Seroprevalence of Bartonella in Eastern China and analysis of risk factors

<p>Abstract</p> <p>Background</p> <p><it>Bartonella </it>infections are emerging in the Zhejiang Province of China. However, there has been no effort to date to explore the epidemiology of these infections in this region, nor to identify risk factors associated with exposure to <it>Bartonella</it>. The aim of this study was to investigate the seroprevalence of <it>Bartonella </it>in both patients bitten by dogs and blood donors (for control) in Eastern China, and to identify risk factors associated with exposure to <it>Bartonella</it>. As no previous data for this region have been published, this study will provide baseline data useful for <it>Bartonella </it>infection surveillance, control, and prevention.</p> <p>Methods</p> <p>Blood samples were collected from industrial rabies clinic attendees and blood donors living in eight areas of the Zhejiang Province of China, between December 2005 and November 2006. An indirect immunofluorescent antibody test was used to determine the presence of <it>Bartonella </it>in these samples. Risk factors associated with <it>Bartonella </it>exposure were explored using Chi-square tests and logistic regression analysis of epidemiological data relating to the study's participants.</p> <p>Results</p> <p><it>Bartonella </it>antibodies were detected in 19.60% (109/556) of blood samples. Seroprevalence varied among the eight areas surveys, ranging from over 32% in Hangzhou to only 2% in Jiangshan (X²= 28.22, P < 0.001). We detected a significantly higher prevalence of <it>Bartonella </it>antibodies in people who had been bitten by dogs than in blood donors (X²= 13.86, P < 0.001). Seroprevalence of <it>Bartonella </it>was similar among males (18.61%, n = 317) and females (20.92%, n = 239).</p> <p>Conclusions</p> <p><it>Bartonella </it>antibodies were encountered in people living across Zhejiang Province and the seropositivity rate among those exposed to dog bites was significantly higher than that among blood donors, indicating that dog bites may be a risk factor for <it>Bartonella </it>infection.</p

Partial preservation of the normal thyroid gland based on tumor diameter may be possible in small medullary thyroid carcinoma: a two-center 15-year retrospective study

BackgroundAt present, there are some controversies in the formulation of surgical protocol for small medullary thyroid carcinoma(s-MTC). We wanted to explore the feasibility of normal thyroid gland retention in small medullary thyroid carcinoma based on different tumor diameters and its prognostic impact on the tumor.MethodsThe data of patients with stage T1 MTC treated at Tianjin Cancer Hospital and Sichuan Cancer Hospital from 2006 to 2021 were analyzed. The tumor diameters of 0.5 cm and 1.0 cm were used as dividing points. The outcomes were tumor recurrence, metastasis, or patient death. Survival was estimated by the Kapan–Meier curve.ResultsA total of 121 T1 s-MTC patients were included, including 55 with total thyroidectomy (TT) and 66 with subthyroidectomy (Sub-TT). There were eleven cases of tumor recurrence and metastasis, and four patients died. When the tumor diameter was 1.0 cm as the cut-off point, tumor diameter (p = 0.010), TT (p = 0.028), unilateral and bilateral type (p = 0.009), and TNM staging (p = 0.007) had significant effects on progression-free survival (PFS). The tumor diameter, unilateral and bilateral type, and TT were risk factors for the prognosis of T1 MTC (p &lt; 0.05).ConclusionThe tumor diameter of 1.0 cm can be used as a cut-off point for stage T1 MTC. Alt-hough there was no significant difference in overall survival (OS) between T1a and T1b in patients, tumor diameter significantly influenced PFS. TT is not necessary for patients with sporadic MTC with T1a

Quantitatively analyzing the failure processes of rechargeable Li metal batteries.

Practical use of lithium (Li) metal for high–energy density lithium metal batteries has been prevented by the continuous formation of Li dendrites, electrochemically isolated Li metal, and the irreversible formation of solid electrolyte interphases (SEIs). Differentiating and quantifying these inactive Li species are key to understand the failure mode. Here, using operando nuclear magnetic resonance (NMR) spectroscopy together with ex situ titration gas chromatography (TGC) and mass spectrometry titration (MST) techniques, we established a solid foundation for quantifying the evolution of dead Li metal and SEI separately. The existence of LiH is identified, which causes deviation in the quantification results of dead Li metal obtained by these three techniques. The formation of inactive Li under various operating conditions has been studied quantitatively, which revealed a general “two-stage” failure process for the Li metal. The combined techniques presented here establish a benchmark to unravel the complex failure mechanism of Li metal

P2-Na0.67 Alx Mn1-x O2 : Cost-Effective, Stable and High-Rate Sodium Electrodes by Suppressing Phase Transitions and Enhancing Sodium Cation Mobility.

