Situational Awareness of Influenza Activity Based on Multiple Streams of Surveillance Data Using Multivariate Dynamic Linear Model

BACKGROUND: Multiple sources of influenza surveillance data are becoming more available; however integration of these data streams for situational awareness of influenza activity is less explored. METHODS AND RESULTS: We applied multivariate time-series methods to sentinel outpatient and school absenteeism surveillance data in Hong Kong during 2004-2009. School absenteeism data and outpatient surveillance data experienced interruptions due to school holidays and changes in public health guidelines during the pandemic, including school closures and the establishment of special designated flu clinics, which in turn provided 'drop-in' fever counts surveillance data. A multivariate dynamic linear model was used to monitor influenza activity throughout epidemics based on all available data. The inferred level followed influenza activity closely at different times, while the inferred trend was less competent with low influenza activity. Correlations between inferred level and trend from the multivariate model and reference influenza activity, measured by the product of weekly laboratory influenza detection rates and weekly general practitioner influenza-like illness consultation rates, were calculated and compared with those from univariate models. Over the whole study period, there was a significantly higher correlation (rho = 0.82, p</=0.02) for the inferred trend based on the multivariate model compared to other univariate models, while the inferred trend from the multivariate model performed as well as the best univariate model in the pre-pandemic and the pandemic period. The inferred trend and level from the multivariate model was able to match, if not outperform, the best univariate model albeit with missing data plus drop-in and drop-out of different surveillance data streams. An overall influenza index combining level and trend was constructed to demonstrate another potential use of the method. CONCLUSIONS: Our results demonstrate the potential use of multiple streams of influenza surveillance data to promote situational awareness about the level and trend of seasonal and pandemic influenza activity.published_or_final_versio

Data-driven approach for creating synthetic electronic medical records

<p>Abstract</p> <p>Background</p> <p>New algorithms for disease outbreak detection are being developed to take advantage of full electronic medical records (EMRs) that contain a wealth of patient information. However, due to privacy concerns, even anonymized EMRs cannot be shared among researchers, resulting in great difficulty in comparing the effectiveness of these algorithms. To bridge the gap between novel bio-surveillance algorithms operating on full EMRs and the lack of non-identifiable EMR data, a method for generating complete and synthetic EMRs was developed.</p> <p>Methods</p> <p>This paper describes a novel methodology for generating complete synthetic EMRs both for an outbreak illness of interest (tularemia) and for background records. The method developed has three major steps: 1) synthetic patient identity and basic information generation; 2) identification of care patterns that the synthetic patients would receive based on the information present in real EMR data for similar health problems; 3) adaptation of these care patterns to the synthetic patient population.</p> <p>Results</p> <p>We generated EMRs, including visit records, clinical activity, laboratory orders/results and radiology orders/results for 203 synthetic tularemia outbreak patients. Validation of the records by a medical expert revealed problems in 19% of the records; these were subsequently corrected. We also generated background EMRs for over 3000 patients in the 4-11 yr age group. Validation of those records by a medical expert revealed problems in fewer than 3% of these background patient EMRs and the errors were subsequently rectified.</p> <p>Conclusions</p> <p>A data-driven method was developed for generating fully synthetic EMRs. The method is general and can be applied to any data set that has similar data elements (such as laboratory and radiology orders and results, clinical activity, prescription orders). The pilot synthetic outbreak records were for tularemia but our approach may be adapted to other infectious diseases. The pilot synthetic background records were in the 4-11 year old age group. The adaptations that must be made to the algorithms to produce synthetic background EMRs for other age groups are indicated.</p

Probabilistic Daily ILI Syndromic Surveillance with a Spatio-Temporal Bayesian Hierarchical Model

BACKGROUND: For daily syndromic surveillance to be effective, an efficient and sensible algorithm would be expected to detect aberrations in influenza illness, and alert public health workers prior to any impending epidemic. This detection or alert surely contains uncertainty, and thus should be evaluated with a proper probabilistic measure. However, traditional monitoring mechanisms simply provide a binary alert, failing to adequately address this uncertainty. METHODS AND FINDINGS: Based on the Bayesian posterior probability of influenza-like illness (ILI) visits, the intensity of outbreak can be directly assessed. The numbers of daily emergency room ILI visits at five community hospitals in Taipei City during 2006-2007 were collected and fitted with a Bayesian hierarchical model containing meteorological factors such as temperature and vapor pressure, spatial interaction with conditional autoregressive structure, weekend and holiday effects, seasonality factors, and previous ILI visits. The proposed algorithm recommends an alert for action if the posterior probability is larger than 70%. External data from January to February of 2008 were retained for validation. The decision rule detects successfully the peak in the validation period. When comparing the posterior probability evaluation with the modified Cusum method, results show that the proposed method is able to detect the signals 1-2 days prior to the rise of ILI visits. CONCLUSIONS: This Bayesian hierarchical model not only constitutes a dynamic surveillance system but also constructs a stochastic evaluation of the need to call for alert. The monitoring mechanism provides earlier detection as well as a complementary tool for current surveillance programs

