Metal-Organic Framework-Based Nanomaterials for Regulation of the Osteogenic Microenvironment.

As the global population ages, bone diseases have become increasingly prevalent in clinical settings. These conditions often involve detrimental factors such as infection, inflammation, and oxidative stress that disrupt bone homeostasis. Addressing these disorders requires exogenous strategies to regulate the osteogenic microenvironment (OME). The exogenous regulation of OME can be divided into four processes: induction, modulation, protection, and support, each serving a specific purpose. To this end, metal-organic frameworks (MOFs) are an emerging focus in nanomedicine, which show tremendous potential due to their superior delivery capability. MOFs play numerous roles in OME regulation such as metal ion donors, drug carriers, nanozymes, and photosensitizers, which have been extensively explored in recent studies. This review presents a comprehensive introduction to the exogenous regulation of OME by MOF-based nanomaterials. By discussing various functional MOF composites, this work aims to inspire and guide the creation of sophisticated and efficient nanomaterials for bone disease management.

Symmetry-Engineering-Induced In-Plane Polarization Enhancement in Ta2NiS5/CrOCl van der Waals Heterostructure.

Van der Waals (vdW) interfaces can be formed via layer stacking regardless of the lattice constant or symmetry of the individual building blocks. Herein, we constructed a vdW interface of layered Ta2NiS5 and CrOCl, which exhibited remarkably enhanced in-plane anisotropy via polarized Raman spectroscopy and electrical transport measurements. Compared with pristine Ta2NiS5, the anisotropy ratio of the Raman intensities for the B2g, 2Ag, and 3Ag modes increased in the heterostructure. More importantly, the anisotropy ratios of conductivity and mobility in the heterostructure increased by one order of magnitude. Specifically speaking, the conductivity ratio changed from ~2.1 (Ta2NiS5) to ~15 (Ta2NiS5/CrOCl), while the mobility ratio changed from ~2.7 (Ta2NiS5) to ~32 (Ta2NiS5/CrOCl). Such prominent enhancement may be attributed to the symmetry reduction caused by lattice mismatch at the heterostructure interface and the introduction of strain into the Ta2NiS5. Our research provides a new perspective for enhancing artificial anisotropy physics and offers feasible guidance for future functionalized electronic devices.

Multifunctional modifications of polyetheretherketone implants for bone repair: A comprehensive review.

Polyetheretherketone (PEEK) has emerged as a highly promising orthopedic implantation material due to its elastic modulus which is comparable to that of natural bone. This polymer exhibits impressive properties for bone implantation such as corrosion resistance, fatigue resistance, self-lubrication and chemical stability. Significantly, compared to metal-based implants, PEEK implants have mechanical properties that are closer to natural bone, which can mitigate the "stress shielding" effect in bone implantation. Nevertheless, PEEK is incapable of inducing osteogenesis due to its bio-inert molecular structure, thereby hindering the osseointegration process. To optimize the clinical application of PEEK, researchers have been working on promoting its bioactivity and endowing this polymer with beneficial properties, such as antibacterial, anti-inflammatory, anti-tumor, and angiogenesis-promoting capabilities. Considering the significant growth of research on PEEK implants over the past 5 years, this review aims to present a timely update on PEEK's modification methods. By highlighting the latest advancements in PEEK modification, we hope to provide guidance and inspiration for researchers in developing the next generation bone implants and optimizing their clinical applications.

Targeting hepatitis B virus cccDNA levels: Recent progress in seeking small molecule drug candidates.

Hepatitis B virus (HBV) infection is a major global health problem that puts people at high risk of death from cirrhosis and liver cancer. The presence of covalently closed circular DNA (cccDNA) in infected cells is considered to be the main obstacle to curing chronic hepatitis B. At present, the cccDNA cannot be completely eliminated by standard treatments. There is an urgent need to develop drugs or therapies that can reduce HBV cccDNA levels in infected cells. We summarize the discovery and optimization of small molecules that target cccDNA synthesis and degradation. These compounds are cccDNA synthesis inhibitors, cccDNA reducers, core protein allosteric modulators, ribonuclease H inhibitors, cccDNA transcriptional modulators, HBx inhibitors and other small molecules that reduce cccDNA levels.

Effects of optical and radar satellite observations within Google Earth Engine on soil organic carbon prediction models in Spain.

