Ningdong Granule Upregulates the Striatal DA Transporter and Attenuates Stereotyped Behavior of Tourette Syndrome in Rats

This study aimed to evaluate the possible mechanism of Ningdong granule (NDG) for the treatment of Tourette syndrome (TS). The rats with stereotyped behavior were established by microinjection with TS patients’ sera; then, the model rats were divided into NDG and haloperidol (Hal) group, and the nonmedication model rats were regarded as treatment control (TS group). The stereotyped behavior of the rats was recorded, the level of dopamine (DA) in striatum, and the content of homovanillic acid (HVA) in sera were tested, and dopamine transporter (DAT) expression was measured in the study. The experimental results showed that NDG effectively inhibited the stereotyped behavior (P<0.01), decreased the levels of DA in the striatum (P<0.05), increased the content of sera HVA (P<0.01), and enhanced the protein and mRNA expression of DAT in the striatum (P<0.01). Additionally, the results also revealed Hal could improve the stereotyped behavior as well but had no remarkable influence on DAT expression and DA metabolism. In conclusion, NDG attenuates stereotyped behavior, and its mechanism of action might be associated with the upregulation of DAT expression to regulate DA metabolism in the brain

Analysis of the association between urinary glyphosate exposure and fatty liver index: a study for US adults

Abstract Background Non-alcoholic fatty liver disease (NAFLD) is a prevalent condition that often goes unrecognized in the population, and many risk factors for this disease are not well understood. Glyphosate (GLY) is one of the most commonly used herbicides worldwide, and exposure to this chemical in the environment is significant. However, studies exploring the association between GLY exposure and NAFLD remain limited. Therefore, the aim of this study was to assess the association between urinary glyphosate (uGLY) level and fatty liver index (FLI) using data from the National Health and Nutrition Examination Survey (NHANES), which includes uGLY measurements. Methods The log function of uGLY was converted and expressed as Loge(uGLY) with the constant “e” as the base and used for subsequent analysis. The association between Loge(uGLY) (the independent variable) level and FLI (the dependent variable) was assessed by multiple linear regression analysis. Smoothing curve fitting and a generalized additive model were used to assess if there was a nonlinear association between the independent and the dependent variables. A subgroup analysis was used to find susceptible individuals of the association between the independent variable and the dependent variable. Results A final total of 2238 participants were included in this study. Participants were categorized into two groups (< -1.011 and ≥ -1.011 ng/ml) based on the median value of Loge(uGLY). A total of 1125 participants had Loge(uGLY) levels ≥ -1.011 ng/ml and higher FLI. The result of multiple linear regression analysis showed a positive association between Loge(uGLY) and FLI (Beta coefficient = 2.16, 95% CI: 0.71, 3.61). Smoothing curve fitting and threshold effect analysis indicated a linear association between Loge(uGLY) and FLI [likelihood ratio(LLR) = 0.364]. Subgroup analyses showed that the positive association between Loge(uGLY) and FLI was more pronounced in participants who were female, aged between 40 and 60 years, had borderline diabetes history, and without hypertension history. In addition, participants of races/ethnicities other than (Mexican American, White and Black) were particularly sensitive to the positive association between Loge(uGLY) and FLI. Conclusions A positive linear association was found between Loge(uGLY) level and FLI. Participants who were female, 40 to 60 years old, and of ethnic backgrounds other than Mexican American, White, and Black, deserve more attention

Dynamic Recrystallization Behavior and Processing Map of the Cu–Cr–Zr–Nd Alloy

Hot deformation behavior of the Cu–Cr–Zr–Nd alloy was studied by hot compressive tests in the temperature range of 650–950 °C and the strain rate range of 0.001–10 s−1 using Gleeble-1500D thermo-mechanical simulator. The results showed that the flow stress is strongly dependent on the deformation temperature and the strain rate. With the increase of temperature or the decrease of strain rate, the flow stress significantly decreases. Hot activation energy of the alloy is about 404.84 kJ/mol and the constitutive equation of the alloy based on the hyperbolic-sine equation was established. Based on the dynamic material model, the processing map was established to optimize the deformation parameters. The optimal processing parameters for the Cu–Cr–Zr–Nd alloy hot working are in the temperature range of 900–950 °C and strain rate range of 0.1–1 s−1. A full dynamic recrystallization structure with fine and homogeneous grain size can be obtained at optimal processing conditions. The microstructure of specimens deformed at different conditions was analyzed and connected with the processing map. The surface fracture was observed to identify instability conditions

