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Abstract Objective To explore the optimal models for predicting the formation of high-quality embryos in Poor Ovarian Response (POR) Patients with Progestin-Primed Ovarian Stimulation (PPOS) using machine learning algorithms. Methods A retrospective analysis was conducted on the clinical data of 4,216 POR cycles who underwent in vitro fertilization (IVF) / intracytoplasmic sperm injection (ICSI) at Sichuan Jinxin Xinan Women and Children’s Hospital from January 2015 to December 2021. Based on the presence of high-quality cleavage embryos 72 h post-fertilization, the samples were divided into the high-quality cleavage embryo group (N = 1950) and the non-high-quality cleavage embryo group (N = 2266). Additionally, based on whether high-quality blastocysts were observed following full blastocyst culture, the samples were categorized into the high-quality blastocyst group (N = 124) and the non-high-quality blastocyst group (N = 1800). The factors influencing the formation of high-quality embryos were analyzed using logistic regression. The predictive models based on machine learning methods were constructed and evaluated accordingly. Results Differential analysis revealed that there are statistically significant differences in 14 factors between high-quality and non-high-quality cleavage embryos. Logistic regression analysis identified 14 factors as influential in forming high-quality cleavage embryos. In models excluding three variables (retrieved oocytes, MII oocytes, and 2PN fertilized oocytes), the XGBoost model performed slightly better (AUC = 0.672, 95% CI = 0.636–0.708). Conversely, in models including these three variables, the Random Forest model exhibited the best performance (AUC = 0.788, 95% CI = 0.759–0.818). In the analysis of high-quality blastocysts, significant differences were found in 17 factors. Logistic regression analysis indicated that 13 factors influence the formation of high-quality blastocysts. Including these variables in the predictive model, the XGBoost model showed the highest performance (AUC = 0.813, 95% CI = 0.741–0.884). Conclusion We developed a predictive model for the formation of high-quality embryos using machine learning methods for patients with POR undergoing treatment with the PPOS protocol. This model can help infertility patients better understand the likelihood of forming high-quality embryos following treatment and help clinicians better understand and predict treatment outcomes, thus facilitating more targeted and effective interventions.

Abstract Over the past century, the field of antibody discovery has undergone significant evolution, excluding the current exploration stage of artificial intelligence‐based antibody generation and the often overlooked non‐animal sourced antibody discovery, which typically requires mature in vitro affinity and the selection of high‐quality antigen formulations. This journey has traversed various stages, from methods involving serum‐based antibody acquisition, the isolation of B cells capable of perpetual antibody production through hybridoma technology, to the in‐depth exploration of genetic material using the phage display system, and the current stage involving diverse single B cell screening techniques. Additionally, the emergence of machine learning has brought impressive scientific and technological breakthroughs across research domains, proving to be a powerful application in the field of antibody discovery. However, each technique comes with its limitations, such as variability and control challenges in serum‐based acquisition, lengthy and difficult hybridoma‐derived antibody development, potential limitations in sequence and epitope diversity due to immunization biases in phage display techniques, and costly single B cell screening. Protein mass spectrometry sequencing, with shorter acquisition time and lower costs, is seen as a shortcut by diagnostic companies, impacting traditional antibody development. In diagnostic antibody development, methodological differences in downstream assays and the impact of constant regions outside the Fv core are often neglected. This paper deeply analyzes challenges, proposing innovative strategies for the next generation of diagnostic antibody development. Aimed at moving closer to the gold standard of antibody discovery, these strategies enhance the competitiveness of diagnostic reagent products.

