Accurate prediction and intelligent control of COD and other parameters removal from pharmaceutical wastewater using electrocoagulation coupled with catalytic ozonation process.

In this study, we employed the response surface method (RSM) and the long short-term memory (LSTM) model to optimize operational parameters and predict chemical oxygen demand (COD) removal in the electrocoagulation-catalytic ozonation process (ECOP) for pharmaceutical wastewater treatment. Through RSM simulation, we quantified the effects of reaction time, ozone dose, current density, and catalyst packed rate on COD removal. Then, the optimal conditions for achieving a COD removal efficiency exceeding 50% were identified. After evaluating ECOP performance under optimized conditions, LSTM predicted COD removal (56.4%), close to real results (54.6%) with a 0.2% error. LSTM outperformed RSM in predictive capacity for COD removal. In response to the initial COD concentration and effluent discharge standards, intelligent adjustment of operating parameters becomes feasible, facilitating precise control of the ECOP performance based on this LSTM model. This intelligent control strategy holds promise for enhancing the efficiency of ECOP in real pharmaceutical wastewater treatment scenarios. PRACTITIONER POINTS: This study utilized the response surface method (RSM) and the long short-term memory (LSTM) model for pharmaceutical wastewater treatment optimization. LSTM predicted COD removal (56.4%) closely matched experimental results (54.6%), with a minimal error of 0.2%. LSTM demonstrated superior predictive capacity, enabling intelligent parameter adjustments for enhanced process control. Intelligent control strategy based on LSTM holds promise for improving electrocoagulation-catalytic ozonation process efficiency in pharmaceutical wastewater treatment.

A novel insight of degradation ibuprofen in aqueous by catalytic ozonation with supported catalyst: Supports effect on ozone mass transfer.

Catalytic ozonation is an effective wastewater purification process. However, the low ozone mass transfer in packed bubble columns leads to low ozone utilization efficiency (OUE), poor organic degradation performance, and high energy consumption. Therefore, there is an urgent need to develop efficient supported catalysts that can enhance mass transfer and performance. However, the reaction mechanism of the support on ozone mass transfer remains unclear, which hinders the development of catalytic ozonation applications. In this study, lava rocks (LR)-supported catalysts, specifically CuMn2O4@LR and MnO2Co3O4@LR, were proposed for catalytic ozonation of IBP degradation due to their superior catalytic activity, stability, and high OUE. Addition of CuMn2O4@LR or MnO2Co3O4@LR increased IBP removal efficiency from 85% to 91% or 88%, and reduced energy consumption from 2.86 to 2.14 kWh/m3 or 2.60 kWh/m3, respectively. This improvement was attributed to LR-supported catalysts enhancing mass transfer and promoting O3 decomposition to generate •OH and •O2-, leading to IBP degradation. Furthermore, this study investigated the effects of ozone dose, supporter sizes, and catalyst components on ozone-liquid mass transfer. The results revealed that the size of the supporter influenced stacked porosity and consequently affected ozone mass transfer. Larger-sized LR (kLa= 0.172 min-1) exhibited better mass transfer compared to smaller-sized supports. Based on these findings, it was concluded that both CuMn2O4@LR and MnO2Co3O4@LR are potential catalysts for catalytic ozonation in residual IBP degradation of pharmaceutical wastewater, and LR showed good credibility as a catalyst supporter. Understanding the effects of supporters and active components on ozone mass transfer provides a fundamental principle for designing supported catalysts in catalytic ozonation applications.

Shared genetics of psychiatric disorders and type 2 diabetes:a large-scale genome-wide cross-trait analysis.

BACKGROUND: Individuals with psychiatric disorders have elevated rates of type 2 diabetes comorbidity. Although little is known about the shared genetics and causality of this association. Thus, we aimed to investigate shared genetics and causal link between different type 2 diabetes and psychiatric disorders. METHODS: We conducted a large-scale genome-wide cross-trait association study(GWAS) to investigate genetic overlap between type 2 diabetes and anorexia nervosa, attention deficit/hyperactivity disorder, autism spectrum disorder, bipolar disorder, major depressive disorder, obsessive-compulsive disorder, schizophrenia, anxiety disorders and Tourette syndrome. By post-GWAS functional analysis, we identify variants genes expression in various tissues. Enrichment pathways, potential protein interaction and mendelian randomization also provided to research the relationship between type 2 diabetes and psychiatric disorders. RESULTS: We discovered that type 2 diabetes and psychiatric disorders had a significant correlation. We identified 138 related loci, 32 were novel loci. Post-GWAS analysis revealed that 86 differentially expressed genes were located in different brain regions and peripheral blood in type 2 diabetes and related psychiatric disorders. MAPK signaling pathway plays an important role in neural development and insulin signaling. In addition, there is a causal relationship between T2D and mental disorders. In PPI analysis, the central genes of the DEG PPI network were FTO and TCF7L2. CONCLUSION: This large-scale genome-wide cross-trait analysis identified shared genetics andpotential causal links between type 2 diabetes and related psychiatric disorders, suggesting potential new biological functions in common among them.

Obsessive-Compulsive Disorder and Metabolic Disorders: Overview and Future Perspective.

ABSTRACT: Obsessive-compulsive disorder (OCD) has a bidirectional relationship with metabolic disorders. The purposes of this review are to decipher the links between OCD and metabolic disorders and to explore the etiological mechanism of OCD in metabolism, which may aid in early identification of and tailored interventions for OCD and metabolic disorders.

Shared genetics between classes of obesity and psychiatric disorders: A large-scale genome-wide cross-trait analysis.

AIMS: Epidemiological studies demonstrate an association between classes of obesity and psychiatric disorders, although little is known about shared genetics and causality of association. Thus, we aimed to investigate shared genetics and causal link between different classes of obesity and psychiatric disorders. METHODS: We used genome-wide association study (GWAS) summary data range from 9725 to 500,199 sample sizes of European descent, conducted a large-scale genome-wide cross-trait association study to investigate genetic overlap between the classes of obesity and anorexia nervosa, attention-deficit/hyperactivity disorder, autism spectrum disorder, bipolar disorder, major depressive disorder, obsessive-compulsive disorder, schizophrenia, anxiety disorders and Tourette syndrome. We conducted transcriptome-wide association study analysis (TWAS) to identified variants regulated gene expression in those related disorders. Finally, pathway enrichment analysis to identified major pathways. RESULTS: In the combined analysis, we replicated 211 previously reported loci and discovered 58 novel independent loci that were associated with all three classes of obesity and related psychiatric disorders. Functional analysis revealed that the identified variants regulated gene expression in major tissues belonging to exocrine/endocrine, digestive, circulatory, adipose, digestive, respiratory, and nervous systems, such as DCC, NEGR1, INO80E. Mendelian randomization analyses suggested that there may be a two-way or one-way causal relationship between obesity and psychiatric disorders. CONCLUSION: This large-scale genome-wide cross-trait analysis identified shared genetics and potential causal links between classes of obesity and psychiatric disorders (attention deficit hyperactivity disorder, autism spectrum disorder, anorexia nervosa, major depressive disorder, schizophrenia, and obsessive-compulsive disorder). Such shared genetics suggests potential new biological functions in common among them.