ABSTRACT
The influences of landscape pattern on water quality are dependent on spatial-temporal scales. However, the effects of landscape composition, landscape configuration, and landscape slope metrics on seasonal water quality at different spatial scales remain unclear. Based on the total nitrogen, total phosphorus, nitrate-N, and ammonium-N data from 26 sampling sites in the Qingshan Lake watershed, this study coupled landscape pattern analysis, redundancy analysis, and partial redundancy analysis to quantify the spatiotemporal scale effects of landscape pattern on riverine nitrogen ï¼Nï¼ and phosphorus ï¼Pï¼ concentrations. The results showed thatï¼ â The explanatory ability of landscape pattern at the sub-watershed scale on riverine N and P concentrations was 6.8%-8.4% higher than that at the buffer scale, and this effect was more obvious in the dry season. â¡ At the sub-watershed scale, the percentage of forestland and the interspersion and juxtaposition degree of residential land had a greater influence on riverine N and P concentrations. At the buffer scale, the slope of farmland and residential land and the aggregation degree of forestland patches were the key factors affecting riverine N and P concentrations. ⢠The contribution rate of landscape configuration to riverine N and P concentration variations ï¼20.1%-36.5%ï¼ was the highest. The sensitivity of the effect of landscape configuration on riverine N and P concentrations to seasonal changes was the highest, and the effect of landscape slope on riverine N and P concentrations had the highest sensitivity to spatial scale changes. Therefore, landscape pattern-regulated non-point source pollution should be considered from a multi-scale perspective. These results can provide scientific basis for the formulation of landscape pattern optimization measures aiming at non-point source pollution control.
ABSTRACT
The introduction of immune checkpoint inhibitors (ICIs) has revolutionized the treatment of lung cancer. Given the limited clinical benefits of immunotherapy in patients with non-small cell lung cancer (NSCLC), various predictors have been shown to significantly influence prognosis. However, no single predictor is adequate to forecast patients' survival benefit. Therefore, it's imperative to develop a prognostic model that integrates multiple predictors. This model would be instrumental in identifying patients who might benefit from ICIs. Retrospective analysis and small case series have demonstrated the potential role of these models in prognostic prediction, though further prospective investigation is required to evaluate more rigorously their application in these contexts. This article presents and summarizes the latest research advancements on immunotherapy prognostic models for NSCLC from multiple omics perspectives and discuss emerging strategies being developed to enhance the domain.
ABSTRACT
Urinary sensing of synthetic biomarkers that are released into urine after specific activation in an in vivo disease environment is an emerging diagnosis strategy to overcome the insensitivity of a previous biomarker assay. However, it remains a great challenge to achieve sensitive and a specific urinary photoluminescence (PL) diagnosis. Herein, we report a novel urinary time-resolved PL (TRPL) diagnosis strategy by exploiting europium complexes of diethylenetriaminepentaacetic acid (Eu-DTPA) as synthetic biomarkers and designing the activatable nanoprobes. Notably, TRPL of Eu-DTPA in the enhancer can eliminate the urinary background PL for ultrasensitive detection. We achieved sensitive urinary TRPL diagnosis of mice kidney and liver injuries by using simple Eu-DTPA and Eu-DTPA-integrated nanoprobes, respectively, which cannot be realized by traditional blood assays. This work demonstrates the exploration of lanthanide nanoprobes for in vivo disease-activated urinary TRPL diagnosis for the first time, which might advance the noninvasive diagnosis of diverse diseases via tailorable nanoprobe designs.
