Proteomic analysis discovers potential biomarkers of early traumatic axonal injury in the brainstem.

OBJECTIVE: Application of Tandem Mass Tags (TMT)-based LC-MS/MS analysis to screen for differentially expressed proteins (DEPs) in traumatic axonal injury (TAI) of the brainstem and to predict potential biomarkers and key molecular mechanisms of brainstem TAI. METHODS: A modified impact acceleration injury model was used to establish a brainstem TAI model in Sprague-Dawley rats, and the model was evaluated in terms of both functional changes (vital sign measurements) andstructural changes (HE staining, silver-plating staining and ß-APP immunohistochemical staining). TMT combined with LC-MS/MS was used to analyse the DEPs in brainstem tissues from TAI and Sham groups. The biological functions of DEPs and potential molecular mechanisms in the hyperacute phase of TAI were analysed by bioinformatics techniques, and candidate biomarkers were validated using western blotting and immunohistochemistry on brainstem tissues from animal models and humans. RESULTS: Based on the successful establishment of the brainstem TAI model in rats, TMT-based proteomics identified 65 DEPs, and bioinformatics analysis indicated that the hyperacute phase of TAI involves multiple stages of biological processes including inflammation, oxidative stress, energy metabolism, neuronal excitotoxicity and apoptosis. Three DEPs, CBR1, EPHX2 and CYP2U1, were selected as candidate biomarkers and all three proteins were found to be significantly expressed in brainstem tissue 30 min-7 days after TAI in both animal models and humans. CONCLUSION: Using TMT combined with LC-MS/MS analysis for proteomic study of early TAI in rat brainstem, we report for the first time that CBR1, EPHX2 and CYP2U1 can be used as biomarkers of early TAI in brainstem by means of western blotting and immunohistochemical staining, compensating for the limitations of silver-plating staining and ß-APP immunohistochemical staining, especially in the case of very short survival time after TAI (shorter than 30 min). A number of other proteins that also have a potential marker role are also presented, providing new insights into the molecular mechanisms, therapeutic targets and forensic identification of early TAI in brainstem.

Establishment of colloidal gold immunochromatography strip for rapid detection of Karen mikimotoi and its application.

In China, harmful algal blooms (HABs) are one of the most prominent ecological disasters in the coastal areas. Among the harmful algae species that cause HABs, Karen mikimotoi is a kind of algae that appear frequently. It can secrete hemolytic toxins and fish toxins such as glycolipids and unsaturated fatty, posing a significant threat to marine life. In order to establish a fast and effective detection technology for Karen mikimotoi that can be promoted and applied on site, we have developed a test strip which is based on monoclonal antibody technology and the colloidal gold immune-chromatography assay (GICA). The experimental results show that this test strip can detect different growth stages including growth, and stable and recession period of Karen mikimotoi. The detection results can be displayed within 3-15 min. It had high sensitivity and specificity, with a detection limit of 754 cells/mL. A colorimetric card was made to further determine the concentration of algae detected. What is more, we had developed a method that can be used for on-site enrichment of algae cells using a syringe to detect lower concentrations of Karen mikimotoi, with a minimum detection concentration of 100 cells/mL. Also the test strip was used for on-site testing along the coast of China. This test strip not only serves as a warning for the outbreak of red tide, but also provides a new approach for the development of rapid detection technology for red tide algae.

Governing public health emergencies during the coronavirus disease outbreak: Lessons from four Chinese cities in the first wave.

The ongoing coronavirus disease (COVID-19) pandemic has had a far-reaching impact on urban living, prompting emergency preparedness and response from public health governance at multiple levels. The Chinese government has adopted a series of policy measures to control infectious disease, for which cities are the key spatial units. This research traces and reports analyses of those policy measures and their evolution in four Chinese cities: Zhengzhou, Hangzhou, Shanghai and Chengdu. The theoretical framework stems from conceptualisations of urban governance and its role in public health emergencies, wherein crisis management and emergency response are highlighted. In all four cities, the trend curves of cumulative diagnosed cases, critical policies launched in key time nodes and local governance approaches in the first wave were identified and compared. The findings suggest that capable local leadership is indispensable for controlling the coronavirus epidemic, yet local governments' approaches are varied, contributing to dissimilar local epidemic control policy pathways and positive outcomes in the fight against COVID-19. The effectiveness of disease control is determined by how local governments' measures have adapted to geospatial and socioeconomic heterogeneity. The coordinated actions from central to local governments also reveal an efficient, top-down command transmission and execution system for coping with the pandemic. This article argues that effective control of pandemics requires both a holistic package of governance strategies and locally adaptive governance measures/processes, and concludes with proposals for both a more effective response at the local level and identification of barriers to achieving these responses within diverse subnational institutional contexts.

The Role of Non-coding RNAs in Methamphetamine-Induced Neurotoxicity.

