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BackgroundSevere acute pancreatitis (SAP) is characterized by inflammation, with inflammatory immune cells playing a pivotal role in disease progression. This study aims to understand variations in specific immune cell subtypes in SAP, uncover their mechanisms of action, and identify potential biological markers for predicting Acute Pancreatitis (AP) severity.MethodsWe collected peripheral blood from 7 untreated SAP patients and employed single-cell RNA sequencing for the first time to construct a transcriptome atlas of peripheral blood mononuclear cells (PBMCs) in SAP. Integrating SAP transcriptomic data with 6 healthy controls from the GEO database facilitated the analysis of immune cell roles in SAP. We obtained comprehensive transcriptomic datasets from AP samples in the GEO database and identified potential biomarkers associated with AP severity using the “Scissor” tool in single-cell transcriptomic data.ResultsThis study presents the inaugural construction of a peripheral blood single-cell atlas for SAP patients, identifying 20 cell subtypes. Notably, there was a significant decrease in effector T cell subsets and a noteworthy increase in monocytes compared to healthy controls. Moreover, we identified a novel monocyte subpopulation expressing high levels of PPBP and PF4 which was significantly elevated in SAP. The proportion of monocyte subpopulations with high CCL3 expression was also markedly increased compared to healthy controls, as verified by flow cytometry. Additionally, cell communication analysis revealed insights into immune and inflammation-related signaling pathways in SAP patient monocytes. Finally, our findings suggest that the subpopulation with high CCL3 expression, along with upregulated pro-inflammatory genes such as S100A12, IL1B, and CCL3, holds promise as biomarkers for predicting AP severity.ConclusionThis study reveals monocytes’ crucial role in SAP initiation and progression, characterized by distinct pro-inflammatory features intricately linked to AP severity. A monocyte subpopulation with elevated PPBP and CCL3 levels emerges as a potential biomarker and therapeutic target.

IntroductionThis study examined the prevalence and correlates of probable mental health disorders, including psychological distress, somatization, depression, anxiety, phobic anxiety (PHO), obsessive-compulsive disorder (OCD), post-traumatic stress disorder (PTSD), and insomnia among Chinese primary health care (PHC) physicians and nurses amid the post-pandemic period in 2022.MethodRegion-stratified sampling was conducted to recruit a national sample of 4,246 respondents from 31 July 2022 to 12 August 2022. A total of 692 primary healthcare institutions were identified in 30 provincial-level administrative regions of China. An online questionnaire was used for assessing probable mental health disorders using Symptoms Checklist-90-Revised (SCL-90-R) and PTSD Checklist for DSM-5 (PCL-5), and sleeping problems using Insomnia Severity Index (ISI). Data on demographics and work were also collected. Bivariate analysis and multiple logistic regression were conducted to identify significant correlates of probable mental health disorders.ResultsA total of 4,246 valid questionnaires were identified. Results showed that relative to the prevalence of probable mental health disorders among health care workers at the early stage of the pandemic in China, there was an overall decreased prevalence except for somatization, PHO, and OCD among the current PHC physicians and nurses. Multiple logistic regressions showed that significant risk factors of common probable mental health disorders, namely psychological distress, SOM, DEP, ANX, PHO, OCD, PTSD, and insomnia, were female gender, multimorbidity, history of psychiatric disorders, quarantine experience, never asking anyone for help, and overtime work.ConclusionAttention should be given to preexisting psychiatric and multimorbid conditions, social support, and work-related stressors. Regular assessment and psychological interventions are needed to enhance the mental health of PHC professionals even after public health crisis.

Summary: Chronic inflammatory demyelinating polyneuropathy (CIDP) is a rare, immune-mediated disorder in which an aberrant immune response causes demyelination and axonal damage of the peripheral nerves. Genetic contribution to CIDP is unclear and no genome-wide association study (GWAS) has been reported so far. In this study, we aimed to identify CIDP-related risk loci, genes, and pathways. We first focused on CIDP, and 516 CIDP cases and 403,545 controls were included in the GWAS analysis. We also investigated genetic risk for inflammatory polyneuropathy (IP), in which we performed a GWAS study using FinnGen data and combined the results with GWAS from the UK Biobank using a fixed-effect meta-analysis. A total of 1,261 IP cases and 823,730 controls were included in the analysis. Stratified analyses by gender were performed. Mendelian randomization (MR), colocalization, and transcriptome-wide association study (TWAS) analyses were performed to identify associated genes. Gene-set analyses were conducted to identify associated pathways. We identified one genome-wide significant locus at 20q13.33 for CIDP risk among women, the top variant located at the intron region of gene CDH4. Sex-combined MR, colocalization, and TWAS analyses identified three candidate pathogenic genes for CIDP and five genes for IP. MAGMA gene-set analyses identified a total of 18 pathways related to IP or CIDP. Sex-stratified analyses identified three genes for IP among males and two genes for IP among females. Our study identified suggestive risk genes and pathways for CIDP and IP. Functional analyses should be conducted to further confirm these associations.