Sodium layered P2-stacking Na0.67 MnO2 materials have shown great promise for sodium-ion batteries. However, the undesired Jahn-Teller effect of the Mn4+ /Mn3+ redox couple and multiple biphasic structural transitions during charge/discharge of the materials lead to anisotropic structure expansion and rapid capacity decay. Herein, by introducing abundant Al into the transition-metal layers to decrease the number of Mn3+ , we obtain the low cost pure P2-type Na0.67 Alx Mn1-x O2 (x=0.05, 0.1 and 0.2) materials with high structural stability and promising performance. The Al-doping effect on the long/short range structural evolutions and electrochemical performances is further investigated by combining in situ synchrotron XRD and solid-state NMR techniques. Our results reveal that Al-doping alleviates the phase transformations thus giving rise to better cycling life, and leads to a larger spacing of Na+ layer thus producing a remarkable rate capability of 96 mAh g-1 at 1200 mA g-1

Fetal brain tissue annotation and segmentation challenge results

In-utero fetal MRI is emerging as an important tool in the diagnosis and analysis of the developing human brain. Automatic segmentation of the developing fetal brain is a vital step in the quantitative analysis of prenatal neurodevelopment both in the research and clinical context. However, manual segmentation of cerebral structures is time-consuming and prone to error and inter-observer variability. Therefore, we organized the Fetal Tissue Annotation (FeTA) Challenge in 2021 in order to encourage the development of automatic segmentation algorithms on an international level. The challenge utilized FeTA Dataset, an open dataset of fetal brain MRI reconstructions segmented into seven different tissues (external cerebrospinal fluid, gray matter, white matter, ventricles, cerebellum, brainstem, deep gray matter). 20 international teams participated in this challenge, submitting a total of 21 algorithms for evaluation. In this paper, we provide a detailed analysis of the results from both a technical and clinical perspective. All participants relied on deep learning methods, mainly U-Nets, with some variability present in the network architecture, optimization, and image pre- and post-processing. The majority of teams used existing medical imaging deep learning frameworks. The main differences between the submissions were the fine tuning done during training, and the specific pre- and post-processing steps performed. The challenge results showed that almost all submissions performed similarly. Four of the top five teams used ensemble learning methods. However, one team's algorithm performed significantly superior to the other submissions, and consisted of an asymmetrical U-Net network architecture. This paper provides a first of its kind benchmark for future automatic multi-tissue segmentation algorithms for the developing human brain in utero

Fetal Brain Tissue Annotation and Segmentation Challenge Results

In-utero fetal MRI is emerging as an important tool in the diagnosis and analysis of the developing human brain. Automatic segmentation of the developing fetal brain is a vital step in the quantitative analysis of prenatal neurodevelopment both in the research and clinical context. However, manual segmentation of cerebral structures is time-consuming and prone to error and inter-observer variability. Therefore, we organized the Fetal Tissue Annotation (FeTA) Challenge in 2021 in order to encourage the development of automatic segmentation algorithms on an international level. The challenge utilized FeTA Dataset, an open dataset of fetal brain MRI reconstructions segmented into seven different tissues (external cerebrospinal fluid, grey matter, white matter, ventricles, cerebellum, brainstem, deep grey matter). 20 international teams participated in this challenge, submitting a total of 21 algorithms for evaluation. In this paper, we provide a detailed analysis of the results from both a technical and clinical perspective. All participants relied on deep learning methods, mainly U-Nets, with some variability present in the network architecture, optimization, and image pre- and post-processing. The majority of teams used existing medical imaging deep learning frameworks. The main differences between the submissions were the fine tuning done during training, and the specific pre- and post-processing steps performed. The challenge results showed that almost all submissions performed similarly. Four of the top five teams used ensemble learning methods. However, one team's algorithm performed significantly superior to the other submissions, and consisted of an asymmetrical U-Net network architecture. This paper provides a first of its kind benchmark for future automatic multi-tissue segmentation algorithms for the developing human brain in utero.Comment: Results from FeTA Challenge 2021, held at MICCAI; Manuscript submitte

