Activated aggregation strategies to construct size-increasing nanoparticles for cancer therapy.

The development of novel therapeutic strategies and modalities for tumors is still one of the important areas of current scientific research. Low permeability and short residence time of drugs in solid tumor areas are important reasons for the low efficiency of existing therapeutic strategies. Typically, nanoparticles with large size displayed enhanced residence time but low permeability. Therefore, to prolong the retention time of materials in solid tumors, size-increasing strategies have been developed to directly generate large-scale nanoparticles using small molecular compounds or increase the size of small nanoparticles in solid tumor areas. In this review, we summarize recently reported activatable aggregation systems that could be activated by cancer-related substances for cancer therapy and classify them by the mechanisms that lead to aggregation. In the end, we propose some potential challenges briefly from the view of our opinion. This article is categorized under: Therapeutic Approaches and Drug Discovery > Nanomedicine for Oncologic Disease.

An excipient-free "sugar-coated bullet" for the targeted treatment of orthotopic hepatocellular carcinoma.

Several components of traditional nanoformulations that result in structural heterogeneity, poor reproducibility, excipient-trigged biotoxicity, and low retention of antitumor drugs in neoplastic foci are important barriers limiting clinical translation. We report an excipient-free nanoformulation prepared by a reactive oxygen species (ROS)-responsive amphiphilic prodrug (Gal-MB-DOX) for the targeted treatment of orthotopic hepatocellular carcinoma (HCC). Gal-MB-DOX can form monocomponent nanoparticles with a galactose-rich surface similar to a "sugar-coated bullet" through self-assembly in aqueous solution. This nanoformulation can be decomposed quickly by ROS and release free hydrophobic drugs that further precipitate into larger particles, potentially promoting the retention of drugs in tumor cells. These sugar-coated bullets selectively target tumor cells through passive and active targeting, resulting in high in vivo therapeutic efficacy in an orthotopic HCC mouse model. This monocomponent nanomedicine system provides a simple but effective strategy for the treatment of tumors.

Effects of Meteorological Factors and Air Pollutants on COVID-19 Transmission under the Action of Control Measures.

At present, COVID-19 is still spreading, and its transmission patterns and the main factors that affect transmission behavior still need to be thoroughly explored. To this end, this study collected the cumulative confirmed cases of COVID-19 in China by 8 April 2020. Firstly, the spatial characteristics of the COVID-19 transmission were investigated by the spatial autocorrelation method. Then, the factors affecting the COVID-19 incidence rates were analyzed by the generalized linear mixed effect model (GLMMs) and geographically weighted regression model (GWR). Finally, the geological detector (GeoDetector) was introduced to explore the influence of interactive effects between factors on the COVID-19 incidence rates. The results showed that: (1) COVID-19 had obvious spatial aggregation. (2) The control measures had the largest impact on the COVID-19 incidence rates, which can explain the difference of 34.2% in the COVID-19 incidence rates, while meteorological factors and pollutant factors can only explain the difference of 1% in the COVID-19 incidence rates. It explains that some of the literature overestimates the impact of meteorological factors on the spread of the epidemic. (3) The influence of meteorological factors was stronger than that of air pollution factors, and the interactive effects between factors were stronger than their individual effects. The interaction between relative humidity and NO2 was stronger. The results of this study will provide a reference for further prevention and control of COVID-19.

Spatio-temporal evolution characteristics analysis and optimization prediction of urban green infrastructure: a case study of Beijing, China.

The reasonable layout of green infrastructure is conducive to the low-carbon, livable and high-quality sustainable development of cities. The framework of spatio-temporal evolution characteristics and prediction analysis of Urban Green Infrastructure (UGI) was constructed by integrating morphological spatial pattern analysis (MSPA) and CA-Markov in the study. We analyzed the spatio-temporal evolution characteristics of UGI in Beijing from 1990 to 2019, predicted its future change trend in 2030, and put forward the optimization scheme for the ecological network of UGI. The area change of UGI presented a "V" shape from 1990 to 2019 in Beijing, and the turning point was around 2009. Its spatial distribution revealed a significant heterogeneity. The comprehensive change rate index showed a "rising and then falling" trend from 1990 to 2019. Core with an area of over 1000 km2 had inclined "C" shape, connecting the north, west and south of the study area. Among the three prediction scenarios for 2030, the area of UGI under the ecological conservation priority scenario is the largest, accounting for 86.35% of the total area. The area of UGI under the economic development priority scenario is the smallest, accounting for 76.85%. The optimization of zoning and road network are effective measures to improve the connectivity of UGI in Beijing. This study is beneficial to extend the research ideas of UGI and promote sustainable urban development.

A Smart Theranostic Prodrug System Activated by Reactive Oxygen Species for Regional Chemotherapy of Metastatic Cancer.

Metastatic cancer is difficult to cure because of its uncontrollable nature and side effects during treatment. We constructed a reactive oxygen species (ROS)-activated smart theranostic prodrug system based on an ROS active site linked with both a targeting group and an anticancer drug for efficient regional chemotherapy of metastatic cancers. The optimized prodrug (Bio-(8)-MB-CPT) with biotin as the targeting group displayed high sensitivity towards ROS and selectively targeting ability towards cervical cancer cells, showing highly efficient drug release (up to 92 %) in vitro. Bio-(8)-MB-CPT thus exerted strong toxicity towards cervical cancer cells, but unlike the parent drug (camptothecin), showed no toxicity towards normal cells. Moreover, the prodrug displayed significantly enhanced antitumor efficacy in vivo and eradicated the tumor with no obvious side effects (inhibition of the tumor reached up to 99.9 %).

