Pro-CRISPR PcrIIC1-associated Cas9 system for enhanced bacterial immunity.

The CRISPR system is an adaptive immune system found in prokaryotes that defends host cells against the invasion of foreign DNA1. As part of the ongoing struggle between phages and the bacterial immune system, the CRISPR system has evolved into various types, each with distinct functionalities2. Type II Cas9 is the most extensively studied of these systems and has diverse subtypes. It remains uncertain whether members of this family can evolve additional mechanisms to counter viral invasions3,4. Here we identify 2,062 complete Cas9 loci, predict the structures of their associated proteins and reveal three structural growth trajectories for type II-C Cas9. We found that novel associated genes (NAGs) tended to be present within the loci of larger II-C Cas9s. Further investigation revealed that CbCas9 from Chryseobacterium species contains a novel ß-REC2 domain, and forms a heterotetrameric complex with an NAG-encoded CRISPR-Cas-system-promoting (pro-CRISPR) protein of II-C Cas9 (PcrIIC1). The CbCas9-PcrIIC1 complex exhibits enhanced DNA binding and cleavage activity, broader compatibility for protospacer adjacent motif sequences, increased tolerance for mismatches and improved anti-phage immunity, compared with stand-alone CbCas9. Overall, our work sheds light on the diversity and 'growth evolutionary' trajectories of II-C Cas9 proteins at the structural level, and identifies many NAGs-such as PcrIIC1, which serves as a pro-CRISPR factor to enhance CRISPR-mediated immunity.

Landscape Classification System Based on RKM Clustering for Soil Survey UAV Images-Case Study of the Small Hilly Areas in Jurong City.

With the advantages of high accuracy, low cost, and flexibility, Unmanned Aerial Vehicle (UAV) images are now widely used in the fields of land survey, crop monitoring, and soil property prediction. Since the distribution of soil and landscape are closely related, this study makes use of the advantages of UAV images to classify the landscape to build a landscape classification system for soil investigation. Firstly, land use, object, and topographic factor were selected as landscape factors based on soil-forming factors. Then, based on multispectral images and Digital Elevation Models (DEM) acquired by UAV, object-oriented classification of different landscape factors was carried out. Additionally, we selected 432 sample data and validation data from the field survey. Finally, the landscape factor classification results were superimposed to obtain the landscape unit applicable to the system classification. The landscape classification system oriented to the soil survey was constructed by clustering 11,897 landscape units through the rough K-mean clustering algorithm. Compared to K-mean clustering, the rough K-mean clustering was better, with a Silhouette Coefficient of 0.26247 significantly higher than that of K-mean clustering. From the classification results, it can be found that the overall classification results are somewhat fragmented, but the landscape boundaries at the small area scale are consistent with the actual situation and the fragmented small spots are less. Comparing the small number of landscape boundaries obtained from the actual survey, we can find that the landscape boundaries in the landscape classification map are generally consistent with the actual landscape boundaries. In addition, through the analysis of two soil profile data within a landscape category, we found that the identified soil type of soil formation conditions and the landscape factor type of the landscape category is approximately the same. Therefore, this landscape classification system can be effectively used for soil surveys, and this landscape classification system is important for soil surveys to carry out the selection of survey routes, the setting of profile points, and the determination of soil boundaries.

Digital Mapping of Soil Organic Carbon Based on Machine Learning and Regression Kriging.

In the last two decades, machine learning (ML) methods have been widely used in digital soil mapping (DSM), but the regression kriging (RK) model which combines the advantages of the ML and kriging methods has rarely been used in DSM. In addition, due to the limitation of a single-model structure, many ML methods have poor prediction accuracy in undulating terrain areas. In this study, we collected the SOC content of 115 soil samples in a hilly farming area with continuous undulating terrain. According to the theory of soil-forming factors in pedogenesis, we selected 10 topographic indices, 7 vegetation indices, and 2 soil indices as environmental covariates, and according to the law of geographical similarity, we used ML and RK methods to mine the relationship between SOC and environmental covariates to predict the SOC content. Four ensemble models-random forest (RF), Cubist, stochastic gradient boosting (SGB), and Bayesian regularized neural networks (BRNNs)-were used to fit the trend of SOC content, and the simple kriging (SK) method was used to interpolate the residuals of the ensemble models, and then the SOC and residual were superimposed to obtain the RK prediction result. Moreover, the 115 samples were divided into calibration and validation sets at a ratio of 80%, and the tenfold cross-validation method was used to fit the optimal parameters of the model. From the results of four ensemble models: RF performed best in the calibration set (R2c = 0.834) but poorly in the validation set (R2v = 0.362); Cubist had good accuracy and stability in both the calibration and validation sets (R2c = 0.693 and R2v = 0.445); SGB performed poorly (R2c = 0.430 and R2v = 0.336); and BRNN had the lowest accuracy (R2c = 0.323 and R2v = 0.282). The results showed that the R2 of the four RK models in the validation set were 0.718, 0.674, 0.724, and 0.625, respectively. Compared with the ensemble models without superimposed residuals, the prediction accuracy was improved by 0.356, 0.229, 0.388, and 0.343, respectively. In conclusion, Cubist has high prediction accuracy and generalization ability in areas with complex topography, and the RK model can make full use of trends and spatial structural factors that are not easy to mine by ML models, which can effectively improve the prediction accuracy. This provides a reference for soil survey and digital mapping in complex terrain areas.