Study on the effect of heat treatment on the microstructure and properties of C17300 beryllium copper alloy microwires

Abstracts: The effects of solution time and aging temperature on the microstructure and properties of C17300 beryllium copper alloy wire were studied by means of SEM, XRD, electronic universal tester and resistance tester. The results show that after solution treatment, the deformed twin structure disappears and the distorted crystal grows into equiaxed crystal. The optimum solution time is 90 min, and the hardness and resistivity are 115HV and 1.13 × 10−7 Ω m respectively. The optimum solution time is 90 min, and the hardness and resistivity are 115HV and 9.52 × 10−7 Ω m respectively. When the aging temperature is increased from 260 °C to 340 °C, the amount of grain boundary reaction is increased from 3.84% to 17.05%, and the precipitated phase changes from point to tumor-like distribution. When the aging temperature rises to 320 °C, the mechanical properties are the best, with tensile strength of 1192.86 MPa, hardness of 373.81HV and elongation of 5.16%. With the increase of aging temperature, the work hardening rate gradually increases, the number of dimples and micropores on the fracture surface decreases, and the granular precipitates at the bottom of dimples increase. In summary, the differences of microstructure and mechanical properties of beryllium bronze at different solution time and aging temperature are compared, which provides some basis for preparing high strength beryllium copper alloy microwires

Dynamic Recrystallization Behavior and Processing Map of the Cu–Cr–Zr–Nd Alloy

Hot deformation behavior of the Cu–Cr–Zr–Nd alloy was studied by hot compressive tests in the temperature range of 650–950 °C and the strain rate range of 0.001–10 s−1 using Gleeble-1500D thermo-mechanical simulator. The results showed that the flow stress is strongly dependent on the deformation temperature and the strain rate. With the increase of temperature or the decrease of strain rate, the flow stress significantly decreases. Hot activation energy of the alloy is about 404.84 kJ/mol and the constitutive equation of the alloy based on the hyperbolic-sine equation was established. Based on the dynamic material model, the processing map was established to optimize the deformation parameters. The optimal processing parameters for the Cu–Cr–Zr–Nd alloy hot working are in the temperature range of 900–950 °C and strain rate range of 0.1–1 s−1. A full dynamic recrystallization structure with fine and homogeneous grain size can be obtained at optimal processing conditions. The microstructure of specimens deformed at different conditions was analyzed and connected with the processing map. The surface fracture was observed to identify instability conditions

Efficacy of Compounds Isolated from the Essential Oil of Artemisia lavandulaefolia in Control of the Cigarette Beetle, Lasioderma serricorne

To develop natural product resources to control cigarette beetles (Lasioderma serricorne), the essential oil from Artemisia lavandulaefolia (Compositae) was investigated. Oil was extracted by hydrodistillation of the above-ground portion of A. lavandulaefolia and analyzed using gas chromatography-mass spectrometer (GC-MS). Extracted essential oil and three compounds isolated from the oil were then evaluated in laboratory assays to determine the fumigant, contact, and repellent efficacy against the stored-products’ pest, L. serricorne. The bioactive constituents from the oil extracts were identified as chamazulene (40.4%), 1,8-cineole (16.0%), and β-caryophyllene (11.5%). In the insecticidal activity assay, the adults of L. serricorne were susceptible to fumigant action of the essential oil and 1,8-cineole, with LC50 values of 31.81 and 5.18 mg/L air. The essential oil, 1,8-cineole, chamazulene, and β-caryophyllene exhibited contact toxicity with LD50 values of 13.51, 15.58, 15.18 and 35.52 μg/adult, respectively. During the repellency test, the essential oil and chamazulene had repellency approximating the positive control. The results indicated that chamazulene was abundant in A. lavandulaefolia essential oil and was toxic to cigarette beetles

Image_3_Machine learning models including insulin resistance indexes for predicting liver stiffness in United States population: Data from NHANES.TIF

