A systematic review of maggot debridement therapy for chronically infected wounds and ulcers

SummaryObjectiveThis study aimed to systematically evaluate maggot debridement therapy (MDT) in the treatment of chronically infected wounds and ulcers.MethodsWe performed a meta-analysis referring to the PRISMA statement (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). We searched for published articles in the following databases: PubMed, Web of Science, Embase, Wanfang (Chinese), and the China National Knowledge Infrastructure (CNKI). The latest search was updated on March 14, 2014. For dichotomous outcomes, the effects of MDT were expressed as the relative risk (RR) and 95% confidence interval (CI). For continuous outcomes with different measurement scales, we calculated the standardized mean difference (SMD). The pooled effects were estimated using a fixed effect model or random effect model based on the heterogeneity test. Subgroup analyses were performed according to the types of wounds or ulcers.ResultsMDT had a significantly increased positive effect on wound healing compared with conventional therapies, with a pooled RR of 1.80 (95% CI 1.24–2.60). The subgroup analysis revealed that the combined RRs were 1.79 (95% CI 0.95–3.38) for patients with diabetic foot ulcers (DFU) and 1.70 (95% CI 1.28–2.27) for patients with other types of ulcers. The time to healing of the ulcers was significantly shorter among patients treated with MDT, with a pooled SMD of −0.95 (95% CI −1.24, −0.65). For patients with DFU, the SMD was −0.79 (95% CI −1.18, −0.41), and for patients with other types of ulcers, the SMD was −1.16 (95% CI −1.63, −0.69).ConclusionMDT not only shortened the healing time but also improved the healing rate of chronic ulcers. Therefore, MDT may be a feasible alternative in the treatment of chronic ulcers

Prevalence of ideal cardiovascular health and its relationship with relative handgrip strength in rural northeast China

ObjectivesWe aimed to investigate ideal cardiovascular health (CVH), its relationship with handgrip strength, and its components in rural China.MethodsWe conducted a cross-sectional study of 3,203 rural Chinese individuals aged ≥35 years in Liaoning Province, China. Of these, 2,088 participants completed the follow-up survey. Handgrip strength was estimated using a handheld dynamometer and was normalized to body mass. Ideal CVH was assessed using seven health indicators (smoking, body mass index, physical activity, diet, cholesterol, blood pressure, and glucose). Binary logistic regression analyses were performed to assess the correlation between handgrip strength and ideal CVH.ResultsWomen had a higher rate of ideal cardiovascular health (CVH) than men (15.7% vs. 6.8%, P < 0.001). Higher handgrip strength correlated with a higher proportion of ideal CVH (P for trend <0.001). After adjusting for confounding factors, the odds ratios (95% confidence interval) of ideal CVH across increasing handgrip strength tripartite were 1.00 (reference), 2.368 (1.773, 3.164), and 3.642 (2.605, 5.093) in the cross-sectional study and 1.00 (reference), 2.088 (1.074, 4.060), and 3.804 (1.829, 7.913) in the follow-up study (all P < 0.05).ConclusionIn rural China, the ideal CVH rate was low, and positively correlated with handgrip strength. Grip strength can be a rough predictor of ideal CVH and can be used to provide guidelines for improving CVH in rural China

Inter-comparison and evaluation of global satellite XCO2 products

ABSTRACTCarbon dioxide (CO2) is one of the main greenhouse gases and has become a major concern as its concentration has been growing in recent years. Satellite remote sensing is an efficient way to monitor CO2 in the atmosphere, and several satellites are already used for CO2 monitoring. It is imperative to investigate the spatial coverage and spatio-temporal trends of satellite products, as well as identify the satellites with higher levels of accuracy. Additionally, examining the disparities between the older and new generations of satellites would be meaningful. Therefore, this paper provides a comprehensive evaluation and inter-comparison for the commonly used satellite column-averaged dry-air mole fraction of CO2 (XCO2) products. Specifically, the temporal trends and monthly coverage of the Greenhouse Gases Observing SATellite (GOSAT), Greenhouse Gases Observing SATellite-2 (GOSAT-2), Orbiting Carbon Observatory-2 (OCO-2), Orbiting Carbon Observatory-3 (OCO-3), and SCanning Imaging Absorption spectroMeter for Atmospheric CartograpHY (SCIAMACHY) are investigated. The accuracy of these satellite products is evaluated and analyzed based on Total Carbon Column Observing Network (TCCON) data. The results indicate that the XCO2 of all the satellite products show a year-by-year increase, with seasonal periodicity. In terms of overall accuracy, the OCO series satellites exhibit a slightly higher level of accuracy compared to the GOSAT series. The products of the new generation of satellites are less stable than those of the older generation, probably due to the impacts of the inversion algorithm and platforms

