Search

This study examines how multicomponent exercise nursing interventions affect the state of frailty, daily activities, and quality of life in elderly stroke patients with frailty. A total of 125 elderly stroke patients with frailty were randomly assigned to either a control group (n = 62) or an intervention group (n = 63). The control group received standard nursing care, while the intervention group received a multicomponent exercise nursing intervention in addition to standard care. Patients were assessed using the FRAIL Frailty Scale, Modified Barthel Index (MBI), and Short Form Health Survey (SF-36) before the intervention, 4 weeks after the intervention, and 12 weeks after the intervention. Significant differences were observed between the two groups in terms of frailty status, activities of daily living, and quality of life (p

Abstract Febrile seizures (FS) are the most prevalent type of seizures in children. Existing predictive models for FS exhibit limited predictive ability. To build a better-performing predictive model, a retrospective analysis study was conducted on febrile children who visited the Children's Hospital of Shanghai from July 2020 to March 2021. These children were divided into training set (n = 1453), internal validation set (n = 623) and external validation set (n = 778). The variables included demographic data and complete blood counts (CBCs). The least absolute shrinkage and selection operator (LASSO) method was used to select the predictors of FS. Multivariate logistic regression analysis was used to develop a predictive model. The coefficients derived from the multivariate logistic regression were used to construct a nomogram that predicts the probability of FS. The calibration plot, area under the receiver operating characteristic curve (AUC), and decision curve analysis (DCA) were used to evaluate model performance. Results showed that the AUC of the predictive model in the training set was 0.884 (95% CI 0.861 to 0.908, p

Genomic instability facilitates the evolution of cells, tissues, organs, and species. The progression of human malignancies can be regarded as the accumulation of genomic instability, which confers a high evolutionary potential for tumor cells to adapt to continuous changes in the tumor microenvironment. Nasopharyngeal carcinoma (NPC) is a head-and-neck squamous-cell carcinoma closely associated with Epstein–Barr virus (EBV) infection. NPC progression is driven by a combination of accumulated genomic instability and persistent EBV infection. Here, we present a review of the key characteristics of genomic instability in NPC and the profound implications of EBV infection. We further discuss the significance of profiling genomic instability for the assessment of disease progression and treatment efficacy, as well as the opportunities and challenges of targeted therapies for NPC based on its unique genomic instability.

In order to explore the influence of hydraulic cavitation modification on the aggregate structure and physicochemical properties of corn starch, this experiment took corn starch as the research object, and treated it with hydraulic cavitation for 10, 20, 30, 40 and 50 min, respectively. After treatment, the particle size distribution, microscopic morphology structure, crystalline structure, molecular short-range order structure, the swelling power and solubility of corn starch were tested. The results showed that after hydrodynamic cavitation treatment of starch, the average particle size of corn starch granules increased, conical cracks and unevenly distributed circular pores appeared on the surface of starch granules, and the relative crystallinity increased from 22.25% to 29.23%. The degree of molecular short-range order first increased and then decreased, the swelling power increased from 9.87 to 13.10 g·g−1, and the solubility increased from 8.94% to 11.76%. The cavitation effect produced by hydraulic cavitation could change the aggregated structure of corn starch.

Immune cell changes are associated with Kawasaki disease (KD) pathogenesis. Here, using single cell RNA sequencing of PBMC, the authors show monocyte inflammatory genes are over-expressed in KD and TCR and BCR clonotype sequences show oligoclonal expansions after intravenous immunoglobulin therapy.

BackgroundPrimary pulmonary lymphoma (PPL) is defined as clonal abnormal hyperplasia of lung parenchyma or bronchial lymphoid tissue originating from bronchial mucosal tissue. However, PPL is rare, which accounts for approximately 3-4% of extraneurotic lymphomas and 0.5-1% of all primary tumors in the lung. Owing to the lack of any typical clinical symptoms and radiological features, it is challenging to accurately diagnose PPL, which affects its clinical management and prognosis. Considering this, herein, we aim to raise awareness of this disease and help physicians understand the role of 18F-FDG PET/CT in the diagnosis of PPL.MethodA retrospective analysis was performed on the clinical and 18F-FDG PET/CT imaging data of 19 patients diagnosed with PPL by biopsy pathology at our hospital from April 2014 to December 2021.ResultsOf the 19 PPL patients, 15 patients showed clinical symptoms with the most common being fever and cough. In addition, there were 4 cases that had no clinical symptoms, and all of them were MALT lymphoma. In fact, 16 patients were misdiagnosed as lobar pneumonia, lung cancer, tuberculosis, and diffuse interstitial inflammation, representing a misdiagnosis rate of 84.2%. Also, 73.7% were MALT lymphomas, representing the most common pathological pattern, along with 3 DLBCL and 2 T-cell lymphomas. With reguard to CT signs, the air-bronchial sign was found to be the most common, followed by the halo sign and the collapsed leaf sign. On the basis of the predominant radiologic features, lesions were categorized as pneumonic consolidation, nodular/mass type, diffuse interstitial type, and mixed type. The average SUVmax of lesions was 7.23 ± 4.75, the ratio of SUVmax (lesion/liver) was 3.46 ± 2.25, and the ratio of SUVmax (lesion/mediastinal blood pool) was found to be 5.25 ± 3.27. Of interest, the different pathological types of PPL showed different values of 18F-FDG uptake. The 18F-FDG uptake of DLCBL was the most prominent with a SUVmax of 15.33 ± 6.30 and was higher than that of MALT lymphoma with a SUVmax of 5.74 ± 2.65. There appeared similarity in 18F-FDG uptake between MALT lymphoma and T-cell lymphoma. For the SUVmax of lesion, we found statistical significance between MALT lymphoma and DLCBL (P value