Abstract Current influenza vaccines need to be updated annually owing to constant antigenic drift in the globular head of the viral surface hemagglutinin (HA) glycoprotein. The immunogenic subdominant stem domain of HA is highly conserved and can be recognized by antibodies capable of binding multiple HA subtypes. Therefore, the HA stem antigen is a promising target for the design of universal influenza vaccines. On the basis of an established lipid nanoparticle‐encapsulated mRNA vaccine platform, we designed and developed a novel universal influenza mRNA vaccine (mHAs) encoding the HA stem antigen of the influenza A (H1N1) virus. We tested the efficacy of the mHAs vaccine using a mouse model. The vaccine induced robust humoral and specific cellular immune responses against the stem region of HA. Importantly, two doses of the mHAs vaccine fully protected mice from lethal challenges of the heterologous H1N1 and heterosubtypic H5N8 influenza viruses. Vaccinated mice had less pathological lung damage and lower viral titers than control mice. These results suggest that an mRNA vaccine using the conserved stem region of HA may provide effective protection against seasonal and other possible influenza variants.

Background and objectiveStent-assisted coil (SAC) embolization is a commonly used endovascular treatment for unruptured intracranial aneurysms (UIAs) but can be associated with symptomatic delayed intracerebral hemorrhage (DICH). Our study aimed to investigate the hemodynamic risk factors contributing to DICH following SAC embolization and to establish a classification for DICH predicated on hemodynamic profiles.MethodsThis retrospective study included patients with UIAs located in the internal carotid artery (ICA) treated with SAC embolization at our institution from January 2021 to January 2022. We focused on eight patients who developed postoperative DICH and matched them with sixteen control patients without DICH. Using computational fluid dynamics, we evaluated the hemodynamic changes in distal arteries [terminal ICA, the anterior cerebral artery (ACA), and middle cerebral artery (MCA)] pre-and post-embolization. We distinguished DICH-related arteries from unrelated ones (ACA or MCA) and compared their hemodynamic alterations. An imbalance index, quantifying the differential in flow velocity changes between ACA and MCA post-embolization, was employed to gauge the flow distribution in distal arteries was used to assess distal arterial flow distribution.ResultsWe identified two types of DICH based on postoperative flow alterations. In type 1, there was a significant lower in the mean velocity increase rate of the DICH-related artery compared to the unrelated artery (−47.25 ± 3.88% vs. 42.85 ± 3.03%; p

Highly contagious respiratory illnesses like influenza and COVID-19 pose serious risks to public health. A two-in-one vaccine would be ideal to avoid multiple vaccinations for these diseases. Here, we generated a chimeric receptor binding domain of the spike protein (S-RBD) and hemagglutinin (HA)-stalk-based vaccine for both SARS-CoV-2 and influenza viruses. The S-RBD from SARS-CoV-2 Delta was fused to the headless HA from H1N1 (H1Delta), creating a chimera that forms trimers in solution. The cryo-electron microscopy structure of the chimeric protein complexed with the RBD-targeting CB6 and the HA-stalk-targeting CR9114 antibodies shows that the trimeric protein is stable and accessible for neutralizing antibody binding. Immunization with the vaccine elicited high and long-lasting neutralizing antibodies and effectively protected mice against the challenges of lethal H1N1 or heterosubtypic H5N8, as well as the SARS-CoV-2 Delta or Omicron BA.2 variants. Overall, this study offers a two-in-one universal vaccine design to combat infections caused by both SARS-CoV-2 variants of concern and influenza viruses.

Lung cancer patients tend to have strong intratumoral and intertumoral heterogeneity and complex tumor microenvironment, which are major contributors to the efficacy of and drug resistance to immunotherapy. From a new perspective, single-cell techniques offer an innovative way to look at the intricate cellular interactions between tumors and the immune system and help us gain insights into lung cancer and its response to immunotherapy. This article reviews the application of single-cell techniques in lung cancer, with focuses directed on the heterogeneity of lung cancer and the efficacy of immunotherapy. This review provides both theoretical and experimental information for the future development of immunotherapy and personalized treatment for the management of lung cancer.

