Identification of genotype 4 Hepatitis E virus binding proteins on swine liver cells

Hepatitis E virus (HEV) is a zoonotic pathogen of which several species of animal were reported as reservoirs. Swine stands out as the major reservoir for HEV infection in humans, as suggested by the close genetic relationship of swine and human virus and cross-species infection of HEV. Up to now, the mechanism of cross-species infection of HEV from swine to humans is still unclear. This study sought to identify receptor element for genotype 4 HEV on swine liver cells using the viral overlay protein binding assay (VOPBA) technique and Mass Spectrometry fingerprinting. A single virus binding band with natural molecular weight about 55 kDa was observed, and mass spectrometry revealed that this virus binding band contained 31 different proteins. Infection inhibition assay suggested that this 55 kDa protein could prevent HEV from infecting its susceptible A549 cell line, which was further confirmed by the HEV genome detecting in the inoculated cells. Further research should be performed to elucidate the accurate receptor of HEV on the swine liver cells

A Fluorescent Sensor for Zinc Detection and Removal Based on Core-Shell Functionalized Fe 3

The magnetic Fe3O4@SiO2 nanoparticles (NPs) functionalized with 8-chloroacetylaminoquinoline as a fluorescent sensor for detection and removal of Zn2+ have been synthesized. The core-shell structures of the nanoparticles and chemical composition have been confirmed by TEM, XRD, FTIR, and XPS techniques. The addition of functionalized Fe3O4@SiO2 NPs into the acetonitrile solution of Zn2+ had an effect of visual color change as well as significant fluorescent enhancement. High-saturated magnetizations (24.7 emu/g) of functionalized Fe3O4@SiO2 NPs could help to separate the metal ions from the aqueous solution. The magnetic sensor exhibited high removal efficiency towards Zn2+ (92.37%). In this work, we provided an easy and efficient route to detect Zn2+ and simultaneously remove Zn2+

Moving Targets Detection with Low-bit Quantization in Distributed Radar on Moving Platforms

Distributed radar with moving platforms can enhance the survivability and detection performance of a system, however, it is difficult to equip these platforms with sufficient communication bandwidth to transmit high-precision observed data, posing a great challenge to the high-performance detection of a distributed radar system. Because low-bit quantization can effectively reduce the computation cost and resource consumption of distributed radar systems, in this paper, we investigate the high-performance detection of multiple moving targets using the distributed radar system on moving platforms by adopting the low-bit quantization strategy. First, according to system resources, multipulse observed data of each node may be quantized with a low-bit quantizer and the likelihood function relative to the quantizer and states of multiple targets are derived. Subsequently, based on the convexity of the likelihood function relative to the unknown reflection coefficients, a joint estimation algorithm is designed for the Doppler shifts and reflection coefficients. Then, a generalized likelihood ratio test based multi-target detector is designed for detecting multiple targets in the surveillance area with unknown states, and deriving the constant false alarm rate detection threshold. Finally, the optimal low-bit quantizer is designed by deriving the asymptotic detection performance of the system, which effectively improves the detection performance and ensures robustness. Simulation experiments are conducted to analyze the detection and estimation performance of the proposed algorithm, thereby demonstrating the effectiveness of the proposed algorithm for weak signals, and showing that the low-bit quantized data can achieve detection and estimation performance close to that of the high-precision (16-bit quantization) data while consuming a complementary 20% of the communication bandwidth. Besides, according to the simulated results, the two-bit quantization strategy may be a trade-off between the detection performance and resource consumption of the distributed radar system

Evaluation of the learning curve for robotic single-anastomosis duodenal–ileal bypass with sleeve gastrectomy

BackgroundThe robotic surgical system is being used in various bariatric procedures. However, only a few studies with very small sample size are present on robotic single-anastomosis duodenal–ileal bypass with sleeve gastrectomy (SADI-S). Moreover, to date, the learning curve of robotic SADI-S has been poorly evaluated yet.ObjectiveThis retrospective study aimed to estimate the learning curve of robotic SADI-S.Methods102 consecutive patients who underwent robotic SADI-S between March 2020 and December 2021 were included. Textbook outcome standard was performed to comprehensively evaluate clinical outcome of robotic SADI-S. Based on the textbook outcome, we evaluated the learning curve of robotic SADI-S by the cumulative sum (CUSUM) method.ResultsThe mean operative time was 186.13 ± 36.91 min. No conversion to laparotomy or deaths occurred during the study period. The rate of complications was 6.9% (n = 7), of which major complications were identified in 2.9% (n = 3), including 2 gastric leakages and 1 respiratory failure. A total of 60 patients reached the textbook outcome standard. The rate of textbook outcome was positive and was steadily increasing after the number of surgical cases accumulated to the 58th case. Taking the 58th case as the boundary, all the patients were divided into the learning stage group (the first 58 patients) and mastery stage group (the last 44 patients). The rate of complications, proportion of abdominal drainage tubes and postoperative hospital stay were significantly higher in the learning stage group compared with the mastery stage group (P < 0.05). No significant difference was observed between the two groups in terms of patient demographic data, operative times, reoperations and readmission.ConclusionRobotic SADI-S is a feasible and reproducible surgical technique with a learning curve of 58 cases