Abstract In this paper, we model the insurance company’s surplus by a compound Poisson risk model, where the surplus process can only be observed at random observation times. It is assumed that the insurer observes its surplus level periodically to decide on dividend payments and capital injection at the interobservation time having an Erlang ( n ) $\operatorname{Erlang}(n)$ distribution. If the observed surplus level is greater than zero but less than injection line b 1 > 0 $b_{1} > 0$ , the shareholders should immediately inject a certain amount of capital to bring the surplus level back to the injection line b 1 $b_{1}$ . If the observed surplus level is larger than dividend line b 2 $b_{2}$ ( b 2 > b 1 $b_{2} > b_{1}$ ), any excess of the surplus over b 2 $b_{2}$ is immediately paid out as dividends to the shareholders of the company. Ruin is declared when the observed surplus level is negative. We derive the explicit expressions of the Gerber–Shiu function, the expected discounted capital injection, and the expected discounted dividend payments. Numerical illustrations are also given to analyze the effect of random observation times on actuarial quantities.

Distant metastasis remains the primary cause of treatment failure and suggests a poor prognosis in nasopharyngeal carcinoma (NPC). Epithelial-mesenchymal transition (EMT) is a critical cellular process for initiating a tumor invasion and remote metastasis. Our previous study showed that the blockage of the stromal interaction molecule 1 (STIM1)-mediated Ca2+ signaling blunts the Epstein–Barr virus (EBV)-promoted cell migration and inhibits the dissemination and lymphatic metastasis of NPC cells. However, the upstream signaling pathway that regulates the STIM1 expression remains unknown. In this follow-up study, we demonstrated that the miRNA-185-5p/STIM1 axis is implicated in the regulation of the metastatic potential of 5–8F cells, a highly invasive NPC cell line. We demonstrate that the knockdown of STIM1 attenuates the migration ability of 5–8F cells by inhibiting the epidermal growth factor receptor (EGFR) phosphorylation-induced switch from E- to N-cadherin in vitro. In addition, the STIM1 knockdown inhibited the locoregional lymphatic invasion of the 5–8F cells in mice. Furthermore, we identified miRNA-185-5p as an upstream regulator that negatively regulates the expression of STIM1. Our findings suggest that the miRNA-185-5p/STIM1 axis regulates the invasiveness of NPC cell lines by affecting the EGFR activation-modulated cell adhesiveness. The miRNA-185-5p/STIM1 axis may serve as a potentially effective therapeutic target for the treatment of NPC.

Resting state networks comprise several brain regions that exhibit complex patterns of interaction. Switching from eyes closed (EC) to eyes open (EO) during the resting state modifies these patterns of connectivity, but precisely how these change remains unclear. Here we use functional magnetic resonance imaging to scan healthy participants in two resting conditions (viz., EC and EO). Seven resting state networks were chosen for this study: salience network (SN), default mode network (DMN), central executive network (CEN), dorsal attention network (DAN), visual network (VN), motor network (MN) and auditory network (AN). We performed functional connectivity (FC) analysis for each network, comparing the FC maps for both EC and EO. Our results show increased connectivity between most networks during EC relative to EO, thereby suggesting enhanced integration during EC and greater modularity or specialization during EO. Among these networks, SN is distinctive: during the transition from EO to EC it evinces increased connectivity with DMN and decreased connectivity with VN. This change might imply that SN functions in a manner analogous to a circuit switch, modulating resting state relations with DMN and VN, when transitioning between EO and EC.

