The effects of (+)-Gossypol on 11β-HSD and the concentration of corticosterone and dehydrocorticosterone in mice serum and tissues

11β-hydroxysteroid dehydrogenase (11β-HSD) plays an important part in mediating glucocorticoid action, catalyzing the interconversion of corticosterone (B) and dehydrocorticosterone (A) in rodents. The aim of our study is to investigate the effects of (+)-gossypol (G+) on 11β-HSD. Adult ICR mice were given B and B + (G+) by intraperitoneal injection. The activity of 11β-HSD was evaluated by measuring the ratio of A and B, meanwhile the effects of (+)-gossypol on the conversion rate of B to A was determined with HPLC. Serum A/B levels of the B+(G+) group decreased by 2.42, 7.32, 17.85, 31.39, and 40.02 % compared to the B group at each measured time interval. A/B levels at 1 h for the B + (G+) group decreased by 43.78, 21.29 and 34.47% in liver, kidney and adrenal glands, respectively, in comparison to the B group. However, (+)-gossypol had no effect on brain and testis. (+)-Gossypol was an inhibitor of 11β-HSD.Colegio de Farmacéuticos de la Provincia de Buenos Aire

The effects of (+)-Gossypol on 11β-HSD and the concentration of corticosterone and dehydrocorticosterone in mice serum and tissues

11β-hydroxysteroid dehydrogenase (11β-HSD) plays an important part in mediating glucocorticoid action, catalyzing the interconversion of corticosterone (B) and dehydrocorticosterone (A) in rodents. The aim of our study is to investigate the effects of (+)-gossypol (G+) on 11β-HSD. Adult ICR mice were given B and B + (G+) by intraperitoneal injection. The activity of 11β-HSD was evaluated by measuring the ratio of A and B, meanwhile the effects of (+)-gossypol on the conversion rate of B to A was determined with HPLC. Serum A/B levels of the B+(G+) group decreased by 2.42, 7.32, 17.85, 31.39, and 40.02 % compared to the B group at each measured time interval. A/B levels at 1 h for the B + (G+) group decreased by 43.78, 21.29 and 34.47% in liver, kidney and adrenal glands, respectively, in comparison to the B group. However, (+)-gossypol had no effect on brain and testis. (+)-Gossypol was an inhibitor of 11β-HSD.Colegio de Farmacéuticos de la Provincia de Buenos Aire

Helicobacter pylori infection is associated with decreased serum levels of high density lipoprotein, but not with the severity of coronary atherosclerosis

<p>Abstract</p> <p>Objective</p> <p>The objective of this survey was to study the association between <it>Helicobacter pylori </it>infection and the severity of coronary atherosclerosis.</p> <p>Methods</p> <p>The study population consisted of 961 consecutive patients (711 males and 250 females) who underwent coronary angiography for suspected or known coronary atherosclerosis. The patients' body mass index, blood pressure, the blood lipid, blood glucose, leukocyte count (10⁹/L), neutrophil count (10⁹/L), and Helicobacter <it>pylori</it>-specific IgG antibodies were performed. Coronary angiograms were scored according to vessel score and Gensini's score.</p> <p>Results</p> <p>A significant association between <it>H. pylori </it>infection and coronary atherosclerosis as well as its severity was not find in this cross section study (<it>p </it>= 0.858). And, the level distribution of vessel score (<it>p </it>= 0.906) and Gensini's score (<it>p </it>= 0.905) were similar in the seropositivity group and seronegativity group of Helicobacter <it>pylori </it>infection. However, the level of fasting high-density lipoprotein cholesterol (mmol/L) (<it>p </it>= 0.013) was significantly lower in the seropositivity group than that in the seronegativity group of Helicobacter <it>pylori </it>infection.</p> <p>Conclusions</p> <p>In conclusion, in the present study, a significantly correlation between Helicobacter <it>pylori </it>seropositivity and angiographically evaluated severity of atherosclerosis was not find. And, the present study showed a good correlation between Helicobacter <it>pylori </it>infection and decreased HDL cholesterol. However, the exact mechanisms need further study.</p

Automatic Data Transformation Using Large Language Model: An Experimental Study on Building Energy Data

Existing approaches to automatic data transformation are insufficient to meet the requirements in many real-world scenarios, such as the building sector. First, there is no convenient interface for domain experts to provide domain knowledge easily. Second, they require significant training data collection overheads. Third, the accuracy suffers from complicated schema changes. To bridge this gap, we present a novel approach that leverages the unique capabilities of large language models (LLMs) in coding, complex reasoning, and zero-shot learning to generate SQL code that transforms the source datasets into the target datasets. We demonstrate the viability of this approach by designing an LLM-based framework, termed SQLMorpher, which comprises a prompt generator that integrates the initial prompt with optional domain knowledge and historical patterns in external databases. It also implements an iterative prompt optimization mechanism that automatically improves the prompt based on flaw detection. The key contributions of this work include (1) pioneering an end-to-end LLM-based solution for data transformation, (2) developing a benchmark dataset of 105 real-world building energy data transformation problems, and (3) conducting an extensive empirical evaluation where our approach achieved 96% accuracy in all 105 problems. SQLMorpher demonstrates the effectiveness of utilizing LLMs in complex, domain-specific challenges, highlighting the potential of their potential to drive sustainable solutions.Comment: 10 pages, 7 figure

