Phosphorylated STAT3 as a potential diagnostic and predictive biomarker in ALK- ALCL vs. CD30high PTCL, NOS.

Aims: The differential diagnosis between ALK-negative anaplastic large cell lymphoma (ALK- ALCL) and peripheral T-cell lymphoma, not otherwise specified (PTCL, NOS) with high expression of CD30 (CD30high) are essential. However, no reliable biomarker is available in daily practice except CD30. STAT3 is characteristically activated in ALCL. We aimed to investigate whether the status of STAT3 phosphorylation could help the differential diagnosis. Methods: The status of phosphorylation of STAT3 was examined using two antibodies against pSTAT3-Y705 and pSTAT3-S727 by immunohistochemistry in ALK+ ALCL (n=33), ALK- ALCL (n=22) and PTCL, NOS (n=34). Ten PTCL, NOS with diffuse CD30 expression were defined as CD30high PTCL, NOS. Flowcytometric analysis were performed to evaluate the expression of pSTAT3-Y705/S727 in PTCL, NOS (n=3). Results: The median H-scores of pSTAT3-Y705 and S727 were 280 and 260 in ALK+ ALCL, 250 and 240 in ALK- ALCL, and 45 and 75 in CD30high subgroup, respectively. Using H score of 145 as the cutoff value, pSTAT3-S727 alone distinguished between ALK- ALCL and CD30high PTCL, NOS with a sensitivity of 100% and specificity of 83%. Additionally, pSTAT3-S727, but not pSTAT3-Y705, was also expressed by background tumor-infiltrating lymphocytes (S727TILs) in PTCL, NOS. PTCL, NOS patients with high S727TILs H score had a favorable prognosis than those with no TILs (3-year OS rate: 43% vs. 0, p=0.013) or low S727TILs (3-year OS rate: 43% vs. 0, p=0.099). Flowcytometric analysis revealed that of the three patients investigated, two had enhanced pSTAT-S727 signals in neoplastic cell populations, and all three patients were negative for pSTAT3-Y705 expression in both tumor cells and background lymphocytes. Conclusions: pSTAT3-Y705/S727 can be used to help distinguish ALK- ALCL from CD30high PTCL, NOS and pSTAT3-S727 expression by TILs predicts the prognosis of a subset of PTCL, NOS.

Can vertical environmental regulation induce enterprise green innovation? A new perspective from automatic air quality monitoring station in China.

Based on panel data of listed companies in China from 2006 to 2020, this study takes the establishment of automatic air quality monitoring stations as a quasi-natural experiment and uses the staggered difference-in-differences method to explore whether the establishment of monitoring stations promotes green innovation of listed companies. The empirical results show that: (1) The green innovation of companies achieves an increase of 3.5% with monitoring stations in their locations, and an increase of 2.3% with the establishment of each additional monitoring station. This conclusion is valid after a series of robustness tests and exclusive tests. (2) The heterogeneity analyses show that monitoring stations have a greater role in promoting green innovation for non-state-owned enterprises, enterprises in heavy polluting industries and enterprises in key cities for environmental protection. (3) The transmission mechanism test results show that the establishment of automatic air monitoring station has crowding-out effect rather than leverage effect on green innovation, substantial innovation rather than strategic innovation. (4) The further analyses manifest the promotion of end-to-end green innovation, independent invention and quality of green patents.

The effect of emission trading system on infant health: evidence from China.

Although existing literature has explored the effect of the emission trading system (ETS) on economic growth and pollution emissions, little is known about the impact of the ETS on residents' heath, especially infant health. Based on a "big sample" data set from 1773 county-level administrative regions in China from 2001 to 2012 and a differences-in-differences (DID) strategy, for the first time, this paper investigates the effect of China's SO2 ETS pilot policy on infant health measured by infant mortality. In particular, from the perspectives of pollution emission reduction and green and high-quality economic growth, we empirically identify the mechanism through which the ETS influences infant mortality. The results show that the implementation of the ETS pilot policy significantly reduces infant mortality, and with the implementation of the pilot policy, such a health improvement effect is strengthened. This finding is consolidated through a series of robustness checks, including employing the method of the propensity score matching combined by the DID, using the thermal inversion strength as the instrumental variable, excluding the impacts of other environmental policies, and conducting a placebo test. In addition, the results of the mechanism analysis indicate that the ETS pilot policy significantly lowers SO2 emission density and PM2.5 concentration and raises energy efficiency and per capita GDP. Therefore, the ETS pilot policy can improve infant health by promoting pollution emission reduction and green and high-quality economic growth. This study provides some empirical evidence for the causal relationship between environmental regulation policies and infant health, as well as some reference for the formulation and improvement of related environmental regulation policies.