The modeling and mapping of soil organic carbon (SOC) has advanced through the rapid growth of Earth observation data (e.g., Sentinel) collection and the advent of appropriate tools such as the Google Earth Engine (GEE). However, the effects of differing optical and radar sensors on SOC prediction models remain uncertain. This research aims to investigate the effects of different optical and radar sensors (Sentinel-1/2/3 and ALOS-2) on SOC prediction models based on long-term satellite observations on the GEE platform. We also evaluate the relative impact of four synthetic aperture radar (SAR) acquisition configurations (polarization mode, band frequency, orbital direction and time window) on SOC mapping with multiband SAR data from Spain. Twelve experiments involving different satellite data configurations, combined with 4027 soil samples, were used for building SOC random forest regression models. The results show that the synthesis mode and choice of satellite images, as well as the SAR acquisition configurations, influenced the model accuracy to varying degrees. Models based on SAR data involving cross-polarization, multiple time periods and "ASCENDING" orbits outperformed those involving copolarization, a single time period and "DESCENDING" orbits. Moreover, combining information from different orbital directions and polarization modes improved the soil prediction models. Among the SOC models based on long-term satellite observations, the Sentinel-3-based models (R2 = 0.40) performed the best, while the ALOS-2-based model performed the worst. In addition, the predictive performance of MSI/Sentinel-2 (R2 = 0.35) was comparable with that of SAR/Sentinel-1 (R2 = 0.35); however, the combination (R2 = 0.39) of the two improved the model performance. All the predicted maps involving Sentinel satellites had similar spatial patterns that were higher in northwest Spain and lower in the south. Overall, this study provides insights into the effects of different optical and radar sensors and radar system parameters on soil prediction models and improves our understanding of the potential of Sentinels in developing soil carbon mapping.

Discovery of novel AdipoRon analogues as potent anti-inflammatory agents against nonalcoholic steatohepatitis.

Nonalcoholic steatohepatitis (NASH) is a rising public health burden, and there is a lack of effective therapeutic drugs in clinical practice. Sustained hepatic inflammation is considered as the key histopathological feature and dangerous fact for NASH. Different causes vary from one NASH patient to another, while sustained hepatic inflammation affects all NASH patients. AdipoRon is the first small-molecule AdipoR agonist, exerting antidiabetic and anti-inflammatory effect. In order to find novel AdipoRon analogues with more potent anti-inflammatory activity, we designed, synthesized and biologically evaluated 32 analogues. Among them, Q7 exerted potent anti-inflammatory activity and less cytotoxicity. Q7 could dose-dependently stimulate the increasing of AMPK phosphorylation, the widely recognized downstream effector of AdipoR1 activation. In NASH model mice, Q7 showed significant anti-inflammatory, anti-fibrotic and lipid-lowering effect in mice liver, and was superior to AdipoRon. Together, Q7 holds promise for developing anti-inflammatory and anti-NASH agents.

Exploring the environmental impact of crop production in China using a comprehensive footprint approach.

The carbon-nutrient-water cycles of farmland ecosystem not only provides support for crop production, but also has an impact on the environment. Comprehensively quantifying the impact of crop production on the environment can provide a basis for crop sustainable production. A series of environmental footprint approaches, including carbon footprint (CF), nitrogen footprint (NF) and water footprint (WF), were optimized to evaluate greenhouse gas (GHG) emissions, reactive nitrogen (Nr) loss and water resource consumption in crop production, and a comprehensive footprint method based on Endpoint modeling was proposed to evaluate the overall environmental impact of crop production in China. The CF, NF and WF of 28 forms of crop production varied from 1206.29 kg CO2 equivalent (CO2-eq) ha-1 of oil crops to 7326.37 kg CO2-eq ha-1 of fiber crops, 16.07 kg Nr-eq ha-1 of oil crops to 60.70 kg Nr-eq ha-1 of sugar crops, and 4032.04 m3 ha-1 oil crops to 12,476.28 m3 ha-1 of sugar crops, respectively. The contribution of each component to footprints varied greatly among different crops, and fertilizer manufacture, NH3 volatilization and green WF were generally the main contributors of CF, NF and WF, respectively. The total GHG emissions, Nr loss and water consumption were estimated to be 670.11 Tg CO2-eq, 5.50 Tg Nr-eq and 837.06 G m3 for all crop production of China. The greenhouse vegetable with the highest area-scaled comprehensive footprint was 4.5 times that of the oil crops which had the lowest one. The contribution of crop production to the corresponding environmental impact in China was as low as 3.7%, of which NH3 volatilization contributed 48% and grain production contributed 72%. Mineral N fertilization was the main driver of the variation of comprehensive footprint between provinces, with reduction of N fertilizer application as an important way to reduce the environmental impact of crop production.