Methamphetamine (METH) is an amphetamine-type stimulant that is highly toxic to the central nervous system (CNS). Repeated intake of METH can lead to addiction, which has become a globalized problem, resulting in multiple public health and safety problems. Recently, the non-coding RNA (ncRNA) has been certified to play an essential role in METH addiction through various mechanisms. Herein, we mainly focused on three kinds of ncRNAs including long non-coding RNAs (lncRNAs), microRNAs (miRNAs), and circular RNAs (circRNAs), which are involved in neurotoxicity effects such as cognitive impairment, behavioral abnormalities, and psychiatric disorders due to METH abuse. In addition, differential expression (DE) ncRNAs also suggest that specific responses and sensitivity to METH neurotoxicity exist in different brain regions and cells. We summarized the relationships between the ncRNAs and METH-induced neurotoxicity and psychiatric disturbances, respectively, hoping to provide new perspectives and strategies for the prevention and treatment of METH abuse. Schematic diagram of the non-coding RNAs (ncRNAs) was involved in methamphetamine (METH)-induced neurotoxicity. The ncRNAs were involved in METH-induced blood-brain barrier disruption, neuronal, astrocyte, and microglial damage, and synaptic neurotransmission impairment. The study of ncRNAs is a hot spot in the future to further understand the neurotoxicity of METH and provide more favorable scientific support for clinical diagnosis and innovation of related treatments.

Pathological Changes and Cause of Death Associated with the Global Novel Coronavirus Disease (COVID-19).

The coronavirus disease 2019 (COVID-19) has been a global epidemic for more than three years, causing more than 6.9 million deaths. COVID-19 has the clinical characteristics of strong infectivity and long incubation period, and can cause multi-system damage, mainly lung damage, clinical symptoms of acute respiratory distress syndrome (ARDS) and systemic multiple organ damage. The SARS-CoV-2 virus is still constantly mutating. At present, there is no global consensus on the pathological changes of COVID-19 associated deaths and even no consensus on the criteria for determining the cause of death. The investigation of the basic pathological changes and progression of the disease is helpful to guide the clinical treatment and the development of therapeutic drugs. This paper reviews the autopsy reports and related literature published worldwide from February 2020 to June 2023, with a clear number of autopsy cases and corresponding pathological changes of vital organs as the inclusion criteria. A total of 1 111 autopsy cases from 65 papers in 18 countries are included. Pathological manifestations and causes of death are classified and statistically analyzed, common pathological changes of COVID-19 are summarized, and analytical conclusions are drawn, suggesting that COVID-19 infection can cause life-threatening pathological changes in vital organs. On the basis of different health levels of infected groups, the direct cause of death is mainly severe lung damage and secondary systemic multiple organ failure.

Multi-omics analysis reveals GABAergic dysfunction after traumatic brainstem injury in rats.

Background: Traumatic brainstem injury (TBSI) is one of the forms of brain injury and has a very high mortality rate. Understanding the molecular mechanism of injury can provide additional information for clinical treatment. Materials and methods: In this study, we detected transcriptome, proteomics, and metabolome expression changes in the brainstem of TBSI rats, and comprehensively analyzed the underlying mechanisms of TBSI. Results: After TBSI, there was significant diffuse axonal injury (DAI) in the brainstem of rats. A total of 579 genes, 70 proteins, and 183 metabolites showed significant changes in brainstem tissue. Through molecular function and pathway analysis, the differentially expressed genes, proteins, and metabolites of TBSI were mainly attributed to neural signal regulation, inflammation, neuroprotection, and immune system. In addition, a comprehensive analysis of transcripts, proteins, and metabolites showed that the genes, proteins, and metabolic pathways regulated in the brainstem after TBSI were involved in neuroactive ligand-receptor interaction. A variety of GCPR-regulated pathways were affected, especially GAGA's corresponding receptors GABAA, GABAB, GABAC, and transporter GAT that were inhibited to varying degrees. Conclusion: This study provides insights into the development of a rapid diagnostic kit and making treatment strategies for TBSI.

Development of a test strip for rapid detection of Gymnodinium catenatum.

Harmful algal blooms (HABs) are major ecological and environmental problems in China's coastal waters and seriously threaten the stability of the marine ecosystem and human health. Gymnodinium catenatum is a toxic red tide dinoflagellate. It can produce paralytic shellfish toxins (PSP), which cause serious hazards to marine organisms, public health, and safety. In this paper, a test strip based on colloidal gold immunochromatography (GICG) was developed for the rapid detection of Gymnodinium catenatum. The experimental results showed that the test strip has good specificity and sensitivity. It not only detects the different components of Gymnodinium catenatum but also may detect algal toxins. The lowest density of Gymnodinium catenatum that can be detected by this test strip is approximately 120 cells/mL. Cross-reaction indicated that the test strip had a high specificity for Gymnodinium catenatum. This test strip provides a rapid method for in situ detection of Gymnodinium catenatum and a reference method for the monitoring of other harmful algae to serve as an early warning of upcoming red tides. It also provides a new way to prepare more detection methods for toxic algal toxins.