Abstract Expression quantitative trait methylation (eQTM) analysis identifies DNA CpG sites at which methylation is associated with gene expression. The present study describes an eQTM resource of CpG-transcript pairs derived from whole blood DNA methylation and RNA sequencing gene expression data in 2115 Framingham Heart Study participants. We identified 70,047 significant cis CpG-transcript pairs at p

In fuel assemblies of pressurized water reactors (PWRs), mixing-vane spacer grids are extensively employed to array fuel rods and enhance inter- and intra-subchannel mixing. The turbulent flow downstream of spacer grids is essential knowledge to support the design of spacer grids and mixing vanes. As a high-fidelity approach for turbulence simulation, large eddy simulation (LES) results are assessed with the high-resolution flow field measured with particle image velocimetry (PIV). The LES results reveal a mixing vane effect on vortex structures and Reynolds stresses. The mechanism of vortex generation at the mixing vanes and the development downstream of the spacer grid are visualized by the Q-criterion. The distributions of the root mean square fluctuating velocity and Reynolds stresses decay rapidly downstream of the spacer grid. The secondary flow intensity downstream of the spacer grid predicted by LES is evaluated and follows an exponentially decaying law with distance from the spacer grid.

Impulsive blind deconvolution (IBD) is a popular method to recover impulsive sources for bearing fault diagnosis. Its underpinnings are in the design of objective functions based on prior knowledge of impulsive sources and a transfer function to describe transmission path influences. However, popular objective functions cannot retain waveform impulsiveness and periodicity cyclostationarity simultaneously, and the single convolution operation of IBD methods is insufficient to describe transmission paths composed of multiple linear and nonlinear units. Inspired by the MaxPooling period modulation intensity (MPMI) and convolutional sparse learning (CSL), an adaptive multi-D-norm-driven sparse unfolding deconvolution network (AMD-SUDN) is proposed in this paper. The core strategy is that one target vector with simultaneous impulsiveness and cyclostationarity is constructed automatically through the MPMI; then, this vector is substituted into the multi D-norm to design objective functions. Moreover, an iterative soft threshold algorithm (ISTA) for the CSL model is derived, and its iterative steps are unfolded into one deconvolution network. The algorithm’s performance and the hyperparameter configuration are investigated by a set of numerical simulations. Finally, the proposed AMD-SUDN is applied to detect the impulsive features of bearing faults. All comparative results verify that the proposed AMD-SUDN achieves a better deconvolution accuracy than state-of-the-art IBD methods.

Introduction The independent and causal cardiovascular disease risk factor lipoprotein(a) (Lp(a)) is elevated in >1.5 billion individuals worldwide, but studies have prioritised European populations.Methods Here, we examined how ancestrally diverse studies could clarify Lp(a)’s genetic architecture, inform efforts examining application of Lp(a) polygenic risk scores (PRS), enable causal inference and identify unexpected Lp(a) phenotypic effects using data from African (n=25 208), East Asian (n=2895), European (n=362 558), South Asian (n=8192) and Hispanic/Latino (n=8946) populations.Results Fourteen genome-wide significant loci with numerous population specific signals of large effect were identified that enabled construction of Lp(a) PRS of moderate (R2=15% in East Asians) to high (R2=50% in Europeans) accuracy. For all populations, PRS showed promise as a ‘rule out’ for elevated Lp(a) because certainty of assignment to the low-risk threshold was high (88.0%–99.9%) across PRS thresholds (80th–99th percentile). Causal effects of increased Lp(a) with increased glycated haemoglobin were estimated for Europeans (p value =1.4×10−6), although inverse effects in Africans and East Asians suggested the potential for heterogeneous causal effects. Finally, Hispanic/Latinos were the only population in which known associations with coronary atherosclerosis and ischaemic heart disease were identified in external testing of Lp(a) PRS phenotypic effects.Conclusions Our results emphasise the merits of prioritising ancestral diversity when addressing Lp(a) evidence gaps.