BackgroundPrevention and treatment of liver fibrosis at an early stage is of great prognostic importance, whereas changes in liver stiffness are often overlooked in patients before the onset of obvious clinical symptoms. Recognition of liver fibrosis at an early stage is therefore essential.ObjectiveAn XGBoost machine learning model was constructed to predict participants' liver stiffness measures (LSM) from general characteristic information, blood test metrics and insulin resistance-related indexes, and to compare the fit efficacy of different datasets for LSM.MethodsAll data were obtained from the National Health and Nutrition Examination Survey (NHANES) for the time interval January 2017 to March 2020. Participants' general characteristics, Liver Ultrasound Transient Elastography (LUTE) information, indicators of blood tests and insulin resistance-related indexes were collected, including homeostasis model assessment of insulin resistance (HOMA-IR) and metabolic score for insulin resistance (METS-IR). Three datasets were generated based on the above information, respectively named dataset A (without the insulin resistance-related indexes as predictor variables), dataset B (with METS-IR as a predictor variable) and dataset C (with HOMA-IR as a predictor variable). XGBoost regression was used in the three datasets to construct machine learning models to predict LSM in participants. A random split was used to divide all participants included in the study into training and validation cohorts in a 3:1 ratio, and models were developed in the training cohort and validated with the validation cohort.ResultsA total of 3,564 participants were included in this study, 2,376 in the training cohort and 1,188 in the validation cohort, and all information was not statistically significantly different between the two cohorts (p > 0.05). In the training cohort, datasets A and B both had better predictive efficacy than dataset C for participants' LSM, with dataset B having the best fitting efficacy [±1.96 standard error (SD), (-1.49,1.48) kPa], which was similarly validated in the validation cohort [±1.96 SD, (-1.56,1.56) kPa].ConclusionsXGBoost machine learning models built from general characteristic information and clinically accessible blood test indicators are practicable for predicting LSM in participants, and a dataset that included METS-IR as a predictor variable would improve the accuracy and stability of the models.</p

Table_1_Machine learning models including insulin resistance indexes for predicting liver stiffness in United States population: Data from NHANES.doc

BackgroundPrevention and treatment of liver fibrosis at an early stage is of great prognostic importance, whereas changes in liver stiffness are often overlooked in patients before the onset of obvious clinical symptoms. Recognition of liver fibrosis at an early stage is therefore essential.ObjectiveAn XGBoost machine learning model was constructed to predict participants' liver stiffness measures (LSM) from general characteristic information, blood test metrics and insulin resistance-related indexes, and to compare the fit efficacy of different datasets for LSM.MethodsAll data were obtained from the National Health and Nutrition Examination Survey (NHANES) for the time interval January 2017 to March 2020. Participants' general characteristics, Liver Ultrasound Transient Elastography (LUTE) information, indicators of blood tests and insulin resistance-related indexes were collected, including homeostasis model assessment of insulin resistance (HOMA-IR) and metabolic score for insulin resistance (METS-IR). Three datasets were generated based on the above information, respectively named dataset A (without the insulin resistance-related indexes as predictor variables), dataset B (with METS-IR as a predictor variable) and dataset C (with HOMA-IR as a predictor variable). XGBoost regression was used in the three datasets to construct machine learning models to predict LSM in participants. A random split was used to divide all participants included in the study into training and validation cohorts in a 3:1 ratio, and models were developed in the training cohort and validated with the validation cohort.ResultsA total of 3,564 participants were included in this study, 2,376 in the training cohort and 1,188 in the validation cohort, and all information was not statistically significantly different between the two cohorts (p > 0.05). In the training cohort, datasets A and B both had better predictive efficacy than dataset C for participants' LSM, with dataset B having the best fitting efficacy [±1.96 standard error (SD), (-1.49,1.48) kPa], which was similarly validated in the validation cohort [±1.96 SD, (-1.56,1.56) kPa].ConclusionsXGBoost machine learning models built from general characteristic information and clinically accessible blood test indicators are practicable for predicting LSM in participants, and a dataset that included METS-IR as a predictor variable would improve the accuracy and stability of the models.</p