Abstract Background Family education investment is a key factor in reducing intergenerational transmission of poverty. At the price of higher health risk, the poor may overdraw their bodies to earn more money to invest in education. This study investigates the effect of physical overdraft, health risks and health insurance on poverty and economic growth. Methods This paper proposes an economic development model of endogenous health risks and poverty by setting up a physical overdraft decision. Furthermore, we introduce mutual health insurance mechanism to analyze its poverty alleviation effects. Results First, this study shows that health risks weaken the economy and are among the leading causes of poverty. Second, mutual health insurance can alleviate, but not completely eliminate, the negative impact of health risks on the economy. Third, appropriate health insurance arrangements can lift some or even all poor households out of poverty. Conclusion Health risks have a significant effect on poverty. Furthermore, health insurance mechanisms have the advantages of transferring health risks, reducing poverty and improving health equity.

Thyroid tumors, one of the common tumors in the endocrine system, while the discrimination between benign and malignant thyroid tumors remains insufficient. The aim of this study is to construct a diagnostic model of benign and malignant thyroid tumors, in order to provide an emerging auxiliary diagnostic method for patients with thyroid tumors. The patients were selected from the Chongqing General Hospital (Chongqing, China) from July 2020 to September 2021. And peripheral blood, BRAFV600E gene, and demographic indicators were selected, including sex, age, BRAFV600E gene, lymphocyte count (Lymph#), neutrophil count (Neu#), neutrophil/lymphocyte ratio (NLR), platelet/lymphocyte ratio (PLR), red blood cell distribution width (RDW), platelets count (PLT), red blood cell distribution width—coefficient of variation (RDW–CV), alkaline phosphatase (ALP), and parathyroid hormone (PTH). First, feature selection was executed by univariate analysis combined with least absolute shrinkage and selection operator (LASSO) analysis. Afterward, we used machine learning algorithms to establish three types of models. The first model contains all predictors, the second model contains indicators after feature selection, and the third model contains patient peripheral blood indicators. The four machine learning algorithms include extreme gradient boosting (XGBoost), random forest (RF), light gradient boosting machine (LightGBM), and adaptive boosting (AdaBoost) which were used to build predictive models. A grid search algorithm was used to find the optimal parameters of the machine learning algorithms. A series of indicators, such as the area under the curve (AUC), were intended to determine the model performance. A total of 2,042 patients met the criteria and were enrolled in this study, and 12 variables were included. Sex, age, Lymph#, PLR, RDW, and BRAFV600E were identified as statistically significant indicators by univariate and LASSO analysis. Among the model we constructed, RF, XGBoost, LightGBM and AdaBoost with the AUC of 0.874 (95% CI, 0.841–0.906), 0.868 (95% CI, 0.834–0.901), 0.861 (95% CI, 0.826–0.895), and 0.837 (95% CI, 0.802–0.873) in the first model. With the AUC of 0.853 (95% CI, 0.818–0.888), 0.853 (95% CI, 0.818–0.889), 0.837 (95% CI, 0.800–0.873), and 0.832 (95% CI, 0.797–0.867) in the second model. With the AUC of 0.698 (95% CI, 0.651–0.745), 0.688 (95% CI, 0.639–0.736), 0.693 (95% CI, 0.645–0.741), and 0.666 (95% CI, 0.618–0.714) in the third model. Compared with the existing models, our study proposes a model incorporating novel biomarkers which could be a powerful and promising tool for predicting benign and malignant thyroid tumors.

BackgroundCoronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused significant loss of life and property. In response to the serious pandemic, recently developed vaccines against SARS-CoV-2 have been administrated to the public. Nevertheless, the research on human immunization response against COVID-19 vaccines is insufficient. Although much information associated with vaccine efficacy, safety and immunogenicity has been reported by pharmaceutical companies based on laboratory studies and clinical trials, vaccine evaluation needs to be extended further to better understand the effect of COVID-19 vaccines on human beings.MethodsWe performed a comparative peptidome analysis on serum samples from 95 participants collected at four time points before and after receiving CoronaVac. The collected serum samples were analyzed by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) to profile the serum peptides, and also subjected to humoral and cellular immune response analyses to obtain typical immunogenicity information.ResultsSignificant difference in serum peptidome profiles by MALDI-TOF MS was observed after vaccination. By supervised statistical analysis, a total of 13 serum MALDI-TOF MS feature peaks were obtained on day 28 and day 42 of vaccination. The feature peaks were identified as component C1q receptor, CD59 glycoprotein, mannose-binding protein C, platelet basic protein, CD99 antigen, Leucine-rich alpha-2-glycoprotein, integral membrane protein 2B, platelet factor 4 and hemoglobin subunits. Combining with immunogenicity analysis, the study provided evidence for the humoral and cellular immune responses activated by CoronaVac. Furthermore, we found that it is possible to distinguish neutralizing antibody (NAbs)-positive from NAbs-negative individuals after complete vaccination using the serum peptidome profiles by MALDI-TOF MS together with machine learning methods, including random forest (RF), partial least squares-discriminant analysis (PLS-DA), linear support vector machine (SVM) and logistic regression (LR).ConclusionsThe study shows the promise of MALDI-TOF MS-based serum peptidome analysis for the assessment of immune responses activated by COVID-19 vaccination, and discovered a panel of serum peptides biomarkers for COVID-19 vaccination and for NAbs generation. The method developed in this study can help not only in the development of new vaccines, but also in the post-marketing evaluation of developed vaccines.

The COVID-19 pandemic caused by SARS-CoV-2 is exerting huge pressure on global healthcare. Understanding of the molecular pathophysiological alterations in COVID-19 patients with different severities during disease is important for effective treatment. In this study, we performed proteomic profiling of 181 serum samples collected at multiple time points from 79 COVID-19 patients with different severity levels (asymptomatic, mild, moderate, and severe/critical) and 27 serum samples from non-COVID-19 control individuals. Dysregulation of immune response and metabolic reprogramming was found in severe/critical COVID-19 patients compared with non-severe/critical patients, whereas asymptomatic patients presented an effective immune response compared with symptomatic COVID-19 patients. Interestingly, the moderate COVID-19 patients were mainly grouped into two distinct clusters using hierarchical cluster analysis, which demonstrates the molecular pathophysiological heterogeneity in COVID-19 patients. Analysis of protein-level alterations during disease progression revealed that proteins involved in complement activation, the coagulation cascade and cholesterol metabolism were restored at the convalescence stage, but the levels of some proteins, such as anti-angiogenesis protein PLGLB1, would not recovered. The higher serum level of PLGLB1 in COVID-19 patients than in control groups was further confirmed by parallel reaction monitoring (PRM). These findings expand our understanding of the pathogenesis and progression of COVID-19 and provide insight into the discovery of potential therapeutic targets and serum biomarkers worth further validation.

The detection of surface parameters of pressure vessel welds guarantees safe operation. To address the problems of low efficiency and poor accuracy of traditional manual inspection methods, a method for welding morphological parameters combined with vision and structured light is proposed in this study. First, a feature point extraction algorithm for weld parameters based on deep convolution was proposed. An accurate extraction method of weld image feature point coordinates was designed based on the combination of the loss function via seam undercut feature recognition and weld feature point extraction network structure. Second, a training data enhancement method based on the third-order non-uniform rational B-spline (NURBS) curve was proposed to reduce the amount of data collection for training. Finally, a pressure vessel measurement device was designed, and the feature point extraction performance of the deep network and common feature point extraction networks, DeepLabCut and HR-net, proposed in this study were compared to analyze the theoretical accuracy of the surface parameter measurement. The results indicated that the theoretical accuracy of the parameter measurements was within 0.065 mm.