The stiffness confinement method (SCM) is frequently employed to solve the reactor dynamics equations because it confines the stiffness of the problem by frequency transformation. However, the balance between the error and efficiency of the SCM has not been well studied. This paper reports the error analysis of the SCM. The error by SCM is derived mathematically and written as integral error, driven by the integral of the frequency interpolation function. An easy-to-implement adaptive time-stepping (ATS) algorithm is proposed based on the error analysis by controlling the neutron flux amplitude error. First, a fine-step PKE is leveraged to estimate the second-order derivative of the flux amplitude-frequency, which is used to predict the error of the neutron flux amplitude. The low cost of solving the PKE incurs a negligible effect on the algorithm’s efficiency. Second, based on the error analysis, an error estimator proposed to determine an optimal time-step size for the neutron temporal-spatial equation. With a pre-set error tolerance, the ATS algorithm is exempted from the empirical selection of the time-step size in transient simulations. Numerical tests with TWIGL and modified 2D LMW benchmark problems show that the optimal time-step size effectively confines the local truncation error of the flux amplitude within the pre-set tolerance. The ATS algorithm yields a higher accuracy at a commensurate computational cost than calculations with fixed time-steps.

Abstract Background Assessment the impact of disability on mortality among the elderly is vital to healthy ageing. The present study aimed to assess the long-term influence of disability on death in the elderly based on a longitudinal study. Method This study used the Chinese Longitudinal Healthy Longevity Study (CLHLS) data from 2002 to 2014, including 13,666 participants aged 65 years and older in analyses. The Katz ADL index was used to assess disability status and levels. Cumulative mortality rates were estimated by the Kaplan-Meier method. Cox proportional hazards models were conducted to estimate associations between disability and all-cause mortality for overall participants, two age groups as well as specific chronic disease groups. All reported results were adjusted by survey weights to account for the complex survey design. Results During the 12-year follow-up, the death density was 6.01 per 100 person-years. The 3-years’ cumulative mortality rate of nondisabled elderly was 11.9% (95%CI: 10.9, 12.9%). As the level of disability increased, the cumulative mortality rate was from 28.1% (95%CI: 23.0, 33.1%) to 77.6% (95%CI: 63.8, 91.4%). Compared with non-disabled elderly, the multiple-adjusted hazard ratio of death due to disability was 1.68 (95% CI: 1.48, 1.90). The hazard ratios varied from 1.44 (95%CI: 1.23, 1.67) to 4.45 (95%CI: 2.69, 7.38) after classifying the disability levels. The hazard ratios of death in the young-old group (65–79 years) were higher than the old-old group (80 years and over) in both level B (HR = 1.58, 95%CI: 1.25, 2.00 vs. HR = 1.22, 95%CI: 1.06, 1.39, P = 0.029) and level G (HR = 24.09, 95%CI: 10.83, 53.60 vs. HR = 2.56, 95%CI: 1.75, 3.74, P

Abstract Background To study the competency of general practitioners (GPs) in Shanghai, China on prevention and management of type 2 diabetes, also understand factors that may prohibit it. Methods A survey questionnaire with 25 questions was designed based on 2013 Chinese Type 2 Diabetes Prevention Guidelines and Chinese Type 2 Diabetes Prevention Guidelines (Grassroots Edition) and conducted among 789 GPs who work at 54 community healthcare centers (CHCs) within 16 districts at Shanghai, China. Excel 2016 and SPSS 24.0 were used for data analysis, and a difference of P

GWAS have enhanced our understanding for the genetic basis of breast cancer, but the majority of them were performed for European ancestry populations. Here, the authors use a cross-ancestry approach and report seven new variants associated with breast cancer risk among women of African ancestry.

