Detecting influenza and emerging avian influenza virus by influenza and pneumonia surveillance systems in a large city in China, 2005 to 2016.

BACKGROUND(#br)Detecting avian influenza virus has become an important public health strategy for controlling the emerging infectious disease.(#br)METHODS(#br)The HIS (hospital information system) modified influenza surveillance system (ISS) and a newly built pneumonia surveillance system (PSS) were used to monitor the influenza viruses in Changsha City, China. The ISS was used to monitor outpatients in two sentinel hospitals and to detect mild influenza and avian influenza cases, and PSS was used to monitor inpatients in 49 hospitals and to detect severe and death influenza cases.(#br)RESULTS(#br)From 2005 to 2016, there were 3,551,917 outpatients monitored by the ISS system, among whom 126,076 were influenza-like illness (ILI) cases, with the ILI proportion (ILI%) of 3.55%. After the HIS was used, the reported incident cases of ILI and ILI% were increased significantly. From March, 2009 to September, 2016, there were 5,491,560 inpatient cases monitored by the PSS system, among which 362,743 were pneumonia cases, with a proportion of 6.61%. Among pneumonia cases, about 10.55% (38,260/362,743) of cases were severe or death cases. The pneumonia incidence increased each year in the city. Among 15 avian influenza cases reported from January, 2005 to September, 2016, there were 26.7% (4/15) mild cases detected by the HIS-modified ISS system, while 60.0% (9/15) were severe or death cases detected by the PSS system. Two H5N1 severe cases were missed by the ISS system in January, 2009 when the PSS system was not available.(#br)CONCLUSIONS(#br)The HIS was able to improve the efficiency of the ISS for monitoring ILI and emerging avian influenza virus. However, the efficiency of the system needs to be verified in a wider area for a longer time span in China

Risk of imported Ebola virus disease in China

© 2014 Elsevier Ltd. Background More than 600,000 annual arrivals from Africa, 1.4 billion population and developing health care systems render China at non-negligible risk of imported Ebola virus disease (EVD). Method According to the natural history of EVD, we constructed a deterministic SEIR model. Three published EVD outbreaks in Africa were enrolled to calculate the basic reproduction number (R0) of EVD. Scenarios representing unreported and reported (with n weeks delay) imported EVD in China were simulated to evaluate the effectiveness of interventions assumed to be implemented in different periods of the outbreaks. Results Based on previous Africa outbreak incidence datasets, our mathematical model predicted the basic reproduction number of EVD in the range of 1.53-3.54. Adopting EVD prevalence at 0.04-0.16% from the same datasets and estimated missing information and monitoring rates at 1-10%, a total of 6-194 imported cases were predicted. Be a single case left unidentified/unreported, total attack rate was predicted to reach 60.19%-96.74%. Curve fitting results showed that earlier intervention benefits in exponential and linear decrease in prevalence and duration of outbreak respectively. Conclusion Based on past outbreak experience in China, there is a need to implement an internet-based surveillance and monitoring system in order to reinforce health policy, track suspected cases and protect the general public by timely interventions.Link_to_subscribed_fulltex

Excellent Liquid Unidirectional Transport Inner Tilted‐Sector Arrayed Tubes

Abstract Liquid unidirectional transport exhibits critical applications from water harvesting to microfluidics. Despite extensive progress, implementation of liquid unidirectional transport that is not subjected to the liquid surface tension and injecting velocity also remains a great challenge. Here, a tilted‐sector arrayed tube for excellent liquid unidirectional transport is proposed that applies to a vast width domain of liquid surface tension and injecting velocity. In addition, the transport direction is abnormally against the tilted direction of structure, in stark contrast to the traditional understanding that is along tilted direction. This excellent and unique liquid unidirectional transport is caused by synergistic effects of tilted sectors and tube structures, which induce a unique 3D liquid propagation mode as well as a large Laplace pressure asymmetry between the front and rear sides of the liquid. Moreover, the antigravity climbing, circuit isolating, and chemical reaction controlling can be achieved based on the excellent liquid unidirectional transport. It is envisioned that the design can be extensively applied in microfluidics, lab‐on‐a‐chip devices, and biochemistry microreactors

Molecular Cloning, Heterologous Expression, and Functional Characterization of an NADPH-Cytochrome P450 Reductase Gene from Camptotheca acuminata, a Camptothecin-Producing Plant.

Camptothecin (CAM), a complex pentacyclic pyrroloqinoline alkaloid, is the starting material for CAM-type drugs that are well-known antitumor plant drugs. Although many chemical and biological research efforts have been performed to produce CAM, a few attempts have been made to uncover the enzymatic mechanism involved in the biosynthesis of CAM. Enzyme-catalyzed oxidoreduction reactions are ubiquitously presented in living organisms, especially in the biosynthetic pathway of most secondary metabolites such as CAM. Due to a lack of its reduction partner, most catalytic oxidation steps involved in the biosynthesis of CAM have not been established. In the present study, an NADPH-cytochrome P450 reductase (CPR) encoding gene CamCPR was cloned from Camptotheca acuminata, a CAM-producing plant. The full length of CamCPR cDNA contained an open reading frame of 2127-bp nucleotides, corresponding to 708-amino acid residues. CamCPR showed 70 ~ 85% identities to other characterized plant CPRs and it was categorized to the group II of CPRs on the basis of the results of multiple sequence alignment of the N-terminal hydrophobic regions. The intact and truncate CamCPRs with N- or C-terminal His6-tag were heterologously overexpressed in Escherichia coli. The recombinant enzymes showed NADPH-dependent reductase activity toward a chemical substrate ferricyanide and a protein substrate cytochrome c. The N-terminal His6-tagged CamCPR showed 18- ~ 30-fold reduction activity higher than the C-terminal His6-tagged CamCPR, which supported a reported conclusion, i.e., the last C-terminal tryptophan of CPRs plays an important role in the discrimination between NADPH and NADH. Co-expression of CamCPR and a P450 monooxygenase, CYP73A25, a cinnamate 4-hydroxylase from cotton, and the following catalytic formation of p-coumaric acid suggested that CamCPR transforms electrons from NADPH to the heme center of P450 to support its oxidation reaction. Quantitative real-time PCR analysis showed that CamCPR was expressed in the roots, stems, and leaves of C. acuminata seedlings. The relative transcript level of CamCPR in leaves was 2.2-fold higher than that of roots and the stems showed 1.5-fold transcript level higher than the roots. The functional characterization of CamCPR will be helpful to disclose the mysterious mechanisms of the biosynthesis of CAM. The present study established a platform to characterize the P450 enzymes involved in the growth, development, and metabolism of eukaryotic organisms

Results of simulation of efficacy of main intervention strategy options for controlling shigellosis outbreak.

<p>Prevalence = <i>I</i>/<i>N</i> = <i>i</i>, where <i>I</i> is the infectious and <i>N</i> is the total number of persons.</p

Flowchart of development of the SEIARW model (1).

<p>Flowchart of development of the SEIARW model (1).</p

Curve fitting of data from the baseline of the outbreak simulation from October 11 to 29.

<p>Since the local CDC investigated and implemented the actual combined strategies at day 8 in this outbreak, SEIARW model with no intervention was employed for curve fitting during 0∼8 days (pink line), and SEIARW model with combined strategies of S1wIAPW was employed for curve fitting for the days thereafter (green line). Prevalence = <i>I</i>/<i>N</i> = <i>i</i>, where <i>I</i> is the infectious and <i>N</i> is the total number of persons.</p