Different responses of Chlorella vulgaris to silver nanoparticles and silver ions under modulation of nitric oxide.

Silver nanoparticles (Ag-NPs) are widely used in daily life because of their antibacterial properties. A fraction of Ag-NPs are released into the ecosystem during their production and utilization. The toxicity of Ag-NPs has been reported. However, it is still disputed whether the toxicity is mainly due to the released silver ions (Ag+). In addition, few studies have reported the response of algae to metal nanoparticles under modulation of nitric oxide (NO). In this study, Chlorella vulgaris (C. vulgaris) was used as a model organism to study the toxic effects of Ag-NPs and Ag+ released from Ag-NPs on algae under the modulation of NO. The results showed that the biomass inhibition rate of Ag-NPs (44.84%) to C. vulgaris was higher than that of Ag+ (7.84%). Compared with Ag+, Ag-NPs induced more severe damage to photosynthetic pigments, photosynthetic system II (PSII) performance, and lipid peroxidation. More serious damage to cell permeability led to higher internalization of Ag under Ag-NPs stress. Application of exogenous NO reduced the inhibition ratio of photosynthetic pigments and chlorophyll autofluorescence. Further, NO reduced the MDA levels by scavenging reactive oxygen species induced by Ag-NPs. NO modulated the secretion of extracellular polymers and hampered the internalization of Ag. All these results showed that NO alleviates the toxicity of Ag-NPs to C. vulgaris. However, NO did not improve the toxic effects of Ag+. Our results provide new insights into the toxicity mechanism of Ag-NPs to algae modulated by the signal molecule NO.

Distinct immune and inflammatory response patterns contribute to the identification of poor prognosis and advanced clinical characters in bladder cancer patients.

Due to the molecular heterogeneity, most bladder cancer (BLCA) patients show no pathological responses to immunotherapy and chemotherapy yet suffer from their toxicity. This study identified and validated three distinct and stable molecular clusters of BLCA in cross-platform databases based on personalized immune and inflammatory characteristics. H&E-stained histopathology images confirmed the distinct infiltration of immune and inflammatory cells among clusters. Cluster-A was characterized by a favorable prognosis and low immune and inflammatory infiltration but showed the highest abundance of prognosis-related favorable immune cell and inflammatory activity. Cluster-B featured the worst prognosis and high immune infiltration, but numerous unfavorable immune cells exist. Cluster-C had a favorable prognosis and the highest immune and inflammatory infiltration. Based on machine learning, a highly precise predictive model (immune and inflammatory responses signature, IIRS), including FN1, IL10, MYC, CD247, and TLR2, was developed and validated to identify the high IIRS-score group that had a poor prognosis and advanced clinical characteristics. Compared to other published models, IIRS showed the highest AUC in 5 years of overall survival (OS) and a favorable predictive value in predicting 1- and 3- year OS. Moreover, IIRS showed an excellent performance in predicting immunotherapy and chemotherapy's response. According to immunohistochemistry and qRT-PCR, IIRS genes were differentially expressed between tumor tissues with corresponding normal or adjacent tissues. Finally, immunohistochemical and H&E-stained analyses were performed on the bladder tissues of 13 BLCA patients to further demonstrate that the IIRS score is a valid substitute for IIR patterns and can contribute to identifying patients with poor clinical and histopathology characteristics. In conclusion, we established a novel IIRS depicting an IIR pattern that could independently predict OS and acts as a highly precise predictive biomarker for advanced clinical characters and the responses to immunotherapy and chemotherapy.

Roles of polystyrene micro/nano-plastics as carriers on the toxicity of Pb2+ to Chlamydomonas reinhardtii.

Little information could be consulted on the impacts of micro-plastics as carriers on toxicity of heavy metals, especially for micro-plastics of different sizes. Therefore, this study investigated the adsorption and desorption of Pb2+ on polystyrene plastics with nano- and micro-size (NPs and MPs), and further evaluated the roles of NPs and MPs as carriers on the toxicity of Pb2+ to Chlamydomonas reinhardtii (C. reinhardtii). The results showed that NPs showed higher adsorption capacities and a lower desorption rate for Pb2+ than MPs. The growth inhibitory rates (IR) of mixed and loaded Pb2+ with MPs to C. reinhardtii were 18.29% and 15.76%, respectively, which were lower than that of Pb2+ (22.28%). The presence of MPs decreased the bioavailability of Pb2+ to C. reinhardtii by a competitive adsorption for Pb2+ between MPs and algal cells, and suppressed membrane damage and oxidative stress caused by Pb2+. Maximum IR was observed for the mixture of NPs with Pb2+ (35.64%), followed by Pb2+ loaded on NPs (30.13%), single NPs (26.71%) and Pb2+ (21.01%). The internalization of NPs with absorbed Pb2+ intensified lipid peroxidation. The mixed and loaded microplastics with Pb2+ had more negative effects on C. reinhardtii than the single microplastics. The size-dependent effect was observed in the capacity of heavy metal ions carried by microplastics and the roles of microplastics as carriers on the toxicity of Pb2+. The results showed that the indirect risk of microplastics as 'carriers' could not be ignored, especially for NPs.