Abstract Purpose Previous studies have shown that serum carcinoembryonic antigen (CEA) is independently associated with metabolic syndrome (MetS). However, these studies were mainly cross-sectional analyses, and cause was not clarified. In the present study, two bidirectional cohort studies were conducted to investigate the bidirectional associations between CEA and MetS using a Chinese male sample cohort. Methods The initial longitudinal cohort included 9629 Chinese males enrolled from January 2010 to December 2015. Two bidirectional cohorts were conducted in the study: subcohort A (from CEA to MetS, n = 6439) included participants without MetS at baseline to estimate the risk of developing incident MetS; subcohort B (from MetS to CEA, n = 8533) included participants without an elevated CEA level (Hyper-CEA) at baseline to examine the risk of developing incident Hyper-CEA. Hazard ratios (HRs) and 95% confidence intervals (CIs) were estimated using Cox proportional hazards models. Results In subcohort A, the incidence densities of MetS among participants with and without Hyper-CEA were 84.56 and 99.28 per 1000 person-years, respectively. No significant effects of Hyper-CEA on incident MetS were observed in subcohort A (HR, 0.89; 95% CI, 0.71 to 1.12; P = 0.326). In subcohort B, a higher incidence density of Hyper-CEA was found among participants with MetS (33.42 and 29.13 per 1000 person-years for those with and without MetS, respectively). For nonsmoking participants aged > 65 years, MetS increased the risk of incident Hyper-CEA (HR, 1.87; 95% CI, 1.09 to 3.20; P = 0.022). Conclusion For the direction of CEA on incident MetS, no significant association was observed. For the direction of MetS on incident Hyper-CEA, MetS in nonsmoking elderly men could increase the risk of incident Hyper-CEA, while this association was not found in other stratified participants. The clinical implications of the association between CEA and MetS should be interpreted with caution.

Abstract: Persons of African ancestry (AA) have a twofold higher risk for multiple myeloma (MM) compared with persons of European ancestry (EA). Genome-wide association studies (GWASs) support a genetic contribution to MM etiology in individuals of EA. Little is known about genetic risk factors for MM in individuals of AA. We performed a meta-analysis of 2 GWASs of MM in 1813 cases and 8871 controls and conducted an admixture mapping scan to identify risk alleles. We fine-mapped the 23 known susceptibility loci to find markers that could better capture MM risk in individuals of AA and constructed a polygenic risk score (PRS) to assess the aggregated effect of known MM risk alleles. In GWAS meta-analysis, we identified 2 suggestive novel loci located at 9p24.3 and 9p13.1 at P < 1 × 10−6; however, no genome-wide significant association was noted. In admixture mapping, we observed a genome-wide significant inverse association between local AA at 2p24.1-23.1 and MM risk in AA individuals. Of the 23 known EA risk variants, 20 showed directional consistency, and 9 replicated at P < .05 in AA individuals. In 8 regions, we identified markers that better capture MM risk in persons with AA. AA individuals with a PRS in the top 10% had a 1.82-fold (95% confidence interval, 1.56-2.11) increased MM risk compared with those with average risk (25%-75%). The strongest functional association was between the risk allele for variant rs56219066 at 5q15 and lower ELL2 expression (P = 5.1 × 10−12). Our study shows that common genetic variation contributes to MM risk in individuals with AA.

The accuracy and real-time performance of the speed measurement method were important factors influencing the accuracy of the seeding spacing monitoring. In this study, three different speed measurement methods (including GNSS (Global Navigation Satellite System) receiver speed measurement, radar speed measurement, and encoder measurement rotation speed) were used to compare and analyze the monitoring results of seeding spacing. The same monitoring system was used to calculate the seeding spacing under three different speed measurement methods. The encoder directly measured the rotation speed of the seeding disc instead of the traditional method of measuring the rotation speed of the driving wheel. The monitoring results of uniform-speed and variable-speed seeding field tests showed that the three speed measurement methods have extremely high correlations, with a correlation coefficient R > 0.95. There was small difference among the three speed measurement methods, and they all met the needs of field seeding operations. After comprehensively considering the use cost and installation complexity, it was recommended to use GNSS receiver speed measurement to monitor the speed of the seeding operation and the real-time seeding spacing.

