Information disclosure, multifaceted collaborative governance, and carbon total factor productivity---an evaluation of the effects of the 'environmental information disclosure pilot' policy based on double machine learning.

As an environmental institutional arrangement related to the information factor of the diversified participation of the government, enterprises, the media and the public, the environmental information disclosure pilot policy, can and how to affect the carbon emission efficiency through multiple collaborative governance? This study uses the Environmental Information Disclosure Pilot Policy implemented in China in 2007 as a quasi-natural experiment. It examines 284 prefecture-level cities from 2004 to 2021 and A-share listed companies from 2004 to 2021, constructing an evolutionary game dynamic model involving government, public, enterprises, and media. Through mathematical derivation and assignment analysis, it explores how environmental information impacts carbon emission efficiency under multifaceted collaborative governance, assessing the strategic choices and evolutionary paths of stakeholders before and after policy implementation, using methods like double machine learning for empirical testing. The study highlights several key findings: First, the implementation of the Environmental Information Disclosure Pilot Policy significantly enhanced carbon total factor productivity in pilot cities, as revealed through Double Machine Learning (DML) policy effect evaluation. Second, adjustments for potential estimation biases using Doubly Debiased LASSO (DDL) regression indicated that environmental information disclosure impacts carbon productivity via a governance mechanism involving government, public, media, and enterprises. Third, a causal pathway analysis suggested a sequential logic in governance effectiveness, starting from governmental environmental focus to corporate environmental responsibility. Lastly, integrating DML with a moderation effect model revealed a regulatory role for environmental legislation construction, offering new insights for achieving dual carbon goals and enriching empirical evidence on information's impact on carbon emission efficiency.

A study on the emotional and attitudinal behaviors of social media users under the sudden reopening policy of the Chinese government.

Introduction: Since the outbreak of the COVID-19 pandemic, the Chinese government has implemented a series of strict prevention and control policies to prevent the spread of the virus. Recently, the Chinese government suddenly changed its approach and lifted all prevention and control measures. This sudden change in policy is expected to lead to a widespread outbreak of COVID-19 in China, and the public and local governments are not adequately prepared for the unknown impact on society. The change in the "emergency" prevention and control policy provides a unique research perspective for this study. Methods: The purpose of this study is to analyze the public's attitudes and emotional responses to COVID-19 under the sudden opening policy, identify the key factors that contribute to these attitudes and emotions, and propose solutions. In response to this sudden situation, we conducted data mining on topics and discussions related to the opening of the epidemic on Sina Weibo, collecting 125,686 interactive comments. We used artificial intelligence technology to analyze the attitudes and emotions reflected in each data point, identify the key factors that contribute to these attitudes and emotions, explore the underlying reasons, and find corresponding solutions. Results: The results of the study show that in the face of the sudden release of the epidemic, the public mostly exhibited negative emotions and behaviors, with many people experiencing anxiety and panic. However, the gradual resumption of daily life and work has also led some people to exhibit positive attitudes. Conclusion: The significance of this study is to help the government and institutions understand the impact of policy implementation on users, and to enable them to adjust policies in a timely manner to respond to potential social risks. The government, emergency departments, and the public can all prepare for similar situations based on the conclusions of this study.

Gut microbiota dynamics in a 1-year follow-up after adult liver transplantation in Northeast China.

Background: Liver transplantation (LTx) is the most effective treatment for end-stage liver diseases. Gut microorganisms influence the host physiology. We aim to profile the dynamics of gut microbiota in the perioperative period and a 1-year follow-up of LTx recipients in Northeast China. Methods: A total of 257 fecal samples were longitudinally collected from 85 LTx patients using anal swabs from pre-LTx to 1-year post-LTx. A total of 48 fecal samples from end-stage liver disease patients without LTx served as the control. 16S rRNA sequencing was used to analyze gut microbiota diversity, bacterial genera, phenotype classification, and metabolic pathways. Results: The diversity of gut microbiota decreased significantly after transplantation, accompanied by a profound change in the microbial structure, which is characterized by increased abundance of facultative anaerobic bacteria dominated by g_Enterococcus and reduced anaerobic bacteria composition. Predicted functional analysis also revealed disturbances in the metabolic pathway of the gut microbiota. After LTx, the diversity of microbiota gradually recovered but to a less preoperative level after 1 year of recovery. Compared with pre-transplantation, the microbiome structure was characterized by an increase in Acidaminococcus and Acidithiobacillus after 1 year of transplantation. Conclusion: LTx and perioperative treatment triggered gut microbial dysbiosis. The gut microbiota was restructured after LTx to near to but significantly differed from that of pre-LTx.

Fibrinogen, Neutrophil-to-Lymphocyte Rate and Platelet-to-Neutrophil Rate as Novel Acute Phase Indicators in Patients with Thromboangiitis Obliterans.

BACKGROUND/OBJECTIVES: The acute exacerbations and progressive deterioration seen in thromboangiitis obliterans (TAO) have been related to poor clinical outcomes. Here, we have studied the association of laboratory biomarkers with the acute phase of TAO (AP-TAO). METHODS/RESULTS: We conducted a retrospective case-control study on 112 patients with TAO and 98 healthy controls; comparing the neutrophil-to-lymphocyte rate (NLR), lymphocyte-to-monocyte rate (LMR), platelet-to-neutrophil rate (PNR), fibrinogen (FIB), and apolipoprotein A-I (ApoA-I). Significantly higher NLR level, as well as lower LMR, PNR, and ApoA-I levels were observed in patients with TAO, particularly the acute phase. Significantly increased FIB was only observed in AP-TAO. A positive correlation was found between NLR and with C-reactive protein (CRP) in the acute phase (r = 0.817, P < 0.001). Moreover, NLR, PNR, and FIB levels of 3.38, 45.12, and 3.69 were shown to be the predictive cut-off values for the AP-TAO (sensitivity 72.5, 82,4, and 66,7%, specificity 92.2, 78.4, and 96.1%; area under the curve [AUC] 0.875, 0.855, and 0.872), respectively. The FIB level was independently associated with the AP-TAO (OR = 11.420, P = 0.007). CONCLUSIONS: NLR, PNR, and FIB may be useful markers for the identification of inflammation and the AP-TAO. FIB may be an independent risk factor for the acute phase.