Integrated spatial metabolomics and network pharmacology to explore the pharmacodynamic substances and mechanism of Radix ginseng-Schisandra chinensis Herb Couple on Alzheimer's disease.

Radix ginseng and Schisandra chinensis have been extensively documented in traditional Chinese medicine (TCM) for their potential efficacy in treating dementia. However, the precise mechanism of their therapeutic effects remains to be fully elucidated. In this study, air flow-assisted desorption electrospray ionization mass spectrometry imaging (AFADESI-MSI) and network pharmacology are used to investigate the pharmacodynamics and mechanism underlying the herbal combination consisting of Radix ginseng-Schisandra chinensis (RS) in a rodent model for Alzheimer's disease (AD). Brain histopathological findings suggested that RS attenuates hippocampal damage in AD mice, making this combination a potential AD treatment. Twenty-eight biomarkers were identified by spatial metabolomics analysis, which are intricately linked to neuroinflammation, neurotransmitter imbalance, energy deficiency, oxidative stress, and aberrant fatty acid metabolism in AD. The total extract of RS (TE) affected 22 of these biomarkers, with the small molecule components of RS (SN) significantly influencing 19 and the large molecule components of RS (PR) impacting 14. Nine small molecule components are likely to dominate the pharmacodynamics of RS. We constructed a target interaction network based on the corresponding bioactivities that revealed relationships amongst 11 key biomarkers, 8 active ingredients and 12 critical targets. This research illustrates the immense potential of spatial metabolomics and network pharmacology in the study of TCM, revealing the targets and mechanisms underlying herbal formulas.

Genome-wide identification and integrated analysis of the FAR1/FHY3 gene family and genes expression analysis under methyl jasmonate treatment in Panax ginseng C. A. Mey.

Ginseng (Panax ginseng C. A. Mey.) is an important and valuable medicinal plant species used in traditional Chinese medicine, and its metabolite ginsenoside is the primary active ingredient. The FAR1/FHY3 gene family members play critical roles in plant growth and development as well as participate in a variety of physiological processes, including plant development and signaling of hormones. Studies have indicated that methyl jasmonate treatment of ginseng adventitious roots resulted in a significant increase in the content of protopanaxadiol ginsenosides. Therefore, it is highly significant to screen the FAR1/FHY3 gene family members in ginseng and preliminarily investigate their expression patterns in response to methyl jasmonic acid signaling. In this study, we screened and identified the FAR1/FHY3 family genes in the ginseng transcriptome databases. And then, we analyzed their gene structure and phylogeny, chromosomal localization and expression patterns, and promoter cis-acting elements, and made GO functional annotations on the members of this family. After that, we treated the ginseng adventitious roots with 200 mM methyl jasmonate and investigated the trend of the expression of four genes containing the largest number of methyl jasmonate cis-acting elements at different treatment times. All four genes were able to respond to methyl jasmonate, the most significant change was in the PgFAR40 gene. This study provides data support for subsequent studies of this family member in ginseng and provides experimental reference for subsequent validation of the function of this family member under methyl jasmonic acid signaling.

Screening of key immune-related gene in Parkinson's disease based on WGCNA and machine learning. / 基于WGCNAå’Œæœºå™¨å­¦ä¹ ç­›é€‰å¸•é‡‘æ£®ç— å ç–«ç›¸å ³å ³é”®åŸºå› .

