History of Medicine Schedule 2010-2011

William Osler (1849-1919): America’s Most Famous Physician (Robert E. Rakel) The Assassination of John F. Kennedy: A Neurosurgeon’s Eyewitness Account of the Medical Aspect of the Events of November 22, 1963 (Robert G. Grossman) Making Cancer History: Disease and Discovery at the University of Texas M.D. Anderson Cancer Center (James S. Olson) The History of Pathology as a Biological Science and Medical Specialty (L. Maximillian Buja) “Medicine in the Mid-19th Century America” (Student Essay Contest Winner) (David Hunter) The Achievements and Enduring Relevance of Rudolph Virchow (Nathan Grohmann) Medicine: Perspectives in History and Art (Robert E. Greenspan) What Every Physician Should Know: Lessons from the Past (Robert E. Greenspan) Medicine in Ancient Mesopotamia (Sajid Haque) The History of Texas Children’s Hospital (B. Lee Ligon) Visualizing Disease: Motion Pictures in the History of Medical Education (Kirsten Ostherr)

Baylor History of Medicine Society Schedule 2007-2008

"Medicine: Perspectives in History and Art" (Robert E. Greenspan) Eight Practical Lessons from Osler That Will Better Your Life (Bryan Boutwell) History of the American Mental Hospital: From networking to not working & Back (Ed Fann) Ambiguities and Amputations: Methods, mishaps, and the surgical quest to cure breast cancer (Student Essay Contest Winner) (Matt Luedke) An Automated, Algorithmic, Retrospective Analysis of the Growing Influence of Statistics in Medicine (Student Essay Contest Winner) (Ryan Rochat) What’s Special about William Osler? (Charles S. Bryan) The Virtuous Physician: Lessons from Medical Biography (Charles S. Bryan) Legacy: 50 Years of Loving Care – The History of Texas Children’s Hospital, 1954-2004 (Betsy Parish) The Education of a University President: Edgar Odell Lovett of Rice University (John B. Boles) Artists and Illness: The Effect of Illness on an Artist’s Work (David Bybee)

The processing of human pro-tumor necrosis factor

Human pro-TNF-$\alpha$ is a 26 kd type II transmembrane protein, and it is the precursor of 17 kd mature TNF. Pro-TNF release mature from its extracellular domain by proteolytic cleavage between resideu Ava ($-$1) and Val (+1). Both forms of TNF are biologically active and the native form of mature TNF is a bell-shaped trimer. The structure of pro-TNF was studied both in intact cell system and in an in vitro translation system by chemical crosslinking. We found that human pro-TNF protein exist as a trimer in intact cells (LPS-induced THP-1 cells and TNF cDNA transfected COS-3 cells) and this trimeric structure is assembled intracellularly, possibly in the ER. By analysis several deletion mutants, we observed a correlation between expression of pro-TNF cytotoxicity in a juxtacrine fashion and detection of the trimer, suggesting the trimeric structure is very important for its biologic activity. With a series of deletion mutants in the linking domain, we found that the small deletion did not block the cleavage and large deletion did regardless of the presence or absence of the native cleavage site, suggesting that the length of the residues between the plasma membrane and the base of the trimer determines the rate of the cleavage, possibly by blocking the accessibility of the cleavage enzyme to its action site. Our data also suggest that the native cleavage site is not sufficient for the release of mature TNF and alternative cleavage site(s) exists. ^