ObjectiveThe purpose of this study was to evaluate the efficacy of denosumab treatment in patients with rheumatoid arthritis (RA).MethodsThe Medline, Embase and Cochrane Library databases were searched for relevant clinical studies. Studies that assessed the efficacy of denosumab in patients with RA were identified. The primary endpoints were the percent changes in bone mineral density (BMD), and the changes in modified total Sharp score (mTSS), modified Sharp erosion score and joint space narrowing (JSN) score. Pooled analyses were calculated using random-effect models.ResultsAfter searching the literature and performing further detailed assessments, 10 studies with a total of 1758 patients were included in the quantitative analysis. Pooled analyses showed that denosumab treatment significantly increased the percent changes in lumbar spine BMD [mean difference (MD): 5.12, confidence intervals (CI): 4.15 to 6.09], total hip BMD (MD: 2.72, 95% CI: 1.80 to 3.64) and femoral neck BMD (MD: 2.20, 95% CI: 0.94 to 3.46) compared with controls. Moreover, denosumab treatment significantly decreased the changes in mTSS (MD: -0.63, 95% CI: -0.86 to -0.41) and modified Sharp erosion score (MD: -0.62, 95% CI: -0.88 to -0.35). Subgroup analysis indicated that denosumab was superior to bisphosphonates for the improvement of BMD and the mitigation of joint destruction.ConclusionDenosumab treatment was associated with increased BMD and alleviated progression of joint destruction in RA patients, even when compared with bisphosphonates.

Coronavirus disease 2019 (COVID-19) remains a major health challenge globally. Previous studies have suggested that changes in the glycosylation of IgG are closely associated with the severity of COVID-19. This study aimed to compare the profiles of IgG N-glycome between COVID-19 patients and healthy controls. A case-control study was conducted, in which 104 COVID-19 patients and 104 age- and sex-matched healthy individuals were recruited. Serum IgG N-glycome composition was analyzed by hydrophilic interaction liquid chromatography with the ultra-high-performance liquid chromatography (HILIC-UPLC) approach. COVID-19 patients have a decreased level of IgG fucosylation, which upregulates antibody-dependent cell cytotoxicity (ADCC) in acute immune responses. In severe cases, a low level of IgG sialylation contributes to the ADCC-regulated enhancement of inflammatory cytokines. The decreases in sialylation and galactosylation play a role in COVID-19 pathogenesis via the activation of the lectin-initiated alternative complement pathway. IgG N-glycosylation underlines the complex clinical phenotypes of SARS-CoV-2 infection.

Understanding COVID-19 induced mortality risk is significant for life insurers to better analyze their financial sustainability after the outbreak of COVID-19. To capture the mortality effect caused by COVID-19 among all ages, this study proposes a temporary adverse mortality jump model to describe the dynamics of mortality in a post-COVID-19 pandemic world based on the weekly death numbers from 2015 to 2021 in the United States. As a comparative study, the Lee-Carter model is used as the base case to represent the dynamics of mortality without COVID-19. Then we compare the force of mortality, the survival probability and the liability of a life insurer by considering COVID-19 and those without COVID-19. We show that a life insurer's financial sustainability will deteriorate because of the higher mortality rates than expected in the wake of COVID-19. Our results remain unchanged when we also consider the effect of interest rate risk by adopting the Vasicek and CIR models.

The effect of calcium sensing receptor (CaSR) on tumor cell proliferation has been studied in several human cancers, and great discrepancies were found in different tumors. However, the role of CaSR in lung adenocarcinomas (LUADs) is not clear. Therefore, we investigated the function of CaSR on regulating the growth of human LUAD and its possible mechanism. The expression of CaSR protein and its relationship with pathological parameters were examined in paraffin sections from 51 LUAD patients, by immunohistochemistry. The results showed that CasR expression was negatively correlated with the Ki-67 index as well as the grade of malignancy in LUAD. Further, CaSR demonstrated an in vitro inhibitory effect on the proliferation of human LUAD A549 cells by regulating CaSR activity with agonist cinacalcet, antagonist NPS2143, or shRNA-CaSR transfection. Tumor xenograft models also verified the in vivo proliferation-inhibiting role of CaSR by subcutaneous injecting A549 cells into nude mice with or without changes of CaSR activity. Molecularly, Western blotting showed that CaSR positively regulated the activity of glycogen synthase kinase 3β (GSK3β), followed by the downregulation of Cyclin D1. We used the dominant negative mutant and the constitutively active mutant plasmid of GSK3β to alter GSK3β activity. Our functional experiments showed that the proliferation–inhibition of CaSR was suppressed by the inactivation of GSK3β and enhanced by the activation of GSK3β. These results suggested that CaSR played a proliferation-inhibiting role in LUAD, at least partially by regulating the GSK3β/Cyclin D1 pathway.

