Microbial spectrum and risk factors of endogenous endophthalmitis in a tertiary center of Northern China

AIM: To study the clinical features, microbial spectrum, associated factors and prognosis of endogenous endophthalmitis(EE) in a group of Chinese patients.METHODS: The medical records from 32 eyes of 29 patients diagnosed with EE in Peking Union Medical College Hospital from January 2009 to October 2019 were reviewed. RESULTS: The initial visual acuity(VA) of 30 eyes in this study was worse than 20/400. Twenty-three eyes were diagnosed with fungal endophthalmitis and nine with bacterial endophthalmitis. The most common fungal and bacterial isolates were Candida and Klebsiella pneumoniae, respectively. Several rare fungi and bacteria species were also isolated from our patients, including Cryptococcus, Paecilomyces, Brucella, and Bacillus licheniformis. The leading risk factor for EE was diabetes. The most common extraocular infection locus was genitourinary tract. Vitrectomy was performed on twenty-nine eyes. Eight eyes achieved final VA of 20/400 or better. EE caused by Candida had a better prognosis.CONCLUSION: The visual outcome of EE is based on pathogens and prompt intervention. Early vitrectomy and antimicrobial treatment are beneficial for EE.

Asymmetric relationship between global and national factors and domestic food prices:evidence from Turkey with novel nonlinear approaches

This study investigates the asymmetric relationship between global and national fac-tors and domestic food prices in Turkey,considering the recent rapid and continuous increase in domestic food prices.In this context,six global and three national explana-tory variables were included,and monthly data for the period from January 2004 to June 2021 were used.In addition,novel nonlinear time-series econometric approaches,such as wavelet coherence,Granger causality in quantiles,and quantile-on-quantile regression,were applied for examination at different times,frequencies,and quan-tiles.Moreover,the Toda-Yamamoto(TY)causality test and quantile regression(QR)approach were used for robustness checks.The empirical results revealed that(i)there is a significant relationship between domestic food prices and explanatory variables at different times and frequencies;(ii)a causal relationship exists in most quantiles,excluding the lowest quantile,some middle quantiles,and the highest quantile for some variables;(iii)the power of the effect of the explanatory variables on domestic food prices varies according to the quantiles;and(iv)the results were validated by the TY causality test and QR,which show that the results were robust.Overall,the empiri-cal results reveal that global and national factors have an asymmetric relationship with domestic food prices,highlighting the effects of fluctuations in global and national variables on domestic food prices.Thus,the results imply that Turkish policymakers should consider the asymmetric effects of global and national factors on domestic food prices at different times,frequencies,and quantiles.

The analysis of the influence factors of China＇s real estate prices

The real estate market is an ilnportant part of China＇s socialist market economy. Real estate price xs not only a profound role on the level of consumption of the residents, but a profound role on the steady development of the banking sector-based financial sector. Real estate price have a significant impact not only on the development ofmacroeconomic stability, but on the safety of financial system. Chinese economy is currently on the period of transition from investment and export promotion of economic growth mode to a demand-led growth, it is an important role to China＇s economic growth for sustained and healthy development of real estate market.

Effects of economic factors on median list and selling prices in the U.S.housing market

This study investigates the effects of key economic factors on the median list price and median selling price in the U.S.housing market.Key economic factors such as interest rates,unemployment rates,inflation rates,real gross domestic product,money supply,mortgage rate,Standard&Poor’s(S&P)500,and government expenditure are investigated to understand their relationships with housing prices.Conventional econometric models are typically used for housing market analysis;however,advancements in data science and machine learning allow these relationships to be examined more accurately.This study employs a decision tree regressor,k-nearest neighbors,random forest,and gradient boosting to enhance analysis accuracy and feature selection,thus enriching literature pertaining to machine learning in the housing market domain.The significance of housing market data as an indicator of economic growth is emphasized,and its effect on the overall economy,consumer spending,investment patterns,and financial stability is discussed.By utilizing a robust dataset and performing rigorous preprocessing,this study aims to provide valuable insights for policymakers,investors,and individuals involved in the housing sector.

