Search

IntroductionCoronavirus disease 2019 occurred unexpectedly in late December 2019, it was difficult to immediately develop an effective vaccine or propose targeted medical interventions in the early stages of the outbreak. At this point, non-pharmaceutical interventions (NPIs) are essential components of the public health response to COVID-19. How to combine different NPIs in the early stages of an outbreak to control the spread of epidemics and ensure that the policy combination does not incur high socio-economic costs became the focus of this study.MethodsWe mainly used the fuzzy set qualitative comparative analysis to assess the impact of different combinations of NPIs on the effectiveness of control in the COVID-19 pandemic early stage, using open datasets containing case numbers, country populations and policy responses.ResultsWe showed that the configuration of high morbidity results includes one, which is the combination of non-strict face covering, social isolation and travel restrictions. The configuration of non-high morbidity results includes three, one is strict mask wearing measures, which alone constitute sufficient conditions for interpreting the results; the second is strict testing and contact tracing, social isolation; the third is strict testing and contact tracing, travel restriction. The results of the robustness test showed that the number, components and consistency of the configurations have not changed after changing the minimum case frequency, which proved that the analysis results are reliable.ConclusionIn the early stages of the epidemic, the causes of high morbidity are not symmetrical with the causes of non-high morbidity. Strict face covering is the most basic measure required to prevent and control epidemics, and the combination of non-strict face covering and containment is the most important factor leading to poor prevention and control, and the combination of strict containment and proactive pursuit is the way to achieve superior prevention and control, timely and proactive containment strategies have better prevention and control, and should mobilize the public to cooperate.

The root systems of Brassica species are complex. Eight root system architecture (RSA) traits, including total root length, total root surface area, root average diameter, number of tips, total primary root length, total lateral root length, total tertiary root length, and basal link length, were phenotyped across 379 accessions representing six Brassica species (B. napus, B. juncea, B. carinata, B. oleracea, B. nigra, and B. rapa) using a semi-hydroponic system and image analysis software. The results suggest that, among the assessed species, B. napus and B. oleracea had the most intricate and largest root systems, while B. nigra exhibited the smallest roots. The two species B. juncea and B. carinata shared comparable root system complexity and had root systems with larger root diameters. In addition, 313 of the Brassica accessions were genotyped using a 19K Brassica single nucleotide polymorphism (SNP) array. After filtering by TASSEL 5.0, 6,213 SNP markers, comprising 5,103 markers on the A-genome (covering 302,504 kb) and 1,110 markers on the C-genome (covering 452,764 kb), were selected for genome-wide association studies (GWAS). Two general linear models were tested to identify the genomic regions and SNPs associated with the RSA traits. GWAS identified 79 significant SNP markers associated with the eight RSA traits investigated. These markers were distributed across the 18 chromosomes of B. napus, except for chromosome C06. Sixty-five markers were located on the A-genome, and 14 on the C-genome. Furthermore, the major marker-trait associations (MTAs)/quantitative trait loci (QTLs) associated with root traits were located on chromosomes A02, A03, and A06. Brassica accessions with distinct RSA traits were identified, which could hold functional, adaptive, evolutionary, environmental, pathological, and breeding significance.

ObjectiveThe aim of this study is to understand the job burnout of village doctors during the COVID-19 epidemic and its influencing factors, and to provide a reference for effectively alleviating the job burnout of village doctors.MethodsA cross-sectional survey was conducted among village doctors in S province in December 2021. The survey included a general information questionnaire and the CMBI Burnout Scale. Epidata was used for dual input, and descriptive analysis, t-test, chi-square test, and binary Logistic regression for statistical analysis were used.ResultsA total of 993 village doctors participated in the survey. Most of them were male village doctors (62.84%), with an average age of 46.57 (SD = 7.50). Village doctors believed that the impact of the epidemic on work was serious, with a score of 3.87 ± 0.91. The economic support was small, with a score of 2.31 ± 0.99. The development space was low, with a score of 2.62 ± 0.98. The overall incidence of burnout was 53.47%. In the burnout group, 54.05% were mild, 33.14% were moderate, and 12.81% were severe. The high degree of difficulty in using WeChat (OR = 1.436, 95%CI: 1.229–1.679), high work pressure (OR = 1.857, 95%CI: 1.409–2.449), high risk of practice (OR = 1.138, 95%CI: 1.004–1.289), less economic support (OR = 0.825, 95%CI: 0.684–0.995), less technical support (OR = 0.696, 95%CI: 0.565–0.858), and poor emotional support (OR = 0.632, 95%CI: 0.513–0.780) were more likely to have job burnout.ConclusionBurnout is a common phenomenon among village doctors during the COVID-19 pandemic, which needs to be prevented and alleviated by various measures.