TIME SERIES ANALYSIS AS INPUT FOR PREDICTIVE MODELING: PREDICTING CARDIAC ARREST IN A PEDIATRIC INTENSIVE CARE UNIT

The first manuscript, entitled "Time-Series Analysis as Input for Clinical Predictive Modeling: Modeling Cardiac Arrest in a Pediatric ICU" lays out the theoretical background for the project. There are several core concepts presented in this paper. First, traditional multivariate models (where each variable is represented by only one value) provide single point-in-time snapshots of patient status: they are incapable of characterizing deterioration. Since deterioration is consistently identified as a precursor to cardiac arrests, we maintain that the traditional multivariate paradigm is insufficient for predicting arrests. We identify time series analysis as a method capable of characterizing deterioration in an objective, mathematical fashion, and describe how to build a general foundation for predictive modeling using time series analysis results as latent variables. Building a solid foundation for any given modeling task involves addressing a number of issues during the design phase. These include selecting the proper candidate features on which to base the model, and selecting the most appropriate tool to measure them. We also identified several unique design issues that are introduced when time series data elements are added to the set of candidate features. One such issue is in defining the duration and resolution of time series elements required to sufficiently characterize the time series phenomena being considered as candidate features for the predictive model. Once the duration and resolution are established, there must also be explicit mathematical or statistical operations that produce the time series analysis result to be used as a latent candidate feature. In synthesizing the comprehensive framework for building a predictive model based on time series data elements, we identified at least four classes of data that can be used in the model design. The first two classes are shared with traditional multivariate models: multivariate data and clinical latent features. Multivariate data is represented by the standard one value per variable paradigm and is widely employed in a host of clinical models and tools. These are often represented by a number present in a given cell of a table. Clinical latent features derived, rather than directly measured, data elements that more accurately represent a particular clinical phenomenon than any of the directly measured data elements in isolation. The second two classes are unique to the time series data elements. The first of these is the raw data elements. These are represented by multiple values per variable, and constitute the measured observations that are typically available to end users when they review time series data. These are often represented as dots on a graph. The final class of data results from performing time series analysis. This class of data represents the fundamental concept on which our hypothesis is based. The specific statistical or mathematical operations are up to the modeler to determine, but we generally recommend that a variety of analyses be performed in order to maximize the likelihood that a representation of the time series data elements is produced that is able to distinguish between two or more classes of outcomes. The second manuscript, entitled "Building Clinical Prediction Models Using Time Series Data: Modeling Cardiac Arrest in a Pediatric ICU" provides a detailed description, start to finish, of the methods required to prepare the data, build, and validate a predictive model that uses the time series data elements determined in the first paper. One of the fundamental tenets of the second paper is that manual implementations of time series based models are unfeasible due to the relatively large number of data elements and the complexity of preprocessing that must occur before data can be presented to the model. Each of the seventeen steps is analyzed from the perspective of how it may be automated, when necessary. We identify the general objectives and available strategies of each of the steps, and we present our rationale for choosing a specific strategy for each step in the case of predicting cardiac arrest in a pediatric intensive care unit. Another issue brought to light by the second paper is that the individual steps required to use time series data for predictive modeling are more numerous and more complex than those used for modeling with traditional multivariate data. Even after complexities attributable to the design phase (addressed in our first paper) have been accounted for, the management and manipulation of the time series elements (the preprocessing steps in particular) are issues that are not present in a traditional multivariate modeling paradigm. In our methods, we present the issues that arise from the time series data elements: defining a reference time; imputing and reducing time series data in order to conform to a predefined structure that was specified during the design phase; and normalizing variable families rather than individual variable instances. The final manuscript, entitled: "Using Time-Series Analysis to Predict Cardiac Arrest in a Pediatric Intensive Care Unit" presents the results that were obtained by applying the theoretical construct and its associated methods (detailed in the first two papers) to the case of cardiac arrest prediction in a pediatric intensive care unit. Our results showed that utilizing the trend analysis from the time series data elements reduced the number of classification errors by 73%. The area under the Receiver Operating Characteristic curve increased from a baseline of 87% to 98% by including the trend analysis. In addition to the performance measures, we were also able to demonstrate that adding raw time series data elements without their associated trend analyses improved classification accuracy as compared to the baseline multivariate model, but diminished classification accuracy as compared to when just the trend analysis features were added (ie, without adding the raw time series data elements). We believe this phenomenon was largely attributable to overfitting, which is known to increase as the ratio of candidate features to class examples rises. Furthermore, although we employed several feature reduction strategies to counteract the overfitting problem, they failed to improve the performance beyond that which was achieved by exclusion of the raw time series elements. Finally, our data demonstrated that pulse oximetry and systolic blood pressure readings tend to start diminishing about 10-20 minutes before an arrest, whereas heart rates tend to diminish rapidly less than 5 minutes before an arrest.