Immune responses to the enduring hypoxic response antigen Rv0188 are preferentially detected in Mycobacterium bovis infected cattle with low pathology

The DosR regulon and the Enduring Hypoxic Response (EHR) define a group of M. tuberculosis genes that are specifically induced in bacilli exposed in vitro to conditions thought to mimic the environment encountered by Mycobacteria during latent infection. Although well described in humans, latent mycobacterial infection in cattle remains poorly understood. Thus, the aim of this study was to identify antigens that may potentially disclose cattle with latent M. bovis infection. To this end, we initially screened 57 pools of overlapping peptides representing 4 DosR regulon and 29 EHR antigens for their ability to stimulate an immune response in whole blood from TB-reactor cattle using IFN-γ and IL-2 as readouts. All 4 DosR regulon proteins were poorly recognized (maximum responder frequency of 10%). For the EHR antigens, both IFN-γ and IL-2 revealed similar response hierarchies, with responder frequencies ranging from 54% down to 3% depending on the given EHR antigen. Furthermore, these results demonstrated that responses in the infected cattle were largely IFN-γ biased. To support the concept for their role in latency, we evaluated if EHR antigen responses were associated with lower pathology. The EHR antigen Rv0188 was recognised predominantly in animals presenting with low pathology scores, whereas responses to ESAT-6/CFP-10 or the other EHR antigens tested were prevalent across the pathology spectrum. However, when we determined the production of additional cytokines induced by the M. bovis antigens PPD-B or ESAT-6/CFP-10, we detected significantly greater PPD-B-induced production of the pro-inflammatory cytokine IL-1β in animals recognizing Rv0188 (i.e. those with limited or no pathology). Thus, these results are consistent with the idea that responses to Rv0188 may identify a subset of animals at early stages of infection or in which disease progression may be limited.     

Signs of the 2009 Influenza Pandemic in the New York-Presbyterian Hospital Electronic Health Records

Background

In June of 2009, the World Health Organization declared the first influenza pandemic of the 21^st century, and by July, New York City''s New York-Presbyterian Hospital (NYPH) experienced a heavy burden of cases, attributable to a novel strain of the virus (H1N1pdm).

Methods and Results

We present the signs in the NYPH electronic health records (EHR) that distinguished the 2009 pandemic from previous seasonal influenza outbreaks via various statistical analyses. These signs include (1) an increase in the number of patients diagnosed with influenza, (2) a preponderance of influenza diagnoses outside of the normal flu season, and (3) marked vaccine failure. The NYPH EHR also reveals distinct age distributions of patients affected by seasonal influenza and the pandemic strain, and via available longitudinal data, suggests that the two may be associated with distinct sets of comorbid conditions as well. In particular, we find significantly more pandemic flu patients with diagnoses associated with asthma and underlying lung disease. We further observe that the NYPH EHR is capable of tracking diseases at a resolution as high as particular zip codes in New York City.

Conclusion

The NYPH EHR permits early detection of pandemic influenza and hypothesis generation via identification of those significantly associated illnesses. As data standards develop and databases expand, EHRs will contribute more and more to disease detection and the discovery of novel disease associations.     

IHE cross-enterprise document sharing for imaging: interoperability testing software

Background  

With the deployments of Electronic Health Records (EHR), interoperability testing in healthcare is becoming crucial. EHR enables access to prior diagnostic information in order to assist in health decisions. It is a virtual system that results from the cooperation of several heterogeneous distributed systems. Interoperability between peers is therefore essential. Achieving interoperability requires various types of testing. Implementations need to be tested using software that simulates communication partners, and that provides test data and test plans.     

Recommendations for lipid testing and reporting by Australian pathology laboratories

The importance of measuring blood lipids in determining the absolute risk of a cardiovascular event is now well established. In Australia, the National Heart Foundation of Australia and the Cardiac Society of Asutralia and New Zealand (NHFA/CSANZ) have done much to educate doctors. In recent years the recommendations of the NHFA/CSANZ have been based on values for Low-density lipoprotein (LDL-C) as well as High-density lipoprotein cholesterol (HDL-C) and Triglyceride (TG). This change has been reflected in requests to pathology laboratories. However the interpretation of these results may be difficult and the NHFA guidelines outline desirable values for patients at high risk only. There are no formal recommendations for reference intervals or interpretive comments. With the availability of expert systems, some pathology laboratories are now in a better position to provide specific comments to assist with the interpretation of test results.An ad hoc committee of private and public chemical pathologists met to draft recommendations for lipid testing and reporting by Australian pathology providers, on the basis of current guidelines and their own expertise. Provisions in the current Medicare Benefits Schedule (MBS) for lipid testing were reviewed, and the indications for lipid testing, recommended tests, the logistics of managing specimens, methods of analysis and availability of specialised tests have been documented. Recommendations are made on the provision of desirable values for lipid tests. Suggestions are provided on interpretive comments which could accompany reports of lipid test results, including categorisation of the likely associated lipoprotein abnormalities, their causes, contribution to risk for cardiovascular disease (CVD) and targets for treatment. Current and future approaches to the assessment of risk for CVD are discussed.     