The BP neural network model is a hot issue in recent academic research, and it has been successfully applied to many other fields, but few researchers apply the BP neural network model to the field of automobile insurance. The main method that has been used in the prediction of the total claim amount in automobile insurance is the generalized linear model, where the BP neural network model could provide a different approach to estimate the total claim loss. This paper uses a genetic algorithm to optimize the structure of the BP neural network at first, and the calculation speed is significantly improved. At the same time, by considering the overfitting problem, an early stop method is introduced to avoid the overfitting problem. In the model, a three-layer BP neural network model, which includes the input layer, hidden layer, and output layer, is trained. With consideration of various factors, a total claim amount prediction model is established, and the trained BP neural network model is used to predict the total claim amount of automobile insurance based on the data of the training set. The results show that the accuracy of the prediction by using the BP neural network model to both the data of Shandong Province and to the data of six cities is over 95%. Then, the predicted total claim amount is used to calculate premiums for five cities in Shandong Province according to credibility theory. The results show that the average premium of the five cities is slightly higher than the actual claim amount of the city. The combination of BP neural network and credibility theory can perform accurate claim amount estimation and pricing for automobile insurance, which can effectively improve the current situation of the automobile insurance business and promote the development of insurance industry.

Early warning of plant diseases and pests is critical to ensuring food safety and production for economic crops. Data sources such as the occurrence, frequency, and infection locations are crucial in forecasting plant diseases and pests. However, at present, acquiring such data relies on fixed-point observations or field experiments run by agricultural institutions. Thus, insufficient data and low rates of regional representative are among the major problems affecting the performance of forecasting models. In recent years, the development of mobile internet technology and conveniently accessible multi-source agricultural information bring new ideas to plant diseases’ and pests’ forecasting. This study proposed a forecasting model of Alternaria Leaf Spot (ALS) disease in apple that is based on mobile internet disease survey data and high resolution spatial-temporal meteorological data. Firstly, a mobile internet-based questionnaire was designed to collect disease survey data efficiently. A specific data clean procedure was proposed to mitigate the noise in the data. Next, a sensitivity analysis was performed on the temperature and humidity data, to identify disease-sensitive meteorological factors as model inputs. Finally, the disease forecasting model of the apple ALS was established using four machine learning algorithms: Logistic regression(LR); Fisher linear discriminant analysis(FLDA); Support vector machine(SVM); and K-Nearest Neighbors (KNN). The KNN algorithm is recommended in this study, which produced an overall accuracy of 88%, and Kappa of 0.53. This paper shows that through mobile internet disease survey and a proper data clean approach, it is possible to collect necessary data for disease forecasting in a short time. With the aid of high resolution spatial-temporal meteorological data and machine learning approaches, it is able to achieve disease forecast at a regional scale, which will facilitate efficient disease prevention practices.

BACKGROUND:Placental protein expression plays a crucial role during pregnancy. We hypothesized that: (1) circulating levels of pregnancy-associated, placenta-related proteins throughout gestation reflect the temporal progression of the uncomplicated, full-term pregnancy, and can effectively estimate gestational ages (GAs); and (2) preeclampsia (PE) is associated with disruptions in these protein levels early in gestation; and can identify impending PE. We also compared gestational profiles of proteins in the human and mouse, using pregnant heme oxygenase-1 (HO-1) heterozygote (Het) mice, a mouse model reflecting PE-like symptoms. METHODS:Serum levels of placenta-related proteins-leptin (LEP), chorionic somatomammotropin hormone like 1 (CSHL1), elabela (ELA), activin A, soluble fms-like tyrosine kinase 1 (sFlt-1), and placental growth factor (PlGF)-were quantified by ELISA in blood serially collected throughout human pregnancies (20 normal subjects with 66 samples, and 20 subjects who developed PE with 61 samples). Multivariate analysis was performed to estimate the GA in normal pregnancy. Mean-squared errors of GA estimations were used to identify impending PE. The human protein profiles were then compared with those in the pregnant HO-1 Het mice. RESULTS:An elastic net-based gestational dating model was developed (R2 = 0.76) and validated (R2 = 0.61) using serum levels of the 6 proteins measured at various GAs from women with normal uncomplicated pregnancies. In women who developed PE, the model was not (R2 = -0.17) associated with GA. Deviations from the model estimations were observed in women who developed PE (P = 0.01). The model developed with 5 proteins (ELA excluded) performed similarly from sera from normal human (R2 = 0.68) and WT mouse (R2 = 0.85) pregnancies. Disruptions of this model were observed in both human PE-associated (R2 = 0.27) and mouse HO-1 Het (R2 = 0.30) pregnancies. LEP outperformed sFlt-1 and PlGF in differentiating impending PE at early human and late mouse GAs. CONCLUSIONS:Serum placenta-related protein profiles are temporally regulated throughout normal pregnancies and significantly disrupted in women who develop PE. LEP changes earlier than the well-established biomarkers (sFlt-1 and PlGF). There may be evidence of a causative action of HO-1 deficiency in LEP upregulation in a PE-like murine model.