Impact of Environmental,Social,and Governance(ESG)Factors on Stock Prices and Investment Performance

This study examines the relationship between Environmental,Social,and Governance(ESG)factors and stock prices as well as investment performance.ESG factors have become increasingly relevant in investment decisions as investors prioritize companies with sustainable practices.Using a sample of publicly-traded companies,this research analyzes the impact of ESG factors on stock prices and investment returns.The findings suggest that companies with strong ESG performance tend to have higher stock prices and better investment performance than those with weak ESG performance.The study also highlights the significance of the individual components of ESG,such as environmental policies and corporate governance practices,on stock prices and investment returns.Overall,this research provides valuable insights for investors seeking to incorporate ESG factors into their investment decision-making processes.

Active and Passive Factors of Oil Prices

Price volatility analysis is a basic problem in the price modification,financial risk estimation and management process.Among the global commodities,oil plays an important role in the development of modern industry and economy.Hence the price of crude oil analysis is a hot topic.It is also a difficult topic since there are so many factors associating the price volatilities.And some factors give the different influences in the different periods.Based on data computing,people generally classify the factors into positive and negative ones.But some factors do not affect the price as the nominal effect.For instance,the output of OPEC gave the positive contributions to the oil price in the past long time.Hence,the investigation of the historic WTI oil price is well proposed and the factors are classified into active and passive ones.And then the better explanations are given using this type of classification.

Influence of endogenous ciliary neurotrophic factor on neural differentiation of adult rat hippocampal progenitors

Ciliary neurotrophic factor is the only known neurotrophic factor that can promote differentiation of hippocampal neural progenitor cells to glial cells and neurons in adult rats. This process is similar to spontaneous differentiation. Therefore, ciliary neurotrophic factor may be involved in spontaneous differentiation of neural stem cells. To verify this hypothesis, the present study isolated neural progenitor cells from adult male rats and cultured them in vitro. Results showed that when neural progenitor cells were cultured in the absence of mitogen fibroblast growth factor-2 or epidermal growth factor, they underwent spontaneous differentiation into neurons and glial cells. Western blot and immunocytochemical staining showed that exogenous ciliary neurotrophic factor strongly induced adult hippocampal progenitor cells to differentiate into neurons and glial cells. Moreover, passage 4 adult hippocampal progenitor cells expressed high levels of endogenous ciliary neurotrophic factor, and a neutralizing antibody against ciliary neurotrophic factor prevented the spontaneous neuronal and glial differentiation of adult hippocampal progenitor cells. These results suggest that the spontaneous differentiation of adult hippocampal progenitor cells is mediated partially by endogenous ciliary neurotrophic factor.

Effects of leukemia inhibitory factor and basic fibroblast growth factor on free radicals and endogenous stem cell proliferation in a mouse model of cerebral infarction

The present study established a mouse model of cerebral infarction by middle cerebral artery occlusion, and monitored the effect of 25 tJg/kg leukemia inhibitory factor and （or） basic fibroblast growth factor administration 2 hours after model establishment. Results showed that following administration, the number of endogenous neural stem cells in the infarct area significantly increased, malondialdehyde content in brain tissue homogenates significantly decreased, nitric oxide content, glutathione peroxidase and superoxide dismutase activity significantly elevated, and mouse motor function significantly improved as confirmed by the rotarod and bar grab tests. In particular, the effect of leukemia inhibitory factor in combination with basic fibroblast growth factor was the most significant. Results indicate that leukemia inhibitory factor and basic fibroblast growth factor can improve the microenvironment after cerebral infarction by altering free radical levels, improving the quantity of endogenous neural stem cells, and promoting neurological function of mice with cerebral infarction.