Optimizing Provider Recruitment for Influenza Surveillance Networks

The increasingly complex and rapid transmission dynamics of many infectious diseases necessitates the use of new, more advanced methods for surveillance, early detection, and decision-making. Here, we demonstrate that a new method for optimizing surveillance networks can improve the quality of epidemiological information produced by typical provider-based networks. Using past surveillance and Internet search data, it determines the precise locations where providers should be enrolled. When applied to redesigning the provider-based, influenza-like-illness surveillance network (ILINet) for the state of Texas, the method identifies networks that are expected to significantly outperform the existing network with far fewer providers. This optimized network avoids informational redundancies and is thereby more effective than networks designed by conventional methods and a recently published algorithm based on maximizing population coverage. We show further that Google Flu Trends data, when incorporated into a network as a virtual provider, can enhance but not replace traditional surveillance methods

Evidence for Escherichia coli DcuD carrier dependent FOF1-ATPase activity during fermentation of glycerol

During fermentation Escherichia coli excrete succinate mainly via Dcu family carriers. Current work reveals the total and N,N’-dicyclohexylcarbodiimide (DCCD) inhibited ATPase activity at pH 7.5 and 5.5 in E. coli wild type and dcu mutants upon glycerol fermentation. The overall ATPase activity was highest at pH 7.5 in dcuABCD mutant. In wild type cells 50% of the activity came from the FOF1-ATPase but in dcuD mutant it reached ~80%. K+ (100 mM) stimulate total but not DCCD inhibited ATPase activity 40% and 20% in wild type and dcuD mutant, respectively. 90% of overall ATPase activity was inhibited by DCCD at pH 5.5 only in dcuABC mutant. At pH 7.5 the H+ fluxes in E. coli wild type, dcuD and dcuABCD mutants was similar but in dcuABC triple mutant the H+ flux decreased 1.4 fold reaching 1.15 mM/min when glycerol was supplemented. In succinate assays the H+ flux was higher in the strains where DcuD is absent. No significant differences were determined in wild type and mutants specific growth rate except dcuD strain. Taken together it is suggested that during glycerol fermentation DcuD has impact on H+ fluxes, FOF1-ATPase activity and depends on potassium ions

Zinc uptake promotes myoblast differentiation via Zip7 transporter and activation of Akt signalling transduction pathway

[EN] Myogenic regeneration occurs through a chain of events beginning with the output of satellite cells from quiescent state, formation of competent myoblasts and later fusion and differentiation into myofibres. Traditionally, growth factors are used to stimulate muscle regeneration but this involves serious off-target effects, including alterations in cell homeostasis and cancer. In this work, we have studied the use of zinc to trigger myogenic differentiation. We show that zinc promotes myoblast proliferation, differentiation and maturation of myofibres. We demonstrate that this process occurs through the PI3K/Akt pathway, via zinc stimulation of transporter Zip7. Depletion of zinc transporter Zip7 by RNA interference shows reduction of both PI3K/Akt signalling and a significant reduction of multinucleated myofibres and myotubes development. Moreover, we show that mature myofibres, obtained through stimulation with high concentrations of zinc, accumulate zinc and so we hypothesise their function as zinc reservoirs into the cell.P.R. and R.S. acknowledges support from the Spanish Ministry of Economy and Competitiveness (MINECO) (MAT2015-69315-C3-1-R). P.R. acknowledges the Fondo Europeo de Desarrollo Regional (FEDER). CIBER-BBN is an initiative funded by the VI National R&D&I Plan 2008-2011, Iniciativa Ingenio 2010, Consolider Program, CIBER Actions and financed by the Instituto de Salud Carlos III with assistance from the European Regional Development Fund. R.S. acknowledges the support from the Spanish MECD through the PRX16/00208 grant. MSS acknowledges support from the European Research Council (ERC - HealInSynergy 306990) and the UK Engineering and Physical Sciences Research Council (EPSRC - EP/P001114/1)Mnatsakanyan, H.; Sabater I Serra, R.; Rico Tortosa, PM.; Salmerón Sánchez, M. (2018). 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Mitochondrial ATP synthase: architecture, function and pathology

Human mitochondrial (mt) ATP synthase, or complex V consists of two functional domains: F1, situated in the mitochondrial matrix, and Fo, located in the inner mitochondrial membrane. Complex V uses the energy created by the proton electrochemical gradient to phosphorylate ADP to ATP. This review covers the architecture, function and assembly of complex V. The role of complex V di-and oligomerization and its relation with mitochondrial morphology is discussed. Finally, pathology related to complex V deficiency and current therapeutic strategies are highlighted. Despite the huge progress in this research field over the past decades, questions remain to be answered regarding the structure of subunits, the function of the rotary nanomotor at a molecular level, and the human complex V assembly process. The elucidation of more nuclear genetic defects will guide physio(patho)logical studies, paving the way for future therapeutic interventions

MODERN ISSUES OF DEVELOPMENT OF INVESTMENT ENVIRONMENT IN THE REPUBLIC OF ARMENIA

Abstract: The current, problems and prospects of the development of the investment environment in the Republic of Armenia are presented. New problems associated with attraction of investments in countries with transition economies in the recent global economical crisis and revealed. The problems connected with the increasing of investment attractiveness in the transition economy of the Republic of Armenia are considered. Factors affecting the investment environment are described and areas for improvement of the investment environment are proposed which involves improvement of the corporate governance system and protection of property rights, creation of equal conditions for competition, improvement of state control of the economy and information support of business, accounting and statistics