This paper studies the statistical estimation of the Gerber-Shiu discounted penalty functions in a general spectrally negative Lévy risk model. Suppose that the claims process and the surplus process can be observed at a sequence of discrete time points. Using the observed data, the Gerber-Shiu functions are estimated by the Laguerre series expansion method. Consistent properties are studied under the large sample setting, and simulation results are also presented when the sample size is finite.

In this paper, we consider the compound Poisson risk model with stochastic premium income. We propose a new estimation of Gerber-Shiu function by an efficient method: Fourier-cosine series expansion. We show that the estimator is easily computed and has a fast convergence rate. Some simulation examples are illustrated to show that the estimation has a good performance when the sample size is finite.

This paper considers the estimation of ruin probability in an insurance risk model with stochastic premium income. We first show that the ruin probability can be approximated by the complex Fourier series (CFS) expansion method. Then, we construct a nonparametric estimator of the ruin probability and analyze its convergence. Numerical examples are also provided to show the efficiency of our method when the sample size is finite.

Taking mortality distribution, surrender value, and tax relief factors into consideration, the authors construct an actuarial model for the influence of personal income tax deferred commercial pension insurance on changes in personal pension wealth and adopts a numerical simulation to deliver the corresponding changes in personal pension wealth to different initial insured age and different initial insured annual salary. In order to better measure the security level of the commercial pension insurance, the model for the net replacement rate of pension of the commercial pension insurance was further constructed. The results show that the effect of participating in the personal income tax deferred commercial pension insurance on the present value of personal pension wealth depends on the combined action of the initial insured age and the initial annual salary. Under the same insured age, because men retire later and work longer than women, men can obtain a higher accumulation of personal pension wealth than women. For insured persons with different income levels, high-income groups can obtain higher personal pension wealth growth, and although low-income groups cannot obtain higher personal pension wealth growth, they can obtain a significant increase in the pension replacement rate by participating in the insurance, thereby better guaranteeing their living standards after retirement. Regardless of the income level, tax relief can be obtained once participating in the insurance, but the value may vary. The optimal tax-saving age for men is 23 years old, and for women 25 years old.

In this paper, we assume that the reserve level of an insurance company can only be observed at discrete time points, then a new risk model is proposed by introducing a periodic capital injection strategy and a barrier dividend strategy into the classical risk model. We derive the equations and the boundary conditions satisfied by the Gerber-Shiu function, the expected discounted capital injection function and the expected discounted dividend function by assuming that the observation interval and claim amount are exponentially distributed, respectively. Numerical examples are also given to further analyze the influence of relevant parameters on the actuarial function of the risk model.

In this paper, we propose a new generalized Gerber−Shiu discounted penalty function for a compound Poisson risk model, which can be used to study the moments of the ruin time. First, by taking derivatives with respect to the original Gerber−Shiu discounted penalty function, we construct a relation between the original Gerber−Shiu discounted penalty function and our new generalized Gerber−Shiu discounted penalty function. Next, we use Laplace transform to derive a defective renewal equation for the generalized Gerber−Shiu discounted penalty function, and give a recursive method for solving the equation. Finally, when the claim amounts obey the exponential distribution, we give some explicit expressions for the generalized Gerber−Shiu discounted penalty function. Numerical illustrations are also given to study the effect of the parameters on the generalized Gerber−Shiu discounted penalty function.

Recently, the valuation of variable annuity products has become a hot topic in actuarial science. In this paper, we use the Fourier cosine series expansion (COS) method to value the guaranteed minimum death benefit (GMDB) products. We first express the value of GMDB by the discounted density function approach, then we use the COS method to approximate the valuation Equations. When the distribution of the time-until-death random variable is approximated by a combination of exponential distributions and the price of the fund is modeled by an exponential Lévy process, explicit equations for the cosine coefficients are given. Some numerical experiments are also made to illustrate the efficiency of our method.

In this paper, we consider an insurance risk model with mixed premium income, in which both constant premium income and stochastic premium income are considered. We assume that the stochastic premium income process follows a compound Poisson process and the premium sizes are exponentially distributed. A new method for estimating the expected discounted penalty function by Fourier-cosine series expansion is proposed. We show that the estimation is easily computed, and it has a fast convergence rate. Some numerical examples are also provided to show the good properties of the estimation when the sample size is finite.