The effects of (+)-Gossypol on 11β-HSD and the concentration of corticosterone and dehydrocorticosterone in mice serum and tissues

11β-hydroxysteroid dehydrogenase (11β-HSD) plays an important part in mediating glucocorticoid action, catalyzing the interconversion of corticosterone (B) and dehydrocorticosterone (A) in rodents. The aim of our study is to investigate the effects of (+)-gossypol (G+) on 11β-HSD. Adult ICR mice were given B and B + (G+) by intraperitoneal injection. The activity of 11β-HSD was evaluated by measuring the ratio of A and B, meanwhile the effects of (+)-gossypol on the conversion rate of B to A was determined with HPLC. Serum A/B levels of the B+(G+) group decreased by 2.42, 7.32, 17.85, 31.39, and 40.02 % compared to the B group at each measured time interval. A/B levels at 1 h for the B + (G+) group decreased by 43.78, 21.29 and 34.47% in liver, kidney and adrenal glands, respectively, in comparison to the B group. However, (+)-gossypol had no effect on brain and testis. (+)-Gossypol was an inhibitor of 11β-HSD.Colegio de Farmacéuticos de la Provincia de Buenos Aire

A general route via formamide condensation to prepare atomically dispersed metal-nitrogen-carbon electrocatalysts for energy technologies

Single-atom electrocatalysts (SAECs) have gained tremendous attention due to their unique active sites and strong metal–substrate interactions. However, the current synthesis of SAECs mostly relies on costly precursors and rigid synthetic conditions and often results in very low content of single-site metal atoms. Herein, we report an efficient synthesis method to prepare metal–nitrogen–carbon SAECs based on formamide condensation and carbonization, featuring a cost-effective general methodology for the mass production of SAECs with high loading of atomically dispersed metal sites. The products with metal inclusion were termed as formamide-converted metal–nitrogen–carbon (shortened as f-MNC) materials. Seven types of single-metallic f-MNC (Fe, Co, Ni, Mn, Zn, Mo and Ir), two bi-metallic (ZnFe and ZnCo) and one tri-metallic (ZnFeCo) SAECs were synthesized to demonstrate the generality of the methodology developed. Remarkably, these f-MNC SAECs can be coated onto various supports with an ultrathin layer as pyrolysis-free electrocatalysts, among which the carbon nanotube-supported f-FeNC and f-NiNC SAECs showed high performance for the O2 reduction reaction (ORR) and the CO2 reduction reaction (CO2RR), respectively. Furthermore, the pyrolysis products of supported f-MNC can still render isolated metallic sites with excellent activity, as exemplified by the bi-metallic f-FeCoNC SAEC, which exhibited outstanding ORR performance in both alkaline and acid electrolytes by delivering ∼70 and ∼20 mV higher half-wave potentials than that of commercial 20 wt% Pt/C, respectively. This work offers a feasible approach to design and manufacture SAECs with tuneable atomic metal components and high density of single-site metal loading, and thus may accelerate the deployment of SAECs for various energy technology applications

Using dual evolutionary search to construct decision tree based ensemble classifier

Abstract A typical ensemble learning process typically uses a forward integration mechanism to construct the ensemble classifier with a large number of base classifiers. Based on this mechanism, it is difficult to adjust the diversity among base classifiers and optimize the structure inside ensemble since the generation process has a certain amount of randomness, which makes the performance of ensemble classifiers heavily dependent on the human design decisions. To address this issue, we proposed an automatic ensemble classifier construction method based on a dual-layer evolutionary search mechanism, which includes a tree coding-based base classifier population and a binary coding-based ensemble classifier population. Through a collaborative searching process between the two populations, the proposed method can be driven by training data to update the base classifier population and optimize the ensemble classifiers globally. To verify the effectiveness of the dual evolutionary ensemble learning method (DEEL), we tested it on 22 classification tasks from 4 data repositories. The results show that the proposed method can generate a diverse decision tree population on the training data while searching and constructing ensemble classifiers from them. Compared with 9 competitor algorithms, the proposed method achieved the best performance on 17 of 22 test tasks and improved the average accuracies by 0.97–7.65% over the second place. In particular, the generated ensemble classifiers show excellent structure, which involve small number and diverse decision trees. That increases the transparency of ensembles and helps to perform interpretability analysis on them