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Abstract Background Antiviral drugs show significant efficacy in non-severe COVID-19 cases, yet there remains a subset of moderate COVID-19 patients whose pneumonia continues to progress post a complete course of treatment. Plasma-activated water (PAW) possesses anti-SARS-CoV-2 properties. To explore the potential of PAW in improving pneumonia in COVID-19 patients following antiviral treatment failure, we conducted this study. Methods This was a randomized, controlled trial. Moderate COVID-19 patients with antiviral treatment failure were randomly assigned to the experimental group or the control group. They inhaled nebulized PAW or saline respectively. This was done twice daily for four consecutive days. We assessed improvement in chest CT on day 5, the rate of symptom resolution within 10 days, and safety. Results A total of 23 participants were included, with 11 receiving PAW and 12 receiving saline. The baseline characteristics of both groups were comparable. The experimental group showed a higher improvement rate in chest CT on day 5 (81.8% vs. 33.3%, p = 0.036). The cumulative disappearance rate of cough within 10 days was higher in the experimental group. Within 28 days, 4 patients in each group progressed to severe illness, and no patients died. No adverse reactions were reported from inhaling nebulized PAW. Conclusion This pilot trial preliminarily confirmed that nebulized inhalation of PAW can alleviate pneumonia in moderate COVID-19 patients with antiviral treatment failure, with no adverse reactions observed. This still needs to be verified by large-scale studies. Trial registration Chinese Clinical Trial Registry; No.: ChiCTR2300078706 (retrospectively registered, 12/15/2023); URL: www.chictr.org.cn .

Qinghai Lake, the largest inland saltwater lake in China, is an important water body that maintains the ecological security of the northeastern Tibetan Plateau. Global navigation satellite system reflectometry technology (GNSS-R) is rarely used in lake detection, especially in lake boundary detection under the circumstances of spaceborne. This article attempts to implement lake boundary detection using cyclone global navigation satellite system (CYGNSS) raw intermediate frequency (IF) data for the first time. First, the second-order frequency-locked loop is used to assist the third-order phase-locked loop closed-loop tracking to successfully generate higher delay resolution and Doppler resolution (67 × 51) delay Doppler maps (DDM) than CYGNSS compressed DDM (11 × 17); second, based on the principle of scattering characteristics of different reflectors of GNSS-R, and the fusion method of signal power ratio and effective area delay distance (DLR), the 2018 Qinghai Lake boundary detection inversion was carried out in the lake area to explore the feasibility of achieving higher spatial resolution (0.7 km) and high-precision lake boundary detection with the raw IF data of CYGNSS. The inversion detection results are compared with the topographic distribution results of Google Earth and global land analysis and discovery, and the inversion result error is about 0.5 km, which is consistent with the theoretical spatial resolution. It proves that spaceborne GNSS-R technology and CYGNSS raw IF data have great potential in the field of lake boundary detection.

The whale optimization algorithm has several advantages, such as simple operation, few control parameters, and a strong ability to jump out of the local optimum, and has been used to solve various practical optimization problems. In order to improve its convergence speed and solution quality, a reinforced whale optimization algorithm (RWOA) was designed. Firstly, an opposition-based learning strategy is used to generate other optima based on the best optimal solution found during the algorithm’s iteration, which can increase the diversity of the optimal solution and accelerate the convergence speed. Secondly, a dynamic adaptive coefficient is introduced in the two stages of prey and bubble net, which can balance exploration and exploitation. Finally, a kind of individual information-reinforced mechanism is utilized during the encircling prey stage to improve the solution quality. The performance of the RWOA is validated using 23 benchmark test functions, 29 CEC-2017 test functions, and 12 CEC-2022 test functions. Experiment results demonstrate that the RWOA exhibits better convergence accuracy and algorithm stability than the WOA on 20 benchmark test functions, 21 CEC-2017 test functions, and 8 CEC-2022 test functions, separately. Wilcoxon’s rank sum test shows that there are significant statistical differences between the RWOA and other algorithms