Global soil-derived ammonia emissions from agricultural nitrogen fertilizer application: A refinement based on regional and crop-specific emission factors.

Ammonia (NH3 ) emissions from fertilized soils to the atmosphere and the subsequent deposition to land surface exert adverse effects on biogeochemical nitrogen (N) cycling. The region- and crop-specific emission factors (EFs) of N fertilizer for NH3 are poorly developed and therefore the global estimate of soil NH3 emissions from agricultural N fertilizer application is constrained. Here we quantified the region- and crop-specific NH3 EFs of N fertilizer by compiling data from 324 worldwide manipulative studies and focused to map the global soil NH3 emissions from agricultural N fertilizer application. Globally, the NH3 EFs averaged 12.56% and 14.12% for synthetic N fertilizer and manure, respectively. Regionally, south-eastern Asia had the highest NH3 EFs of synthetic N fertilizer (19.48%) and Europe had the lowest (6%), which might have been associated with the regional discrepancy in the form and rate of N fertilizer use and management practices in agricultural production. Global agricultural NH3 emissions from the use of synthetic N fertilizer and manure in 2014 were estimated to be 12.32 and 3.79 Tg N/year, respectively. China (4.20 Tg N/year) followed by India (2.37 Tg N/year) and America (1.05 Tg N/year) together contributed to over 60% of the total global agricultural NH3 emissions from the use of synthetic N fertilizer. For crop-specific emissions, the NH3 EFs averaged 11.13%-13.95% for the three main staple crops (i.e., maize, wheat, and rice), together accounting for 72% of synthetic N fertilizer-induced NH3 emissions from croplands in the world and 70% in China. The region- and crop-specific NH3 EFs of N fertilizer established in this study offer references to update the default EF in the IPCC Tier 1 guideline. This work also provides an insight into the spatial variation of soil-derived NH3 emissions from the use of synthetic N fertilizer in agriculture at the global and regional scales.

Increased soil release of greenhouse gases shrinks terrestrial carbon uptake enhancement under warming.

Warming can accelerate the decomposition of soil organic matter and stimulate the release of soil greenhouse gases (GHGs), but to what extent soil release of methane (CH4 ) and nitrous oxide (N2 O) may contribute to soil C loss for driving climate change under warming remains unresolved. By synthesizing 1,845 measurements from 164 peer-reviewed publications, we show that around 1.5°C (1.16-2.01°C) of experimental warming significantly stimulates soil respiration by 12.9%, N2 O emissions by 35.2%, CH4 emissions by 23.4% from rice paddies, and by 37.5% from natural wetlands. Rising temperature increases CH4 uptake of upland soils by 13.8%. Warming-enhanced emission of soil CH4 and N2 O corresponds to an overall source strength of 1.19, 1.84, and 3.12 Pg CO2 -equivalent/year under 1°C, 1.5°C, and 2°C warming scenarios, respectively, interacting with soil C loss of 1.60 Pg CO2 /year in terms of contribution to climate change. The warming-induced rise in soil CH4 and N2 O emissions (1.84 Pg CO2 -equivalent/year) could reduce mitigation potential of terrestrial net ecosystem production by 8.3% (NEP, 22.25 Pg CO2 /year) under warming. Soil respiration and CH4 release are intensified following the mean warming threshold of 1.5°C scenario, as compared to soil CH4 uptake and N2 O release with a reduced and less positive response, respectively. Soil C loss increases to a larger extent under soil warming than under canopy air warming. Warming-raised emission of soil GHG increases with the intensity of temperature rise but decreases with the extension of experimental duration. This synthesis takes the lead to quantify the ecosystem C and N cycling in response to warming and advances our capacity to predict terrestrial feedback to climate change under projected warming scenarios.

Differences in the responses of flow and nutrient load to isolated and coupled future climate and land use changes.

