Image Data Sharing for Biomedical Research—Meeting HIPAA Requirements for De-identification

Data sharing is increasingly recognized as critical to cross-disciplinary research and to assuring scientific validity. Despite National Institutes of Health and National Science Foundation policies encouraging data sharing by grantees, little data sharing of clinical data has in fact occurred. A principal reason often given is the potential of inadvertent violation of the Health Insurance Portability and Accountability Act privacy regulations. While regulations specify the components of private health information that should be protected, there are no commonly accepted methods to de-identify clinical data objects such as images. This leads institutions to take conservative risk-averse positions on data sharing. In imaging trials, where images are coded according to the Digital Imaging and Communications in Medicine (DICOM) standard, the complexity of the data objects and the flexibility of the DICOM standard have made it especially difficult to meet privacy protection objectives. The recent release of DICOM Supplement 142 on image de-identification has removed much of this impediment. This article describes the development of an open-source software suite that implements DICOM Supplement 142 as part of the National Biomedical Imaging Archive (NBIA). It also describes the lessons learned by the authors as NBIA has acquired more than 20 image collections encompassing over 30 million images.     

Medical Image Data and Datasets in the Era of Machine Learning—Whitepaper from the 2016 C-MIMI Meeting Dataset Session

At the first annual Conference on Machine Intelligence in Medical Imaging (C-MIMI), held in September 2016, a conference session on medical image data and datasets for machine learning identified multiple issues. The common theme from attendees was that everyone participating in medical image evaluation with machine learning is data starved. There is an urgent need to find better ways to collect, annotate, and reuse medical imaging data. Unique domain issues with medical image datasets require further study, development, and dissemination of best practices and standards, and a coordinated effort among medical imaging domain experts, medical imaging informaticists, government and industry data scientists, and interested commercial, academic, and government entities. High-level attributes of reusable medical image datasets suitable to train, test, validate, verify, and regulate ML products should be better described. NIH and other government agencies should promote and, where applicable, enforce, access to medical image datasets. We should improve communication among medical imaging domain experts, medical imaging informaticists, academic clinical and basic science researchers, government and industry data scientists, and interested commercial entities.     

How is the z-Average Molar Mass Related to the Sedimentation Data?

Nowadays, students, professors, and researchers in the field of polymer education and research know how different average molar masses of polymer chains are defined in textbooks, but not everyone knows that these different averages actually come from different experimental methods, not purely from different mathematical definitions and calculations. Some of the detailed knowledge have been lost in literature and even textbooks during the polymer development process. The z-average molar mass (M_z) is one of the typical examples; namely, why it is called the z-average and how it is related to the concentration profile inside an ultracentrifuge cell. Currently, it is rather difficult, if not impossible, to find how to derive the final equation used in the calculation of M_z. It is the purpose of this paper to show a simple and nontedious derivation of how to obtain M_z from a sedimentation experiment, very different from the original old paper published by Lansing and Kraemer in 1935. It is also experimentally shown how to calculate M_z by using the ultracentrifuge data from a mixture of three polystyrene standards.     

The susceptibility of the kidney to alternative pathway activation—A hypothesis

The glomerulus is often the prime target of dysregulated alternative pathway (AP) activation. In particular, AP activation is the key driver of two severe kidney diseases: atypical hemolytic uremic syndrome and C3 glomerulopathy. Both conditions are associated with a variety of predisposing molecular defects in AP regulation, such as genetic variants in complement regulators, autoantibodies targeting AP proteins, or autoantibodies that stabilize the AP convertases (C3- and C5-activating enzymes). It is noteworthy that these are systemic AP defects, yet in both diseases pathologic complement activation primarily affects the kidneys. In particular, AP activation is often limited to the glomerular capillaries. This tropism of AP-mediated inflammation for the glomerulus points to a unique interaction between AP proteins in plasma and this particular anatomic structure. In this review, we discuss the pre-clinical and clinical data linking the molecular causes of aberrant control of the AP with activation in the glomerulus, and the possible causes of this tropism. Based on these data, we propose a model for why the kidney is so uniquely and frequently targeted in patients with AP defects. Finally, we discuss possible strategies for preventing pathologic AP activation in the kidney.     

