Increased glyburide clearance in the pregnant mouse model. Drug Metab Dispos 38:1403–1406. Address correspondence to:

c) Number of text pages: 22 Abstract Glyburide (GLB) is an oral sulfonylurea, commonly used for the treatment of gestational diabetes mellitus. It has been reported that the clearance of GLB in pregnant women is significantly higher than that in non-pregnant women. The molecular mechanism by which pregnancy increases the clearance of GLB is not known, but may be caused by increased CYP3A activity. As liver tissue from pregnant women is not readily available, in the present study, we investigated the mechanism of such pregnancy-related changes in GLB disposition in a mouse model. We demonstrated that the systemic clearance of GLB in pregnant mice was increased approximately 2-fold (p < 0.01) as compared with non-pregnant mice, a magnitude of change similar to that observed in the clinical study. Plasma protein binding of GLB in mice was not altered by pregnancy. The half-life of GLB depletion in hepatic S-9 fractions of pregnant mice was significantly shorter than that of non-pregnant mice. Moreover, GLB depletion was markedly inhibited by ketoconazole, a potent inhibitor of mouse Cyp3a, suggesting that GLB metabolism in mice is primarily mediated by hepatic Cyp3a. These data suggest that the increased systemic clearance of GLB in pregnant mice is likely caused by an increase in hepatic Cyp3a activity during pregnancy, and provide a basis for further mechanistic understanding and analysis of pregnancy-induced alterations in the disposition of GLB and drugs that are predominantly and extensively metabolized by CYP3A/Cyp3a. DMD #33837

A Multi-Objective Optimisation Mathematical Model with Constraints Conducive to the Healthy Rhythm for Lighting Control Strategy

Studies have shown that illuminance and correlated colour temperature (CCT) are strongly correlated with body responses such as circadian rhythm, alertness, and mood. It is worth noting that these responses show a complex and variable coupling, which needs to be solved using accurate mathematical models for the regulation of indoor light parameters. Therefore, in this study, by weighing the evaluations of visual comfort, alertness, valence, and arousal of mood, a multi-objective optimisation mathematical model was developed with constraints conducive to the healthy rhythm. The problem was solved with the multi-objective evolutionary algorithm based on the decomposition differential evolution (MOEA/D-DE) algorithm. Taking educational space as the analysis goal, a dual-parameter setting strategy for illuminance and CCT covering four modes was proposed: focused learning, comfortable learning, soothing learning, and resting state, which could provide a scientific basis for the regulation of the lighting control system. The alertness during class time reached 3.01 compared to 2.34 during break time, showing a good light facilitation effect. The proposed mathematical model and analysis method also have the potential for application in the lighting design and control in other spaces to meet the era of intelligent, highly flexible, and sustainable buildings

A Modified Inherent Strain Method for Fast Prediction of Residual Deformation in Additive Manufacturing of Metal Parts

Effective prediction of residual deformation is very important to guarantee the quality of metal parts produced by additive manufacturing (AM). However, analytical or numerical models for the AM process are complicated and time consuming thus far. The conventional inherent strain theory can predict residual distortion of the regular metal welding problem. Typically, it is not applicable to the complicated layer-by-layer laser-sintering depositing process. In this paper, a modified inherent strain method is presented to predict the residual deformation much more efficiently. Calculation of the modified inherent strain is based on small-scale thermomechanical simulation of the AM process. Next, the strain values are assigned to the heat-affected zone as material thermal property and a one-time static mechanical analysis is performed. Residual deformation obtained by the new method and the thermomechanical simulation indicates good accuracy and efficiency of the proposed method.Mechanical Engineerin

Data-Driven Building Energy Consumption Prediction Model Based on VMD-SA-DBN

Prediction of building energy consumption using mathematical modeling is crucial for improving the efficiency of building energy utilization, assisting in building energy consumption planning and scheduling, and further achieving the goal of energy conservation and emission reduction. In consideration of the non-linear and non-smooth characteristics of building energy consumption time series data, a short-term, hybrid building energy consumption prediction model combining variational mode decomposition (VMD), a simulated annealing (SA) algorithm, and a deep belief network (DBN) is proposed in this study. In the proposed VMD-SA-DBN model, the VMD algorithm decomposes the time series into different modes to reduce the fluctuation of the data. The SA-DBN prediction model is built for each mode separately, and the DBN network structure parameters are optimized by the SA algorithm. The prediction results of each model are aggregated and reconstructed to obtain the final prediction output. The validity and prediction performance of the proposed model is evaluated on a publicly available dataset, and the results show that the proposed new model significantly improves the accuracy and stability of building energy consumption prediction compared with several typical machine learning methods. The mean absolute percent error (MAPE) of the VMD-SA-DBN model is 63.7%, 65.5%, 46.83%, 64.82%, 44.1%, 36.3%, and 28.3% lower than that of the long short-term memory (LSTM), gated recurrent unit (GRU), VMD-LSTM, VMD-GRU, DBN, SA-DBN, and VMD-DBN models, respectively. The results will help managers formulate more-favorable low-energy emission reduction plans and improve building energy efficiency

