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Natural products, recognized for their potential in disease prevention and treatment, have been integrated with advanced nano-delivery systems to create natural product-based nanomedicines, offering innovative approaches for various diseases. Natural products derived from traditional Chinese medicine have their own targeting effect and remarkable therapeutic effect on many diseases, but there are some shortcomings such as poor physical and chemical properties. The construction of nanomedicines using the active ingredients of natural products has become a key step in the modernization research process, which could be used to make up for the defects of natural products such as low solubility, large dosage, poor bioavailability and poor targeting. Nanotechnology enhances the safety, selectivity, and efficacy of natural products, positioning natural product-based nanomedicines as promising candidates in medicine. This review outlines the current status of development, the application in different diseases, and safety evaluation of natural product-based nanomedicines, providing essential insights for further exploration of the synergy between natural products and nano-delivery systems in disease treatment. [ABSTRACT FROM AUTHOR]

In single-site validations, site-pixel, synthetic pixel and footprint-integrated methods are commonly used to validate evapotranspiration (ET) retrieval at the pixel scale. To reveal the reliability of these methods, this study analysed the performance of the three methods for ET retrieval validation based on eddy-covariance observations at the Daman site which is numerously used for validation in Heihe Watershed Allied Telemetry Experimental Research (HiWATER). The analysis at different spatial scales in different seasons showed that the validation results of the site-pixel method were more similar that of the footprint-integrated method than the synthetic pixel method. However, the validation results of the site-pixel method showed significant discrepancies from those of the footprint-integrated method, especially in summer at the 480 M scale which is near to the spatial resolution of MODIS with a large difference of more than 125.65 W M−2. This error is due to aggregation and high spatial heterogeneity. The use of synthetic pixel can avoid this error. Normally, the error probably appears when the wind direction is 270°–360°, usually in summer, autumn and winter. In addition, the different aggregation methods had a slight effect on the ET estimation results. This study is helpful in determining the reliability of different single-point validation methods and provides a reliable way to select a validation method for single-site validation of remote sensing retrieval of ET. [ABSTRACT FROM AUTHOR]

Evapotranspiration (ET) serves as a key indicator of the water change between the Earth's surface and atmosphere, significantly influencing the hydrology cycle, surface energy cycle, and carbon cycle. Existing remote sensing models for estimating ET usually necessitate the parameterization of resistance parameters. In this study, we proposed the Remote Sensed Non-Parametric (RSNP) model, which leverages the nonparametric (NP) and Surface Flux Equilibrium-nonparametric (SFE-NP) approaches, and adapted remote sensing and reanalysis datasets of meteorological and surface parameters as model inputs. We estimate global monthly ET from 2001 to 2019 in the spatial resolution of 0.1° with RSNP model. Validation against FLUXNET sites globally yield RMSE of 23 mm/month (278 mm/yr), while regional-scale validation against water-balance ET results in a Root Mean Square Error (RMSE) of 113 mm/yr. In addition, the produced ET dataset have great accuracy in forest underlying and obtains spatial details of land surface ET. Furthermore, compared with ETMonitor, PEW and PML_V2, our dataset offers a continuous and seamless ET dataset suitable for global research. This study contributes to the advancement of global ET estimation and informs future water balance studies. The dataset presented in this article has been published in National Tibetan Plateau Data Center at https://doi.org/10.11888/Terre.tpdc.301343 (Pan, 2024). [ABSTRACT FROM AUTHOR]