OBJECTIVES: Abnormal immune system activation and inflammation are crucial in causing Parkinson's disease. However, we still don't fully understand how certain immune-related genes contribute to the disease's development and progression. This study aims to screen key immune-related gene in Parkinson's disease based on weighted gene co-expression network analysis (WGCNA) and machine learning. METHODS: This study downloaded the gene chip data from the Gene Expression Omnibus (GEO) database, and used WGCNA to screen out important gene modules related to Parkinson's disease. Genes from important modules were exported and a Venn diagram of important Parkinson's disease-related genes and immune-related genes was drawn to screen out immune related genes of Parkinson's disease. Gene ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) were used to analyze the the functions of immune-related genes and signaling pathways involved. Immune cell infiltration analysis was performed using the CIBERSORT package of R language. Using bioinformatics method and 3 machine learning methods [least absolute shrinkage and selection operator (LASSO) regression, random forest (RF), and support vector machine (SVM)], the immune-related genes of Parkinson's disease were further screened. A Venn diagram of differentially expressed genes screened using the 4 methods was drawn with the intersection gene being hub nodes (hub) gene. The downstream proteins of the Parkinson's disease hub gene was identified through the STRING database and a protein-protein interaction network diagram was drawn. RESULTS: A total of 218 immune genes related to Parkinson's disease were identified, including 45 upregulated genes and 50 downregulated genes. Enrichment analysis showed that the 218 genes were mainly enriched in immune system response to foreign substances and viral infection pathways. The results of immune infiltration analysis showed that the infiltration percentages of CD4+ T cells, NK cells, CD8+ T cells, and B cells were higher in the samples of Parkinson's disease patients, while resting NK cells and resting CD4+ T cells were significantly infiltrated in the samples of Parkinson's disease patients. ANK1 was screened out as the hub gene. The analysis of the protein-protein interaction network showed that the ANK1 translated and expressed 11 proteins which mainly participated in functions such as signal transduction, iron homeostasis regulation, and immune system activation. CONCLUSIONS: This study identifies the Parkinson's disease immune-related key gene ANK1 via WGCNA and machine learning methods, suggesting its potential as a candidate therapeutic target for Parkinson's disease.

Bisphenol S exposed changes in intestinal microflora and metabolomics of freshwater crayfish, Procambarus clarkii.

Bisphenol S (BPS), a typical endocrine-disrupting chemical (EDC), can cause hepatopancreas damage and intestinal flora disturbance. Comprehensive studies on the mechanisms of acute toxicity in crustaceans are lacking. In this study, 16S rRNA and liquid chromatography were used to investigate intestinal microbiota and metabolites of freshwater crayfish (Procambarus clarkii). In this study, freshwater crayfish were exposed to BPS (10 µg/L and 100 µg/L). The results showed a significant decrease in catalase (CAT) and superoxide dismutase (SOD) activities after exposure to BPS, which inhibited the Nrf2-Keap1 signaling pathway and induced oxidative stress toxicity in freshwater crayfish. In addition, BPS exposure induced the structural changes of intestinal microbial in the freshwater crayfish, showing different patterns of effects. The number of potentially pathogenic bacteria increased, such as Citrobacter, Hafnia-Obesumbacterium, and RsaHf231. A total of 128 different metabolites were analyzed by LC-MS/MS. The inositol and leukotriene (LT) contents in the hepatopancreas of freshwater crayfish were significantly decreased after 10 µg/L BPS exposure, which in turn led to the accumulation of lipids causing hepatopancreas damage. In conclusion, when the concentration of BPS in the water environment exceeded 10 µg/L, the freshwater crayfish intestinal microbiota was dysbiosis and the hepatopancreas metabolism was disturbed.

Effects of replacing fishmeal with soybean meal on the immune and antioxidant capacity, and intestinal metabolic functions of red swamp crayfish Procambarus clarkii.