HOCl-Activated Aggregation of Gold Nanoparticles for Multimodality Therapy of Tumors.

Tumor microenvironment-responsive nanodrugs offer promising opportunities for imaging-guided precision therapy with reduced side effects. Considering that the antitumor effect is closely related to the size of the nanodrugs, it is particularly important to develop a therapeutic system with size adjustability in the tumor microenvironment, which is still a great challenge in the field of nanotheranostics. Herein, a reactive oxygen species (ROS)-activated aggregation strategy is reported for imaging-guided precision therapy of tumors. The ROS-activated nanoplatform is constructed based on gold nanoparticles (AuNPs) coated with an HOCl probe on its surface (namely, Au-MB-PEG NPs). The Au-MB-PEG NPs show high sensitivity toward HOCl, resulting in the modulation of surface charge and rapid aggregation of AuNPs, and simultaneous release of methylene blue as a photosensitizer for photodynamic therapy (PDT). In the tumor environment, the aggregated AuNPs ensure higher tumor accumulation and retention. Furthermore, the redshift of the absorption of aggregated AuNPs leads to activated photoacoustic imaging signals and photothermal therapy (PTT) under near-infrared irradiation. Au-MB-PEG NPs thus efficiently inhibit the tumor growth through combined PTT-PDT therapy. This work contributes to the design of stimuli-induced size-aggregation nanodrugs, thereby attaining advanced performance in cancer diagnosis and treatment.

Time series prediction for the epidemic trends of COVID-19 using the improved LSTM deep learning method: Case studies in Russia, Peru and Iran.

The COVID-19 outbreak in late December 2019 is still spreading rapidly in many countries and regions around the world. It is thus urgent to predict the development and spread of the epidemic. In this paper, we have developed a forecasting model of COVID-19 by using a deep learning method with rolling update mechanism based on the epidemical data provided by Johns Hopkins University. First, as traditional epidemical models use the accumulative confirmed cases for training, it can only predict a rising trend of the epidemic and cannot predict when the epidemic will decline or end, an improved model is built based on long short-term memory (LSTM) with daily confirmed cases training set. Second, considering the existing forecasting model based on LSTM can only predict the epidemic trend within the next 30 days accurately, the rolling update mechanism is embedded with LSTM for long-term projections. Third, by introducing Diffusion Index (DI), the effectiveness of preventive measures like social isolation and lockdown on the spread of COVID-19 is analyzed in our novel research. The trends of the epidemic in 150 days ahead are modeled for Russia, Peru and Iran, three countries on different continents. Under our estimation, the current epidemic in Peru is predicted to continue until November 2020. The number of positive cases per day in Iran is expected to fall below 1000 by mid-November, with a gradual downward trend expected after several smaller peaks from July to September, while there will still be more than 2000 increase by early December in Russia. Moreover, our study highlights the importance of preventive measures which have been taken by the government, which shows that the strict controlment can significantly reduce the spread of COVID-19.

Dual-Modality Detection of Early-Stage Drug-Induced Acute Kidney Injury by an Activatable Probe.

Early detection of drug-induced acute kidney injury (AKI) is crucial for effective treatment and prevention of further injury. It remains challenging, however, because of the lack of activatable indicators with multimodality imaging capability that could increase the accuracy of diagnosis by mutual verification. Herein, we report an activatable probe, FDOCl-22, that enabled dual-modality detection of the early-stage drug-induced AKI. FDOCl-22 was completely soluble in water and highly sensitive to hypochlorous acid (HOCl). Dramatic increases of both near-infrared (NIR) emission and absorption were observed after reaction with HOCl. A correlation between HOCl concentration and drug-induced AKI was established using FDOCl-22 as a tool. As a consequence, the HOCl-activated probe was able to detect the early-stage drug-induced AKI by dual-modality imaging, irrespective of the drug stimulation time or dosage, by combining NIR fluorescence and photoacoustic imaging.

Fluorescent turn-off/on bioassay for hemoglobin based on dual-emission carbon nanodots-graphene oxide system with multi-detection strategies.

As two members of the carbon materials family, carbon nanodots (CNDs) and graphene oxide (GO) possess many excellent optical properties resulting in a wide range of applications. In this work, the fluorescence of resultant dual-emission carbon nanodots (DECNDs) could be quenched by GO. In the presence of hemoglobin (Hb), the fluorescence would recover resulting from two interactions: one was the direct stacking effect of Hb on GO; the other one was that Hb could cover the surfaces of DECNDs; both of them would prevent the fluorescence quenching of DECNDs by GO. In the light of this mechanism, a novel fluorescent turn-off/on method has been developed for the detection of Hb based on DECNDs-GO system. By virtue of the dual emissions of these CNDs, it is noteworthy that both a single emission and ratiometric of dual emissions can be used to establish linear relationships of Hb: 0.05-300 nM (λem = 386 nm), 5-500 nM (λem = 530 nm), and 50-500 nM (I530/I410), with the corresponding limit of detection (LOD) as low as 20 pM, 2 nM and 20 nM, respectively. This present system is highly selective toward Hb over other proteins and this reliable method has been successfully applied for the detection of Hb in whole blood samples.