Hypoxia-inducible Factor 1α Contributes to Matrix Metalloproteinases 2/9 and Inflammatory Responses in Periodontitis.

Periodontitis is a prevalent condition characterized by inflammation and tissue destruction within the periodontium, with hypoxia emerging as a contributing factor to its pathogenesis. Hypoxia-inducible factor 1α (HIF-1α) has a crucial role in orchestrating adaptive responses to hypoxic microenvironments and has been implicated in various inflammatory-related diseases. Understanding the interplay between HIF-1α, matrix metalloproteinases (MMPs), and inflammatory responses in periodontitis could provide insights into its molecular mechanisms. We investigated the relationship between HIF-1α, MMP2, and MMP9 in gingival crevicular fluid (GCF) and periodontal ligament stem cells (PDLSCs) from periodontitis patients. The expression levels of HIF-1α, MMP2, MMP9, and inflammatory factors (IL-6, IL-1ß, TNF-α) were assessed using enzyme-linked immunosorbent assay (ELISA) and real-time PCR (RT-PCR). Additionally, osteogenic differentiation of PDLSCs was identified by alkaline phosphatase activity. Significantly elevated levels of HIF-1α, MMP2, and MMP9 were observed in GCF of periodontitis patients compared to controls. Positive correlations were found between HIF-1α and MMP2/MMP9, as well as with IL-6, IL-1ß, and TNF-α. Modulation of HIF-1α expression in PDLSCs revealed its involvement in MMP2/9 secretion and inflammatory responses, with inhibition of HIF-1α mitigating these effects. Furthermore, HIF-1α inhibition alleviated the reduction in osteogenic differentiation induced by inflammatory stimuli. Our findings elucidate the regulatory role of HIF-1α in MMP expression, inflammatory responses, and osteogenic differentiation in periodontitis. In conclusion, targeting HIF-1α signaling pathways may offer therapeutic opportunities for managing periodontitis and promoting periodontal tissue regeneration.

Great Lakes Runoff Intercomparison Project Phase 3: Lake Erie (GRIP-E).

Hydrologic model intercomparison studies help to evaluate the agility of models to simulate variables such as streamflow, evaporation, and soil moisture. This study is the third in a sequence of the Great Lakes Runoff Intercomparison Projects. The densely populated Lake Erie watershed studied here is an important international lake that has experienced recent flooding and shoreline erosion alongside excessive nutrient loads that have contributed to lake eutrophication. Understanding the sources and pathways of flows is critical to solve the complex issues facing this watershed. Seventeen hydrologic and land-surface models of different complexity are set up over this domain using the same meteorological forcings, and their simulated streamflows at 46 calibration and seven independent validation stations are compared. Results show that: (1) the good performance of Machine Learning models during calibration decreases significantly in validation due to the limited amount of training data; (2) models calibrated at individual stations perform equally well in validation; and (3) most distributed models calibrated over the entire domain have problems in simulating urban areas but outperform the other models in validation.