Excess utilization of plant protein sources in animal feed has been found to adversely affect the antioxidant properties and immunity of animals. While the role of gut microbes in plant protein-induced inflammation has been identified in various models, the specific mechanisms regulating gut microbes in crustaceans remain unclear. Accordingly, this study was designed to investigate the effects of replacing fishmeal with soybean meal (SM) on the hepatopancreas antioxidant and immune capacities, and gut microbial functions of crayfish, as well as the potential microbial regulatory mechanisms. 750 crayfish (4.00 g) were randomly divided into five groups: SS0, SS25, SS50, SS75, and SS100, and fed diets with different levels of soybean meal substituted for fishmeal for six weeks. High SM supplementation proved detrimental to maintaining hepatopancreas health, as indicated by an increase in hemolymph MDA content, GPT, and GOT activities, the observed rupture of hepatopancreas cell basement membranes, along with the decreased number of hepatopancreatic F cells. Moreover, crayfish subjected to high SM diets experienced obvious inflammation in hepatopancreas, together with up-regulated mRNA expression levels of nfkb, alf, and tlr (p<0.05), whereas the lzm mRNA expression level exhibited the highest value in the SS25 group. Furthermore, hepatopancreas antioxidant properties highly attenuated by the level of dietary SM substitution levels, as evidenced by the observed increase in MDA content (p<0.05), decrease in GSH content (p<0.05), and inhabitation of SOD, CAT, GPx, and GST activities (p<0.05), along with down-regulated hepatopancreas cat, gpx, gst, and mmnsod mRNA expression levels via inhibiting nrf2/keap1 pathway. Functional genes contributing to metabolism identified that high SM diets feeding significantly activated lipopolysaccharide biosynthesis, revealing gut dysfunction acted as the cause of inflammation. The global microbial co-occurrence network further indicated that the microbes contributing more to serum indicators and immunity were in module eigengene 17 (ME17). A structural equation model revealed that the genes related to alf directly drove the serum enzyme activities through microbes in ME17, with OTU399 and OTU533 identified as major biomarkers and classified into Proteobacteria that secrete endotoxins. To conclude, SM could replace 25 % of fishmeal in crayfish diets without negatively affecting immunity, and antioxidant capacity. Excessive SM levels contributed to gut dysfunction and weakened the innate immune system of crayfish.

Health literacy in patients with gout: A latent profile analysis.

OBJECTIVES: Understanding the health literacy status of patients with gout diagnosis is essential for improving the health of this population. Our study aimed to investigate the latent profiles of health literacy in patients with gout and to analyze differences in characteristics across potential profiles. METHODS: This was a cross-sectional study. Eligible participants attended the Shandong Gout Medical Center, from March 2023 to May 2023 and self-reported gout diagnosis. We used the Health Literacy Scale for Patients with Gout designed and validated by our team. The scale had good reliability and validity among patients with gout. 243 patients completed the Demographic Information Questionnaire and the Health Literacy Scale for Patients with Gout. We used latent profile analysis to identify the latent profiles of gout patients' health literacy. We used Chi-square tests with Bonferroni correction to analyze differences in demographics and illness characteristics across identified profiles. RESULTS: Three profiles of patients with gout emerged (prevalence): the low literacy-low critical group (21.81%), the moderate literacy group (42.79%), and the high literacy-stable group (35.39%). The three groups differed in age, education level, monthly income, disease duration, and place of residence (P<0.01). CONCLUSIONS: The health literacy of patients with gout was heterogeneous. Healthcare professionals should adopt targeted interventions based on the characteristics of each latent health literacy profile to improve the health literacy level of patients with gout.

Antibiotic resistance and virulence genes in Helicobacter pylori strains isolated from children in Shanghai, China (2019-2022).

BACKGROUND: The increasing prevalence of antibiotic-resistant Helicobacter pylori strains poses a significant threat to children's health. This study investigated antibiotic resistance rates in Helicobacter pylori strains isolated from children in Shanghai and analyzed the presence of virulence genes in these strains. METHODS: We obtained 201 Helicobacter pylori strains from pediatric patients with upper gastrointestinal symptoms who underwent gastrointestinal endoscopy between 2019 and 2022. Subsequently, we performed antibiotic susceptibility tests and virulence gene PCR assays on these strains. RESULTS: Helicobacter pylori resistance rates of 45.8%, 15.4%, 1.0%, and 2.5% were detected for metronidazole, clarithromycin, amoxicillin, and levofloxacin, respectively. Among all isolates, 64.7% exhibited resistance to at least one antibiotic. Resistance to metronidazole and clarithromycin increased from 2019 to 2022. The predominant vacA gene subtype was vacA s1a/m2. The prevalence of vacA m2 and dupA exhibited an upward trend, while oipA presented a decreasing trend from 2019 to 2022. The prevalence of dupA was significantly higher in gastritis than peptic ulcer disease, and in non-treatment compared to treatment groups. CONCLUSIONS: Helicobacter pylori antibiotic resistance remains high in children and has risen in recent years. Therefore, the increasing use of metronidazole and clarithromycin requires increased monitoring in children. No association was observed between antibiotic resistance and virulence gene phenotypes.

Deciphering the environmental adaptation and functional trait of core and noncore bacterial communities in impacted coral reef seawater.