Cyclone Global Navigation Satellite System (CYGNSS) launched in recent years, provides a large amount of spaceborne GNSS Reflectometry data with all-weather, global coverage, high space-time resolution, and multiple signal sources, which provides new opportunities for the machine learning (ML) study of sea surface height (SSH) inversion. This article proposes for the first time two different CYGNSS SSH inversion models based on two widely used ML methods, back propagation (BP) neural network and convolutional neural network (CNN). The SSH calculated by using Danmarks Tekniske Universitet (DTU) 18 ocean wide mean SSH (MSSH) model (DTU18) with DTU global ocean tide model is used for verification. According to the strategy of independent analysis of data from different signal sources, the mean absolute error (MAE) of the BP and CNN models’ inversion specular points’ results during 7 days is 1.04 m and 0.63 m, respectively. The CLS 2015 product and Jason-3 data were also used for further validation. In addition, the generalization ability of the model, for 6 days and 13 days training sets, was also evaluated. For 6 days training set, the prediction results’ MAE of the BP model is 11.59 m and 5.90 m for PRN2 and PRN4, and the MAE of the CNN model is 1.37 m and 0.97 m for PRN2 and PRN4, respectively. The results show that BP and CNN inversions are in high agreement with each product, and the CNN model has relatively higher accuracy and better generalization ability.

Rheumatoid arthritis (RA) is a chronic autoimmune disease characterized by chronic synovitis and the destruction of bones and joints. Exosomes are nanoscale lipid membrane vesicles originating from multivesicular bodies and are used as a vital means of intercellular communication. Both exosomes and the microbial community are essential in RA pathogenesis. Multiple types of exosomes from different origins have been demonstrated to have effects on various immune cells through distinct mechanisms in RA, which depend on the specific cargo carried by the exosomes. Tens of thousands of microorganisms exist in the human intestinal system. Microorganisms exert various physiological and pathological effects on the host directly or through their metabolites. Gut microbe-derived exosomes are being studied in the field of liver disease; however, information on their role in the context of RA is still limited. Gut microbe-derived exosomes may enhance autoimmunity by altering intestinal permeability and transporting cargo to the extraintestinal system. Therefore, we performed a comprehensive literature review on the latest progress on exosomes in RA and provided an outlook on the potential role of microbe-derived exosomes as emerging players in clinical and translational research on RA. This review aimed to provide a theoretical basis for developing new clinical targets for RA therapy.

Sea surface height (SSH) retrieval based on spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) usually uses the GNSS-R geometric principle and delay-Doppler map (DDM). The traditional method condenses the DDM information into a single scalar measure and requires error model correction. In this article, the idea of using machine learning methods to retrieve SSH is proposed. Specifically, two widely-used methods, principal component analysis combined with support vector regression (PCA-SVR) and convolution neural network (CNN), are used for verification and comparative analysis based on the observation data provided by Techdemosat-1 (TDS-1). According to the DDM inversion method, ten features from TDS-1 Level 1 data are selected as inputs; The SSH verification model based on the Danmarks Tekniske Universitet (DTU) 15 ocean wide mean SSH model and the DTU global ocean tide model is used as output verification of SSH. For the hyperparameters in the machine learning model, a grid search strategy is used to find the optimal values. By analyzing the TDS-1 data from 31 GPS satellites, the mean absolute error (MAE), root-mean-square error (RMSE) and coefficient of determination (R2) of the PCA-SVR inversion model are 0.61 m, 1.72 m, and 99.56%, respectively; and the MAE, RMSE, and R2 of the CNN inversion model is 0.71 m, 1.27 m, and 99.76%, respectively. In addition, the time required to train the PCA-SVR and CNN inversion models is also analyzed. Overall, the technique proposed in this article can be confidently applied to SSH inversion based on TDS-1 data.

