Natural products as glycolytic inhibitors for cervical cancer treatment: A comprehensive review.

Cervical cancer, a prevalent gynaecological malignancy, presents challenges in late-stage treatment efficacy. Aerobic glycolysis, a prominent metabolic trait in cervical cancer, emerges as a promising target for novel drug discovery. Natural products, originating from traditional medicine, represent a significant therapeutic avenue and primary source for new drug development. This review explores the regulatory mechanisms of glycolysis in cervical cancer and summarises natural compounds that inhibit aerobic glycolysis as a therapeutic strategy. The glycolytic phenotype in cervical cancer is regulated by classical molecules such as HIF-1, HPV virulence factors and specificity protein 1, which facilitate the Warburg effect in cervical cancer. Various natural products, such as artemisinin, shikonin and kaempferol, exert inhibitory effects by downregulating key glycolytic enzymes through signalling pathways such as PI3K/AKT/HIF-1α and JAK2/STAT3. Despite challenges related to drug metabolism and toxicity, these natural compounds provide novel insights and promising avenues for cervical cancer treatment.

Modelling method of inter-building movement for campus-scale occupancy simulation: A case study.

As an important factor in the investigation of building energy consumption, occupant behavior (OB) has been widely studied on the building level. However so far, studies of OB modelling on the district scale remain limited. Indeed, district-scale OB modelling has been facing the challenges from the scarcity of district-scale data, modelling methods, as well as simulation application. This study initiates the extrapolation of occupancy modelling methodology from building level to district scale through proposing modelling methods of inter-building movements. The proposed modelling methods utilize multiple distribution fittings and Bayesian network to upscale the event description methods from inter-zone movement events at the building level to inter-building movement events at the district level. This study provides a framework on the application of the proposed modelling methods for a university campus in the suburbs of Shanghai, taking advantages of data sensing, monitoring and survey techniques. With the collected campus-scale occupancy data, this paper defines five patterns of inter-building movement. One pattern represents the dominated inter-building movement events for one kind of students in their daily campus life. Based on the quantitative descriptions for various inter-building movement events, this study performs the stochastic simulation for the campus district, using Markov chain models. The simulation results are then validated with the campus-scale occupancy measurement data. Furthermore, the impact of inter-building movement modelling methods on building energy demand is evaluated for the library building, taking the deterministic occupancy schedules suggested by current building design standard as a baseline.