The absolute ruin insurance risk model is modified by including some valuable market economic information factors, such as credit interest, debit interest and dividend payments. Such information is especially important for insurance companies to control risks. We further assume that the insurance company is able to finance and continue to operate when its reserve is negative. We investigate the integro-differential equations for some interest actuarial diagnostics. We also provide numerical examples to explain the effects of relevant parameters on actuarial diagnostics.

Background and objective Erlotinib is a targeted treatment for advanced non-small cell lung cancer. Smoking status may be one of influencing factors of the efficacy of erlotinib. The aim of this study is to explore the impact of smoking status on the efficacy of erlotinib in patients with advanced non-small cell lung cancer. Methods Patients with nonsmall cell lung cancer who had been previously treated with at least one course of platinum based chemotherapy received 150 mg oral doses of erlotinib once daily until disease progression. Response rate, progression-free survival, overall survival were analyzed in the different smoking status groups. Kaplan-Meier method was used to analyze the survival rate. Results Fortyeight patients were enrolled into the study from December 2005 to September 2006. We followed up these patients until 28th December, 2008. Median follow up time was 30 months. The compliance rate was 100%. The response rate was 32.1% in the smoking group and 35% in the never smoking group (P=0.836); The median progression-free survival was 3 months and 9 months, respectively (P=0.033). The median overall survival was 5 months and 17 months, respectively (P=0.162). Conclusion Erlotinib is an effective drug for advanced non-small cell lung cancer patients with different smoking status. Progressionfree survival is better in the never smoking patients than the smoking patients.

Background and objective All the advanced NSCLC patients that received EGFR-TKI therapy will eventually relapse after a period of efficacy. The aim of this study is to investigate the serum biomarkers as potential predictive factors for the efficacy of epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor (TKI) targeted therapy in advanced non-small cell lung cancer. Methods Twenty self-paired serum samples were collected from 9 advanced NSCLC patients that evaluated as disease control (SD or PR) after gefinitib therapy, at the time points of before and after gefinitib treatment but 2 weeks before being evaluated as disease progress. All samples were pre-separated by WCX microbeads, and then detected on the MALDI-TOF-MS platform of Bruker AutoflexTM. ClinProTools (Version: 2.1) was used to analyze the differentially expressed proteins. Results There were 7 protein peaks (m/z), 3242.09, 8 690.36, 2 952.64, 3 224.04, 1 450.51, 1 887.8 and 3 935.73 found statistically differentially expressed between the self-paired samples. Three proteins (3 242.09, 2 952.64 and 3 224.04) were down-regulated and four proteins (8 690.36, 1 450.51, 1 887.8 and 3 935.73) up-regulated in gefinitib treated sera. Conclusion The data here suggest that several specific protein peaks might indicate gefinitib resistance, yet the identities of these proteins and the mechanisms underlying the responsiveness to gefinitib treatment need further investigation.

Background and objective Erlotinib is a targeted drug for non-small cell lung cancer (NSCLC). The aim of this study is to evaluate the efficacy, influencing factors and toxicity of erlotinib in patients with NSCLC. Methods Patients with NSCLC who had been previously treated with at least one course of platinum based chemotherapyreceived 150 mg oral doses of erlotinib once daily until disease progression. Response rate, progression free survival, overall survival and toxicity profile were analyzed. Kaplan-Meier methods was used to analyze the survival rate. Cox regression was used to define the predictive factors. Results Forty-eight patients were enrolled into the study from Dec, 2005 to Sep, 2006. We followed up these patients until 08.Dec.2008. Median follow up time was 30 months. The compliance rate was 100%. The median symptom improving time was 7 days. Partial response 33.4% (16/48), stable disease 22.9% (11/48), and progressive disease 43.7% (21/48). Response rate was 33.4% (16/48). Disease control rate was 56.3% (27/48). One and two-year progression-free survival rates and overall survival rates were 25％(events 36), 8.3％ (events 40) and 43.8％ (death 27), 20.8％ (death 38); three-year overall survival 5.6％. The median progression-free survival time and median overall survival time was 5 months and 8 months, respectively. Performance status was the only predictor for overall survival in the Cox model (P

This paper mainly studies a generalized double Poisson-Geometric insurance risk model. By martingale and stopping time approach, we obtain adjustment coefficient equation, the Lundberg inequality, and the formula for the ruin probability. Also the Laplace transformation of the time when the surplus reaches a given level for the first time is discussed, and the expectation and its variance are obtained. Finally, we give the numerical examples.