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The advanced treatment of bio-treated coking wastewater was carried out using peroxymonosulfate(PMS)oxidation activated by base combined with sequencing biofilm batch reactor(SBBR). The effects of pH, PMS concentration, and temperature on the removal of COD and chroma of bio-treated coking wastewater were investigated by base/PMS system with sodium hydroxide as activater. The optimal conditions were temperature of 25 ℃, pH of 10, PMS concentration of 7 mmol/L, reaction time of 4 h. Under this condition,the removal rates of COD and chroma were 45.9% and 96.1%,respectively. EPR analyse indicated that 1O2, O2·-, ∙OH and SO4·- existed in the base/PMS system. After base/PMS system,the biodegradabality of waste water improved. The COD of effluent after SBBR treatment was less than 60 mg/L. The average removal rate of COD reached 66.7% with PMS oxidation activated by base combined with sequencing biofilm batch reactor. The three-dimensional fluorescence spectrum showed that the base/PMS system combined with SBBR could remove aromatic-like protein substances and humic-like acids.

Kashin-Beck disease (KBD) is an endemic osteochondropathy. A specific gene called SRY-box transcription factor 6 (SOX6) is important for forming cartilage. This study aims to explore the potential correlation between SOX6 single nucleotide polymorphisms (SNPs) and KBD risk for the first time. In the case–control study, 735 unrelated Chinese Han individuals were enrolled. The four mutation sites of the SOX6 gene (rs4539287 G/A, rs3203295 C/A, rs7928675 C/A, and rs10832681 A/G) were screened and genotyped on the Agena MassARRAY platform. The correlation between SOX6 SNPs and KBD risk was explored based on logistic regression analysis. The interaction between SNP and SNP was analyzed based on the multi-factor dimensionality reduction (MDR) method. Overall analysis revealed a remarkable correlation between rs7928675 and rs10832681 and the reduction of KBD risk (p < 0.05). Subgroup analyses further indicated that these two SNPs have a significant protective effect on KBD risk among participants aged ≤65 years, males, and non-smokers (p < 0.05). MDR displayed a marked interaction between rs3203295 and rs10832681. Our study revealed that SOX6 rs7928675 and rs10832681 are markedly correlated with a reduced risk of KBD in the Chinese Han population, providing a new direction for the prevention, diagnosis, and treatment of KBD.

Introduction: Severe fever with thrombocytopenia syndrome (SFTS) is an emerging infectious disease caused by the SFTS virus (SFTSV), which has a wide geographic distribution. The primary clinical manifestations of SFTS are fever and thrombocytopenia, with multiorgan failure being the leading cause of death. While most patients recover with treatment, little is known about the potential long-term metabolic effects of SFTSV infection. Objectives: This study aimed to shed light on dysregulated metabolic pathways and cytokine responses following SFTSV infection, which pose significant risks to the short-term and long-term health of affected individuals. Methods: Fourteen laboratory-confirmed clinical SFTS cases and thirty-eight healthy controls including 18 SFTSV IgG-positive and 20 IgG-negative individuals were recruited from Taizhou city of Zhejiang province, Eastern China. Inclusion criteria of healthy controls included residing in the study area for at least one year, absence of fever or other symptoms in the past two weeks, and no history of SFTS diagnosis. Ultrahigh-performance liquid chromatography-mass spectrometry (UHPLC-MS) was used to obtain the relative abundance of plasma metabolites. Short-term metabolites refer to transient alterations present only during SFTSV infection, while long-term metabolites persistently deviate from normal levels even after recovery from SFTSV infection. Additionally, the concentrations of 12 cytokines were quantified through fluorescence intensity measurements. Differential metabolites were screened using orthogonal projections to latent structures discriminant analysis (OPLS-DA) and the Wilcoxon rank test. Metabolic pathway analysis was performed using MetaboAnalyst. Between-group differences of metabolites and cytokines were examined using the Wilcoxon rank test. Correlation matrices between identified metabolites and cytokines were analyzed using Spearman's method. Results and conclusions: We screened 122 long-term metabolites and 108 short-term metabolites by analytical comparisons and analyzed their correlations with 12 cytokines. Glycerophospholipid metabolism (GPL) was identified as a significant short-term metabolic pathway suggesting that the activation of GPL might be linked to the self-replication of SFTSV, whereas pentose phosphate pathway and alanine, aspartate, and glutamate metabolism were indicated as significant long-term metabolic pathways playing a role in combating long-standing oxidative stress in the patients. Furthermore, our study suggests a new perspective that α-ketoglutarate could serve as a dietary supplement to protect recovering SFTS patients. [ABSTRACT FROM AUTHOR]