Microorganisms play pivotal roles in different biogeochemical cycles within coral reef waters. Nevertheless, our comprehension of the microbially mediated processes following environmental perturbation is still limited. To gain a deeper insight into the environmental adaptation and nutrient cycling, particularly within core and noncore bacterial communities, it is crucial to understand reef ecosystem functioning. In this study, we delved into the microbial community structure and function of seawater in a coral reef under different degrees of anthropogenic disturbance. To achieve this, we harnessed the power of 16S rRNA gene high-throughput sequencing and metagenomics techniques. The results showed that a continuous temporal succession but little spatial heterogeneity in the bacterial communities of core and noncore taxa and functional profiles involved in nitrogen (N) and phosphorus (P) cycling. Eutrophication state (i.e., nutrient concentration and turbidity) and temperature played pivotal roles in shaping both the microbial community composition and functional traits of coral reef seawater. Within this context, the core subcommunity exhibited a remarkably broader habitat niche breadth, stronger phylogenetic signal and lower environmental sensitivity when compared to the noncore taxa. Null model analysis further revealed that the core subcommunity was governed primarily by stochastic processes, while deterministic processes played a more significant role in shaping the noncore subcommunity. Furthermore, our observations indicated that changes in function related to N cycling were correlated to the variations in noncore taxa, while core taxa played a more substantial role in critical processes such as P cycling. Collectively, these findings facilitated our knowledge about environmental adaptability of core and noncore bacterial taxa and shed light on their respective roles in maintaining diverse nutrient cycling within coral reef ecosystems.

Lipoproteins and metabolites in diagnosing and predicting Alzheimer's disease using machine learning.

BACKGROUND: Alzheimer's disease (AD) is a chronic neurodegenerative disorder that poses a substantial economic burden. The Random forest algorithm is effective in predicting AD; however, the key factors influencing AD onset remain unclear. This study aimed to analyze the key lipoprotein and metabolite factors influencing AD onset using machine-learning methods. It provides new insights for researchers and medical personnel to understand AD and provides a reference for the early diagnosis, treatment, and early prevention of AD. METHODS: A total of 603 participants, including controls and patients with AD with complete lipoprotein and metabolite data from the Alzheimer's disease Neuroimaging Initiative (ADNI) database between 2005 and 2016, were enrolled. Random forest, Lasso regression, and CatBoost algorithms were employed to rank and filter 213 lipoprotein and metabolite variables. Variables with consistently high importance rankings from any two methods were incorporated into the models. Finally, the variables selected from the three methods, with the participants' age, sex, and marital status, were used to construct a random forest predictive model. RESULTS: Fourteen lipoprotein and metabolite variables were screened using the three methods, and 17 variables were included in the AD prediction model based on age, sex, and marital status of the participants. The optimal random forest modeling was constructed with "mtry" set to 3 and "ntree" set to 300. The model exhibited an accuracy of 71.01%, a sensitivity of 79.59%, a specificity of 65.28%, and an AUC (95%CI) of 0.724 (0.645-0.804). When Mean Decrease Accuracy and Gini were used to rank the proteins, age, phospholipids to total lipids ratio in intermediate-density lipoproteins (IDL_PL_PCT), and creatinine were among the top five variables. CONCLUSIONS: Age, IDL_PL_PCT, and creatinine levels play crucial roles in AD onset. Regular monitoring of lipoproteins and their metabolites in older individuals is significant for early AD diagnosis and prevention.

Machine Tool Wear Prediction Technology Based on Multi-Sensor Information Fusion.

The intelligent monitoring of cutting tools used in the manufacturing industry is steadily becoming more convenient. To accurately predict the state of tools and tool breakages, this study proposes a tool wear prediction technique based on multi-sensor information fusion. First, the vibrational, current, and cutting force signals transmitted during the machining process were collected, and the features were extracted. Next, the Kalman filtering algorithm was used for feature fusion, and a predictive model for tool wear was constructed by combining the ResNet and long short-term memory (LSTM) models (called ResNet-LSTM). Experimental data for thin-walled parts obtained under various machining conditions were utilized to monitor the changes in tool conditions. A comparison between the ResNet and LSTM tool wear prediction models indicated that the proposed ResNet-LSTM model significantly improved the prediction accuracy compared to the individual LSTM and ResNet models. Moreover, ResNet-LSTM exhibited adaptive noise reduction capabilities at the front end of the network for signal feature extraction, thereby enhancing the signal feature extraction capability. The ResNet-LSTM model yielded an average prediction error of 0.0085 mm and a tool wear prediction accuracy of 98.25%. These results validate the feasibility of the tool wear prediction method proposed in this study.