This study presents a correction algorithm to remove the backlobe intrusion from the hot-load reflector of microwave radiation imager (MWRI) on-board China FengYun-3C (FY-3C) meteorological satellite. Discontinuities in the radiometric gain of MWRI, due to inaccurate backlobe spillover of the hot-load reflector, have been diagnosed. A physical correction method, relating the radiometric gain difference between two distinct backlobe scenes to the spillover of the hot-load reflector, is thus established to estimate the in-orbit spillover factor of the MWRI hot-load reflector. Meanwhile, it is found that brightness temperature (TB) at each 1° × 1° grid could not address the backlobe pattern appropriately, leading to abrupt changes in the radiometric gain occurring near the coastlines. To better represent the backlobe spillover, the effective backlobe TB is now averaged over a 4° × 4° area from advanced microwave scanning radiometer-observed TBs, which is now in a finer resolution of 0.25° × 0.25°. With the adjusted spillover for the hot-load reflector, anomalous behaviors of the radiometric gain are mitigated effectively and the improvement at the coastlines is pronounced, inducing TB variation up to 2 K in magnitude at the 10.65 GHz channel. Although the algorithm presented in this study is only restricted to channel frequencies up to 23.8 GHz due to its accuracy limitation, it could tackle similar issues for those microwave radiometers not capable of in-orbit backlobe maneuvers and receiving the radiances inevitably from the outer edge of the hot-load or cold-sky reflectors.

Background: Monosodium urate (MSU)-mediated inflammatory response is a crucial inducing factor in gouty arthritis. Here, we explored the underlying mechanism of total glucosides of paeony (TGP) in MSU-induced inflammation of THP-1 macrophages in gouty arthritis. Methods: 3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay was used to detect cell viability. Enzyme-linked immunosorbent assay (ELISA) was utilized to measure the production of interleukin 1β (IL-1β) and tumor necrosis factor α (TNF-α). Real-time quantitative polymerase chain reaction (RT-qPCR) and Western blot assay were conducted to determine RNA and protein expression. Dual-luciferase reporter assay, RNA immunoprecipitation (RIP) assay and RNA pull down assay were used to confirm the interaction between miR-876-5p and MALAT1 or NLR family pyrin domain containing 3 (NLRP3). Results: MSU-induced damage and inflammatory response in THP-1 macrophages were alleviated by the treatment of TGP in a dose-dependent manner. Overexpression of NLRP3 or MALAT1 reversed the protective effects of TGP in MSU-induced THP-1 macrophages. The binding relation between miR-876-5p and MALAT1 or NLRP3 was identified in THP-1 macrophages. MALAT1 up-regulated the expression of NLRP3 by sponging miR-876-5p in THP-1 macrophages. TGP suppressed MSU-induced inflammation in THP-1 macrophages through regulating MALAT1/miR-876-5p/NLRP3 axis. TGP suppressed MSU-induced activation of TLR4/MyD88/NF-κB pathway through regulating MALAT1/miR-876-5p/NLRP3 axis. Conclusion: In conclusion, TGP suppressed MSU-induced inflammation in THP-1 macrophages through regulating MALAT1/miR-876-5p/NLRP3 axis and TLR4/MyD88/NF-κB pathway, suggesting that TGP was a promising active ingredient for gouty arthritis treatment.

The feasibility of Antarctic sea ice detection based on shipborne global positioning system reflectometry (GPS-R) technology is shown in this paper. Because the permittivity of sea water is much higher than that of sea ice, the reflected left-handed circular polarized (LHCP) GPS signal (RL) reflection coefficient of sea water is markedly higher than that of sea ice. The polarization ratio of RL to the direct right-handed circular polarized (RHCP) GPS signal (DR) is used to distinguish between sea water and sea ice in this paper. The experiment was performed on the ship “XueLong” for approximately 9 days from December 2014 to January 2015 during the 31st Chinese National Antarctic Research Expedition (CHINARE 31). The sea ice concentration data with a 25 km × 25 km spatial resolution derived from the National Snow and Ice Data Center (NSIDC) are used for validation and some pictures of sea ice taken from “XueLong” are shown for comparison. The polarization ratios (RL/DR) are calculated, and the correlation coefficient between the polarization ratios (RL/DR) and the sea ice concentrations is −0.66.