Chapter 13: Mining Electronic Health Records in the Genomics Era

Abstract: The combination of improved genomic analysis methods, decreasing genotyping costs, and increasing computing resources has led to an explosion of clinical genomic knowledge in the last decade. Similarly, healthcare systems are increasingly adopting robust electronic health record (EHR) systems that not only can improve health care, but also contain a vast repository of disease and treatment data that could be mined for genomic research. Indeed, institutions are creating EHR-linked DNA biobanks to enable genomic and pharmacogenomic research, using EHR data for phenotypic information. However, EHRs are designed primarily for clinical care, not research, so reuse of clinical EHR data for research purposes can be challenging. Difficulties in use of EHR data include: data availability, missing data, incorrect data, and vast quantities of unstructured narrative text data. Structured information includes billing codes, most laboratory reports, and other variables such as physiologic measurements and demographic information. Significant information, however, remains locked within EHR narrative text documents, including clinical notes and certain categories of test results, such as pathology and radiology reports. For relatively rare observations, combinations of simple free-text searches and billing codes may prove adequate when followed by manual chart review. However, to extract the large cohorts necessary for genome-wide association studies, natural language processing methods to process narrative text data may be needed. Combinations of structured and unstructured textual data can be mined to generate high-validity collections of cases and controls for a given condition. Once high-quality cases and controls are identified, EHR-derived cases can be used for genomic discovery and validation. Since EHR data includes a broad sampling of clinically-relevant phenotypic information, it may enable multiple genomic investigations upon a single set of genotyped individuals. This chapter reviews several examples of phenotype extraction and their application to genetic research, demonstrating a viable future for genomic discovery using EHR-linked data.

What to Learn in This Chapter

• Describe the types of information available in Electronic Health Records (EHRs), and the relative sensitivity and positive predictive value of each
• Describe the difference between unstructured and structured information in the EHR
• Describe methods for developing accurate phenotype algorithms that integrate structured and unstructured EHR information, and the roles played by billing codes, laboratory values, medication data, and natural language processing
• Describe recent uses of EHR-derived phenotypes to study genome-phenome relationships
• Describe the cost advantages unique to EHR-linked biobanks, and the ability to reuse genetic data for many studies
• Understand the role of EHRs to enable phenome-wide association studies of genetic variants

This article is part of the “Translational Bioinformatics” collection for PLOS Computational Biology.

     

Charisma,crowd psychology and altered states of consciousness

This paper argues that an interpretive meaning-centered analysis is not adequate for understanding collective behavior that is outside the range of calculating rationality. Alternative approaches to collective irrational action are drawn from the work of Weber and Durkheim, as well as from the crowd psychologists Le Bon and Tarde. These approaches are then illustrated in a short analysis of the trajectories and recruitment techniques of two contemporary American religious annunciations: est and Scientology, and the findings applied to the general social formation.     

State Bias and Women's Status

There is another bias beside male bias present in interpretive works about women. I call it "state bias" to refer to a hierarchical way of conceptualizing characteristic of state societies. As regards women, it assumes that sexual equality can come in only one form: androgyny. But recent ethnographic studies and reexaminations of data suggest that for nonstates, equality may take various forms from both the perspectives of outside observers and members of the society as well.     

Mining electronic health records: towards better research applications and clinical care

Clinical data describing the phenotypes and treatment of patients represents an underused data source that has much greater research potential than is currently realized. Mining of electronic health records (EHRs) has the potential for establishing new patient-stratification principles and for revealing unknown disease correlations. Integrating EHR data with genetic data will also give a finer understanding of genotype-phenotype relationships. However, a broad range of ethical, legal and technical reasons currently hinder the systematic deposition of these data in EHRs and their mining. Here, we consider the potential for furthering medical research and clinical care using EHR data and the challenges that must be overcome before this is a reality.     