A Novel Scalable and Reliable Control for DC Microgrids With Varying Number of Agents.

Existing scalable control methods mainly rely on a fixed block-diagonal structure for the Lyapunov matrix, potentially resulting in numerical infeasibility issues. To overcome this limitation, this article proposes a novel scalable and reliable control scheme for dc microgrids. Initially, a general model for dc microgrids is established to enhance reliability, considering scenarios involving loss of control effectiveness (LoCE) and offset faults. Subsequently, a structured free-weight matrix technique is introduced to mitigate negative coupling effects of power lines, and to address numerical infeasibility by avoiding the assumption about the Lyapunov matrix. Furthermore, the stability of the entire dc microgrid is guaranteed by checking local agent conditions, independently of power line couplings. Therefore, the proposed control scheme ensures plug-and-play scalability with varying number of agents. Finally, theoretical results are validated through numerical simulations using the MATLAB/SimPowerSystems toolbox.

Integration of metabolomics and transcriptomics to reveal the mechanism of Gerberae piloselloidis herba in alleviating bronchial asthma.

ETHNOPHARMACOLOGICAL RELEVANCE: Gerberae Piloselloides Herba (GPH) is derived from Gerbera piloselloides (Linn.) Cass. It is a commonly used traditional medicine in China, featured by its special bioactivities as antitussive, expectorant, anti-asthma, anti-bacterial and anti-tumor. It is often used as an effective treatment for cough and sore throat as well as bronchial asthma (BA) in China. It was demonstrated in our previous studies that GPH exerted significant effects on the treatment of BA, but its underlying mechanism remains unclear. AIM OF THE STUDY: This study was aimed at revealing the mechanism through which GPH protects against BA. MATERIALS AND METHODS: The protective effect of GPH against BA was evaluated in a mouse model of BA induced by ovalbumin. Through integrated metabolomics and transcriptomics analysis, the most critical pathways were discovered. The effects of GPH in regulating these pathways was verified through molecular biology experiments and molecular docking. RESULTS: GPH have anti-BA effects. In plasma and lung tissue, 5 and 17 differentially expressed metabolites (DEMs), respectively, showed a reversed tendency in the GPH group compared with the model group; apart from gamma-aminobutyric acid and butyrylcarnitine, these DEMs might aid in BA diagnosis. The DEMs were involved primarily in the regulation of lipid metabolism, followed by glucose metabolism and amino acid metabolism. Transcriptomic analysis indicated that GPH modulated 268 differentially expressed genes (DEGs). Integration analysis of metabolomics and transcriptomics revealed that GPH might regulate the PPAR signaling pathway, thus affecting the expression of key gene targets such as Cyp4a12a, Cyp4a12b, Adh7, Acaa1b and Gpat2; controlling fatty acid degradation, unsaturated fatty acid biosynthesis, glycerophospholipid metabolism and other lipid metabolic pathways; and ameliorating BA. This possibility was confirmed through reverse-transcription quantitative polymerase chain reaction, western blotting, immunofluorescence and molecular docking. CONCLUSION: GPH was found to activate the PPAR signaling pathway, decrease the levels of Cyp4a12a and Cyp4a12b, and increase the levels of Adh7, Acaa1b and Gpat2, thereby regulating lipid metabolism disorder, decreasing the generation of inflammatory mediators and limiting lung injury.

Mortality Study of Patients with Omicron Infection Before and After the Implementation of the New Crown Standard.