Image panels for the special issue related to the meeting “The Nervous System of Drosophila melanogaster: From Development to Function”

These images were composed for the special issue related to the meeting “The Nervous System of Drosophila melanogaster: From Development to Function” that was held in Freiburg (Germany) from 26-29 Sept 2013.     

A Generalized Defries–Fulker Regression Framework for the Analysis of Twin Data

Twin studies compare the similarity between monozygotic twins to that between dizygotic twins in order to investigate the relative contributions of latent genetic and environmental factors influencing a phenotype. Statistical methods for twin data include likelihood estimation and Defries–Fulker regression. We propose a new generalization of the Defries–Fulker model that fully incorporates the effects of observed covariates on both members of a twin pair and is robust to violations of the Normality assumption. A simulation study demonstrates that the method is competitive with likelihood analysis. The Defries–Fulker strategy yields new insight into the parameter space of the twin model and provides a novel, prediction-based interpretation of twin study results that unifies continuous and binary traits. Due to the simplicity of its structure, extensions of the model have the potential to encompass generalized linear models, censored and truncated data; and gene by environment interactions.     

Patients’ Continuing Use of an Online Health Record: A Quantitative Evaluation of 14,000 Patient Years of Access Data

Background

Online access to all or part of their health records is widely demanded by patients and, where provided in form of patient portals, has been substantially used by at least subgroups of patients, particularly those with chronic disease. However, little is reported regarding the longer-term patient use of patient-accessible electronic health record services, which is important in allocating resources. Renal PatientView (RPV) is an established system that gives patients with chronic kidney disease access to live test results and information about their condition and treatment. It is available in most UK renal units with up to 75% of particular patient groups registered in some centers. We have analyzed patient use out to 4 years and investigated factors associated with more persistent use.

Objective

Our aim was to investigate RPV use by patients over time from initial registration in order to understand which patients choose to access RPV and the endurance of its appeal for different patient groups.

Methods

We analyzed an anonymized extract of the database underlying RPV containing information on patient registration and events including patient access and the arrival of new blood test results or letters that patients might wish to view.

Results

At the time of the extract, there were 11,352 patients registered on RPV for 0-42 months (median 17). More than half of registrants became persistent users, logging in a median of 2.0 times each month over post-registration intervals of up to 42 months (median 18.9). Provision of assistance with first logon was strongly associated with becoming a persistent user, even at 3 years. Logons by persistent users occurred around the time of consultations/tests, strongly suggestive of patient engagement. While indices indicative of greater deprivation were the strongest determinants of non-participation, they had negligible influence on drop-out rates among established users.

Conclusions

In this mature patient portal system, a large proportion of patients made regular use of their online health records over protracted periods. The patterns and timing of use indicate strong patient interest in detailed information such as recent test results and clinic letters. Supporting patients through the first steps of establishing access to their online records is associated with much higher rates of long-term use of RPV and likely would increase use of other electronic health records provided for patients with chronic disease.     

A EPC Approach to the Residual Water in Erythrocytes

A novel approach has been developed to determine the amount of residual water in human erythrocyte at room temperature by electronic particle counter. Nacl solutions of 13 osmolalities were prepared and the equilibrium cell volumes in which were measured one by one. The isotonic volume, Vo, was obtained under the isotonic condition. The mean RBC volumes of 5 donors at each osmolality were fitted according to Boyle van‘t Hoff relationship, and the osmotically inactive volume, Vb, of erythrocyte was then determined. The results show that Vb＝50% Vo. More importantly, the final cell volume with regard to the solution of the highest concentration found to be kept at about 0. 5 V0. The difference between these two volumes is unconspicuous. According to the published data that non-water volume of human erythrocyte is about 28.3% of its isotonic volume, residual water of human erythrocyte can be gained by subtracting Vdry from Vf, that is Vrw----21.7% Vo Then it was concluded that the residual water of human lays in 2 states, one is bound water, and the other is free water.