Effects of leukemia inhibitory factor on endogenous neural stem cell proliferation and glycoprotein-130 expression in a mouse model of cerebral infarction

Leukemia inhibitory factor （LIF） has been shown to promote proliferation of endogenous neural stem cells. In this study, we treated mice with cerebral infarction using LIF to investigate whether the LIF receptor subunit glycoprotein （gp）130 is involved in neuroprotection. After LIF treatment, the motor function of model mice was significantly improved. Immunofluorescence histochemistry showed increased numbers of endogenous neural stem cells surrounding the infarct foci. Western blot analysis revealed that gp130 expression was significantly decreased surrounding the infarcted foci. Results demonstrated that LIF promoted the proliferation of endogenous neural stem cells by inhibiting gp130 protein expression.

Analysis on Stability Factors of Grain Price in China

To make grain price stable is an important goal for the Chinese government. The paper compared the grain supply elasticity and demand elasticity to determine the grain price stability in China; used ＂k value＂ method to analyze the grain price fluctuation from 1985 to 2010; divided the grain price volatility into three stages; and analyzed the factors in each phase. On the base, it put forward some countermeasures to guarantee the stability of the grain price.

Relationship between endogenous digitalis－like factor and fluid therapy in burn－blast combined injury

Fifty dogs were inflicted with burn-blast combined injury and divided into 5 groups. All the experimental animals began to receive various amounts of fluid and sodium slat replacement 2 h after injury. Serum level of endogenous digitalis-like factor (EDLF

Electricity Price Influence Factors Analysis Using Stochastic Matrix for Real-Time Electricity Price Forecasting

Real-time electricity price( RTEP) influence factor extraction is essential to forecasting accurate power system electricity prices. At present,new electricity price forecasting models have been studied to improve predictive accuracy,ignoring the extraction and analysis of RTEP influence factors. In this study,a correlation analysis method is proposed based on stochastic matrix theory.Firstly, an augmented matrix is formulated, including RTEP influence factor data and RTEP state data. Secondly, data correlation analysis results are obtained given the statistical characteristics of source data based on stochastic matrix theory.Mean spectral radius( MSR) is used as the measure of correlativity.Finally,the proposed method is evaluated in New England electricity markets and compared with the BP neural network forecasting method. Experimental results show that the extracted index system comprehensively generalizes RTEP influence factors,which play a significant role in improving RTEP forecasting accuracy.

Basic fibroblast growth factor increases the numbe of endogenous neural stem cells and inhibits the expression of amino methyl isoxazole propionic acid receptors in amyotrophic lateral sclerosis mice

This study aimed to investigate the number of amino methyl isoxazole propionic acid （AMPA） receptors and production of endogenous neural stem cells in the SOD1 G93AG1H transgenic mouse model of amyotrophic lateral sclerosis, at postnatal day 60 following administration of basic fibroblast growth factor （FGF-2）. A radioligand binding assay and immunohistochemistry were used to estimate the number of AMPA receptors and endogenous neural stem cells respectively. Results showed that the number of AMPA receptors and endogenous neural stem cells in the brain stem and sensorimotor cortex were significantly increased, while motor function was significantly decreased at postnatal days 90 and 120. After administration of FGF-2 into mice, numbers of endogenous neural stem cells increased, while expression of AMPA receptors decreased, whilst motor functions were recovered. At postnatal day 120, the number of AMPA receptors was negatively correlated with the number of endogenous neural stem cells in model mice and FGF-2-treated mice. Our experimental findings indicate that FGF-2 can inhibit AMPA receptors and increase the number of endogenous neural stem cells, thus repairing neural injury in amyotrophic lateral sclerosis mice.

