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Inhalation represents a significant route of cadmium (Cd) exposure, which is associated with an elevated risk of lung diseases. This research study aims to evaluate the impact of repeated low-dose cadmium inhalation on exacerbating airway inflammation induced by ovalbumin (OVA) in asthma-afflicted mice. Mice were grouped into four categories: control (Ctrl), OVA, cadmium chloride (CdCl2), and OVA + cadmium chloride (OVA + CdCl2). Mice in the OVA group displayed increased airway mucus secretion and peribronchial and airway inflammation characterized by eosinophil cell infiltration, along with elevated levels of Th2 cytokines (IL-4, IL-5, IL-13) in bronchoalveolar lavage fluids (BALFs). These parameters were further exacerbated in the OVA + CdCl2 group. Additionally, the OVA + CdCl2 group exhibited higher levels of the oxidative stress marker malondialdehyde (MDA), greater activity of glutathione peroxidase (GSH-Px), and higher phosphorylation of extracellular regulated kinase (ERK) in lung tissue. Treatment with U0126 (an ERK inhibitor) and α-tocopherol (an antioxidant) in the OVA + CdCl2 group resulted in reduced peribronchial and airway inflammation as well as decreased airway mucus secretion. These findings indicate that CdCl2 exacerbates airway inflammation in OVA-induced allergic asthma mice following airway exposure. ERK and oxidative stress are integral to this process, and the inhibition of these pathways significantly alleviates the adverse effects of CdCl2 on asthma exacerbation. [ABSTRACT FROM AUTHOR]

Indoor environmental parameters are closely related to the energy consumption and indoor thermal comfort of office buildings. Predicting these parameters, especially indoor temperature, can contribute to the management of energy consumption and thermal comfort levels in office buildings. An accurate indoor temperature prediction model is the basis for implementing this process. To this end, this paper first discusses the input and output parameters of the model, and then it compares the prediction effects of mainstream prediction model algorithms based on data mining under the same data conditions. The superiority of the XGBoost integrated learning algorithm is verified, and a further XGBoost-based indoor temperature online prediction method is designed. The effectiveness of the method is validated using actual data from a commercial office building in Haidian District, Beijing. Finally, optimization methods for the prediction method are discussed with regard to the scheduler mechanism proposed in this paper. Overall, this work can assist building operators in optimizing HVAC equipment running strategies, thus improving the indoor thermal comfort and energy efficiency of the building. [ABSTRACT FROM AUTHOR]