flow matrices from the 1960 and 1970 U.S. Censuses.