The 11th edition of Harrison's Principles of Internal Medicine welcomes Anthony Fauci to its editorial staff, in addition to more than 85 new contributors. While the organization of the book is similar to previous editions, major emphasis has been placed on disorders that affect multiple organ systems. Important advances in genetics, immunology, and oncology are emphasized. Many chapters of the book have been rewritten and describe major advances in internal medicine. Subjects that received only a paragraph or two of attention in previous editions are now covered in entire chapters. Among the chapters that have been extensively revised are the chapters on infections in the compromised host, on skin rashes in infections, on many of the viral infections, including cytomegalovirus and Epstein-Barr virus, on sexually transmitted diseases, on diabetes mellitus, on disorders of bone and mineral metabolism, and on lymphadenopathy and splenomegaly. The major revisions in these chapters and many

Harrison's Principles of Internal Medicine

What do a seventeenth-century mortality table (whose causes of death include "fainted in a bath," "frighted," and "itch"); the identification of South Africans during apartheid as European, Asian, colored, or black; and the separation of machine- from hand-washables have in common? All are examples of classification -- the scaffolding of information infrastructures. In Sorting Things Out, Geoffrey C. Bowker and Susan Leigh Star explore the role of categories and standards in shaping the modern world. In a clear and lively style, they investigate a variety of classification systems, including the International Classification of Diseases, the Nursing Interventions Classification, race classification under apartheid in South Africa, and the classification of viruses and of tuberculosis. The authors emphasize the role of invisibility in the process by which classification orders human interaction. They examine how categories are made and kept invisible, and how people can change this invisibility when necessary. They also explore systems of classification as part of the built information environment. Much as an urban historian would review highway permits and zoning decisions to tell a city's story, the authors review archives of classification design to understand how decisions have been made. Sorting Things Out has a moral agenda, for each standard and category valorizes some point of view and silences another. Standards and classifications produce advantage or suffering. Jobs are made and lost; some regions benefit at the expense of others. How these choices are made and how we think about that process are at the moral and political core of this work. The book is an important empirical source for understanding the building of information infrastructures.

https://www.reciis.icict.fiocruz.br/index.php/reciis/article/download/794/1436

Sorting Things Out: Classification and Its Consequences

The UMLS Metathesaurus, the largest thesaurus in the biomedical domain, provides a representation of biomedical knowledge consisting of concepts classified by semantic type and both hierarchical and non-hierarchical relationships among the concepts. This knowledge has proved useful for many applications including decision support systems, management of patient records, information retrieval (IR) and data mining. Gaining effective access to the knowledge is critical to the success of these applications. This paper describes MetaMap, a program developed at the National Library of Medicine (NLM) to map biomedical text to the Metathesaurus or, equivalently, to discover Metathesaurus concepts referred to in text. MetaMap uses a knowledge intensive approach based on symbolic, natural language processing (NLP) and computational linguistic techniques. Besides being applied for both IR and data mining applications, MetaMap is one of the foundations of NLM's Indexing Initiative System which is being applied to both semi-automatic and fully automatic indexing of the biomedical literature at the library.

Effective mapping of biomedical text to the UMLS Metathesaurus: the MetaMap program

This introduction presents the main motivations for the development of Description Logics (DLs) as a formalism for representing knowledge, as well as some important basic notions underlying all systems that have been created in the DL tradition. In addition, we provide the reader with an overview of the entire book and some guidelines for reading it. We first address the relationship between Description Logics and earlier semantic network and frame systems, which represent the original heritage of the field. We delve into some of the key problems encountered with the older efforts. Subsequently, we introduce the basic features of DL languages and related reasoning techniques. DL languages are then viewed as the core of knowledge representation systems, considering both the structure of a DL knowledge base and its associated reasoning services. The development of some implemented knowledge representation systems based on Description Logics and the first applications built with such systems are then reviewed. Finally, we address the relationship of Description Logics to other fields of Computer Science.We also discuss some extensions of the basic representation language machinery; these include features proposed for incorporation in the formalism that originally arose in implemented systems, and features proposed to cope with the needs of certain application domains.

https://assets.cambridge.org/97805218/76254/frontmatter/9780521876254_frontmatter.pdf

The Description Logic Handbook

https://www.acilci.net/wp-content/uploads/2014/02/Guidelines-for-reporting-reliability-and-agreement-studies-GRRAS-were-proposed.pdf

Guidelines for Reporting Reliability and Agreement Studies (GRRAS) were proposed.