Genomic Prediction Strategies for Dry-Down-Related Traits in Maize.

For efficient mechanical harvesting, low grain moisture content at harvest time is essential. Dry-down rate (DR), which refers to the reduction in grain moisture content after the plants enter physiological maturity, is one of the main factors affecting the amount of moisture in the kernels. Dry-down rate is estimated using kernel moisture content at physiological maturity and at harvest time; however, measuring kernel water content at physiological maturity, which is sometimes referred as kernel water content at black layer formation (BWC), is time-consuming and resource-demanding. Therefore, inferring BWC from other correlated and easier to measure traits could improve the efficiency of breeding efforts for dry-down-related traits. In this study, multi-trait genomic prediction models were used to estimate genetic correlations between BWC and water content at harvest time (HWC) and flowering time (FT). The results show there is moderate-to-high genetic correlation between the traits (0.24-0.66), which supports the use of multi-trait genomic prediction models. To investigate genomic prediction strategies, several cross-validation scenarios representing possible implementations of genomic prediction were evaluated. The results indicate that, in most scenarios, the use of multi-trait genomic prediction models substantially increases prediction accuracy. Furthermore, the inclusion of historical records for correlated traits can improve prediction accuracy, even when the target trait is not measured on all the plots in the training set.

Characterization of precipitation from citrus vinegar during ageing: chemical constituents, formation mechanism and anti-proliferative effect.

Precipitation formation commonly occurs in the ageing step of fermented citrus vinegar. Hitherto, the chemical characteristics and biological properties of precipitates remain unveiled. This study focused on investigating the chemical profile, formation mechanism and biological repurposing of precipitates. Nine principal components, two flavonoid glycosides and their aglycones along with five polymethoxyflavones (PMFs), were identified from a methanol extract of precipitates. Using hydrolysis models, we demonstrated that insoluble aglycones were generated through the breakage of glycosidic bonds in flavonoid glycosides under acidic condition. Moreover, soluble bound-PMFs were destroyed by yeast-acid hybrid catalysis to release insoluble free-PMFs to form precipitates. A methanol extract of precipitates exhibited a potent anti-proliferative effect on MCF-7 cells (IC50 = 0.032 µg µL-1) via inhibiting tubulin polymerization. This study will be helpful for the food industry to aid optimizing citrus vinegar brewing and for reutilizing precipitates for functional foods and health products. Furthermore, it also provides a green strategy of PMFs enrichment from citrus using an enzyme-acid hybrid system.

Sustainability performance of global chemical industry based on green total factor productivity.

The sustainability of the chemical industry is crucial for achieving global sustainable production. The sustainability performance of global chemical industry is influenced by many issues synergistically and has not been fully quantified. Systematic analysis from multiple perspectives, such as resource savings, economic growth, and environmental improvement, is urgently needed to support effective macro-policy decisions. This study quantifies the variation trend of the sustainability of the global chemical industry during 2004-2014 and identifies the driving forces under the framework of green total factor productivity (GTFP). Results show that most developed countries performed efficiently (with GTFP values equal to 1) in sustainable production of the chemical industry, while the least developed countries usually performed inefficiently (with GTFP values lower than 1). Notably, a polarization of sustainability in the chemical industry has been confirmed among countries with different production capacities. From 2004 to 2014, the sustainability performance of the global chemical industry has generally improved. It was mainly driven by technological progress (resulting from independent technological innovation) rather than efficiency catching-up (derived from technological learning). Furthermore, technological progress was manifested mainly as the improvement in CO2 reduction performance and capital saving performance, while technological learning was manifested mainly as the improvement in labor saving performance. Based on the conclusions of this study, the international world is suggested to take action to strengthen international technology cooperation, and governments should make prioritized and focused policies to effectively promote the sustainability of global chemical industry.

KDM1A Identified as a Potential Oncogenic Driver and Prognostic Biomarker via Multi-Omics Analysis.

