Evidence indicates that the densely cultivated region of northeastern China acts as a source for the wind-borne agent of Kawasaki disease (KD). KD is an acute, coronary artery vasculitis of young children, and still a medical mystery after more than 40 y. We used residence times from simulations with the flexible particle dispersion model to pinpoint the source region for KD. Simulations were generated from locations spanning Japan from days with either high or low KD incidence. The postepidemic interval and the extreme epidemics (1979, 1982, and 1986) pointed to the same source region. Results suggest a very short incubation period (<24 h) from exposure, thus making an infectious agent unlikely. Sampling campaigns over Japan during the KD season detected major differences in the microbiota of the tropospheric aerosols compared with ground aerosols, with the unexpected finding of the Candida species as the dominant fungus from aloft samples (54% of all fungal strains). These results, consistent with the Candida animal model for KD, provide support for the concept and feasibility of a windborne pathogen. A fungal toxin could be pursued as a possible etiologic agent of KD, consistent with an agricultural source, a short incubation time and synchronized outbreaks. Our study suggests that the causative agent of KD is a preformed toxin or environmental agent rather than an organism requiring replication. We propose a new paradigm whereby an idiosyncratic immune response, influenced by host genetics triggered by an environmental exposure carried on winds, results in the clinical syndrome known as acute KD.northeastern China source | agriculture | heart disease | FLEXPART | cereal croplands

An analysis of a mathematical model, which describes the dynamics of an aerially transmitted disease, and the effects of the emergence of drug resistance after the introduction of treatment as an intervention strategy is presented. Under explicit consideration of asymptomatic and symptomatic infective individuals for the basic model without intervention the analysis shows that the dynamics of the epidemic is determined by a basic reproduction number R0. A disease-free and an endemic equilibrium exist and are locally asymptotically stable when R0<1 and R0>1 respectively. When treatment is included the system has a basic reproduction number, which is the largest of the two reproduction numbers that characterise the drug-sensitive (R1) or resistant (R2) strains of the infectious agent. The system has a disease-free equilibrium, which is stable when both R1 and R2 are less than unity. Two endemic equilibria also exist and are associated with treatment and the development of drug resistance. An endemic equilibrium where only the drug-resistant strain persists exists and is stable when R2>1 and R11 and R1>R2 and both drug-sensitive and drug-resistant strains are present. The analysis of the system provides insights about the conditions under which the infection will persist and whether sensitive and resistant strains will coexist or not.

Despite rapid technical progress and demonstrable effectiveness for some types of diagnosis and therapy, much remains to be learned about clinical genome and exome sequencing (CGES) and its role within the practice of medicine. The Clinical Sequencing Exploratory Research (CSER) consortium includes 18 extramural research projects, one National Human Genome Research Institute (NHGRI) intramural project, and a coordinating center funded by the NHGRI and National Cancer Institute. The consortium is exploring analytic and clinical validity and utility, as well as the ethical, legal, and social implications of sequencing via multidisciplinary approaches; it has thus far recruited 5,577 participants across a spectrum of symptomatic and healthy children and adults by utilizing both germline and cancer sequencing. The CSER consortium is analyzing data and creating publically available procedures and tools related to participant preferences and consent, variant classification, disclosure and management of primary and secondary findings, health outcomes, and integration with electronic health records. Future research directions will refine measures of clinical utility of CGES in both germline and somatic testing, evaluate the use of CGES for screening in healthy individuals, explore the penetrance of pathogenic variants through extensive phenotyping, reduce discordances in public databases of genes and variants, examine social and ethnic disparities in the provision of genomics services, explore regulatory issues, and estimate the value and downstream costs of sequencing. The CSER consortium has established a shared community of research sites by using diverse approaches to pursue the evidence-based development of best practices in genomic medicine.