Clinical data are the building blocks of clinical research. The ways in which data are captured and represented play important roles in how data can be used and interpreted. Care must be taken when designing studies that the data collection methods will be as free of bias as possible and that the true meaning of the data are made explicit. The subjective nature of data and the reproducibility of collection methods must be considered. Representational aspects include noting the degree of precision for quantitative data and the use of controlled terminologies for subjective data. The scope and scale of data storage systems also must be addressed. National collaborative efforts are under way to support the sharing of data and data analysis tools.

Clinical Research Data: Characteristics, Representation, Storage, and Retrieval

Objective. To provide guidelines for enhancing the patient accrual process in clinical trials using communitybased Web-based Information Technology (WebIT). Background and Significance. Previous ITadministered education studies have been conducted in controlled environments: the computers are usually 1) supplied to the patients1, 2) in an setting where a research facilitator can assist patients using the IT-based education2'3'4,5 or 3) are supplied by the patient but the software does not feedback measures of efficacy to the investigators6. In contrast, the MI-HEART Clinical Trial education is a randomized study that measures the changes in cognitive variables of community-based patients exposed to tailored or non-tailored self-administered Web-based and paper-based education7. The education is self-administered, without assistance, using patient's accessible WebIT, at non-controlled times and locations. The MI-HEART System feeds back process and efficacy measurements to the investigators. 62 patients have applied to join the MI-HEART clinical trial, 19 of which have already completed the pre-intervention cognitive test. Content Covered. 1) The presentation will first address the barriers to recruitment and the observed patient accrual rates resulting from several patient enrollment strategies covering a period of eight months will be presented. 2) The lessons learned from interesting and unexpected preliminary demographics, process variables and findings will also be presented to foster enhanced recruitment strategies for clinical studies involving WebIT. Enrollment Strategies. 1) Direct patients solicitation 2) Repetitive brochure distribution in clinics 3) Patients Educational Events 4) Public Announcements 5) National and International Recruitment a) Internet Interest Group b) Electronic Medical Record -Based Qualification c) Personal Contact with Peers Some Lessons Learned. (1/2) 1) Self-enrollment of patients that are required to use WebIT may favor a subset of wealthy, knowledgeable and self-efficacious individuals with a low belief in treatment efficacy. This group may only portray a subset of the assortment of health education needs of a non-biased patient population. (figure 1, table 1). § These authors have contributed equally to the work Fig. I Patient's Web Information Technology 12

Lessons Learned in the Early Phase of the MI-HEART Clinical Trial.

Objective: To report on the how-to of the tailoring strategies used in the MI-HEART project. Background and Significance: Tailored communications are defined as any combination of strategies and information intended for one specific person, based on characteristics that are unique to that person, related to the outcome of interest, and derived from individual assessment. The promise is that communications tailored to variations that exist between different individuals on factors known to influence health related decisions and behaviors will be superior to mass produced materials that consist of generic health information. The first generation of empirical studies testing tailored communications against generic materials has generally shown this to be the case. In health education, studies have shown that tailored materials are more effective than non-tailored ones in helping individuals change behaviors such as smoking, diet, physical activity, cancer and cholesterol screening. How Tailoring Works in MI-HEART: The MIHEART application is essentially an expert system that utilizes empirically based decision rules for selecting an appropriate message from a database of stored messages, referred to as the "message library" (Figure 1). The basis for the selection of a message is the tailoring variables and their values in an individual's data record. The individual's data record is populated from the patient's medical record, supplemented by an on-line questionnaire completed by each participant at baseline. We constructed the online tailoring questionnaire to obtain measurements for each individual's status on the variables underlying the decision to seek help when responding to AMI symptoms. The selection of these variables was based on an explicit theoretically derived decision-making model for responding to AMI symptoms, described in detail elsewhere. We included all the variables in our decision model with a wide enough range of response options to capture most of the variation among individuals. Variations in responses to these measures provide the basis for tailoring our communications in that each response item for each measure is linked by a decision rule to a unique communication. Writing tailored messages for MI-HEART required the authoring of hundreds of different text messages and graphics for each variable and response option. The process demanded effectively translating the background research and underlying decision model into communications to favorably influence each cognitive variable for a particular individual at varying stages of decision-making. Figure 1: MI-HEART Message Library

The MI-HEART Project: Information Technology (IT) for One-to-One Tailored Patient Communication

