An Exosome-Based Therapeutic Strategy Targeting Neuroinflammation in Alzheimer's Disease with Berberine and Palmatine.

Introduction: Neuroinflammation is one of the major pathogeneses in Alzheimer's disease (AD) and mainly involves abnormal inflammatory activation of microglia by multiple pathological stimuli. The treatment of AD remains a major challenge due to the multifactorial characterization of AD and the inefficient ability of therapeutic drugs to permeate through the blood‒brain barrier (BBB). Accordingly, drug combination treatment and drug carrier delivery have become important therapeutic tools for the treatment of multifactorial diseases, especially AD. Methods: Inflammatory cytokine levels in microglia, including NO, TNF-α, IL-1ß, IL-4, and IL-10, were detected. The Morris water maze and object location task were used to investigate the learning and memory functions of APP/PS1 mice in different treatment groups. The number of neurons and plasticity of synapses were evaluated by immunofluorescence double labelling. Additionally, the ratio of ß-amyloid plaques and the number of activated microglia were evaluated by immunofluorescence staining. The concentrations of ß-amyloid plaques and inflammatory factors in the hippocampus were determined by ELISA. Microglia-derived exosomes (Exos) were extracted and purified by size exclusion chromatography. The distribution of exosomes and drugs was investigated in vitro and in vivo. Results: Compared to single drug interventions, the combination of Ber and Pal (Ber/Pal) modulated microglial inflammatory cytokine levels. Ber/Pal promoted the recovery of learning and memory impairment in APP/PS1 mice. Immunofluorescence staining indicated that Ber/Pal restored neurons, inhibited Aß plaque formation and microglial activation, and regulated the secretion of inflammatory factors. Exos promoted the accumulation of drugs in cells and tissues and improved the targeting of drugs across the BBB. Conclusion: Ber/Pal could offer a synergistic and more comprehensive therapeutic effect in AD. Additionally, the microglia-derived Exos-Ber/Pal delivery system promoted the targeting and permeation of drugs into the brain, suggesting a creative strategy for targeting AD therapy by regulating neuroinflammation in microglial cells.

Long-term remote observations of particulate organic phosphorus concentration in eutrophic Lake Taihu based on a novel algorithm.

Monitoring the long-term spatiotemporal variations in particulate organic phosphorus concentration (CPOP) is imperative for clarifying the phosphorus cycle and its biogeochemical behavior in waters. However, little attention has been devoted to this owing to a lack of suitable bio-optical algorithms that allow the application of remote sensing data. In this study, based on Moderate Resolution Imaging Spectroradiometer (MODIS) data, a novel absorption-based algorithm of CPOP was developed for eutrophic Lake Taihu, China. The algorithm yielded a promising performance with a mean absolute percentage error of 27.75% and root mean square error of 21.09 µg/L. The long-term MODIS-derived CPOP demonstrated an overall increasing pattern over the past 19 years (2003-2021) and a significant temporal heterogeneity in Lake Taihu, with higher value in summer (81.97 ± 3.81 µg/L) and autumn (82.07 ± 3.8 µg/L), and lower CPOP in spring (79.52 ± 3.81 µg/L) and winter (78.74 ± 3.8 µg/L). Spatially, relatively higher CPOP was observed in the Zhushan Bay (85.87 ± 7.5 µg/L), whereas the lower value was observed in the Xukou Bay (78.95 ± 3.48 µg/L). In addition, significant correlations (r > 0.6, P < 0.05) were observed between CPOP and air temperature, chlorophyll-a concentration and cyanobacterial blooms areas, demonstrating that CPOP was greatly influenced by air temperature and algal metabolism. This study provides the first record of the spatial-temporal characteristics of CPOP in Lake Taihu over the past 19 years, and the CPOP results and regulatory factors analyses could provide valuable insights for aquatic ecosystem conservation.

Long-term dynamics and drivers of particulate phosphorus concentration in eutrophic lake Chaohu, China.