Urbanization and haze-governance performance: Evidence from China's 248 cities.

Using a panel data set of 248 Chinese cities at the prefecture level and above from 2004 to 2013, this study employs the data envelopment analysis (DEA) method based on a non-angular and non-radial directional distance function (DDF) combined with the overall technology, to measure the haze-governance performance. Furthermore, we construct a composite index based on the nighttime light (NTL) data to reflect the urbanization level, and use a spatial Durbin model (SDM) to investigate the effect and its mechanism of urbanization on the haze-governance performance. The results show a significant U-shaped curve relationship between urbanization and haze-governance performance for the samples of both the whole country and sub-regions. When urbanization exceeds a certain critical level, urbanization is conducive to the improvement of haze-governance performance. The proportion of cities exceeding the critical level in eastern China is higher than in central and western China. The mechanism analysis reveals that urbanization exerts a U-shaped influence on haze-governance performance via the effects of industrial structure, technological innovation, and human capital accumulation. In addition, as for the whole country, urbanization in neighboring regions also has a U-shaped spatial spillover effect on local haze-governance performance; however, the corresponding critical value is relatively small. In eastern China and in central and western China, urbanization in neighboring regions exhibits one-way positive and negative effects on local haze-governance performance, respectively.

Regional carbon imbalance within China: An application of the Kaya-Zenga index.

Considering the enlarging inter-provincial disparities in China as regards carbon emissions and carbon intensity (carbon emissions per unit gross domestic product), this paper is the first study to investigate the inter-provincial carbon imbalance by constructing and employing the Kaya-Zenga index. We use China's panel data of provincial-level carbon emissions over 1995-2016 to quantitatively measure the levels of inter-provincial imbalance and polarization in carbon emissions and carbon intensity. Further, we decompose the Kaya-Zenga index into different contributing factors both regionally and structurally and perform a scenario analysis to identify the corresponding regionally differentiated countermeasures regarding carbon emission reduction. The results show that the imbalance in carbon emissions is mainly caused by imbalances in population scale and income level, while the imbalance in carbon intensity predominantly results from imbalances in energy efficiency and energy mix. In addition, for heavy manufacturing provinces, the respective emission-reduction strategy should aim at lowering energy intensity through local technology improvement and inter-regional technology transfer. For light manufacturing and high technology provinces, carbon emission reduction is harder to be achieved; however, a mix of policies of improving energy efficiency, optimizing energy mix, and industrial upgrading should be implemented. The results of the scenario analysis indicate that reducing imbalance in carbon intensity under different scenarios can lead to a substantial reduction in carbon emissions (up to 10%).

Decoupling PM2.5 emissions and economic growth in China over 1998-2016: A regional investment perspective.

It is crucial to decouple economic growth from environmental pollution in China. This study aims to evaluate China's decoupling level between PM2.5 emissions and economic growth from a regional investment perspective. Using the panel data of 30 Chinese provinces for the period of 1998-2016, this study combines decomposition analysis with decoupling analysis to identify the roles of conventional factors and three novel investment factors in the mitigation and decoupling of PM2.5 emissions in China and its four sub-regions. The results show that China's PM2.5 emissions were weakly decoupled to economic growth during the period of 1998-2016, as well as in China's four sub-regions. At the national level, investment scale played the dominant role while investment structure had a marginal effect in mitigating the decoupling level. In contrast, emission intensity was the largest driver in promoting the decoupling effect. At the regional level, emission intensity and investment efficiency accelerated the regional decoupling level, but the coupling effect from investment scale in the western region far exceeded those in other three sub-regions. At the provincial level, the investment structure of Inner Mongolia and investment scales of Xinjiang and Inner Mongolia had the greatest impacts on PM2.5 emission growth. Finally, several policy recommendations are raised for China to mitigate its PM2.5 emissions.