Global estimates of gap-free and fine-scale CO2 concentrations during 2014–2020 from satellite and reanalysis data

Carbon dioxide (CO2) is a crucial greenhouse gas with substantial effects on climate change. Satellite-based remote sensing is a commonly used approach to detect CO2 with high precision but often suffers from extensive spatial gaps. Thus, the limited availability of data makes global carbon stocktaking challenging. In this paper, a global gap-free column-averaged dry-air mole fraction of CO2 (XCO2) dataset with a high spatial resolution of 0.1° from 2014 to 2020 is generated by the deep learning-based multisource data fusion, including satellite and reanalyzed XCO2 products, satellite vegetation index data, and meteorological data. Results indicate a high accuracy for 10-fold cross-validation (R2 = 0.959 and RMSE = 1.068 ppm) and ground-based validation (R2 = 0.964 and RMSE = 1.010 ppm). Our dataset has the advantages of high accuracy and fine spatial resolution compared with the XCO2 reanalysis data as well as that generated from other studies. Based on the dataset, our analysis reveals interesting findings regarding the spatiotemporal pattern of CO2 over the globe and the national-level growth rates of CO2. This gap-free and fine-scale dataset has the potential to provide support for understanding the global carbon cycle and making carbon reduction policy, and it can be freely accessed at https://doi.org/10.5281/zenodo.7721945

Joint estimation of PM2.5 and O3 over China using a knowledge-informed neural network

China has currently entered a critical stage of coordinated control of fine particulate matter (PM2.5) and ozone (O3), it is thus of tremendous value to accurately acquire high-resolution PM2.5 and O3 data. In contrast to traditional studies that usually separately estimate PM2.5 and O3, this study proposes a knowledge-informed neural network model for their joint estimation, in which satellite observations, reanalysis data, and ground station measurements are used. The neural network architecture is designed with the shared and specific inputs, the PM2.5-O3 interaction module, and the weighted loss function, which introduce the prior knowledge of PM2.5 and O3 into neural network modeling. Cross-validation (CV) results indicate that the inclusion of prior knowledge can improve the estimation accuracy, with R2 increasing from 0.872 to 0.911 and from 0.906 to 0.937 for PM2.5 and O3 estimation under sample-based CV, respectively. In addition, the proposed joint estimation model achieves comparable performance with the separate estimation model, but with higher efficiency. Mapping results of PM2.5 and O3 derived by the proposed model have demonstrated interesting findings in the spatial and temporal trends and variations over China

Characteristics of phosphorus components in surface sediments from a Chinese shallow eutrophic lake (Lake Taihu): new insights from chemical extraction and P-31 NMR spectroscopy

As a primary factor responsible for lake eutrophication, a deeper understanding of the phosphorus (P) composition and its turnover in sediment is urgently needed. In this study, P species in surface sediments from a Chinese large eutrophic lake (Lake Taihu) were characterized by traditional fractionation and P-31 nuclear magnetic resonance (NMR) spectroscopy, and their contributions to the overlying water were also discussed. Fractionation results show that NaOH-P predominated in the algal-dominated zone, accounting for 60.1% to total P in Zhushan Bay. Whereas, refractory fractions including HCl-P and residual-P were the main P burial phases in the macrophyte-dominated zone, the center and lakeshore. Recovery rates of the total P and organic P were greatly improved by using a modified single-step extraction of NaOH-EDTA, ranging from 22.6 to 66.1% and from 15.0 to 54.0%. Ortho-P, monoester-P, and pyro-P are identified as the major P components in the NaOH-EDTA extracts by P-31 NMR analysis. Trace amount of DNA-P appeared only in sediments from algal- and macrophyte-dominated zones, ascribing to its biological origin. The relative content of ortho-P is the highest in the algal-dominated zone, while the biogenic P including ester-P and pyro-P is the highest in the macrophyte-dominated zone. Moreover, ortho-P and pyro-P correlated positively with TP and chlorophyll a in the overlying water, whereas only significant relationships were found between monoester-P, biogenic P, and chlorophyll a. These discrepancies imply that inorganic P, mainly ortho-P, plays a vital role in sustaining the trophic level of water body and algal bloom, while biogenic P makes a minor contribution to phytoplankton growth. This conclusion was supported by the results of high proportion of biogenic P in algae, aquatic macrophytes, and suspended particulate from the published literature. This study has significant implication for better understanding of the biogeochemical cycling of endogenous P and its role in affecting lake eutrophication