To overcome the reliance on large samples and high-quality data in existing evaluation methods, while also improving evaluation efficiency and accuracy, this paper proposes a method for rapid landslide hazard assessment. This method utilizes existing research findings and specific analytical techniques for the study area to conduct rapid assessments. Taking the landslide in Yongxing Town, Mingshan Area, Ya'an City, Sichuan Province as an example, the Analytic Hierarchy Process (AHP) is combined with the Information Value (IV) method, Certainty Factor (CF) method, and Frequency Ratio (FR) method from previous studies, The AHP-IV and AHP-FR methods assess the study area as a moderately hazardous zone, while the AHP-CF method assesses it as a slightly hazardous zone. Affected by the strong 2013 Lushan earthquake, the landslide in the study area caused permanent damage. Field investigation results show that the landslide hazard in the study area is moderate, and the AHP-IV and AHP-FR methods are more consistent with the actual field results. The AHP-CF method, due to not considering the water system factor and having certain errors in its discrimination method, leans towards a safer assessment, The results of the three evaluation methods are somewhat consistent. [ABSTRACT FROM AUTHOR]

Clustered data are a complex and frequently used type of data. Traditional factor analysis methods are effective for non-clustered data, but they do not adequately capture correlations between multiple observed individuals or variables in clustered data. This paper proposes a Bayesian approach utilizing MCMC and Gibbs sampling algorithms to accurately estimate parameters of interest within the clustered factor analysis model. The mean traversal graph of parameters ensures that the Markov chain converges, and the Bayesian case-deletion model is used to analyze the model's impact and identify outliers in clustered data using Cook's posterior mean distance. The applicability and validity of the principal-component-method-based factor analysis model for clustered data are demonstrated by comparing the parameter estimation of this method with the principal component method, the clustered data with and without internal relationships are compared by example analysis, and the anomalous groups are identified by the Cook's posterior mean distance. [ABSTRACT FROM AUTHOR]

Objective Synthesizing and evaluating inconsistent medical evidence is essential in evidence-based medicine. This study aimed to employ ChatGPT as a sophisticated scientific reasoning engine to identify conflicting clinical evidence and summarize unresolved questions to inform further research. Materials and Methods We evaluated ChatGPT's effectiveness in identifying conflicting evidence and investigated its principles of logical reasoning. An automated framework was developed to generate a PubMed dataset focused on controversial clinical topics. ChatGPT analyzed this dataset to identify consensus and controversy, and to formulate unsolved research questions. Expert evaluations were conducted 1) on the consensus and controversy for factual consistency, comprehensiveness, and potential harm and, 2) on the research questions for relevance, innovation, clarity, and specificity. Results The gpt-4-1106-preview model achieved a 90% recall rate in detecting inconsistent claim pairs within a ternary assertions setup. Notably, without explicit reasoning prompts, ChatGPT provided sound reasoning for the assertions between claims and hypotheses, based on an analysis grounded in relevance, specificity, and certainty. ChatGPT's conclusions of consensus and controversies in clinical literature were comprehensive and factually consistent. The research questions proposed by ChatGPT received high expert ratings. Discussion Our experiment implies that, in evaluating the relationship between evidence and claims, ChatGPT considered more detailed information beyond a straightforward assessment of sentimental orientation. This ability to process intricate information and conduct scientific reasoning regarding sentiment is noteworthy, particularly as this pattern emerged without explicit guidance or directives in prompts, highlighting ChatGPT's inherent logical reasoning capabilities. Conclusion This study demonstrated ChatGPT's capacity to evaluate and interpret scientific claims. Such proficiency can be generalized to broader clinical research literature. ChatGPT effectively aids in facilitating clinical studies by proposing unresolved challenges based on analysis of existing studies. However, caution is advised as ChatGPT's outputs are inferences drawn from the input literature and could be harmful to clinical practice. [ABSTRACT FROM AUTHOR]