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

Predictive and prognostic value of aurora kinase A combined with tumor-infiltrating lymphocytes in medullary thyroid carcinoma

BackgroundAurora kinase A (AURKA) and tumor-infiltrating lymphocytes (TILs) are both known to play an essential role in tumorigenesis. However, the expression and prognostic value of the AURKA and TILs in medullary thyroid carcinoma (MTC) have not yet been investigated.Patients and methodsSurgical specimens and clinical data of 137 patients diagnosed with MTC were collected. AURKA expression and TILs infiltration were quantified by immunohistochemistry and hematoxylin-eosin staining. Subsequently, the prognostic value of AURKA expression and TIL infiltration in MTC was evaluated.ResultsAURKA was highly expressed in patients with multifocal tumor, cervical lymph node metastasis, and an advanced TNM stage, indicating a high probability of recurrence. AURKA further exhibited a positive correlation with TILs (R = 0.44, P &lt; 0.001). High expression of AURKA combined with a low numbers of TILs (AURKAhigh/TILslow) was identified as an independent prognostic factor for biochemical recurrence (odds ratio: 4.57, 95% confidence interval: 1.54–14.66, P &lt; 0.01) and recurrence-free survival (hazard ratio: 3.64, 95% confidence interval: 1.52–8.71, P &lt; 0.001). The combination of AURKA and TILs apparently improves the prognostic value for biochemical recurrence (area under the curve: 0.751) and structural recurrence (area under the curve: 0.836) of MTC. Notably, AURKAhigh/TILslow demonstrated a high value for prediction of distant or unresectable locoregional recurrence, with an overall accuracy of 86.9%.ConclusionAURKAhigh is associated with the MTC malignancy. The combination of AURKAhigh/TILslow was identified as novel independent prognostic marker in MTC, predicting incurable disease recurrence with high accuracy

Analysis of the Material Basis of Uric Acid-Lowering Activity of Gynura procumbens Extracts

In order to investigate the material basis of the uric acid-lowering activity of Gynura procumbens, G. procumbens extracts obtained by hot reflux extraction with different ethanol concentrations (0%, 30% and 70%) were evaluated for bioactive ingredients, xanthine oxidase (XOD) inhibitory activity and antioxidant activity. The extracts were analyzed and identified by non-targeted metabolomics. Meanwhile, the 30% and 70% ethanol extracts, which exhibited high XOD inhibitory activity, was evaluated for uric acid-lowering activity in a mouse model of hyperuricemia induced by hypoxanthine and potassium oxonate. The results showed that the XOD inhibitory activity of the extracts was significantly positively correlated with the total flavonoid and total organic acid contents (P < 0.05 and P < 0.01, respectively), and the superoxide anion scavenging capacity was significantly positively correlated with the total phenol content (P < 0.01). A total of 705 differential metabolites were detected by non-targeted metabolomics. In vitro experiments revealed that naringenin, 1,5-dicaffeoylquinic acid, α-linolenic acid, ferulic acid and diosmetin were the key contributors to the uric acid-lowering activity of G. procumbens. Both 30% and 70% ethanol extracts alleviated hyperuricemia by lowering serum uric acid and inhibiting XOD (P < 0.01), and alleviated hyperuricemia-induced oxidative liver damage. In this study, through in vivo and in vitro experiments, the uric acid lowering activity of G. procumbens was verified, and the basis of the uric acid lowering substance was discussed, which could provide a theoretical basis for the preparation of uric acid-lowering substances from G. procumbens and the development of functional foods with uric acid-lowering activity

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

Multi-Center Fetal Brain Tissue Annotation (FeTA) Challenge 2022 Results

Segmentation is a critical step in analyzing the developing human fetal brain. There have been vast improvements in automatic segmentation methods in the past several years, and the Fetal Brain Tissue Annotation (FeTA) Challenge 2021 helped to establish an excellent standard of fetal brain segmentation. However, FeTA 2021 was a single center study, and the generalizability of algorithms across different imaging centers remains unsolved, limiting real-world clinical applicability. The multi-center FeTA Challenge 2022 focuses on advancing the generalizability of fetal brain segmentation algorithms for magnetic resonance imaging (MRI). In FeTA 2022, the training dataset contained images and corresponding manually annotated multi-class labels from two imaging centers, and the testing data contained images from these two imaging centers as well as two additional unseen centers. The data from different centers varied in many aspects, including scanners used, imaging parameters, and fetal brain super-resolution algorithms applied. 16 teams participated in the challenge, and 17 algorithms were evaluated. Here, a detailed overview and analysis of the challenge results are provided, focusing on the generalizability of the submissions. Both in- and out of domain, the white matter and ventricles were segmented with the highest accuracy, while the most challenging structure remains the cerebral cortex due to anatomical complexity. The FeTA Challenge 2022 was able to successfully evaluate and advance generalizability of multi-class fetal brain tissue segmentation algorithms for MRI and it continues to benchmark new algorithms. The resulting new methods contribute to improving the analysis of brain development in utero.Comment: Results from FeTA Challenge 2022, held at MICCAI; Manuscript submitted. Supplementary Info (including submission methods descriptions) available here: https://zenodo.org/records/1062864

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