Bioinspired Strategies for Functionalization of Mg-Based Stents

Magnesium alloys have attracted considerable interest as prospective biodegradable materials in cardiovascular stents because of their metal mechanical properties and biocompatibility. However, fast degradation and slow endothelialization results in the premature disintegration of mechanical integrity and the restenosis of implanted Mg-based stents, which is the primary hurdle limiting their predicted clinical applicability. The development of bioinspired strategies is a burgeoning area in cardiovascular stents’ fields of research. Inspired by the unique features of lotus leaves, pitcher plants, healthy endothelial cells (ECs), marine mussels, and extracellular matrix, various bioinspired strategies have been developed to build innovative artificial materials with tremendous promise for medicinal applications. This perspective focuses on bioinspired strategies to provide innovative ideas for reducing corrosion resistance and accelerating endothelialization. The bioinspired strategies are envisaged to serve as a significant reference for future research on Mg-based medical devices

A Novel Method for Long Time Series Passive Microwave Soil Moisture Downscaling over Central Tibet Plateau

The coarse scale of passive microwave surface soil moisture (SSM) is not suitable for regional agricultural and hydrological applications such as drought monitoring and irrigation management. The optical/thermal infrared (OTI) data-based passive microwave SSM downscaling method can effectively improve its spatial resolution to fine scale for regional applications. However, the estimation capability of SSM with long time series is limited by OTI data, which are heavily polluted by clouds. To reduce the dependence of the method on OTI data, an SSM retrieval and spatio-temporal fusion model (SMRFM) is proposed in the study. Specifically, a model coupling in situ data, MODerate-resolution Imaging Spectro-radiometer (MODIS) OTI data, and topographic information is developed to retrieve MODIS SSM (1 km) using the least squares method. Then the retrieved MODIS SSM and the spatio-temporal fusion model are employed to downscale the passive microwave SSM from coarse scale to 1 km. The proposed SMRFM is implemented in a grassland dominated area over Naqu, central Tibet Plateau, for Advanced Microwave Scanning Radiometer—Earth Observing System sensor (AMSR-E) SSM downscaling in unfrozen period. The in situ SSM and Noah land surface model 0.01° SSM are used to validate the estimated MODIS SSM with long time series. The evaluations show that the estimated MODIS SSM has the same temporal resolution with AMSR-E and obtains significantly improved detailed spatial information. Moreover, the temporal accuracy of estimated MODIS SSM against in situ data (r = 0.673, μbRMSE = 0.070 m3/m3) is better than the AMSR-E (r = 0.661, μbRMSE = 0.111 m3/m3). In addition, the temporal r of estimated MODIS SSM is obviously higher than that of Noah data. Therefore, this suggests that the SMRFM can be used to estimate MODIS SSM with long time series by AMSR-E SSM downscaling in the study. Overall, the study can provide help for the development and application of microwave SSM-related scientific research at the regional scale

A Novel Method for Long Time Series Passive Microwave Soil Moisture Downscaling over Central Tibet Plateau

The coarse scale of passive microwave surface soil moisture (SSM) is not suitable for regional agricultural and hydrological applications such as drought monitoring and irrigation management. The optical/thermal infrared (OTI) data-based passive microwave SSM downscaling method can effectively improve its spatial resolution to fine scale for regional applications. However, the estimation capability of SSM with long time series is limited by OTI data, which are heavily polluted by clouds. To reduce the dependence of the method on OTI data, an SSM retrieval and spatio-temporal fusion model (SMRFM) is proposed in the study. Specifically, a model coupling in situ data, MODerate-resolution Imaging Spectro-radiometer (MODIS) OTI data, and topographic information is developed to retrieve MODIS SSM (1 km) using the least squares method. Then the retrieved MODIS SSM and the spatio-temporal fusion model are employed to downscale the passive microwave SSM from coarse scale to 1 km. The proposed SMRFM is implemented in a grassland dominated area over Naqu, central Tibet Plateau, for Advanced Microwave Scanning Radiometer—Earth Observing System sensor (AMSR-E) SSM downscaling in unfrozen period. The in situ SSM and Noah land surface model 0.01° SSM are used to validate the estimated MODIS SSM with long time series. The evaluations show that the estimated MODIS SSM has the same temporal resolution with AMSR-E and obtains significantly improved detailed spatial information. Moreover, the temporal accuracy of estimated MODIS SSM against in situ data (r = 0.673, μbRMSE = 0.070 m3/m3) is better than the AMSR-E (r = 0.661, μbRMSE = 0.111 m3/m3). In addition, the temporal r of estimated MODIS SSM is obviously higher than that of Noah data. Therefore, this suggests that the SMRFM can be used to estimate MODIS SSM with long time series by AMSR-E SSM downscaling in the study. Overall, the study can provide help for the development and application of microwave SSM-related scientific research at the regional scale