Objective: To investigate the mortality rate of patients with Omicron infection before and after the implementation of the new crown standard, and to evaluate the impact of new treatment protocols on the mortality rate of patients with Omicron infection. Methods: Clinical data of 1419 Omicron-infected patients treated in our hospital from April 10, 2022 to June 3, 2022 were collected(Patients diagnosed with Omicron infection who met the diagnostic criteria in the "Diagnosis and treatment protocol for novel coronavirus pneumonia (trial version 9)"15 and whose nasal/pharyngeal swab samples were typed as Omicron variants by laboratory viral genotyping). They were divided into the observation group (April 25 2022 - June 3 2022) and the control group (April 10 2022 - April 24 2022) before and after the implementation criteria. Clinical data of 1419 patients were collected and compared between the two groups on whether to use anticoagulant drugs, whether to use antiplatelet drugs, gender, whether to use new drugs of thymosin/thymus method, age, whether to use herbal medicine, whether to use Fuzheng prescription, blood routine, liver function, kidney function indicators, mortality of patients. Results: A total of 1419 patients were initially selected; 501 patients with incomplete information were excluded, and finally, 918 patients were included. According to the time period before and after the application criteria, they were divided into an observation group (586 cases) and a control group (332 cases). There were no statistically significant differences in gender, age, antiplatelet drug use, and herbal medicine use between the two groups (P < .05). However, there were significant differences in the use of anticoagulant drugs, thymidine/thymidine drugs, and Fu Zhengfang between the two groups. It was statistically significant that the mortality rate in the observation group (2.39)% was significantly lower than that in the control group (5.12)%. P < .05 White blood cell count, red blood cell ratio, lymphocyte count, hemoglobin, neutrophil count, and neutrophil ratio were not significantly different between the two groups (P < .05) .In comparison to the control group (4.92±8.00)10^9/L, the platelet count in the observation group (4.77±3.41)109/L was considerably lower. The difference was statistically significant (P < .05). The comparison of total bilirubin, total protein values and alkaline phosphatase values between the two groups was not significant (P < .05). In the observation group, albumin (38.71±6.39) g/L, glutamate transaminase (23.93±26.03) U/L, glutathione transaminase (26.12±25.53) U/L, gamma-glutamyltransferase (34.28±52.3) U/L, globulin values (28.13±5.55) g/L were significantly lower than those of the control group (36.66±7.08) g/L, (30.36±65.77) U/L, (33.29±49.72) U/L, (43.76±80.23) U/L, (29.85±5.67) g/L, the difference was statistically significant (P < .05). Between the two groups, there were no significant differences in the values of uric acid or creatinine (P > .05). Levels and uric acid readings did not differ significantly, P > .05. The difference between the urea values of the observation group (7.44±6.34 mmol/L) and the control group (8.75±7.51 mmol/L) was statistically significant (P < .05). Conclusion: After the implementation of the treatment protocol for COVID-19 (Trial Version 9), the number of death cases among patients with Omicron variant infection has significantly decreased. The treatment protocol is safe and feasible and can be widely applied in clinical settings..And it will further promote the development and administration of vaccines to prevent and control the spread of the novel coronavirus, reducing the occurrence of patients and death cases.

MEMS-based two-photon microscopy with Lissajous scanning and image reconstruction under a feed-forward control strategy.

Two-photon microscopy (TPM) based on two-dimensional micro-electro-mechanical (MEMS) system mirrors shows promising applications in biomedicine and the life sciences. To improve the imaging quality and real-time performance of TPM, this paper proposes Lissajous scanning control and image reconstruction under a feed-forward control strategy, a dual-parameter alternating drive control algorithm and segmented phase synchronization mechanism, and pipe-lined fusion-mean filtering and median filtering to suppress image noise. A 10 fps frame rate (512 × 512 pixels), a 140 µm × 140 µm field of view, and a 0.62 µm lateral resolution were achieved. The imaging capability of MEMS-based Lissajous scanning TPM was verified by ex vivo and in vivo biological tissue imaging.

Insights into the efficient mineralization of antibiotic trimethoprim in aqueous media by Fe2+ catalytically enhanced vacuum-UV irradiation: Kinetics, mechanisms, and toxicity evaluation.