Hepatitis C virus (HCV) genotype (GT) distribution in China shows significant geographical and demographic difference. As a routinely tested virus in Chinese blood bank systems, rare molecular epidemiology research in blood donors is reported. Our purpose is to investigate the HCV GT/subtypes distribution, phylogenetic analysis and population genetics in Chinese blood donors. Anti-HCV screen positive samples and donor demographics were collected. HCV Core and E1 gene fragments were amplified by RT-PCR, followed by sequencing and phylogenetic analysis to determine HCV GTs/subtypes using MEGA 7.0. The population genetics were performed using Arlequin v3.0 and Beast v1.10.4. SPSS Statistics 17.0 software was used to analyze the correlation between HCV GTs/subtypes distribution and demographic characteristics. 419 and 293 samples based on Core and E1 gene respectively were successfully amplified. HCV la, lb, 2a, 3a, 3b, 6a, 6e and 6n were found, and the corresponding proportions were 0.66% (3/455), 58.68% (267/455), 17.80% (81/455) and 5.05% (23/455), 3.52% (16/455), 12.31% (56/455), 0.88% (4/455) and 0.66% (3/455). Samples from Guangxi showed the most abundant genetic diversity with 8 subtypes were found. The number of haplotypes in HCV-1b is higher than 2a and 6a. The negative Tajima's D and Fu's Fs values of HCV-1b, 2a and 6a suggested the population expansion of those HCV subtypes. The distribution of HCV GT showed significant statistical difference by age and ethnicity. Conclusion: An abundance of HCV genetic diversity was found in Chinese blood donors with mainly 1b and then 2a subtype. There were significant geographical and demographic differences in HCV GTs/subtypes among Chinese blood donors. HCV subtype 1b has stronger viability and HCV subtype 6a has experienced significant expansion.

Crop insurance in China is currently adopting the premium pricing strategy of “One Province One Rate”, which appears to be in line with the systematic risk characteristics within crop insurance. This research aims to investigate the theoretical rationalization of this pricing strategy and its implications using the spatial lag model and the county-level data from the 45 corn plant counties of Jilin Province, China. Results corroborate that: (1) the spatial spillover effect of the corn yield risk is significant in Jilin but decreases rapidly when the risk unit includes more than eight counties; and (2) separating Jilin Province into eight risk zones for corn insurance will significantly reduce the high cross-subsidy phenomenon arising from the “One Province One Rate” strategy and shall benefit poor peasants in the region as well. This paper not only proves the existence of a systematic risk of crop insurance but also reveals that the spatial correlation and systemic features of the crop yield risk do not create a solid foundation for the current pricing strategy of “One Province One Rate”. These conclusions will undoubtedly provide important references and empirical evidence for the role of China's crop insurance in poverty alleviation.

Abstract Background Infection with Parvovirus B19 (B19V), Cytomegalovirus (CMV) and Herpes Simplex Virus-1/2 (HSV-1/2) may cause fetal loses including spontaneous abortion, intrauterine fetal death and non-immune hydrops fetalis. Few comprehensive studies have investigated first-trimester spontaneous abortions caused by virus infections in Chongqing, China. Our study intends to investigate the infection of B19V, CMV and HSV-1/2 in first-trimester spontaneous abortions and the corresponding immune response. Methods 100 abortion patients aged from 17 to 47 years were included in our study. The plasma samples (100) were analyzed qualitatively for specific IgG/IgM for B19V, CMV and HSV-1/2 (Virion\Serion, Germany) according to the manufacturer’s recommendations. B19V, CMV and HSV-1/2 DNA were quantification by Real-Time PCR. Results No specimens were positive for B19V, CMV, and HSV-1/2 DNA. By serology, 30.0%, 95.0%, 92.0% of patients were positive for B19V, CMV and HSV-1/2 IgG respectively, while 2% and 1% for B19V and HSV-1/2 IgM. Conclusion The low rate of virus DNA and a high proportion of CMV and HSV-1/2 IgG for most major of abortion patients in this study suggest that B19V, CMV and HSV-1/2 may not be the common factor leading to the spontaneous abortion of early pregnancy.