Background: Scientific hypothesis generation is a critical step in scientific research that determines the direction and impact of any investigation. Despite its vital role, we have limited knowledge of the process itself, hindering our ability to address some critical questions. Objective: To what extent can secondary data analytic tools facilitate scientific hypothesis generation during clinical research? Are the processes similar in developing clinical diagnoses during clinical practice and developing scientific hypotheses for clinical research projects? We explore the process of scientific hypothesis generation in the context of clinical research. The study is designed to compare the role of VIADS, our web-based interactive secondary data analysis tool, and the experience levels of study participants during their scientific hypothesis generation processes. Methods: Inexperienced and experienced clinical researchers are recruited. In this 2 by 2 study design, all participants use the same data sets during scientific hypothesis-generation sessions, following pre-determined scripts. The inexperienced and experienced clinical researchers are randomly assigned into groups with and without using VIADS. The study sessions, screen activities, and audio recordings of participants are captured. Participants use the think-aloud protocol during the study sessions. After each study session, every participant is given a follow-up survey, with participants using VIADS completing an additional modified System Usability Scale (SUS) survey. A panel of clinical research experts will assess the scientific hypotheses generated based on pre-developed metrics. All data will be anonymized, transcribed, aggregated, and analyzed. Results: This study is currently underway. Recruitment is ongoing via a brief online survey 1. The preliminary results show that study participants can generate a few to over a dozen scientific hypotheses during a 2-hour study session, regardless of whether they use VIADS or other analytic tools. A metric to assess scientific hypotheses within a clinical research context more accurately, comprehensively, and consistently has also been developed. Conclusion: The scientific hypothesis-generation process is an advanced cognitive activity and a complex process. Clinical researchers can quickly generate initial scientific hypotheses based on data sets and prior experience based on our current results. However, refining these scientific hypotheses is much more time-consuming. To uncover the fundamental mechanisms of generating scientific hypotheses, we need breakthroughs that capture thinking processes more precisely.

Scientific hypothesis generation process in clinical research: a secondary data analytic tool versus experience study protocol

Background Cytomegalovirus (CMV) infection, either de novo or as reactivation after allotransplantation and chronic immunosuppression, is recognized to cause detrimental alloimmune effects, inclusive of higher susceptibility to graft rejection and substantive impact on chronic graft injury and reduced transplant survival. To obtain further insights into the evolution and pathogenesis of CMV infection in an immunocompromised host we evaluated changes in the circulating host proteome serially, before and after transplantation, and during and after CMV DNA replication (DNAemia), as measured by quantitative polymerase chain reaction (QPCR). Methods LC-MS-based proteomics was conducted on 168 serially banked plasma samples, from 62 propensity score-matched kidney transplant recipients. Patients were stratified by CMV replication status into 31 with CMV DNAemia and 31 without CMV DNAemia. Patients had blood samples drawn at protocol times of 3- and 12-months post-transplant. Additionally, blood samples were also drawn before and 1 week and 1 month after detection of CMV DNAemia. Plasma proteins were analyzed using an LCMS 8060 triple quadrupole mass spectrometer. Further, public transcriptomic data on time matched PBMCs samples from the same patients was utilized to evaluate integrative pathways. Data analysis was conducted using R and Limma. Results Samples were segregated based on their proteomic profiles with respect to their CMV Dnaemia status. A subset of 17 plasma proteins was observed to predict the onset of CMV at 3 months post-transplant enriching platelet degranulation (FDR, 4.83E-06), acute inflammatory response (FDR, 0.0018), blood coagulation (FDR, 0.0018) pathways. An increase in many immune complex proteins were observed at CMV infection. Prior to DNAemia the plasma proteome showed changes in the anti-inflammatory adipokine vaspin (SERPINA12), copper binding protein ceruloplasmin (CP), complement activation (FDR=0.03), and proteins enriched in the humoral (FDR=0.01) and innate immune responses (FDR=0.01). Conclusion Plasma proteomic and transcriptional perturbations impacting humoral and innate immune pathways are observed during CMV infection and provide biomarkers for CMV disease prediction and resolution. Further studies to understand the clinical impact of these pathways can help in the formulation of different types and duration of anti-viral therapies for management of CMV infection in the immunocompromised host.

https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0285870&type=printable

James J. Cimino

Papers

Clinical Research Data: Characteristics, Representation, Storage, and Retrieval

Lessons Learned in the Early Phase of the MI-HEART Clinical Trial.

The MI-HEART Project: Information Technology (IT) for One-to-One Tailored Patient Communication

Scientific hypothesis generation process in clinical research: a secondary data analytic tool versus experience study protocol

Plasma proteome perturbation for CMV DNAemia in kidney transplantation