BackgroundHemorrhagic fever with renal syndrome (HFRS) is a zoonotic infectious disease commonly found in Asia and Europe, characterized by fever, hemorrhage, shock, and renal failure. China is the most severely affected region, necessitating an analysis of the temporal incidence patterns in the country.MethodsWe employed Autoregressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), Nonlinear AutoRegressive with eXogenous inputs (NARX), and a hybrid CNN-LSTM model to model and forecast time series data spanning from January 2009 to November 2023 in the mainland China. By comparing the simulated performance of these models on training and testing sets, we determined the most suitable model.ResultsOverall, the CNN-LSTM model demonstrated optimal fitting performance (with Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Mean Absolute Error (MAE) of 93.77/270.66, 7.59%/38.96%, and 64.37/189.73 for the training and testing sets, respectively, lower than those of individual CNN or LSTM models).ConclusionThe hybrid CNN-LSTM model seamlessly integrates CNN’s data feature extraction and LSTM’s recurrent prediction capabilities, rendering it theoretically applicable for simulating diverse distributed time series data. We recommend that the CNN-LSTM model be considered as a valuable time series analysis tool for disease prediction by policy-makers.

ObjectivesThe proportion of middle-aged and older adult people exposed to the Internet continues to grow. Internet use may have an impact on the mental health of the older adult, especially loneliness. This study analyzed the relationship between Internet use and presence of loneliness.MethodsA total of 550 person aged 45 years and above were randomly selected from a province in eastern China at the end of 2022. The outcome variable was presence of loneliness, as measured by self-report. Descriptive analysis, chi-square test and binary logistic analysis were used to analyze the data.Results58.3% of respondents use the Internet. Internet use could reduce the possible of reported loneliness in middle-aged and older adult people (OR = 0.652, 95%CI: 0.465, 0.940), and residence played a moderating role in the relationship between them. Middle-aged and older adults who used the Internet for 1–3 h (OR = 0.464, 95%CI: 0.275, 0.784) and 3–5 h (OR = 0.484, 95%CI: 0.247, 0.946) were less likely to felt lonely than those who used the Internet for less than 1 h per day. In addition, middle-aged and older adult people using the Internet to contact relatives and friends (OR = 0.488, 95%CI:0.292, 0.818), read the news (OR = 0.485, 95%CI:0.277, 0.848), assets management (OR = 0.297, 95%CI:0.109, 0.818) were less likely to report loneliness, while those who made online payment (OR = 3.101, 95%CI:1.413, 6.807) were more likely to report loneliness.ConclusionThere is a significant negative correlation between Internet use and presence of loneliness, but different Internet duration and content have different effects on loneliness in middle-aged and older adult people. We should pay attention to the impact of Internet use on loneliness in middle-aged and older adult people.

BackgroundIntestinal microbiota has been confirmed to influencing the pharmacokinetic processes of a variety of oral drugs. However, the pharmacokinetic effects of the gut microbiota on cyclosporine A, a drug with a narrow therapeutic window, remain to be studied.MethodTwenty-one rats were randomly divided into three groups: (a) control group (CON), (b) antibiotic treatment group (ABT) and (c) fecal microbe transplantation group (FMT). The ABT group was administrated with water containing multiple antibiotics to deplete microorganisms. FMT was with the same treatment, followed by oral administration of conventional rat fecal microorganisms for normalization.ResultThe bioavailability of CSA increased by 155.6% after intestinal microbes were consumed by antibiotics. After intestinal microbiota reconstruction by fecal transplantation, the increased bioavailability was significantly reduced and basically returned to the control group level. Changes in gut microbiota alter the protein expression of CYP3A1, UGT1A1 and P-gp in liver. The expressions of these three proteins in ABT group were significantly lower than those in CON and FMT groups. The relative abundance of Alloprevolleta and Oscillospiraceae UCG 005 was negatively correlated with CSA bioavailability while the relative abundance of Parasutterella and Eubacterium xylanophilum group was negatively correlated with CSA bioavailability.ConclusionIntestinal microbiota affects the pharmacokinetics of CSA by regulating the expression of CYP3A1, UGT1A1 and P-GP.