Defense pathways of Chlamydomonas reinhardtii under silver nanoparticle stress: Extracellular biosorption, internalization and antioxidant genes.

Numerous studies have been investigated the toxic effects of silver nanoparticle (Ag-NPs) on algae; however, little attention has been paid to the defense pathways of algae cells to Ag-NPs. In the study, Chlamydomonas reinhardtii (C. reinhardtii) was selected as a model organism to investigate the defense mechanisms to Ag-NPs exposure. The results showed that exopolysaccharide and protein in bound-extracellular polymeric substances significantly increased under Ag-NPs stress. These metal-binding groups including C-O-C (exopolysaccharide), CH3/CH2 (proteins), O-H/N-H (hydroxyl group) and C-H (alkyl groups) played a key role in extracellular biosorption. The internalized or strongly bound Ag (1.90%-17.45% of total contents) was higher than the loosely surface biosorption (0.31%-1.79%). The accumulation of glutathione disulfide (GSSG), together with the decline of reduced glutathione/GSSG (GSH/GSSG) ratio in C. reinhardtii cells, indicated a significant oxidative stress caused by exposure of Ag-NPs. The increasing phytochelatin accompanied with the decreasing GSH level indicated a critical role to intracellular detoxification of Ag. Furthermore, upregulation of antioxidant genes (MSOD, QTOX2, CAT1, GPX2, APX and VTE3) can cope with oxidative stress of Ag-NPs or Ag+. The up-regulation of ascorbate peroxidase (APX) and glutathione peroxidase (GPX2) genes and the reduction in GSH contents showed that the toxicity of Ag-NPs could be mediated by an intracellular ascorbate-GSH defense pathway. These findings can provide valuable information on ecotoxicity of Ag-NPs, potential bioremediation and adaptation capabilities of algal cells to Ag-NPs.

Recent trends in premature mortality and health disparities attributable to ambient PM2.5 exposure in China: 2005-2017.

In the past decade, particulate matter with aerodynamic diameter less than 2.5 µm (PM2.5) has reached unprecedented levels in China and posed a significant threat to public health. Exploring the long-term trajectory of the PM2.5 attributable health burden and corresponding disparities across populations in China yields insights for policymakers regarding the effectiveness of efforts to reduce air pollution exposure. Therefore, we examine how the magnitude and equity of the PM2.5-related public health burden has changed nationally, and between provinces, as economic growth and pollution levels varied during 2005-2017. We derive long-term PM2.5 exposures in China from satellite-based observations and chemical transport models, and estimate attributable premature mortality using the Global Exposure Mortality Model (GEMM). We characterize national and interprovincial inequality in health outcomes using environmental Lorenz curves and Gini coefficients over the study period. PM2.5 exposure is linked to 1.8 (95% CI: 1.6, 2.0) million premature deaths over China in 2017, increasing by 31% from 2005. Approximately 70% of PM2.5 attributable deaths were caused by stroke and IHD (ischemic heart disease), though COPD (chronic obstructive pulmonary disease) and LRI (lower respiratory infection) disproportionately affected poorer provinces. While most economic gains and PM2.5-related deaths were concentrated in a few provinces, both gains and deaths became more equitably distributed across provinces over time. As a nation, however, trends toward equality were more recent and less clear cut across causes of death. The rise in premature mortality is due primarily to population growth and baseline risks of stroke and IHD. This rising health burden could be alleviated through policies to prevent pollution, exposure, and disease. More targeted programs may be warranted for poorer provinces with a disproportionate share of PM2.5-related premature deaths due to COPD and LRI.

A New Vegetation Index Based on Multitemporal Sentinel-2 Images for Discriminating Heavy Metal Stress Levels in Rice.

Heavy metal stress in crops is a worldwide problem that requires accurate and timely monitoring. This study aimed to improve the accuracy of monitoring heavy metal stress levels in rice by using multiple Sentinel-2 images. The selected study areas are in Zhuzhou City, Hunan Province, China. Six Sentinel-2 images were acquired in 2017, and heavy metal concentrations in soil were measured. A novel vegetation index called heavy metal stress sensitive index (HMSSI) was proposed. HMSSI is the ratio between two red-edge spectral indices, namely the red-edge chlorophyll index (CIred-edge) and the plant senescence reflectance index (PSRI). To demonstrate the capability of HMSSI, the performances of CIred-edge and PSRI in discriminating heavy metal stress levels were compared with that of HMSSI at different growth stages. Random forest (RF) was used to establish a multitemporal monitoring model to detect heavy metal stress levels in rice based on HMSSI at different growth stages. Results show that HMSSI is more sensitive to heavy metal stress than CIred-edge and PSRI at different growth stages. The performance of a multitemporal monitoring model combining the whole growth stage images was better than any other single growth stage in distinguishing heavy metal stress levels. Therefore, HMSSI can be regarded as an indicator for monitoring heavy metal stress levels with a multitemporal monitoring model.