Immunodiagnostics and immunosensor design

This work compiles information on the principles of diagnostic immunochemical methods and the recent advances in this field. It presents an overview of modern techniques for the production of diag- nostic antibodies, their modification with the aim of improving their diagnostic potency, the different types of immunochemical detection systems, and the increasing diagnostic applications for human health that include specific disease markers, individualized diagnosis of cancer subtypes, therapeutic and addictive drugs, food residues, and environmental contaminants. A special focus lies in novel developments of immu- nosensor techniques, promising approaches to miniaturized detection units and the associated microfluidic systems. The trends towards high-throughput systems, multiplexed analysis, and miniaturization of the diag- nostic tools are discussed. It is also made evident that progress in the last few years has largely relied on novel chemical approaches

Efficient modeling methods of large-scale model for Monte Carlo transport simulation

Monte Carlo methods are widely used in simulation of the full-core reactor. They are usually adopted to deal with the geometry model based on the Constructive Solid Geometry. A visual modeling software for the automatic particle transport program, called JLAMT, is developed by the institute of applied physics and computation mathematics. It provides the computing model for Monte Carlo simulation codes, such as Geant4(a software toolkits developed by CERN) and JMCT(a 3D Monte Carlo neutron and photon transport code). To get a better result, the detailed model is needed. For devices as complex as full-core reactors, tens of thousands solids are needed to represent the model. This paper brings up efficient modeling methods of implicit modeling and layer-based modeling for solving this problem. And the effects to the overlap checking are discussed. Taking the full-core reactor of Daya bay power station as an example, experiments show that, by using the efficient modeling methods, both the amount of solids and the time of the overlap checking are reduced

Mosquito surveillance revealed lagged effects of mosquito abundance on mosquito-borne disease transmission: a retrospective study in Zhejiang, China.

Mosquito-borne diseases (MBDs) are still threats to public health in Zhejiang. In this study, the associations between the time-lagged mosquito capture data and MBDs incidence over five years were used to examine the potential effects of mosquito abundance on patterns of MBDs epidemiology in Zhejiang during 2008-2012. Light traps were used to collect adult mosquitoes at 11 cities. Correlation tests with and without time lag were performed to investigate the correlations between MBDs incidence rates and mosquito abundance by month. Selected MBDs consisted of Japanese encephalitis (JE), dengue fever (DF) and malaria. A Poisson regression analysis was performed by using a generalized estimating equations (GEE) approach, and the most parsimonious model was selected based on the quasi-likelihood based information criterion (QICu). We identified five mosquito species and the constituent ratio of Culex pipiens pallens, Culex tritaeniorhynchus, Aedes albopictus, Anopheles sinensis and Armigeres subalbatus was 66.73%, 21.47%, 6.72%, 2.83% and 2.25%, respectively. The correlation analysis without and with time lag showed that Culex mosquito abundance at a lag of 0 or 1 month was positively correlated with JE incidence during 2008-2012, Ae. albopictus abundance at a lag of 1 month was positively correlated with DF incidence in 2009, and An. sinensis abundance at a lag of 0-2 months was positively correlated with malaria incidence during 2008-2010. The Poisson regression analysis showed each 0.1 rise of monthly mosquito abundance corresponded to a positive increase of MBD cases for the period of 2008-2012. The rise of mosquito abundance with a lag of 0-2 months increased the risk of human MBDs infection in Zhejiang. Our study provides evidence that mosquito monitoring could be a useful early warning tool for the occurrence and transmission of MBDs

Towards understanding the lithium transport mechanism in garnet-type solid electrolytes: Li+ ions exchanges and their mobility at octahedral/tetrahedral sites

The cubic garnet-type solid electrolyte Li7La3Zr2O12 with aliovalent doping exhibits a high ionic conductivity, reaching up to ∼10−3 S/cm at room temperature. Fully understanding the Li+ transport mechanism including Li+ mobility at different sites is a key topic in this field, and Li7−2x−3yAlyLa3Zr2−xWxO12 (0 ≤ x ≤ 1) are selected as target electrolytes. X-ray and neutron diffraction as well as ac impedance results show that a low amount of aliovalent substitution of Zr with W does not obviously affect the crystal structure and the activation energy of Li+ ion jumping, but it does noticeably vary the distribution of Li+ ions, electrostatic attraction/repulsion, and crystal defects, which increase the lithium jump rate and the creation energy of mobile Li+ ions. For the first time, high-resolution NMR results show evidence that the 24d, 96h, and 48g sites can be well-resolved. In addition, ionic exchange between the 24d and 96h sites is clearly observed, demonstrating a lithium transport route of 24d−96h− 48g−96h−24d. The lithium mobility at the 24d sites is found to dominate the total ionic conductivity of the samples, with diffusion coefficients of 10−9 m2 s−1 and 10−12 m2 s−1 at the octahedral and tetrahedral sites, respectively