美国区域卫生信息化及有效使用电子健康档案的启示

通过介绍美国区域卫生信息化发展、有效使用EHR计划、区域卫生信息化组织等，探讨推进区域卫生信息化和电子健康档案应用过程中的问题和挑战，包括公共医学术语和技术标准、电子健康档案及区域卫生信息化潜在经济效益、电子健康档案应用保障机制、区域卫生信息化水平评价及区域卫生信息化组织可持续运营等，以提供借鉴。     

Accommodating pathway information in expression quantitative trait locus analysis

The availability of high-throughput genotyping technologies and microarray assays has allowed researchers to consider pursuing investigations whose ultimate goal is the identification of genetic variations that influence levels of gene expression, e.g., "expression quantitative trait locus" or "eQTL" mapping studies. However, the large number of genes whose expression levels can be tested for association with genetic variations in such studies can create both statistical and biological interpretive problems. We consider the integrated analysis of eQTL mapping data that incorporates pathway, function, and disease process information. The goal of this analysis is to determine if compelling patterns emerge from the data that are consistent with the notion that perturbations in the molecular physiologic environment induced by genetic variations implicate the expression patterns of multiple genes via genetic network relationships or feedback mechanisms. We apply available genetic network and pathway analysis software, as well as a novel regression analysis technique, to carry out the proposed studies. We also consider extensions of the proposed strategies and areas of future research.     

The implications of electronic health record for personalized medicine

The emerging concept of an electronic health record (EHR) targeted at a patient centric, cross-institutional and longitudinal information entity (possibly spanning the individuals lifetime) has great promise for personalized medicine. In fact, it is probably the only vehicle through which we may truly realize the personalization of medicine beyond population-based genetic profiles that are expected to become part of medication and treatment indications in the near future. The new EHR standards include mechanisms that integrate clinical data with genomic testing results obtained through applying research-type procedures, such as full DNA sequencing, to an individual patient. Although the most optimal process for the utilization of integrated clinical-genomic data in the EHR framework is still unclear, the new Health Level Seven (HL7) Clinical Genomics Draft Standard for Trial Use suggests using the 'encapsulate & bubble-up' approach, which includes two main phases: the encapsulation of raw genomic data and bubbling-up the most clinically significant portions of that data, while associating it with clinical phenotypes residing in the individual's EHR.     

Regional disparities in cancer survival in Iran: Insight from a National Surveillance of Cancer Survival in Iran (IRANCANSURV)

Background and objectivesWe aimed to investigate geographical disparity in cancer survival in 9 provincial population-based cancer registries in Iran from 2015 to 2016.Material and methodIn the current study, data from 90,862 adult patients (aged >15 years) diagnosed with cancer were retrieved from 9 population-based cancer registries across Iran. Five-year survival rates were estimated by applying relative survival approaches. We also applied the international cancer survival standard weights for age standardization. Finally, we calculated the excess hazard ratio (EHR) for each province adjusted for age, sex, and cancer sites to estimate the excess hazard ratio of mortality compared to the capital province (Tehran).ResultsThe largest gap in survival was observed in more curable cancer types, including melanoma (41.4%), ovary (32.3%), cervix (35.0%), prostate (26.7%), and rectum (21.4%), while the observed geographical disparity in lethal cancers such as lung, brain, stomach, and pancreas was less than 15%. Compared to Tehran, we found the highest excess hazard of death in Western Azerbaijan (EHR=1.60, 95% CI 1.51, 1.65), Kermanshah (EHR=1.52, 95% CI=1.44, 1.61), and Kerman (EHR=1.46, 95% CI=1.38, 1.53). The hazard ratio of death was almost identical in Isfahan (EHR=1.04, 95% CI=1.03, 1.06) and Tehran provinces.ConclusionProvinces with higher HDI had better survival rates. IRANCANSURV study showed regional disparities in cancer survival in Iran. Cancer patients in provinces with a higher Human Development Index (HDI) had a higher survival rate and lived longer compared to the patients in provinces with medium and low HDI regions.     

Hippocampal synaptic activity, pattern separation and episodic-like memory: implications for mouse models of Alzheimer's disease pathology

The present review summarizes converging evidence from animal and human studies that an early target of amyloid pathology is synaptic activity in the DG (dentate gyrus)/CA3 network. We briefly review the computational significance of the DG/CA3 network in the encoding of episodic memory and present new evidence that the CA3/DG pattern of activation is compromised in a mouse model of amyloid pathology. In addition, we present a new behavioural method to test the prediction that amyloid-related synaptic pathology will disrupt the formation of an integrated episodic-like (what, where and when) memory in mice.     