Data_Sheet_1_Machine learning models including insulin resistance indexes for predicting liver stiffness in United States population: Data from NHANES.ZIP

BackgroundPrevention and treatment of liver fibrosis at an early stage is of great prognostic importance, whereas changes in liver stiffness are often overlooked in patients before the onset of obvious clinical symptoms. Recognition of liver fibrosis at an early stage is therefore essential.ObjectiveAn XGBoost machine learning model was constructed to predict participants' liver stiffness measures (LSM) from general characteristic information, blood test metrics and insulin resistance-related indexes, and to compare the fit efficacy of different datasets for LSM.MethodsAll data were obtained from the National Health and Nutrition Examination Survey (NHANES) for the time interval January 2017 to March 2020. Participants' general characteristics, Liver Ultrasound Transient Elastography (LUTE) information, indicators of blood tests and insulin resistance-related indexes were collected, including homeostasis model assessment of insulin resistance (HOMA-IR) and metabolic score for insulin resistance (METS-IR). Three datasets were generated based on the above information, respectively named dataset A (without the insulin resistance-related indexes as predictor variables), dataset B (with METS-IR as a predictor variable) and dataset C (with HOMA-IR as a predictor variable). XGBoost regression was used in the three datasets to construct machine learning models to predict LSM in participants. A random split was used to divide all participants included in the study into training and validation cohorts in a 3:1 ratio, and models were developed in the training cohort and validated with the validation cohort.ResultsA total of 3,564 participants were included in this study, 2,376 in the training cohort and 1,188 in the validation cohort, and all information was not statistically significantly different between the two cohorts (p > 0.05). In the training cohort, datasets A and B both had better predictive efficacy than dataset C for participants' LSM, with dataset B having the best fitting efficacy [±1.96 standard error (SD), (-1.49,1.48) kPa], which was similarly validated in the validation cohort [±1.96 SD, (-1.56,1.56) kPa].ConclusionsXGBoost machine learning models built from general characteristic information and clinically accessible blood test indicators are practicable for predicting LSM in participants, and a dataset that included METS-IR as a predictor variable would improve the accuracy and stability of the models.</p

Image_1_Machine learning models including insulin resistance indexes for predicting liver stiffness in United States population: Data from NHANES.TIF

BackgroundPrevention and treatment of liver fibrosis at an early stage is of great prognostic importance, whereas changes in liver stiffness are often overlooked in patients before the onset of obvious clinical symptoms. Recognition of liver fibrosis at an early stage is therefore essential.ObjectiveAn XGBoost machine learning model was constructed to predict participants' liver stiffness measures (LSM) from general characteristic information, blood test metrics and insulin resistance-related indexes, and to compare the fit efficacy of different datasets for LSM.MethodsAll data were obtained from the National Health and Nutrition Examination Survey (NHANES) for the time interval January 2017 to March 2020. Participants' general characteristics, Liver Ultrasound Transient Elastography (LUTE) information, indicators of blood tests and insulin resistance-related indexes were collected, including homeostasis model assessment of insulin resistance (HOMA-IR) and metabolic score for insulin resistance (METS-IR). Three datasets were generated based on the above information, respectively named dataset A (without the insulin resistance-related indexes as predictor variables), dataset B (with METS-IR as a predictor variable) and dataset C (with HOMA-IR as a predictor variable). XGBoost regression was used in the three datasets to construct machine learning models to predict LSM in participants. A random split was used to divide all participants included in the study into training and validation cohorts in a 3:1 ratio, and models were developed in the training cohort and validated with the validation cohort.ResultsA total of 3,564 participants were included in this study, 2,376 in the training cohort and 1,188 in the validation cohort, and all information was not statistically significantly different between the two cohorts (p > 0.05). In the training cohort, datasets A and B both had better predictive efficacy than dataset C for participants' LSM, with dataset B having the best fitting efficacy [±1.96 standard error (SD), (-1.49,1.48) kPa], which was similarly validated in the validation cohort [±1.96 SD, (-1.56,1.56) kPa].ConclusionsXGBoost machine learning models built from general characteristic information and clinically accessible blood test indicators are practicable for predicting LSM in participants, and a dataset that included METS-IR as a predictor variable would improve the accuracy and stability of the models.</p