Design, Synthesis, and Biological Evaluation of Triazolone Derivatives as Potent PPARα/δ Dual Agonists for the Treatment of Nonalcoholic Steatohepatitis.

Peroxisome proliferator-activator receptors α/δ (PPARα/δ) are regarded as potential therapeutic targets for nonalcoholic steatohepatitis (NASH). However, PPARα/δ dual agonist GFT-505 exhibited poor anti-NASH effects in a phase III clinical trial, probably due to its weak PPARα/δ agonistic activity and poor metabolic stability. Other reported PPARα/δ dual agonists either exhibited limited potency or had unbalanced PPARα/δ agonistic activity. Herein, we report a series of novel triazolone derivatives as PPARα/δ dual agonists. Among them, compound H11 exhibited potent and well-balanced PPARα/δ agonistic activity (PPARα EC50 = 7.0 nM; PPARδ EC50 = 8.4 nM) and a high selectivity over PPARγ (PPARγ EC50 = 1316.1 nM) in PPAR transactivation assays. The crystal structure of PPARδ in complex with H11 revealed a unique PPARδ-agonist interaction. H11, which had excellent PK properties and a good safety profile, showed potent in vivo anti-NASH effects in preclinical models. Together, H11 holds a great promise for treating NASH or other inflammatory and fibrotic diseases.

Climate change may interact with nitrogen fertilizer management leading to different ammonia loss in China's croplands.

Despite research into the response of ammonia (NH3 ) volatilization in farmland to various meteorological factors, the potential impact of future climate change on NH3 volatilization is not fully understood. Based on a database consisting of 1063 observations across China, nonlinear NH3  models considering crop type, meteorological, soil and management variables were established via four machine learning methods, including support vector machine, multi-layer perceptron, gradient boosting machine and random forest (RF). The RF model had the highest R2 of 0.76 and the lowest RMSE of 0.82 kg NH3 -N ha-1 , showing the best simulation capability. Results of model importance indicated that NH3 volatilization was mainly controlled by total input of N fertilizer, followed by meteorological factors, human managements and soil characteristics. The NH3 emissions of China's cereal production (paddy rice, wheat and maize) in 2018 was estimated to be 3.3 Mt NH3 -N. By 2050, NH3 volatilization will increase by 23.1-32.0% under different climate change scenarios (Representative Concentration Pathways, RCPs), and climate change will have the greatest impact on NH3 volatilization in the Yangtze river agro-region of China due to high warming effects. However, the potential increase in NH3 volatilization under future climate change can be mitigated by 26.1-47.5% through various N fertilizer management optimization options.

The role of soils in regulation of freshwater and coastal water quality.

Water quality regulation is an important ecosystem service function of soil. In this study, the mechanism by which soil regulates water quality was reviewed, and the effects of soil management on water quality were explored. A scientometrics analysis was also conducted to explore the research fields and hotspots of water quality regulation of soil in the past 5 years. This review found that the pollutants entering the soil can be mitigated by precipitation, adsorption and desorption, ion exchange, redox and metabolic decomposition. As an optimal substrate, soil in constructed wetlands has perfect performance in the adsorption and passivation of pollutants such as nitrogen, phosphorus and heavy metals in water, and degradation of pesticides and emerging contaminants. Mangrove wetlands play an important role in coastal zone protection and coastal water quality restoration. However, the excessive application of agricultural chemicals causes soil overload, which leads to the occurrence of agricultural non-point source pollution. Under the dual pressures of climate change and food insecurity in the future, developing environmentally friendly and economically feasible sustainable soil management measures is crucial for maintaining the water purification function of soil by relying on the accurate quantification of soil function based on big data and modelling. This article is part of the theme issue 'The role of soils in delivering Nature's Contributions to People'.

