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Purpose of reviewGlomerular filtration rate (GFR) is the best index for kidney function and estimated GFR (eGFR) calculated from endogenous filtration markers like serum creatinine and cystatin C is widely used in clinical practice for chronic kidney disease diagnosis and prognostication. We sought to review the evolution of GFR estimating equations, nuances of eGFR interpretation, and utility of eGFR in drug dosing.Recent findingsThe Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) serum creatinine eGFR equation was recently updated to exclude the race variable and the CKD-EPI creatinine-cystatin C equation demonstrated the highest reliability. Although calculated creatinine clearance by Cockcroft Gault has been traditionally used for drug dosing, the use of eGFR is slowly being adapted by the Food and Drug Administration for pharmacokinetic studies. However, the individual-level accuracy of eGFR using the CKD-EPI 2021 equations remained low, with the distribution of measured GFR at a given eGFR value spanning several CKD stages.SummaryAlthough current methods of estimating GFR have improved in population measures of reliability, all have significant individual-level inaccuracies that can be an issue when clinical decision-making is contingent on the actual level of GFR. Modern methods of GFR measurements should be made widely available to enhance individualized patient decision-making.

Empathy is crucial for our social life, with evidences suggesting that executive function is the cognitive basis of empathy. However, debates about the causal relationship between executive function and empathy still exist. A two-wave and one-year longitudinal follow-up experiment is conducted to investigate this problem, mainly focusing on preschoolers as executive function and empathy develop rapidly in preschoolers. A one-year cross-lagged panel analysis results found that preschoolers' empathy, mainly cognitive empathy, was predicted by inhibitory control. In line with previous studies, these results imply that inhibitory control is the cognitive basis of preschoolers' empathy and future educational practices and training studies should be considered.

Stability is a key characteristic for understanding ecosystem processes and evolution. However, research on the stability of complex ecosystems often faces limitations, such as reliance on single parameters and insufficient representation of continuous changes. This study developed a multidimensional stability assessment system for regional ecosystems based on disturbances. Focusing on the lower reaches of the Yellow River Basin (LR-YRB), we integrated the remote sensing ecological index (RSEI) with texture structural parameters, and applied the Landsat-based detection of trends in disturbance and recovery (LandTrendr) algorithm to analyze the continuous changes in disturbances and recovery from 1986 to 2021, facilitating the quantification and evaluation of resistance, resilience, and temporal stability. The results showed that 72.27% of the pixels experienced 1–9 disturbances, indicating the region's sensitivity to external factors. The maximum disturbances primarily lasted 2–3 years, with resistance and resilience displaying inverse spatial patterns. Over the 35-year period, 61.01% of the pixels exhibited moderate temporal stability. Approximately 59.83% of the pixels recovered or improved upon returning to pre-disturbance conditions after maximum disturbances, suggesting a strong recovery capability. The correlation among stability dimensions was low and influenced by disturbance intensity, underscoring the necessity for a multidimensional assessment of regional ecosystem stability based on satellite remote sensing. [ABSTRACT FROM AUTHOR]

Underwater acoustic target recognition has always played a pivotal role in ocean remote sensing. By analyzing and processing ship-radiated signals, it is possible to determine the type and nature of a target. Historically, traditional signal processing techniques have been employed for target recognition in underwater environments, which often exhibit limitations in accuracy and efficiency. In response to these limitations, the integration of artificial intelligence (AI) methods, particularly those leveraging machine learning and deep learning, has attracted increasing attention in recent years. Compared to traditional methods, these intelligent recognition techniques can autonomously, efficiently, and accurately identify underwater targets. This paper comprehensively reviews the contributions of intelligent techniques in underwater acoustic target recognition and outlines potential future directions, offering a forward-looking perspective on how ongoing advancements in AI can further revolutionize underwater acoustic target recognition in ocean remote sensing. [ABSTRACT FROM AUTHOR]