Cassava sector development in Cameroon: Production and marketing factors affecting price

Regular and available supply is the prerequisite of an effective and efficient commercialization process. Using multivariate regression analysis on field data, this research appraises the production and marketing factors that influence cassava market price. The production factors include cultivated area, planting material, yield, and farmers’ field schools;while farmers access to a paved road, having a telephone, the transportation costs of fresh roots, the level of root perishability, and the prices of rice and maize stand as marketing factors. The results show that farmers who attended farmers’ field school adopted improved planting materials, propagated them in their localities and the yields in these communities increased significantly. The farm size also has a significant influence on the availability of fresh roots. On the marketing side, transportation costs, access to a paved road, the prices of rice and maize significantly affect cassava’s market price and tighten the relationship between producers and marketers. We conclude that to increase fresh roots supply, roads leading to cultivating areas should be paved, better transportation provided, communication costs reduced, even distribution of planting materials and appropriate warehouses.

Research on Influencing Factors of Residential Rental Price Based on Hedonic Price Model:A Case Study of Nanshan District,Shenzhen

Taking Nanshan District,Shenzhen as an example,the paper studies the influencing factors of residential rental price via hedonic price model.The hedonic price index system is constructed and relevant characteristic variables are selected from the perspectives of building characteristics,location characteristics,neighborhood characteristics and rental characteristics.The residential rental data in the second half of 2020 are analyzed by multi-factors linear regression,and hedonic price model of house lease in Nanshan District is obtained to analyze the inherent law of residential rental price structure in this area.

Low Carbon Beijing:Research on the Influencing Factors of Carbon Emission Trading Price

The international community has taken extensive actions to achieve carbon neutrality and sustainable development with the intensification of global warming and climate change.China has also carried out a long-term layout,setting the goal of achieving a carbon peak by 2030 and carbon neutrality by 2060.In 2021,with the official launch of a unified national carbon emissions trading market,China’s nationwide carbon emissions trading kicked off.Carbon emission trading is an important policy tool for China’s carbon peak and carbon-neutral action and an essential part of the country’s promotion of a comprehensive green transformation of the economy and society.This study uses a VAR(Vector Autoregressive)model to analyze the influencing factors of the Beijing carbon emissions trading price from January 2014 to December 2019.The study found that coal prices have the most significant impact on Beijing’s carbon emissions trading prices.Oil prices,industrial development indexes,and AQI(Air Quality Index)impacted Beijing’s carbon emissions trading prices.In contrast,natural gas prices and economic indexes have the most negligible impact.These findings will help decision-makers determine a reasonable price for carbon emissions trading and contribute to the market’s healthy development.

The effect of endogenous transforming growth factor β_1 on the growth of bladder cancer cells in vitro

Objective To investigate the influence of endogenous transforming growth factor β1(TGFβ1) on the cell cycle regulation and proliferation of bladder cancer. Methods A constructed replication defective retroviral vector pRevTβ-AS, which carried antisense RNA of TGFβ1.was transfected to a bladder cancer cell line EJ. The proliferation and clone-formation of transferred cells were observed in vitro,and the alteration of cell cycle was also detected by flow cytometric analysis. Results TGFβ1 antisense RNA was transferred into EJ cell and expressed efficiently. After the inhibition of target gene expression in EJ cells, the reduced growth and clone-formation rates were demonstrated, and the proliferative indexes were decreased by 12 % . The ratios of GO and G1 stage cells to June 2003 Vol12 No2 the antisense RNA-transfected EJ cells were increased, simultaneously,the ratio of S stage cells to the antisense RNA-transfected EJ cells ratios were decreased, compared with the control group. Conclusion The

Exploring prognostic potential of long noncoding RNAs in colorectal cancer based on a competing endogenous RNA network

