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Background: Focal segmental glomerulosclerosis (FSGS) is a histological pattern of glomerular damage that includes idiopathic conditions as well as genetic and non-genetic forms. Among these various etiologies, different phenotypes within the spectrum of congenital anomalies of the kidney and urinary tract (CAKUT) have been associated with FSGS. Summary: Until recently, the main pathomechanism of how congenital kidney and urinary tract defects lead to FSGS was attributed to a reduced number of nephrons, resulting in biomechanical stress on the remaining glomeruli, detachment of podocytes and subsequent inability to maintain normal glomerular architecture. The discovery of deleterious single nucleotide variants in PAX2, a transcription factor crucial in normal kidney development and a known cause of papillorenal syndrome, in individuals with adult-onset FSGS without congenital kidney defects has shed new light on developmental defects that become evident during podocyte injury. Key message: In this mini-review, we challenge the assumption that FSGS in CAKUT is caused by glomerular hyperfiltration alone and hypothesize a multifactorial pathogenesis that includes overlapping cellular mechanisms that are activated in both damaged podocytes as well as nephron progenitor cells.

Research has shown that climate change creates warmer temperatures and drier conditions, leading to longer wildfire seasons and increased wildfire risks in the United States. These factors have, in turn, led to increases in the frequency, extent, and severity of wildfires in recent years. Given the danger posed by wildland fires to people, property, wildlife, and the environment, there is an urgent need to provide tools for effective wildfire management. Early detection of wildfires is essential to minimizing potentially catastrophic destruction. To that end, in this paper, we present our work on integrating multiple data sources into SmokeyNet, a deep learning model using spatiotemporal information to detect smoke from wildland fires. We present Multimodal SmokeyNet and SmokeyNet Ensemble for multimodal wildland fire smoke detection using satellite-based fire detections, weather sensor measurements, and optical camera images. An analysis is provided to compare these multimodal approaches to the baseline SmokeyNet in terms of accuracy metrics, as well as time-to-detect, which is important for the early detection of wildfires. Our results show that incorporating weather data in SmokeyNet improves performance numerically in terms of both F1 and time-to-detect over the baseline with a single data source. With a time-to-detect of only a few minutes, SmokeyNet can be used for automated early notification of wildfires, providing a useful tool in the fight against destructive wildfires. [ABSTRACT FROM AUTHOR]