Population Physiology: Leveraging Electronic Health Record Data to Understand Human Endocrine Dynamics

Studying physiology and pathophysiology over a broad population for long periods of time is difficult primarily because collecting human physiologic data can be intrusive, dangerous, and expensive. One solution is to use data that have been collected for a different purpose. Electronic health record (EHR) data promise to support the development and testing of mechanistic physiologic models on diverse populations and allow correlation with clinical outcomes, but limitations in the data have thus far thwarted such use. For example, using uncontrolled population-scale EHR data to verify the outcome of time dependent behavior of mechanistic, constructive models can be difficult because: (i) aggregation of the population can obscure or generate a signal, (ii) there is often no control population with a well understood health state, and (iii) diversity in how the population is measured can make the data difficult to fit into conventional analysis techniques. This paper shows that it is possible to use EHR data to test a physiological model for a population and over long time scales. Specifically, a methodology is developed and demonstrated for testing a mechanistic, time-dependent, physiological model of serum glucose dynamics with uncontrolled, population-scale, physiological patient data extracted from an EHR repository. It is shown that there is no observable daily variation the normalized mean glucose for any EHR subpopulations. In contrast, a derived value, daily variation in nonlinear correlation quantified by the time-delayed mutual information (TDMI), did reveal the intuitively expected diurnal variation in glucose levels amongst a random population of humans. Moreover, in a population of continuously (tube) fed patients, there was no observable TDMI-based diurnal signal. These TDMI-based signals, via a glucose insulin model, were then connected with human feeding patterns. In particular, a constructive physiological model was shown to correctly predict the difference between the general uncontrolled population and a subpopulation whose feeding was controlled.     

Adeno-associated virus: a key to the human genome?

Adeno-associated viruses (AAV) are widely spread throughout the human population, yet no pathology has been associated with infection. This fact, together with the availability of simple molecular techniques to alter the packaged viral genome, has made AAV a serious contender in the search for an ideal gene therapy delivery vehicle. However, our understanding of the intriguing features of this virus is far from exhausted and it is likely that the mechanisms underlying the viral lifestyle will reveal possible novel strategies that can be employed in future clinical approaches. One such aspect is the unique approach AAV has evolved in order to establish latency. In the absence of a cellular milieu that will support productive viral replication, wild-type AAV can integrate its genome site specifically into a locus on human chromosome 19 (termed AAVS1), where it resides without apparent effects on the host cell until cellular conditions are changed by outside influences, such as adenovirus super-infection, which will lead to the rescue of the viral genome and productive replication. This article will introduce the biology of AAV, the unique viral strategy of targeted genome integration and address relevant questions within the context of attempts to establish therapeutic approaches that will utilize targeted gene addition to the human genome.     

Multi-Institutional Sharing of Electronic Health Record Data to Assess Childhood Obesity

Objective

To evaluate the validity of multi-institutional electronic health record (EHR) data sharing for surveillance and study of childhood obesity.

Methods

We conducted a non-concurrent cohort study of 528,340 children with outpatient visits to six pediatric academic medical centers during 2007–08, with sufficient data in the EHR for body mass index (BMI) assessment. EHR data were compared with data from the 2007–08 National Health and Nutrition Examination Survey (NHANES).

Results

Among children 2–17 years, BMI was evaluable for 1,398,655 visits (56%). The EHR dataset contained over 6,000 BMI measurements per month of age up to 16 years, yielding precise estimates of BMI. In the EHR dataset, 18% of children were obese versus 18% in NHANES, while 35% were obese or overweight versus 34% in NHANES. BMI for an individual was highly reliable over time (intraclass correlation coefficient 0.90 for obese children and 0.97 for all children). Only 14% of visits with measured obesity (BMI ≥95%) had a diagnosis of obesity recorded, and only 20% of children with measured obesity had the diagnosis documented during the study period. Obese children had higher primary care (4.8 versus 4.0 visits, p<0.001) and specialty care (3.7 versus 2.7 visits, p<0.001) utilization than non-obese counterparts, and higher prevalence of diverse co-morbidities. The cohort size in the EHR dataset permitted detection of associations with rare diagnoses. Data sharing did not require investment of extensive institutional resources, yet yielded high data quality.