The widespread existence of antibiotics in the environment has attracted growing concerns regarding the potential adverse effects on aquatic organisms, ecosystems, and human health even at low concentrations. Extensive efforts have been devoted to developing new methods for effective elimination of antibiotics from wastewater. Herein, a novel process of Fe2+ catalytically enhanced vacuum ultraviolet (VUV) irradiation was proposed as a promising approach for the removal of antibiotic trimethoprim (TMP) in water. Compared with UVC photolysis, VUV photolysis, and UVC/Fe2+, VUV/Fe2+ could increase the pseudo-first-order reaction rate constant of TMP removal by 6.6-38.4 times and the mineralization rate by 36.5%-59.9%. The excellent performance might originate from the synergistic effect of VUV and Fe2+, i.e., VUV irradiation could effectively split water and largely accelerate the Fe3+/Fe2+ cycle to generate more reactive oxygen species (ROS). EPR results indicated that •OH and O2•- were identified as the main ROS in the UVC/Fe2+ and VUV/Fe2+ processes, while •OH, O2•-, and 1O2 were involved in the VUV process. The operating parameters, such as Fe2+ dosage and initial TMP contents, were evaluated and optimized. Up to 8 aromatic intermediates derived from hydroxylation, demethylation, carbonylation, and methylene group cleavage were identified by UPLC-QTOF-MS/MS technique, the possible pathways of TMP degradation were proposed. Finally, the acute and chronic toxicity of intermediates formed during TMP degradation in the VUV/Fe2+ process were also evaluated.

Activated carbon fiber as an efficient co-catalyst toward accelerating Fe2+/Fe3+ cycling for improved removal of antibiotic cefaclor via electro-Fenton process using a gas diffusion electrode.

The electro-Fenton (EF) based on gas-diffusion electrodes (GDEs) reveals promising application prospective towards recalcitrant organics degradation because such GDEs often yields superior H2O2 generation efficiency and selectivity. However, the low efficiency of Fe2+/Fe3+ cycle with GDEs is always considered to be the limiting step for the EF process. In this study, activated carbon fiber (ACF) was firstly employed as co-catalyst to facilitate the performance of antibiotic cefaclor (CEC) decomposition in EF process. It was found that the addition of ACF co-catalyst achieved a rapid Fe2+/Fe3+ cycling, which significantly enhanced Fenton's reaction and hydroxyl radicals (•OH) generation. X-ray photoelectron spectroscopy (XPS) results indicated that the functional groups on ACF surface are related to the conversion of Fe3+ into Fe2+. Moreover, DMSO probing experiment confirmed the enhanced •OH production in EF + ACF system compared to conventional EF system. When inactive BDD and Ti4O7/Ti anodes were paired to EF system, the addition of ACF could significantly improve mineralization degree. However, a large amount of toxic byproducts, including chlorate (ClO3-) and perchlorate (ClO4-), were generated in these EF processes, especially for BDD anode, due to their robust oxidation capacity. Higher mineralization efficiency and less toxic ClO4- generation were obtained in the EF + ACF process with Ti4O7/Ti anode. This presents a novel alternative for efficient chloride-containing organic removal during wastewater remediation.

Detoxification of Hyperglycemia-induced Glucose Toxicity by the Hexosamine Biosynthetic Pathway.

The abnormal intermediate glucose metabolic pathways induced by elevated intracellular glucose levels during hyperglycemia often establish the metabolic abnormality that leads to cellular and structural changes in development and to progression of diabetic pathologies. Glucose toxicity generally refers to the hyperglycemia-induced irreversible cellular dysfunctions over time. These irreversible cellular dysfunctions in diabetic nephropathy include: (1) inflammatory responses, (2) mesangial expansion, and (3) podocyte dysfunction. Using these three cellular events in diabetic nephropathy as examples of glucose toxicity in the diabetic complications, this review focuses on: (1) the molecular and cellular mechanisms associated with the hexosamine biosynthetic pathway that underly glucose toxicity; and (2) the potential therapeutic tools to inhibit hyperglycemia induced pathologies. We propose novel therapeutic strategies that directly shunts intracellular glucose buildup under hyperglycemia by taking advantage of intracellular glucose metabolic pathways to dampen it by normal synthesis and secretion of hyaluronan, and/or by intracellular chondroitin sulfate synthesis and secretion. This could be a useful way to detoxify the glucose toxicity in hyperglycemic dividing cells, which could mitigate the hyperglycemia induced pathologies in diabetes.