Summary: Genomic discovery and characterization of risk loci for type 2 diabetes (T2D) have been conducted primarily in individuals of European ancestry. We conducted a multiethnic genome-wide association study of T2D among 53,102 cases and 193,679 control subjects from African, Hispanic, Asian, Native Hawaiian, and European population groups in the Population Architecture Genomics and Epidemiology (PAGE) and Diabetes Genetics Replication and Meta-analysis (DIAGRAM) Consortia. In individuals of African ancestry, we discovered a risk variant in the TGFB1 gene (rs11466334, risk allele frequency (RAF) = 6.8%, odds ratio [OR] = 1.27, p = 2.06 × 10−8), which replicated in independent studies of African ancestry (p = 6.26 × 10−23). We identified a multiethnic risk variant in the BACE2 gene (rs13052926, RAF = 14.1%, OR = 1.08, p = 5.75 × 10−9), which also replicated in independent studies (p = 3.45 × 10−4). We also observed a significant difference in the performance of a multiethnic genetic risk score (GRS) across population groups (pheterogeneity = 3.85 × 10−20). Comparing individuals in the top GRS risk category (40%–60%), the OR was highest in Asians (OR = 3.08) and European (OR = 2.94) ancestry populations, followed by Hispanic (OR = 2.39), Native Hawaiian (OR = 2.02), and African ancestry (OR = 1.57) populations. These findings underscore the importance of genetic discovery and risk characterization in diverse populations and the urgent need to further increase representation of non-European ancestry individuals in genetics research to improve genetic-based risk prediction across populations.

Objectives: The aim of this study was to quantify the relationship between meteorological factors and the occurrence of hand, foot, and mouth disease (HFMD) among children in Shandong Province, China, at a county level, using spatial panel data models. Methods: Descriptive analysis was applied to describe the epidemic characteristics of HFMD from January 2008 to December 2012, and then a global autocorrelation statistic (Moran's I) was used to detect the spatial autocorrelation of HFMD in each year. Finally, spatial panel data models were performed to explore the association between the incidence of HFMD and meteorological factors. Results: Moran's I at the county level were high, from 0.30 to 0.45 (p

Background: Severe fever with thrombocytopenia syndrome (SFTS) is an emerging infectious disease caused by a novel bunyavirus. The spatial distribution has continued to expand, while the areas at potential high risk of SFTS have, to date, remained unclear. Methods: Using ecological factors as predictors, the MaxEnt model was first trained based on the locations of human SFTS occurrence in Shandong Province. The risk prediction map of China was then created by projecting the training model onto the whole country. The performance of the model was assessed using the known locations of disease occurrence in China. Results: The key environmental factors affecting SFTS occurrence were temperature, precipitation, land cover, normalized difference vegetation index (NDVI), and duration of sunshine. The risk prediction maps suggested that central, southwestern, northeastern, and the eastern coast of China are potential areas at high risk of SFTS. Conclusions: The potential high risk SFTS areas are distributed widely in China. The epidemiological surveillance system should be enhanced in these high risk regions.

Sixteen species of the genus Promalactis Meyrick, 1908 from China are described. Among them, twelve species are described as new: P. bifurciprocessa sp. n., P. convexa sp. n., P. papillata sp. n., P. quadratitabularis sp. n., P. quadriloba sp. n., P. ramispinea sp. n., P. scorpioidea sp. n., P. serpenticapitata sp. n., P. similiconvexa sp. n., P. spinosicornuta sp. n., P. strumifera sp. n. and P. uncinispinea sp. n.; the previously unknown male of P. dimolybda Meyrick, 1935 and female of P. flavescens Wang, Zheng & Li, 1997 are described for the first time; P. albipunctata Park & Park, 1998 and P. dierli Lvovsky, 2000 are newly recorded for China. Adults and genitalia are illustrated.

BACKGROUND: Hand, foot, and mouth disease (HFMD) has caused major public health concerns worldwide, and has become one of the leading causes of children death. China is the most serious epidemic area with a total of 3,419,149 reported cases just from 2008 to 2010, and its different geographic areas might have different spatial epidemiology characteristics at different spatial-temporal scale levels. We conducted spatial and spatial-temporal epidemiology analysis to HFMD at county level in Shandong Province, China. METHODS: Based on the China National Disease Surveillance Reporting and Management System, the spatial-temporal database of HFMD from 2007 to 2011 was built. The global autocorrelation statistic (Moran's I) was first used to detect the spatial autocorrelation of HFMD cases in each year. Purely Spatial scan statistics combined with Space-time scan statistic were used to detect epidemic clusters. RESULTS: The annual average incidence rate was 93.70 per 100,000 in Shandong Province. Most HFMD cases (93.94%) were aged within 0-5 years old with an average male-to-female sex ratio 1.71, and the incidence seasonal peak was between April and July. The dominant pathogen was EV71 (47.35%), and CoxA16 (26.59%). HFMD had positive spatial autocorrelation at medium spatial scale level (county level) with higher Moran's I from 0.31 to 0.62 (P