Context Little information exists concerning the frequency of clinically significant incidental findings (IFs) identified in the course of imaging research across a broad spectrum of imaging modalities and body regions. Objective To estimate the frequency with which research imaging IFs generate further clinical action, and the medical benefit/burden of identifying these IFs. Design, Setting, and Participants Retrospective review of subjects undergoing a research imaging exam that was interpreted by a radiologist for IFs in the first quarter of 2004, with 3-year clinical follow-up. An expert panel reviewed IFs generating clinical action to determine medical benefit/burden based on predefined criteria. Main Outcome Measures Frequency of (1) IFs that generated further clinical action by modality, body part, age, gender, and (2) IFs resulting in clear medical benefit or burden. Results 1376 patients underwent 1426 research imaging studies. 40% (567/1426) of exams had at least one IF (1055 total). Risk of an IF increased significantly by age (OR=1.5; [1.4–1.7=95% C.I.] per decade increase). Abdominopelvic CT generated more IFs than other exams (OR=18.9 compared with ultrasound; 9.2% with subsequent clinical action), with CT Thorax and MR brain next (OR=11.9 and 5.9; 2.8% and 2.2% with action, respectively). Overall 6.2% of exams (35/567) with an IF generated clinical action, resulting in clear medical benefit in 1.1% (6/567) and clear medical burden in 0.5% (3/567). In most instances, medical benefit/burden was unclear (4.6%; 26/567). Conclusions The frequency of IFs in imaging research exams varies significantly by imaging modality, body region and age. Research imaging studies at high risk for generating IFs can be identified. Routine evaluation of research images by radiologists may result in identification of IFs in a substantial number of cases and subsequent clinical action to address them in much smaller number. Such clinical action can result in medical benefit to a small number of patients.

In the originally published version of this article, Table 1 unfortunately included c.542G>A instead of c.542G>T. This mutation was correctly notated as c.

Following almost 30 years of relative silence, chikungunya fever reemerged in Kenya in 2004. It subsequently spread to the islands of the Indian Ocean, reaching Southeast Asia in 2006. The virus was first detected in Cambodia in 2011 and a large outbreak occurred in the village of Trapeang Roka Kampong Speu Province in March 2012, in which 44% of the villagers had a recent infection biologically confirmed. The epidemic curve was constructed from the number of biologically-confirmed CHIKV cases per day determined from the date of fever onset, which was self-reported during a data collection campaign conducted in the village after the outbreak. All individuals participating in the campaign had infections confirmed by laboratory analysis, allowing for the identification of asymptomatic cases and those with an unreported date of fever onset. We develop a stochastic model explicitly including such cases, all of whom do not appear on the epidemic curve. We estimate the basic reproduction number of the outbreak to be 6.46 (95% C.I. [6.24, 6.78]). We show that this estimate is particularly sensitive to changes in the biting rate and mosquito longevity. Our model also indicates that the infection was more widespread within the population on the reported epidemic start date. We show that the exclusion of asymptomatic cases and cases with undocumented onset dates can lead to an underestimation of the reproduction number which, in turn, could negatively impact control strategies implemented by public health authorities. We highlight the need for properly documenting newly emerging pathogens in immunologically naive populations and the importance of identifying the route of disease introduction.

Objective: To assess the impact of direct-to-consumer (DTC) predictive genomic risk information on perceived risk and worry in the context of routine clinical care. Patients and MethOds:Patients attending a preventive medicine clinic between June 1 and December 18, 2009, were randomly assigned to receive either genomic risk information from a DTC product plus usual care (n=74) or usual care alone (n=76). At intervals of 1 week and 1 year after their clinic visit, participants completed surveys containing validated measures of risk perception and levels of worry associated with the 12 conditions assessed by the DTC product. [P=.02]). Although differences were not significant, they also reported higher levels of worry for 7 conditions and lower levels for 5 others. At 1 year, there were no significant differences between groups.cOnclusiOn: Predictive genomic risk information modestly influences risk perception and worry. The extent and direction of this influence may depend on the condition being tested and its baseline prominence in preventive health care and may attenuate with time.

Vector-borne diseases, such as dengue, malaria and chikungunya, are increasing across their traditional ranges and continuing to infiltrate new, previously unaffected, regions. The spatio-temporal evolution of these diseases is determined by the interaction of the host and vector, which is strongly dependent on social structures and mobility patterns. We develop an agent-based model (ABM), in which each individual is explicitly represented and vector populations are linked to precipitation estimates in a tropical setting. The model is implemented on both scale-free and regular networks. The spatio-temporal transmission of chikungunya is analysed and the presence of asymptomatic silent spreaders within the population is investigated in the context of implementing travel restrictions during an outbreak. Preventing the movement of symptomatic individuals is found to be an insufficient mechanism to halt the spread of the disease, which can be readily carried to neighbouring nodes via sub-clinical individuals. Furthermore, the impact of topology structure vs. precipitation levels is assessed and precipitation is found to be the dominant factor driving spatio-temporal transmission.