Prediction of soil organic carbon and the C:N ratio on a national scale using machine learning and satellite data: A comparison between Sentinel-2, Sentinel-3 and Landsat-8 images.

Soil organic carbon (SOC) and soil carbon-to-nitrogen ratio (C:N) are the main indicators of soil quality and health and play an important role in maintaining soil quality. Together with Landsat, the improved spatial and temporal resolution Sentinel sensors provide the potential to investigate soil information on various scales. We analyzed and compared the potential of satellite sensors (Landsat-8, Sentinel-2 and Sentinel-3) with various spatial and temporal resolutions to predict SOC content and C:N ratio in Switzerland. Modeling was carried out at four spatial resolutions (800 m, 400 m, 100 m and 20 m) using three machine learning techniques: support vector machine (SVM), boosted regression tree (BRT) and random forest (RF). Soil prediction models were generated in these three machine learners in which 150 soil samples and different combinations of environmental data (topography, climate and satellite imagery) were used as inputs. The prediction results were evaluated by cross-validation. Our results revealed that the model type, modeling resolution and sensor selection greatly influenced outputs. By comparing satellite-based SOC models, the models built by Landsat-8 and Sentinel-2 performed the best and the worst, respectively. C:N ratio prediction models based on Landsat-8 and Sentinel-2 showed better results than Sentinel-3. However, the prediction models built by Sentinel-3 had competitive or better accuracy at coarse resolutions. The BRT models constructed by all available predictors at a resolution of 100 m obtained the best prediction accuracy of SOC content and C:N ratio; their relative improvements (in terms of R2) compared to models without remote sensing data input were 29.1% and 58.4%, respectively. The results of variable importance revealed that remote sensing variables were the best predictors for our soil prediction models. The predicted maps indicated that the higher SOC content was mainly distributed in the Alps, while the C:N ratio shared a similar distribution pattern with land use and had higher values in forest areas. This study provides useful indicators for a more effective modeling of soil properties on various scales based on satellite imagery.

Re-estimating methane emissions from Chinese paddy fields based on a regional empirical model and high-spatial-resolution data.

Quantifying methane (CH4) emissions from paddy fields is essential for evaluating the environmental risks of the paddy rice production system, and improving the accuracy of CH4 modeling is a key issue that needs to be addressed. Based on a database containing 835 field measurements, both single national and region-specific models were established to estimate CH4 emissions from paddy fields considering different environmental factors and management patterns using 70% of the measurements. The remaining 30% of the measurements were then used for model evaluation. The performance of the region-specific model was better than that of the single national model. The region-specific model could simulate CH4 emissions in an unbiased manner with R2 values of 0.15-0.70 and efficiency values of 11-60%. The paddy rice type, water regime, organic amendment, latitude, and soil characteristics (pH and bulk density) were identified as the main drivers in the models. By inputting the high-resolution spatial data of these drivers into the established model, the CH4 emissions from China's paddy fields were estimated to be 4.75 Tg in 2015, with a 95% confidence interval of 4.19-5.61 Tg. The results indicated that establishing and driving a region-specific model with high-resolution data can improve the estimation accuracy of CH4 emissions from paddy fields.

Deriving Emission Factors and Estimating Direct Nitrous Oxide Emissions for Crop Cultivation in China.

Updating and refining the N2O emission factors (N2O-EFs) are vital to reduce the uncertainty in estimates of direct N2O emissions. Based on a database with 1151 field measurements across China, the N2O-EFs were established via three approaches including the maximum likelihood method, a linear regression with an intercept and a linear regression with the intercept set to 0 using 70% of the observations. The remaining 30% of the observations were then used to evaluate the predicted N2O-EFs. The third method had the highest R2 of 0.39 and the best model efficiency of 0.38 with no significant bias, showing the best calculation efficiency. The results showed that the N2O-EFs varied with agroregions, crops, and management patterns. The agroregions of Huang-Huai-Hai and Yangtze River had the higher N2O-EFs in maize and wheat seasons than other regions, and the highest N2O-EFs of 0.66-0.92% in the rice season was found in the South and Southwest agroregions. Both fertilizer types and water regimes had the remarkable effects on N2O-EFs. Based on the best estimation by the selected method, direct N2O emissions from China's crop cultivation were estimated to be 194 Gg N2O-N with a 95% confidence interval of 180-208 Gg N2O-N in the year 2016.