Background and aim: It is commonly noticed that chaotic and inefficient subgenotyping are universally used academically and clinically, a standardized HBV genotype/subgenotype classification criterion is urgently acquired. Sequence similarity, which was commonly used for the last three decades, should be upgraded by phylogenetic analysis in genotyping of recombinant-free HBV strains. Methods: In this study, 4,429 HBV whole-genome sequences were employed to reconstruct the phylogeny of HBV using Bayesian inference. After excluding recombinant sequences, calculating partitioned evolutionary models, excluding recombinant sequences, reconstructing phylogenetic trees, and performing a correlation analysis of genetic distances, geographical distribution and serotypes, we systematically redefined the genotypes and subgenotypes of HBV. Results: Compared to previous taxonomy, fourteen subgenotypes (A5-A7; B5-B9; C2-C4, C7; and D6-D7) were revised in the new standard. Now the HBV is divided into ten genotypes (A-J) and 24 subgenotypes (A1-A3; B1-B5; C1-C6; D1-D6; and F1-F4). Conclusion: Our robust genotype/subgenotype new taxonomy has objectively re-molded the current shape of HBV classification. We believe that all future hepatitis B related researches or diagnosis will be benefited under the new HBV genotyping/subgenotyping standards.

This paper studied the effects of crop insurance on agricultural output with an economic growth model. Based on Ramsey-Cass-Koopmans (RCK) model, a basic model of agriculture economic growth was developed. Extending the basic model to incorporate uncertainty and insurance mechanism, a risk model and a risk-insurance model were built to study the influences of risk and crop insurance on agricultural output. Compared with the steady states of the three models, the following results are achieved: (i) agricultural output decreases if we introduce uncertainty into the risk-free model; (ii) crop insurance promotes agriculture economic growth if insurance mechanism is introduced into the risk model; (iii) premium subsidy constantly improves agricultural output. Our contribution is that we studied the effects of crop insurance and premium subsidy from the perspective of economic growth in a dynamic framework, and proved the output promotion of crop insurance theoretically.

BACKGROUND:Diagnosing obstructive sleep apnea (OSA) is clinically relevant because untreated OSA has been associated with increased morbidity and mortality. The STOP-Bang questionnaire is a validated screening tool for OSA. We conducted a systematic review and meta-analysis to determine the effectiveness of STOP-Bang for screening patients suspected of having OSA and to predict its accuracy in determining the severity of OSA in the different populations. METHODS:A search of the literature databases was performed. Inclusion criteria were: 1) Studies that used STOP-Bang questionnaire as a screening tool for OSA in adult subjects (>18 years); 2) The accuracy of the STOP-Bang questionnaire was validated by polysomnography--the gold standard for diagnosing OSA; 3) OSA was clearly defined as apnea/hypopnea index (AHI) or respiratory disturbance index (RDI) ≥ 5; 4) Publications in the English language. The quality of the studies were explicitly described and coded according to the Cochrane Methods group on the screening and diagnostic tests. RESULTS:Seventeen studies including 9,206 patients met criteria for the systematic review. In the sleep clinic population, the sensitivity was 90%, 94% and 96% to detect any OSA (AHI ≥ 5), moderate-to-severe OSA (AHI ≥15), and severe OSA (AHI ≥30) respectively. The corresponding NPV was 46%, 75% and 90%. A similar trend was found in the surgical population. In the sleep clinic population, the probability of severe OSA with a STOP-Bang score of 3 was 25%. With a stepwise increase of the STOP-Bang score to 4, 5, 6 and 7/8, the probability rose proportionally to 35%, 45%, 55% and 75%, respectively. In the surgical population, the probability of severe OSA with a STOP-Bang score of 3 was 15%. With a stepwise increase of the STOP-Bang score to 4, 5, 6 and 7/8, the probability increased to 25%, 35%, 45% and 65%, respectively. CONCLUSION:This meta-analysis confirms the high performance of the STOP-Bang questionnaire in the sleep clinic and surgical population for screening of OSA. The higher the STOP-Bang score, the greater is the probability of moderate-to-severe OSA.