Adaptive Optimization Method for Prediction and Compensation of Thin-Walled Parts Machining Deformation Based on On-Machine Measurement.

Thin-walled aluminum alloy parts are widely used in the aerospace field because of their favorable characteristics that cater to various applications. However, they are easily deformed during milling, leading to a low pass rate of workpieces. On the basis of on-machine measurement (OMM) and surrogate stiffness models (SSMs), we developed an iterative optimization compensation method in this study to overcome the machining deformation of thin-walled parts. In the error compensation process, the time-varying factors of workpiece stiffness and the impact of prediction model errors were considered. First, we performed machining deformation simulation and information extraction on the key nodes of the machined surface, and an SSM containing the stiffness information of discrete nodes of each cutting layer was established. Subsequently, the machining errors were monitored through intermittent OMM to suppress the adverse impact of prediction model errors. Further, an interlayer correction coefficient was introduced in the compensation process to iteratively correct the prediction model error of each node in the SSM along the depth direction, and a correction coefficient between parts was introduced to realize the iterative correction of the prediction model for the same node position between different parts. Finally, the feasibility of the proposed method was verified through multiple sets of actual machining experiments on thin-walled parts with added pads.

Non-invasive imaging of pathological scars using a portable handheld two-photon microscope.

BACKGROUND: Pathological scars are a disorder that can lead to various cosmetic, psychological, and functional problems, and no effective assessment methods are currently available. Assessment and treatment of pathological scars are based on cutaneous manifestations. A two-photon microscope (TPM) with the potential for real-time non-invasive assessment may help determine the under-surface pathophysiological conditions in vivo . This study used a portable handheld TPM to image epidermal cells and dermal collagen structures in pathological scars and normal skin in vivo to evaluate the effectiveness of treatment in scar patients. METHODS: Fifteen patients with pathological scars and three healthy controls were recruited. Imaging was performed using a portable handheld TPM. Five indexes were extracted from two dimensional (2D) and three dimensional (3D) perspectives, including collagen depth, dermo-epidermal junction (DEJ) contour ratio, thickness, orientation, and occupation (proportion of collagen fibers in the field of view) of collagen. Two depth-dependent indexes were computed through the 3D second harmonic generation image and three morphology-related indexes from the 2D images. We assessed index differences between scar and normal skin and changes before and after treatment. RESULTS: Pathological scars and normal skin differed markedly regarding the epidermal morphological structure and the spectral characteristics of collagen fibers. Five indexes were employed to distinguish between normal skin and scar tissue. Statistically significant differences were found in average depth ( t = 9.917, P <0.001), thickness ( t = 4.037, P <0.001), occupation ( t = 2.169, P <0.050), orientation of collagen ( t = 3.669, P <0.001), and the DEJ contour ratio ( t = 5.105, P <0.001). CONCLUSIONS: Use of portable handheld TPM can distinguish collagen from skin tissues; thus, it is more suitable for scar imaging than reflectance confocal microscopy. Thus, a TPM may be an auxiliary tool for scar treatment selection and assessing treatment efficacy.

Secure Adaptive Event-Triggered Control for Cyber-Physical Power Systems Under Denial-of-Service Attacks.

Secure control for cyber-physical power systems (CPPSs) under cyber attacks is a challenging issue. Existing event-triggered control schemes are generally difficult to mitigate the impact of cyber attacks and improve communication efficiency simultaneously. To solve such two problems, this article studies secure adaptive event-triggered control for the CPPSs under energy-limited denial-of-service (DoS) attacks. A new DoS-dependent secure adaptive event-triggered mechanism (SAETM) is developed, where DoS attacks are taken into account when designing the trigger mechanisms. Sufficient conditions are derived to ensure the CPPSs to be uniformly ultimate boundedness stable, and the entering time when the state trajectories of the CPPSs are guaranteed to stay in the secure region is also given. Finally, numerical simulations are provided to illustrate the effectiveness of the proposed control method.