Reverse transcriptase-quantitative polymerase chain reaction (RT-qPCR) is a reliable technique for quantifying expression levels of targeted genes during various biological processes in numerous areas of clinical and biological research. Selection of appropriate reference genes for RT-qPCR normalization is an elementary prerequisite for reliable measurements of gene expression levels. Here, by analyzing datasets published between 2008 and 2017, we summarized the current trends in reference gene selection for insect gene expression studies that employed the most widely used SYBR Green method for RT-qPCR normalization. We curated 90 representative papers, mainly published in 2013–2017, in which a total of 78 insect species were investigated in 100 experiments. Furthermore, top five journals, top 10 frequently used reference genes, and top 10 experimental factors have been determined. The relationships between the numbers of the reference genes, experimental factors, analysis tools on the one hand and publication date (year) on the other hand was investigated by linear regression. We found that the more recently the paper was published, the more experimental factors it tended to explore, and more analysis tools it used. However, linear regression analysis did not reveal a significant correlation between the number of reference genes and the study publication date. Taken together, this meta-analysis will be of great help to researchers that plan gene expression studies in insects, especially the non-model ones, as it provides a summary of appropriate reference genes for expression studies, considers the optimal number of reference genes, and reviews the average number of experimental factors and analysis tools per study.

Reverse transcriptase-quantitative polymerase chain reaction (RT-qPCR) is a momentous technique for quantifying expression levels of the targeted genes across various biological processes. Selection and validation of appropriate reference genes for RT-qPCR analysis are a pivotal precondition for reliable expression measurement. Henosepilachna vigintioctopunctata is one of the most serious insect pests that attack Solanaceae plants in Asian countries. Recently, the transcriptomes of H. vigintioctopunctata were sequenced, promoting gene functional studies of this insect pest. Unfortunately, the reference genes for H. vigintioctopunctata have not been selected and validated. Here, a total of 7 commonly used reference genes, namely, Actin, GAPDH, RPL13, RPL6, RPL32, RPS18, and ATPB, were selected and assessed for suitability under four experimental conditions, namely, developmental stage, tissue, temperature, and host plant, using RefFinder, which integrates four different analytical tools (Normfinder, geNorm, the ΔCt method, and BestKeeper). The results displayed that RPL13 and RPS18 were the best suitable reference genes for each experimental condition. The relative transcript levels of 2 target genes, lov and TBX1, varied greatly according to normalization with the two most- and least-suited reference genes. Our results will be helpful for improving the accuracy of the RT-qPCR analysis for future functional investigations of target gene expression in H. vigintioctopunctata.

The development of genetically-engineered plants that employ RNA interference (RNAi) to suppress invertebrate pests opens up new avenues for insect control. While this biotechnology shows tremendous promise, the potential for both non-target and off-target impacts, which likely manifest via altered mRNA expression in the exposed organisms, remains a major concern. One powerful tool for the analysis of these un-intended effects is RT-qPCR, a technique for quantifying gene expression using a suite of reference genes for normalization. The seven-spotted ladybeetle Coccinella septempunctata, a commonly-used predator in both classical and augmentative biological controls, is a model surrogate species used in the environmental risk assessment (ERA) of plant incorporated protectants (PIPs). Here, we assessed the suitability of eight reference gene candidates for the normalization and analysis of C. septempunctata v-ATPase A gene expression under both biotic and abiotic conditions. Five computational tools with distinct algorisms, geNorm, Normfinder, BestKeeper, the ΔCt method, and RefFinder, were used to evaluate the stability of these candidates. As a result, unique sets of reference genes were recommended respectively for experiments involving different developmental stages, tissues, and ingested dsRNAs. By providing a foundation for standardized RT-qPCR analysis in C. septempunctata, our work improves the accuracy and replicability of the ERA of PIPs involving RNAi transgenic plants.