BACKGROUND Colorectal cancer(CRC)is one of the most prevalent tumors worldwide.Recently,long noncoding RNAs(lncRNAs)have been shown to influence tumorigenesis and tumor progression by acting as competing endogenous RNAs(ceRNAs).It is difficult to extract prognostic lncRNAs and useful bioinformation from most ceRNA networks constructed previously.AIM To construct a prognostic related ceRNA regulatory network and lncRNA related signature based on risk score in CRC.METHODS RNA transcriptome profile and clinical information of 506 CRC patients were downloaded from the Cancer Genome Atlas database.R packages and Perl program were used for data processing.Cox regression analysis was used for prognostic model construction.Quantitative real-time polymerase chain reaction was used to detect the expression of lncRNAs.RESULTS A prognostic-related ceRNA network was constructed,including 9 lncRNAs,44 mRNAs,and 30 miRNAs.In addition,a four-lncRNA model was constructed using multivariate Cox regression analysis,which could be an independent prognostic model in CRC.The risk score for each patient was calculated,and the 506 patients were divided into high and low-risk groups(253 for each group)based on the median risk score.The results of the survival analysis showed that patients with a high-risk score had a poor survival rate.Furthermore,the predictive value of the four-lncRNA model was evaluated in GSE38832.Patient survival probabilities could be better predicted when combing the risk score and clinical features.Gene Set Enrichment Analysis results verified that a number of cancer-related signaling pathways were enriched with a high-risk score in CRC.Finally,we validated a novel lncRNA(LINC00488)using quantitative real-time polymerase chain reaction in 22 paired CRC patient tumor tissues compared to adjacent non-tumor tissues.CONCLUSION The four-lncRNA model could give better predictive value for CRC patients.Our understanding of the lncRNA-related ceRNA regulatory mechanism could provide a potential diagnostic indicator for CRC patients.

Endogenous versus exogenous cell replacement for Parkinson’s disease: where are we at and where are we going?

Parkinson’s disease is the second most common neurodegenerative disease and has currently no effective treatment,one that would be able to stop or reverse the loss of dopaminergic neurons in the substantia nigra pars compacta.In addition,Parkinson’s disease diagnosis is typically done when a significant percentage of the dopaminergic neurons is already lost.In neurodegenerative disorders,some therapeutic strategies could be effective only at inhibiting further degeneration;on the other hand,cell replacement therapies aim at replacing lost neurons,an approach that would be ideal for the treatment of Parkinson’s disease.Many cell replacement therapies have been tested since the 1970s in the field of Parkinson’s disease;however,there are still significant limitations prohibiting a successful clinical application.From the first fetal midbrain intrastriatal graft to the most recent conversion of astrocytes into dopaminergic neurons,we have gained equally,significant insights and questions still looking for an answer.This review aims to summarize the main milestones in cell replacement approaches against Parkinson’s disease.By focusing on achievements and failures,as well as on the additional research steps needed,we aim to provide perspective on how future cell replacement therapies treats Parkinson’s disease.

Economics,fundamentals,technology,finance,speculation and geopolitics of crude oil prices:an econometric analysis and forecast based on data from 1990 to 2017

It is of real and direct significance for China to cope with oil price fluctuations and ensure oil security. This paper aims to quantitatively analyze the specific contribution ratios of the complex factors influencing international crude oil prices and to establish crude oil price models to forecast long-term international crude oil prices. Six explanatory influential variables, namely Dow Jones Indexes, the Organization for Economic Cooperation and Development oil stocks, US rotary rig count, US dollar index, total open interest, which is the total number of outstanding contracts that are held by market participants at the end of each day, and geopolitical instability are specified, and the samples, from January 1990 to August 2017, are divided into six sub-periods. Moreover, the co-integration relationship among variables shows that the contribution ratios of all the variables influencing Brent crude oil prices are in accordance with the corresponding qualitative analysis. Furthermore, from September 2017 to December 2022 outside of the sample, the Vector Autoregressive forecasts show that annually averaged Brent crude oil prices for 2017-2022 would be $53.0, $61.3, $74.4, $90.0, $105.5, and $120.7 per barrel, respectively. The Vector Error Correction forecasts show that annual average Brent crude oil prices for 2017-2022 would be $53.0, $56.5, $58.5, $60.7, $63.0 and $65.4 per barrel, respectively.