Simultaneous determination of human plasma 5 amino acid neurotransmitters using liquid chromatography-tandem mass spectrometry: Establishment of reference intervals in Chinese adult population and application to patients with schizophrenia.

Schizophrenia is a serious mental disease with unknown etiology that affects approximately 1 % of the population around the world. Altered levels of amino acid neurotransmitters may underlie the physiopathology of schizophrenia (SZ). This study aimed to develop a rapid and robust liquid chromatography-tandem mass spectrometry (LC-MS/MS) method for simultaneous determination of glutamate acid (Glu), aspartic acid (Asp), γ-aminobutyric acid (GABA), glycine acid (Gly), and Taurine acid (Tau) in patients with schizophrenia plasma and establish reference intervals for Chinese adult populations, and applied to patients with schizophrenia for a preliminary exploration of changes in their plasma levels of five amino acid neurotransmitters. Sample treatment involved protein precipitation followed by dansyl chloride (DNS-Cl) derivatization and total run time is 5.8 min. The method was validated according to the latest national and international guidelines, which achieved acceptable precision (0.54-14.54 %) and accuracy (97.06-103.82 %). The reference interval for Glu, Asp, Gly, Tau, and GABA were 55.51-189.06, 27.51-92.38, 204.01-574.55, 107.50-227.65, and <1 µmol/L, respectively. Increased Tau levels and decreased Asp and Glu levels were shown in patients with schizophrenia. This method was suitable for clinical routine detection of plasma 5 amino acid neurotransmitters in Chinese adult populations.

Liver function test indices-based prediction model for post-stroke depression: a multicenter, retrospective study.

BACKGROUND: Post-stroke depression (PSD) was one of the most prevalent and serious neuropsychiatric effects after stroke. Nevertheless, the association between liver function test indices and PSD remains elusive, and there is a lack of effective prediction tools. The purpose of this study was to explore the relationship between the liver function test indices and PSD, and construct a prediction model for PSD. METHODS: All patients were selected from seven medical institutions of Chongqing Medical University from 2015 to 2021. Variables including demographic characteristics and liver function test indices were collected from the hospital electronic medical record system. Univariate analysis, least absolute shrinkage and selection operator (LASSO) and logistic regression analysis were used to screen the predictors. Subsequently, logistic regression, random forest (RF), extreme gradient boosting (XGBoost), gradient boosting decision tree (GBDT), categorical boosting (CatBoost) and support vector machine (SVM) were adopted to build the prediction model. Furthermore, a series of evaluation indicators such as area under curve (AUC), sensitivity, specificity, F1 were used to assess the performance of the prediction model. RESULTS: A total of 464 PSD and 1621 stroke patients met the inclusion criteria. Six liver function test items, namely AST, ALT, TBA, TBil, TP, ALB/GLB, were closely associated with PSD, and included for the construction of the prediction model. In the test set, logistic regression model owns the AUC of 0.697. Compared with the other four machine learning models, the GBDT model has the best predictive performance (F1 = 0.498, AUC = 0.761) and was chosen to establish the prediction tool. CONCLUSIONS: The prediction model constructed using these six predictors with GBDT algorithm displayed a promising prediction ability, which could be used for the participating hospital units or individuals by mobile phone or computer.

Effect of green taxation on pollution emissions under ESG concept.

Given the global ecological environment deteriorating, the ESG (environmental, social, and governance) concept advocates that economic growth and environmental protection must go hand in hand. Green taxations, in particular, are critical to economic development as they serve as a key lever for the government in regulating the national economy and improving environmental quality. This paper aims to examine the pollution reduction effect of green taxation in China. We constructed fixed effect model using provincial panel data from 2012 to 2017 to empirically examine the effect of green taxation-including environment protection tax, consumption tax, resource tax, and vehicle purchase tax-on pollution emissions. The findings show that environmental protection tax has a significant inhibitory effect on exhaust gas emissions but a poor inhibitory effect on wastewater and industrial solid waste emissions. Other green taxations-consumption tax, resource tax, and vehicle purchase tax-do not have an ideal inhibitory effect on pollution emissions. The paper concludes with policy recommendations organized around improving the environmental protection tax, actively promoting the ESG concept, and optimizing the top-level design of the green taxation system.

Research on cost accounting of enterprise carbon emission (in China).

Enterprises in China face two major challenges about their existence and energy supply at present. One is the difficulty in providing enough energy at an acceptable and reasonable price; the other is a severe environmental issue caused by over-consumption of energy. The government and relevant enterprises, therefore, mainly focus on carbon emission reduction, and the cost accounting of carbon emission, an essential prerequisite, and object of carbon emission reduction, should be further attention. The carbon emission cost is divided into internal cost and external cost, combined with the extended accounting model and cost calculation. This can comprehensively measure and reflect the two costs of the life cycle of the product, provide more relevant data and information support for the deepening and development of the circular economy, and provide an effective cost information basis and guide enterprise managers for scientific decision-making and governance.