For enzymatic analysis to quantify a substrate or enzyme, kinetic analysis of reaction curve can be integrated with a proper classical approach. For their integration, they should have consistent slopes and intercepts of linear response and an overlapped region of analyte quantities measurable under optimized conditions. To quantify a substrate after optimizations of tool enzyme activity and reaction duration, the equilibrium method works when the reaction is completed within the reaction duration; otherwise, kinetic analysis of reaction curve applies providing at least seven data with sufficient consumption of substrate. To quantify an enzyme after optimizations of initial substrate concentration and reaction duration, the classical initial rate method works when an estimated initial rate locates within the linear range; otherwise, kinetic analysis of reaction curve applies after the conversion of the quantification index with optimized parameters. This integration strategy has ideal linear ranges and practical efficiency for quantifying an enzyme at moderate substrate levels and for quantifying a substrate at moderate cost on tool enzyme; it has promise to simultaneous assays of multiple enzymes in one reaction vessel each time and ,thus, potential applications to concurrently quantify multiple serum enzymes, screen inhibitors against multiple enzyme targets, and detect multiple serum components by enzymeimmunoassay.

Background/aim: To better optimize the inactivated vaccine-induced immune response and improve vaccine protection efficiency, a preliminary study was conducted on the influencing factors of producing neutralizing antibody (NAb) titers against the inactivated coronavirus disease 2019 (COVID-19) vaccine. Materials and methods: A total of 91 health care volunteers were enrolled from the Immunology Division of the Laboratory Department of Chongqing General Hospital from February to March 2021. The study had a cross-sectional design. All of the volunteers were scheduled to receive a complete dose regimen of the inactivated severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccine and the vaccination interval between 2 doses was 14 days. Clinical and laboratory features were collected for further analysis. Results: The NAb titers gradually increased after COVID-19 vaccination, and 72.53% (n = 66) of the volunteers had NAbs after the second dose. Eight variables, including CD16+CD56+ NK cell level before the first dose (HR = 0.94, p = 0.02), CD16+CD56+ NK cell level after the second dose (HR = 0.94, p = 0.03), interleukin (IL)-2 level before the first dose (HR = 2.09, p = 0.05), mean corpuscular volume (HR = 0.86, p = 0.02), serum urea level (HR = 0.69, p = 0.05), increment of CD19+ B cells (HR = 0.86, p = 0.03), increment of CD4+/CD8+ T cells (HR = 0.21, p = 0.03), and increment of the IL-6 level (HR = 0.75, p = 0.04) demonstrated a correlation with the NAb titers after COVID-19 vaccination. In the multivariate logistical regression analysis, the serum urea level (HR = 2.32, P = 0.03) and increment of CD19+ B cells (HR = 1.96, p = 0.03) were positively correlated with the NAb titers. The principal component analysis effectively distinguished the response after COVID-19 vaccination. The Pearson correlation analysis indicated that the CD19+ B cell level (r = 0.23, p < 0.001) and IL-2 (r = 0.24, p < 0.001) and IL-6 levels (r = 0.22, p < 0.001) were weakly positively correlated with the concentration of NAbs. Conclusion: The NAbs titers of the inactivated vaccines were positively correlated with the ratio of CD19+ B cell, IL-6, and IL-2 levels in the serum, which provide clinical guidance for inactivated SARS-CoV-2 vaccines. [ABSTRACT FROM AUTHOR]