Global navigation satellite system reflectometry technology (GNSS-R) is rarely used for river flow velocity inversion, and in particular, there is currently no research using the BeiDou Navigation Satellite System reflectometry technology (BDS-R) for river flow velocity inversion. In this paper, a carrier phase observation of river flow velocity inversion model is proposed. The interference phase is the integral of the Doppler frequency. The raw intermediate frequency (IF) data sets are processed through an open-loop method to obtain the Doppler frequency observation generated by river flow and then realize velocity inversion. The shore-based river current measurement was conducted on the south bank of Dashengguan Yangtze River in Nanjing city, Jiangsu Province, for nearly two hours on 22 April 2021. After realizing the inversion of river flow velocity in GPS L1, the combined inversion of BDS B1I GEO satellite and IGSO satellite is realized for the first time, which demonstrates the feasibility of river flow velocity inversion using BDS reflected signals. Compared with the real river flow velocity, the GPS L1 PRN 4 (1st period) inversion precision reaches up to 0.028 m/s (mean absolute error, MAE) and 0.036 m/s (root mean square error, RMSE). In parallel, BDS GEO 2 inversion precision can reach 0.048 m/s (MAE) and 0.063 m/s (RMSE), and BDS IGSO 10 inversion precision is 0.061 m/s (MAE) and 0.073 m/s (RMSE). These results illustrate that satellite elevation change rate and distance between specular points and current meter may have a negative effect on the accuracy of river flow velocity inversion. Specular points obstructed by obstacles or too far from the velocity meter may introduce uncertain error in both MAE and RMSE. Neither the satellite elevation nor the signal strength has an obvious correlation with inversion precision, which is consistent with the theoretical principle.

In view of the difficulty of wind direction retrieval in the case of the large space and time span of the global sea surface, a method of sea surface wind direction retrieval using a support vector machine (SVM) is proposed. This paper uses the space-borne global navigation satellite systems reflected signal (GNSS-R) as the remote sensing signal source. Using the Cyclone Global Navigation Satellite System (CYGNSS) satellite data, this paper selects a variety of feature parameters according to the correlation between the features of the sea surface reflection signal and the wind direction, including the Delay Doppler Map (DDM), corresponding to the CYGNSS satellite parameters and geometric feature parameters. The Radial Basis Function (RBF) is selected, and parameter optimization is performed through cross-validation based on the grid search method. Finally, the SVM model of sea surface wind direction retrieval is established. The result shows that this method has a high retrieval classification accuracy using the dataset with wind speed greater than 10 m/s, and the root mean square error (RMSE) of the retrieval result is 26.70°.

In response to the deficiency of the detection capability of traditional remote sensing means (scatterometer, microwave radiometer, etc.) for high wind speed above 25 m/s, this paper proposes a GNSS-R technique combined with a machine learning method to invert high wind speed at sea surface. The L1-level satellite-based data from the Cyclone Global Navigation Satellite System (CYGNSS), together with the European Centre for Medium-Range Weather Forecasts (ECMWF) and the National Centers for Environmental Prediction (NCEP) data, constitute the original sample set, which is processed and trained with Support Vector Regression (SVR), the combination of Principal Component Analysis (PCA) and SVR (PCA-SVR), and Convolutional Neural Network (CNN) methods, respectively, to finally construct a sea surface high wind speed inversion model. The three models for high wind speed inversion are certified by the test data collected during Typhoon Bavi in 2020. The results show that all three machine learning models can be used for high wind speed inversion on sea surface, among which the CNN method has the highest inversion accuracy with a mean absolute error of 2.71 m/s and a root mean square error of 3.80 m/s. The experimental results largely meet the operational requirements for high wind speed inversion accuracy.