Isolation and characterization of a genotype 4 Hepatitis E virus strain from an infant in China

In the present study, a genotype 4 HEV strain was identified in the fecal specimen from a seven months old infant with no symptom of hepatitis in Shanghai Children's hospital. The full capsid protein gene (ORF2) sequence of this strain was determined by RT-PCR method. Sequence analysis based on the full ORF2 sequence indicated that this HEV strain shared the highest sequence identity (97.6%) with another human HEV strain isolated from a Japanese patient who was infected by genotype 4 HEV during traveling in Shanghai. Phylogenetic analysis showed that this genotype 4 HEV was phylogenetically far from the genotype 4 HEV strain that was commonly prevalent in Shanghai swine group, suggesting that this strain may not come from swine group and not involved in zoonotic transmission in this area

Driving forces and typologies behind household energy consumption disparities in China: A machine learning-based approach

Establishing an intuitive link between driving factors of household energy consumption activities and inequalities is important for the understanding of household heterogeneity in energy consumption behaviours. This paper proposes a novel typology framework based on machine learning approaches and data from 3637 Chinese households in 2014 from 85 cities. Activity-based energy consumption was measured, highlighting inequalities across activities, regions and household types. The results showed significant energy consumption disparities between urban/rural and north/south households, especially in cooking, space heating and vehicle activities. By identifying driving factors of energy consumption, a new household typology classified samples into 6 (all), 6 (urban) and 7 (rural) types. Within these types, households with similar demographic structures, lifestyles and energy consumption habits were clustered. Demographic structure, region, and primary energy demand were used as the basis for the typology. The findings demonstrated how household lifestyle differences explained the cause and underlying driving factors of urban-rural energy consumption inequalities and provided suggestions for city-by-city and type-by-type measurements to support effective low-carbon transformation in cities

Effect of Core Values of General Practice on Adherence of Patients with Diabetes

BackgroundTreatment adherence is closely related to disease control for patients with diabetes. Primary care is general, and continuous, which may satisfy the general and continuous healthcare needs of diabetic patients. But the association of core values of general practices with adherence of diabetic patients is not yet clear.ObjectiveTo explore the effect of core values of general practice (first contact/first line care, continuity, accessibility, comprehensiveness, coordination and patient-oriented) on the adherence (medication adherence, diet adherence, exercise adherence, self-monitoring adherence and regular hospital visits adherence) of type 2 diabetic patients, providing a reference for improving the adherence of such patients by precisely enhancing the core values of general practices.MethodsA survey was conducted between August and September 2019 with a convenience sample of type 2 diabetics receiving contacted family doctor services from Shayuan Community Health Center of Guangzhou using a questionnaire consisting of three parts〔demographic information, the Chinese version of Primary Care Assessment Survey (ASPC) , and Adherence to Out-of-hospital Treatment of Type 2 Diabetics (AOTTD) 〕. Treatment adherence was compared by various personal factors. Multiple linear regression was used to analyze the association of the core values of general practice with treatment adherence.ResultsAltogether, 224 cases who handed in responsive questionnaires were included for final analysis. The average scores of AOTTD, and ASPC of the respondents were (80.57±11.27) and (72.95±11.40) , respectively. The scores of AOTTD differed significantly by sex and understanding level of type 2 diabetes (P<0.05) . The total score of ASPC and the score of its each domain were associated with the total score of AOTTD, or the domain score of regular hospital visits (P<0.10) . The scores of two domains (accessibility and coordination) of the ASPC were associated with the medication adherence score (P<0.10) . The domain score of coordination was associated with the diet adherence score (P<0.10) . The score of each domain of the ASPC (except for coordination) was associated with the self-monitoring adherence score (P<0.10) .ConclusionFor type 2 diabetics, strengthening each of the core values of general practice could contribute to the increase of their treatment adherence, and regular hospital visits adherence. Improving the accessibility of general practice could enhance their medication adherence. Improving the coordination of general practice could enhance their medication adherence and diet adherence. Improving first contact/first line care, continuity, accessibility, comprehensiveness, coordination and patient-oriented values of general practice could contribute to the increase of their medication adherence. But no association was found between the core values of general practice and patients'exercise adherence, which suggests that providing more exercise resources and environmental support for these patients may be a solution