Research on Location Selection Model of 5G Micro Base Station Based on Smart Street Lighting System

In order to promote the development and construction of smart cities, the massive equipment requirements of sensing terminals increased the pressure on urban site resource allocation. The light pole is suitable for carrying various urban functional equipment to form a smart street lighting system, which can provide rich site resources for the large-scale construction of urban functional facilities such as 5G micro base stations. However, the selection and combination of equipment mounted in the smart street lighting system only focus on the functional superposition at the physical level, without considering the relevance of each subsystem in practical application scenarios. Therefore, this study proposed a 5G micro base station location model based on a smart street lighting system. The correlation and cooperativity between 5G micro base stations and mounted devices were fully considered, and a universal system-level location selection index was developed to realize rational utilization of urban space site resources and intelligent linkage between subsystems. The results showed that the model is significantly effective for functional areas with different road network characteristics and provides practical, robust, effective, and accurate help for similar location selection problems

α-Mangostin Alleviated Lipopolysaccharide Induced Acute Lung Injury in Rats by Suppressing NAMPT/NAD Controlled Inflammatory Reactions

α-Mangostin (MAN) is a bioactive xanthone isolated from mangosteen. This study was designed to investigate its therapeutic effects on acute lung injury (ALI) and explore the underlying mechanisms of action. Rats from treatment groups were subject to oral administration of MAN for 3 consecutive days beforehand, and then ALI was induced in all the rats except for normal controls via an intraperitoneal injection with lipopolysaccharide. The severity of disease was evaluated by histological examination and hematological analysis. Protein expressions in tissues and cells were examined with immunohistochemical and immunoblotting methods, respectively. The levels of cytokines and nicotinamide adenine dinucleotide (NAD) were determined using ELISA and colorimetric kits, respectively. It was found that MAN treatment significantly improved histological conditions, reduced leucocytes counts, relieved oxidative stress, and declined TNF-α levels in ALI rats. Meanwhile, MAN treatment decreased expressions of nicotinamide phosphoribosyltransferase (NAMPT) and Sirt1 both in vivo and in vitro, which was accompanied with a synchronized decline of NAD and TNF-α. Immunoblotting assay further showed that MAN downregulated HMGB1, TLR4, and p-p65 in RAW 264.7 cells. MAN induced declines of both HMGB1/TLR4/p-p65 and TNF-α were substantially reversed by cotreatment with nicotinamide mononucleotide or NAD. These results suggest that downregulation of NAMPT/NAD by MAN treatments contributes to the alleviation of TLR4/NF-κB-mediated inflammations in macrophage, which is essential for amelioration of ALI in rats

Identification of potential glioma drug resistance target proteins based on ultra-performance liquid chromatography-mass spectrometry differential proteomics

In this study, to screen for candidate markers of temozolomide (TMZ) resistance in glioblastoma, we artificially established TMZ drug-resistant glioblastoma (GBM) cell lines, U251-TMZ and U87-TMZ. In the U251-TMZ and U87-TMZ cell lines, we screened and analyzed differentially expressed proteins using ultra-performance liquid chromatography-mass spectrometry (UPLC-MS) differential proteomics. Compared with the U251 and U87 control cell lines, 95 differential proteins were screened in the U251-TMZ and U87-TMZ cell lines, of which 28 proteins were upregulated and 67 proteins were down-regulated. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of the co-upregulated proteins showed that most of the differentially expressed proteins were located in the cytoplasm and were significantly upregulated in the biological processes related to vesicular transport in the intimal system and inflammatory response mediated by myeloid leukocytes. Seven candidates were identified as potential GBM markers of TMZ resistance. Combined with existing research findings, our study supports that UAP1L1 and BCKDK are promising potential markers of TMZ resistance in GBM. This is important for further understanding the molecular mechanisms that drive the development and enhancement of TMZ resistance