Particulate phosphorus (PP) plays an important biological role in the eutrophication process, and is thus an important water quality parameter for assessing climatic change and anthropogenic activity factors that affect aquatic ecosystems. Here, we used 20-year Moderate Resolution Imaging Spectroradiometer (MODIS) data to explore the patterns and trends of PP concentration (CPP) in eutrophic Lake Chaohu based on a new empirical model. The validation results indicated that the developed model performed satisfactorily in estimating CPP, with a mean absolute percentage error of 31.89% and root mean square error of 0.022 mg/L. Long-term MODIS observations (2000-2019) revealed that the CPP of Lake Chaohu has experienced an overall increasing trend and distinct spatiotemporal heterogeneity. The driving factor analysis revealed that the chemical fertilizer consumption, municipal wastewater, industrial sewage, precipitation, and air temperature were the five potential driving factors and collectively explained more than 81% of the long-term variation in CPP. This study provides the long-term datasets of CPP in inland waters and new insights for future water eutrophication control and restoration efforts.

Remote monitoring of total dissolved phosphorus in eutrophic Lake Taihu based on a novel algorithm: Implications for contributing factors and lake management.

Understanding the spatiotemporal dynamics of total dissolved phosphorus concentration (CTDP) and its regulatory factors is essential to improving our understanding of its impact on inland water eutrophication, but few studies have assessed this in eutrophic inland lakes due to a lack of suitable bio-optical algorithms allowing the use of remote sensing data. We developed a novel semi-analytical algorithm for this purpose and tested it in the eutrophic Lake Taihu, China. Our algorithm produced robust results with a mean absolute square percentage error of 29.65% and root mean square error of 9.54 µg/L. Meanwhile, the new algorithm demonstrates good portability to other waters with different optical properties and could be applied to various image data, including Moderate Resolution Imaging Spectroradiometer (MODIS), Medium Resolution Imaging Spectrometer (MERIS), and Ocean and Land Color Instrument (OLCI). Further analysis based on Geostationary Ocean Color Imager observations from 2011 to 2020 revealed a significant spatiotemporal heterogeneity of CTDP in Lake Taihu. Correlation analysis of the long-term trend between CTDP and driving factors demonstrated that air temperature is the dominant regulating factor in variations of CTDP. This study provides a novel algorithm allowing remote-sensing monitoring of CTDP in eutrophic lakes and can lead to new insights into the role of dissolved phosphorus in water eutrophication.

Monitoring the particulate phosphorus concentration of inland waters on the Yangtze Plain and understanding its relationship with driving factors based on OLCI data.

Tracking the spatiotemporal dynamics of particulate phosphorus concentration (CPP) and understanding its regulating factors is essential to improve our understanding of its impact on inland water eutrophication. However, few studies have assessed this in eutrophic inland lakes, owing to a lack of suitable bio-optical algorithms allowing the use of remote sensing data. Herein, a novel semi-analytical algorithm of CPP was developed to estimate CPP in lakes on the Yangtze Plain, China. The independent validations of the proposed algorithm showed a satisfying performance with the mean absolute percentage error and root mean square error less than 27% and 27 µg/L, respectively. The Ocean and Land Color Instrument observations revealed a remarkable spatiotemporal heterogeneity of CPP in 23 lakes on the Yangtze Plain from 2016 to 2020, with the lowest value in December (62.91 ± 34.59 µg/L) and the highest CPP in August (114.9 ± 51.69 µg/L). Among the 23 examined lakes, the highest mean CPP was found in Lake Poyang (124.58 ± 44.71 µg/L), while the lowest value was found in Lake Qiandao (33.51 ± 4.71 µg/L). Additionally, 13 lakes demonstrated significant decreasing or increasing trends (P < 0.05) of annual mean CPP during the observation period. The driving factor analysis revealed that four natural factors (wind speed, air temperature, precipitation, and sunshine duration) and two anthropogenic factors (the normalized difference vegetation index and nighttime light) combined explained more than 91% of the variation in CPP, while the impacts of these factors on CPP showed considerable differences among lakes. This study offered a novel and scalable algorithm for the study of the spatiotemporal variation of CPP in inland waters and provided new insights into the regulating factors in water eutrophication.