ABSTRACT
Understanding the drivers and peaks of CO2 emissions at the provincial level plays a crucial role in achieving the goals of China's CO2 emissions peak by 2030. This research combines the spatial-temporal Logarithmic Mean Division Index with scenario analysis to empirically explore the drivers and peaks of CO2 emissions in 30 provinces in China during 1997 and 2020, considering COVID-19 effects. The results show that the energy structure has replaced the energy intensity as the main factor of emission reduction in 2020. The CO2 emission driving mechanism at the provincial level is different from that at the country level. The population restrains the CO2 emissions in Heilongjiang, Sichuan, and Guizhou. The energy structure increases CO2 emissions in Hainan, Ningxia, Shanxi, and Xinjiang. The role of driving factors to CO2 emissions varies greatly among provinces. The population effect is strong in Shandong, Guangdong, and Henan. The economic effect is significant in Shanghai, Jiangsu, and Tianjin. The energy intensity effect is remarkable in Shanxi, Ningxia, and Inner Mongolia. The energy structure effect is profound in Shanxi, Inner Mongolia, and Henan. Based on our findings, China's CO2 emissions peak will occur in 2030 and 2025 under baseline and green development scenarios. Many provinces have already reached their peaks, including Chongqing, Yunnan, Beijing, Tianjin, Qinghai, Shanghai, Jilin, Hubei, Heilongjiang, Hebei, Sichuan, Anhui, Guizhou, and Henan. However, Xinjiang and Shanxi will not reach their peaks by 2030. Based on the findings, this paper put forward several policy implications.
ABSTRACT
Jian-Ti-Kang-Yi decoction (JTKY) is widely used in the treatment of COVID-19. However, the protective mechanisms of JTKY against pneumonia remain unknown. In this study, polyinosinic-polycytidylic acid (poly(I:C)), a mimic of viral dsRNA, was used to induce pneumonia in mice;the therapeutic effects of JTKY on poly(I:C)-induced pneumonia model mice were evaluated. In addition, the anti-inflammatory and anti-oxidative potentials of JTKY were also investigated. Lastly, the metabolic regulatory effects of JTKY in poly(I:C)-induced pneumonia model mice were studied using untargeted metabolomics. Our results showed that JTKY treatment decreased the wet-to-dry ratio in the lung tissue, total protein concentration, and total cell count of the bronchoalveolar lavage fluid (BALF). Hematoxylin and Eosin (HE) and Masson staining indicated that the JTKY treatment alleviated the pathological changes and decreased the fibrotic contents in the lungs. JTKY treatment also decreased the expression of pro-inflammatory cytokines [interleukin (IL)-1β, IL-6, and tumor necrosis factor-alpha (TNF-α)] and increased the levels of immunomodulatory cytokines (IL-4 and IL-10) in the BALF and serum. Flow cytometry analysis showed that the JTKY treatment lowered the ratio of CD86+/CD206+ macrophages in the BALF, decreased inducible nitric oxide synthase (iNOS) level, and increased arginase 1 (Arg-1) level in lung. JTKY also lowered CD11b+Ly6G+ neutrophils in BALF and decreased myeloperoxidase (MPO) activity in lung. Moreover, it also elevated superoxide dismutase (SOD) and glutathione peroxidase (GSH-Px) activities and decreased methane dicarboxylic aldehyde (MDA) level in lung. Untargeted metabolomic analysis showed that the JTKY treatment could affect 19 metabolites in lung, such as L-adrenaline, L-asparagine, ornithine, and alpha-ketoglutaric acid. These metabolites are associated with the synthesis and degradation of ketone bodies, butanoate, alanine, aspartate, and glutamate metabolism, and tricarboxylic acid (TCA) cycle processes. In conclusion, our study demonstrated that treatment with JTKY ameliorated poly(I:C)-induced pneumonia. The mechanism of action of JTKY may be associated with the inhibition of the inflammatory response, the reduction of oxidative stress, and the regulation of the synthesis and degradation of ketone bodies, TCA cycle, and metabolism of alanine, aspartate, glutamate, and butanoate processes in lung.
ABSTRACT
Expression and purification of different fragments of the new coronavirus nucleocapsid (N) protein, establish a new coronavirus total antibody fluorescence immunochromatographic method and evaluate the influence of different protein fragments on the method. Using bioinformatics technology to analyze, synthesize, express and purify the N protein sequence, prepare different N protein fragments;use 1-ethyl-(3-dimethylaminopropyl) carbodiimide (1-( 3-Dimethylaminopropyl)-3-ethylcarbodiimide hydrochloride (EDC) method of fluorescent microspheres coupled with antigen was established to establish a sandwich fluorescence chromatography antibody detection method, and the performance was evaluated respectively. In the prepared 4 N protein fragments, the full-length N protein (N419) is preferably coated, and N412 is labeled with 0.5mol/L NaCl as the optimal combination;the 91-120th amino acid (N412) of the N-terminus of the N antigen is deleted It can reduce 87.5% of non-specific interference;the linear range is 0.312-80U/L, the lowest detection limit is 0.165U/L, and the accuracy is above 95%. The fluorescence immunochromatographic detection method for total antibodies of the new coronavirus established by pairing the N protein fragments has a total coincidence rate of 98% compared with the Guangzhou Wanfu test strip. The improvement provides experimental basis and reference.