BACKGROUND: Lysine-specific demethylase 1A (KDM1A) is a histone demethylation enzyme and a crucial epigenetic factor for multiple pathological pathways that mediate carcinogenesis and immunogenicity. Although increasing evidence supposes the association between KDM1A and cancers, no systematic multi-omics analysis of KDM1A is available. METHODS: We systematically evaluated the KDM1A expression of various cancer and normal tissues and the unique relationship between KDM1A expression and prognosis of cancer cases based on The Cancer Genome Atlas (TCGA), Genotype Tissue Expression (GTEx), and Clinical Proteomic Tumor Analysis Consortium (CPTAC) database. The genetic variations, phosphorylation, and DNA methylation of KDM1A were analyzed via various tools. We further analyzed the correlation of KDM1A expression and fibroblasts and immune cell infiltration score of TCGA samples via TIMER2.0. RESULTS: KDM1A was highly expressed in 17 types of total 33 cancers, while it expressed low levels in only 4 cancers. High KDM1A expression was associated with worse survival status in various cancers. KDM1A expression was positively correlated with the cancer-associated fibroblasts and myeloid-derived suppressor cells infiltration levels in most cancer types. Additionally, KDM1A in most cancer types was negatively correlated with Th1 cell infiltration and positively correlated with Th2 cells. Moreover, spliceosome, cell cycle, and RNA transport pathways were involved in the functional mechanisms of KDM1A via enrichment analysis. CONCLUSIONS: Our study describes the epigenetic factor KDM1A as an oncogene and prognostic biomarker. Our findings provide valuable guidance for further analysis of KDM1A function in pathogenesis and potential clinical treatment.

A new method of energy-related carbon dioxide emissions estimation at the provincial-level: A case study of Shandong Province, China.

The accuracy of carbon dioxide (CO2) emissions at the provincial-level is urgently needed for China to peak CO2 emission and implement a carbon reduction plan. However, the current estimation methods have some drawbacks, such as not meeting China's situation, data obsolescence, and relatively high uncertainty. Moreover, there are large differences in estimated results among previous studies. To address these problems, this paper proposes a new provincial-level energy-related CO2 emission estimation method refer to methods at different levels in China and abroad. We re-divide the energy involved in a province based on energy flow and consumption-based responsibility and propose an accounting method for calculating provincial-level CO2 emission factors, which provides clear cross-provincial emission allocation and accurate provincial-level emission estimation. By taking Shandong Province as an example, we calculate the provincial-level CO2 emission factors and obtain the CO2 emissions from 2000 to 2016. Furthermore, the reasons for the large gaps between calculated CO2 emissions in Shandong are further quantitatively analyzed from the perspective of the method and data selection. The results indicate that although different methods greatly influence the estimation results, the gaps that arise from different selections of calculation scope, energy classification, activity data and CO2 emission factor within a method also require attention. Finally, some recommendations are proposed when making CO2 emission calculations or comparisons.

Estimates of carbon dioxide emissions based on incomplete condition information: a case study of liquefied natural gas in China.

Recent calculations of carbon dioxide (CO2) emissions have faced challenges because data consist of only partial information, which is called "incomplete information." According to the emission factor method, energy consumption and CO2 emission factors with incomplete information may lead to unmatched multiplication between themselves, which affects accuracy and increases uncertainties in emission results. To address a specific case of incomplete information that has not been fully explored, we studied the effects of incomplete condition information on the estimates of CO2 emissions from liquefied natural gas (LNG) in China. Based on Chinese LNG sampling data, we obtained the specific-country CO2 emission factor for LNG in China and calculated the corresponding CO2 emissions. By applying hypothesis testing, regression analysis, variance analysis, or Monte Carlo (MC) simulations, the effects of incomplete information on the uncertainty of CO2 emission calculations in three cases were analyzed. The results indicate that calorific values have more than a 9.8% impact on CO2 emission factors and CO2 emissions with incomplete sample information. Regarding incomplete statistical information, the impact of statistical temperature on CO2 emissions exceeds 5.5%. Regarding incomplete sample and statistical information, sample and statistical temperatures can individually increase estimate biases by more than 5.2%. Significantly, the impacts of sample temperature and statistical temperature may offset each other. Therefore, the incomplete condition information is quite important and cannot be ignored in the estimation of CO2 emissions from LNG and international fair comparison.

Inclusion of Machine Learning Kernel Ridge Regression Potential Energy Surfaces in On-the-Fly Nonadiabatic Molecular Dynamics Simulation.

We discuss a theoretical approach that employs machine learning potential energy surfaces (ML-PESs) in the nonadiabatic dynamics simulation of polyatomic systems by taking 6-aminopyrimidine as a typical example. The Zhu-Nakamura theory is employed in the surface hopping dynamics, which does not require the calculation of the nonadiabatic coupling vectors. The kernel ridge regression is used in the construction of the adiabatic PESs. In the nonadiabatic dynamics simulation, we use ML-PESs for most geometries and switch back to the electronic structure calculations for a few geometries either near the S1/S0 conical intersections or in the out-of-confidence regions. The dynamics results based on ML-PESs are consistent with those based on CASSCF PESs. The ML-PESs are further used to achieve the highly efficient massive dynamics simulations with a large number of trajectories. This work displays the powerful role of ML methods in the nonadiabatic dynamics simulation of polyatomic systems.