Role of 5-HT1A-mediated upregulation of brain indoleamine 2,3 dioxygenase 1 in the reduced antidepressant and antihyperalgesic effects of fluoxetine during maintenance treatment

The reduced antidepressant and antihyperalgesic effects of selective serotonin reuptake inhibitors (SSRIs) such as fluoxetine during maintenance treatment has been reported, but little is known about the molecular mechanism of this phenomenon. In three comorbid pain and depression animal models (genetic predisposition, chronic social stress, arthritis), we showed that the fluoxetine’s antidepressant and antihyperalgesic effects were reduced during the maintenance treatment. Fluoxetine exposure induced upregulation of the 5-hydroxytryptamine 1A (5-HT1A) auto-receptor and indoleamine 2,3 dioxygenase 1 (IDO1, a rate-limiting enzyme of tryptophan metabolism) in the brainstem dorsal raphe nucleus (DRN), which shifted the tryptophan metabolism away from the 5-HT biosynthesis. Mechanistically, IDO1 upregulation was downstream to fluoxetine-induced 5-HT1A receptor expression because 1) antagonism of the 5-HT1A receptor with WAY100635 or 5-HT1A receptor knockout blocked the IDO1 upregulation, and 2) inhibition of IDO1 activity did not block the 5-HT1A receptor upregulation following fluoxetine exposure. Importantly, inhibition of either the 5-HT1A receptor or IDO1 activity sustained the fluoxetine’s antidepressant and antihyperalgesic effects, indicating that 5-HT1A-mediated IDO1 upregulation in the brainstem DRN contributed to the reduced antidepressant and antihyperalgesic effects of fluoxetine. These results suggest a new strategy to improving the therapeutic efficacy of SSRI during maintenance treatment

Development of Building CFD Model Design Process Based on BIM

This paper proposes the design process of optimized building Computational Fluid Dynamics (CFD) model based on Building Information Modelling (BIM). The proposed method consists of five-step processes: BIM data extraction, geometry simplification, grid optimization, attribute data matching, and finally, exporting a CFD case folder for OpenFOAM. Validation is performed to evaluate the improvement of the grid model and the accuracy of the simulation result. Validation is conducted for four indoor ventilation models. The number of grids increased or decreased, according to the optimization method, but did not change significantly. On the other hand, the maximum non-orthogonality improved by up to 20.78%, according to the optimization function. This proves that it is sufficiently effective in improving the grid quality. The accuracy of the proposed method is evaluated by relative error rate with the ANSYS simulation result. The error rates for flow and temperature are evaluated. The relative error rate is less than 5% under all conditions. Therefore, the accuracy of the proposed method is verified

Automatic Detection of Linear Thermal Bridges from Infrared Thermal Images Using Neural Network

Detecting thermal bridges in building envelopes should be a priority to improve the thermal performance of buildings. Recently, thermographic surveys are being used to detect thermal bridges. However, conventional methods of detecting thermal bridges from thermal images rely on the subjective judgment of audits. Research has been conducted to automatically detect thermal bridges from thermal images to improve problems caused by such subjective judgment, but most of these studies are still in the early stage. Therefore, this study proposes a linear thermal bridge detection method based on image processing and machine learning. The proposed method includes thermal anomaly area clustering, feature extraction, and an artificial-neural-network-based thermal bridge detection. The proposed method was validated by detecting the thermal bridges in actual buildings. As a result, the average precision, recall, and F-score were 89.29%, 87.29, and 87.63%, respectively

Automated Conversion of Building Information Modeling (BIM) Geometry Data for Window Thermal Performance Simulation

A window set is defined as a window where the frame and the glass are combined and is used at the part that comes into contact with the air. As the performance evaluation of window sets has gained significance, the need for software that can simulate window set performance has also increased accordingly. However, the simulation of window sets is not carried out efficiently due to the difficulty in the window set modeling. Meanwhile, the design of building information modeling has recently proliferated so that the window set BIM library is distributed online. If such a window set BIM library is utilized in the window set simulation, it is expected that the productivity issue that occurs in the simulation process could be improved. Therefore, this study proposes a method to automatically convert the information required in the simulation of the window set heat transfer coefficient from the BIM. In order to achieve the purpose of this study, the following procedure is carried out. First, the framework for converting the information required in the simulation of the window set heat transfer coefficient from the BIM is suggested. Second, the method to extract and convert BIM data based on the suggested framework is proposed. Lastly, the BIM data conversion program is developed, and its performance is validated by applying the window set BIM case. The case study result showed that the information converted and entered from the window set data BIM conversion program coincided with the information entered in the window set BIM. It is expected that the result of this study will increase the productivity of window set simulations, which will lead to the increased use of certification through these simulations

In-situ evaluation of window-wall joint performance using numerical models and thermal images

Infrared thermography is an effective method for assessing the on-site thermal performance and heat exchange of structures. On-site infrared thermography was generally performed under the assumption of quasi-steady sate, which refers to the averaged boundary conditions; however, a description of the actual on-site dynamic conditions is necessary to increase the reproducibility of the assessment. This study, through simulation models, describes the infrared thermal images of the surrounding wall depending on the thermal performance at the joint between the window and wall, through which the thermal performance of the joint is estimated to quantify insulation defects or thermal bridge flaws. As incremental research to the previous chamber-based preliminary research, the main objective was to apply the assessment method to an on-site experiment and verify the reproducibility of the results. In the case of the distinct differentiation of the temperature distribution of the wall according to the material properties of the joint, the reproducibility of the assessed results was verified as values of the material properties of the joint of the physical model that represent the minimum error were the same, regardless of the period of assessments. Furthermore, the difference in the results from the physical model, in which the derived physical property values were applied, and the infrared thermography measurements were also within 5% and 20% (coefficient of variance of the root mean square error and normalized root mean squared error)

Gut Microbiota is critical for the induction of chemotherapy-induced pain

Chemotherapy-induced pain is a dose-limiting condition that affects 30% of patients undergoing chemotherapy. We found that the gut microbiota promotes the development of chemotherapy-induced mechanical hyperalgesia. Oxaliplatin-induced mechnical hyperalgesia was reduced in germ-free mice and in those mice pretreated with antibiotics. Restoration of the microbiota of germ-free mice abrogated this protection. These effects appear to be mediated, in part, by TLR4 expressed on hematopoietic cells, including macrophages