Conclusions

Multi-institutional EHR data sharing is a promising, feasible, and valid approach for population health surveillance. It provides a valuable complement to more resource-intensive national surveys, particularly for iterative surveillance and quality improvement. Low rates of obesity diagnosis present a significant obstacle to surveillance and quality improvement for care of children with obesity.     

Machine learning liver-injuring drug interactions with non-steroidal anti-inflammatory drugs (NSAIDs) from a retrospective electronic health record (EHR) cohort

Drug-drug interactions account for up to 30% of adverse drug reactions. Increasing prevalence of electronic health records (EHRs) offers a unique opportunity to build machine learning algorithms to identify drug-drug interactions that drive adverse events. In this study, we investigated hospitalizations’ data to study drug interactions with non-steroidal anti-inflammatory drugs (NSAIDS) that result in drug-induced liver injury (DILI). We propose a logistic regression based machine learning algorithm that unearths several known interactions from an EHR dataset of about 400,000 hospitalization. Our proposed modeling framework is successful in detecting 87.5% of the positive controls, which are defined by drugs known to interact with diclofenac causing an increased risk of DILI, and correctly ranks aggregate risk of DILI for eight commonly prescribed NSAIDs. We found that our modeling framework is particularly successful in inferring associations of drug-drug interactions from relatively small EHR datasets. Furthermore, we have identified a novel and potentially hepatotoxic interaction that might occur during concomitant use of meloxicam and esomeprazole, which are commonly prescribed together to allay NSAID-induced gastrointestinal (GI) bleeding. Empirically, we validate our approach against prior methods for signal detection on EHR datasets, in which our proposed approach outperforms all the compared methods across most metrics, such as area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve (AUPRC).     

α-Synuclein in Extracellular Vesicles: Functional Implications and Diagnostic Opportunities

Fibrillar inclusions of intraneuronal α-synuclein can be detected in certain brain areas from patients with Parkinson’s disease (PD) and other disorders with Lewy body pathology. These insoluble protein aggregates do not themselves appear to have a prominent neurotoxic effect, whereas various α-synuclein oligomers appear harmful. Although it is incompletely known how the prefibrillar species may be pathogenic, they have been detected both within and on the outside of exosomes and other extracellular vesicles (EVs), suggesting that such structures may mediate toxic α-synuclein propagation between neurons. Vesicular transfer of α-synuclein may thereby contribute to the hierarchical spreading of pathology seen in the PD brain. Although the regulation of α-synuclein release via EVs is not understood, data suggest that it may involve other PD-related molecules, such as LRRK2 and ATP13A2. Moreover, new evidence indicates that CNS-derived EVs in plasma have the potential to serve as biomarkers for diagnostic purposes. In a recent study, levels of α-synuclein were found to be increased in L1CAM-positive vesicles isolated from plasma of PD patients compared to healthy controls, and follow-up studies will reveal whether α-synuclein in EVs could be developed as a future disease biomarker. Preferentially, toxic prefibrillar α-synuclein oligomers should then be targeted as a biomarker—as evidence suggests that they reflect the disease process more closely than total α-synuclein content. In such studies, it will be essential to adopt stringent EV isolation protocols in order to avoid contamination from the abundant pool of free plasma α-synuclein in different aggregational states.     

Case contamination in electronic health records based case‐control studies

Clinically relevant information from electronic health records (EHRs) permits derivation of a rich collection of phenotypes. Unlike traditionally designed studies where scientific hypotheses are specified a priori before data collection, the true phenotype status of any given individual in EHR‐based studies is not directly available. Structured and unstructured data elements need to be queried through preconstructed rules to identify case and control groups. A sufficient number of controls can usually be identified with high accuracy by making the selection criteria stringent. But more relaxed criteria are often necessary for more thorough identification of cases to ensure achievable statistical power. The resulting pool of candidate cases consists of genuine cases contaminated with noncase patients who do not satisfy the control definition. The presence of patients who are neither true cases nor controls among the identified cases is a unique challenge in EHR‐based case‐control studies. Ignoring case contamination would lead to biased estimation of odds ratio association parameters. We propose an estimating equation approach to bias correction, study its large sample property, and evaluate its performance through extensive simulation studies and an application to a pilot study of aortic stenosis in the Penn medicine EHR. Our method holds the promise of facilitating more efficient EHR studies by accommodating enlarged albeit contaminated case pools.