Building digital twins of the human immune system: toward a roadmap

Digital twins, customized simulation models pioneered in industry, are beginning to be deployed in medicine and healthcare, with some major successes, for instance in cardiovascular diagnostics and in insulin pump control. Personalized computational models are also assisting in applications ranging from drug development to treatment optimization. More advanced medical digital twins will be essential to making precision medicine a reality. Because the immune system plays an important role in such a wide range of diseases and health conditions, from fighting pathogens to autoimmune disorders, digital twins of the immune system will have an especially high impact. However, their development presents major challenges, stemming from the inherent complexity of the immune system and the difficulty of measuring many aspects of a patient’s immune state in vivo. This perspective outlines a roadmap for meeting these challenges and building a prototype of an immune digital twin. It is structured as a four-stage process that proceeds from a specification of a concrete use case to model constructions, personalization, and continued improvement

Change Point Estimation in Monitoring Survival Time

Precise identification of the time when a change in a hospital outcome has occurred enables clinical experts to search for a potential special cause more effectively. In this paper, we develop change point estimation methods for survival time of a clinical procedure in the presence of patient mix in a Bayesian framework. We apply Bayesian hierarchical models to formulate the change point where there exists a step change in the mean survival time of patients who underwent cardiac surgery. The data are right censored since the monitoring is conducted over a limited follow-up period. We capture the effect of risk factors prior to the surgery using a Weibull accelerated failure time regression model. Markov Chain Monte Carlo is used to obtain posterior distributions of the change point parameters including location and magnitude of changes and also corresponding probabilistic intervals and inferences. The performance of the Bayesian estimator is investigated through simulations and the result shows that precise estimates can be obtained when they are used in conjunction with the risk-adjusted survival time CUSUM control charts for different magnitude scenarios. The proposed estimator shows a better performance where a longer follow-up period, censoring time, is applied. In comparison with the alternative built-in CUSUM estimator, more accurate and precise estimates are obtained by the Bayesian estimator. These superiorities are enhanced when probability quantification, flexibility and generalizability of the Bayesian change point detection model are also considered

Economic evaluation of three populational screening strategies for cervical cancer in the county of Valles Occidental: CRICERVA clinical trial

Copyright @ 2011 Acera et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.A high percentage of cervical cancer cases have not undergone cytological tests within 10 years prior to diagnosis. Different population interventions could improve coverage in the public system, although costs will also increase. The aim of this study was to compare the effectiveness and the costs of three types of population interventions to increase the number of female participants in the screening programmes for cancer of the cervix carried out by Primary Care in four basic health care areas.Fondo de Investigación Sanitaria del Instituto Carlos III de Madri

Uncovering the knowledge flows and intellectual structures of research in Technological Forecasting and Social Change: A journey through history

© 2020 Technological Forecasting and Social Change (TF&SC) celebrates its fiftieth anniversary this year. The anniversary represents an appropriate time for an introspective analysis of the journal's history and impact. This study presents a bibliometric analysis of TF&SC in terms of how often TF&SC is cited by other journals (citation outflow), how often other journals are cited by TF&SC (citation inflow), citations by Web of Science and SCImago disciplinary categories, most-cited articles in TF&SC, co-citation of journals, and co-occurrence of author keywords. Analysis is conducted by using the Web of Science (WOS) database and Visualization of Similarities (VOS) viewer software. The incoming versus outgoing citation patterns identified here suggest an asymmetry in the knowledge flows of TF&SC. Papers published in TF&SC have increasingly cited knowledge from journals in Technology and Innovation Management (TIM), Engineering, and Decision Sciences, but the journal impacts a different set of disciplinary categories such as Energy, Environmental Sciences, and Social Sciences. From 1969–2018, Innovation, Foresight and Forecasting feature as the most popular keywords. Focus on topics such as Patents/Patent Analysis, Climate Change, Sustainability, and Energy seems to have intensified in the last decade. Findings suggest that focus on two countries of interest, India and China, is emerging in research published in TF&SC. Different regions of the world can be expected to place differential emphasis on various topics based on their socioeconomic-technological environments. The journal needs to be receptive to this diversity of perspectives from a growing community of scholars worldwide

Co-citation, bibliographic coupling and leading authors, institutions and countries in the 50 years of Technological Forecasting and Social Change

Technological Forecasting and Social Change (TF&SC) is a leading international journal that publishes major advances related to technological forecasting and future studies. The journal was launched in 1969 and in 2019 celebrated its 50th anniversary. To celebrate 50 years of outstanding contributions, this study presents a bibliometric analysis of TF&SC publications and patterns of citations within TF&SC in terms of authors, institutions and countries. The analysis relies on the Web of Science Core Collection database for bibliographic content and Visualization of Similarities viewer software for mapping of bibliometric data. Our analysis identifies leading authors, universities and countries that produce publications in TF&SC. This study also applies bibliometric analysis of co-citations and bibliographic coupling. Results suggest that authors and publications originating in the USA and the Netherlands are particularly influential. However, the journal is becoming more geographically diverse. Mapping of co-citations and bibliographic coupling suggests that work published in TF&SC is represented by several heterogeneous clusters

Glossary of Permafrost and Related Ground-Ice Terms

Peer reviewed: NoNRC publication: Ye