Understanding the differences in the responses of river hydrology and water quality to climate and land use changes is particularly crucial for the development and management of water resources in the future. This study was carried out to assess the isolated and coupled effects of future climate change and land use change on the flow and nutrient load of the Xitiaoxi watershed in southeast China by applying the calibrated Hydrological Simulation Program Fortran model. Four representative concentration pathways released by the Intergovernmental Panel on Climate Change and two projected land use change scenarios were used to simulate future conditions. The results indicate that climate change would result in flow increased with an average variation of 25.2% in the future, and the increased flow would be mainly concentrated on the high flow part of the total flow duration curve. Climate change would also induce seasonal shifts to nutrient load. The effects of land use change showed that nutrient load was more sensitive than flow, made Orthophosphate load increase by 2.8%-154.7%, and flow increase by 7.2%-15.1%. The results for coupled climate and land use changes indicate that flow and nutrient load would be more affected by climate change than by land use change. Climate and land use changes may amplify or weaken each other's effects on flow and nutrient load, which suggests that both should be incorporated into hydrologic models when studying the future conditions. The results of this study can help decision-makers guide management practices that aim to minimize flow and nutrient load.

Multi-Feature Classification of Multi-Sensor Satellite Imagery Based on Dual-Polarimetric Sentinel-1A, Landsat-8 OLI, and Hyperion Images for Urban Land-Cover Classification.

This paper focuses on evaluating the ability and contribution of using backscatter intensity, texture, coherence, and color features extracted from Sentinel-1A data for urban land cover classification and comparing different multi-sensor land cover mapping methods to improve classification accuracy. Both Landsat-8 OLI and Hyperion images were also acquired, in combination with Sentinel-1A data, to explore the potential of different multi-sensor urban land cover mapping methods to improve classification accuracy. The classification was performed using a random forest (RF) method. The results showed that the optimal window size of the combination of all texture features was 9 × 9, and the optimal window size was different for each individual texture feature. For the four different feature types, the texture features contributed the most to the classification, followed by the coherence and backscatter intensity features; and the color features had the least impact on the urban land cover classification. Satisfactory classification results can be obtained using only the combination of texture and coherence features, with an overall accuracy up to 91.55% and a kappa coefficient up to 0.8935, respectively. Among all combinations of Sentinel-1A-derived features, the combination of the four features had the best classification result. Multi-sensor urban land cover mapping obtained higher classification accuracy. The combination of Sentinel-1A and Hyperion data achieved higher classification accuracy compared to the combination of Sentinel-1A and Landsat-8 OLI images, with an overall accuracy of up to 99.12% and a kappa coefficient up to 0.9889. When Sentinel-1A data was added to Hyperion images, the overall accuracy and kappa coefficient were increased by 4.01% and 0.0519, respectively.

A meta-analysis of fertilizer-induced soil NO and combined NO+N2 O emissions.

Soils are among the important sources of atmospheric nitric oxide (NO) and nitrous oxide (N2 O), acting as a critical role in atmospheric chemistry. Updated data derived from 114 peer-reviewed publications with 520 field measurements were synthesized using meta-analysis procedure to examine the N fertilizer-induced soil NO and the combined NO+N2 O emissions across global soils. Besides factors identified in earlier reviews, additional factors responsible for NO fluxes were fertilizer type, soil C/N ratio, crop residue incorporation, tillage, atmospheric carbon dioxide concentration, drought and biomass burning. When averaged across all measurements, soil NO-N fluxes were estimated to be 4.06 kg ha-1  yr-1 , with the greatest (9.75 kg ha-1  yr-1 ) in vegetable croplands and the lowest (0.11 kg ha-1  yr-1 ) in rice paddies. Soil NO emissions were more enhanced by synthetic N fertilizer (+38%), relative to organic (+20%) or mixed N (+18%) sources. Compared with synthetic N fertilizer alone, synthetic N fertilizer combined with nitrification inhibitors substantially reduced soil NO emissions by 81%. The global mean direct emission factors of N fertilizer for NO (EFNO ) and combined NO+N2 O (EFc ) were estimated to be 1.16% and 2.58%, with 95% confidence intervals of 0.71-1.61% and 1.81-3.35%, respectively. Forests had the greatest EFNO (2.39%). Within the croplands, the EFNO (1.71%) and EFc (4.13%) were the greatest in vegetable cropping fields. Among different chemical N fertilizer varieties, ammonium nitrate had the greatest EFNO (2.93%) and EFc (5.97%). Some options such as organic instead of synthetic N fertilizer, decreasing N fertilizer input rate, nitrification inhibitor and low irrigation frequency could be adopted to mitigate soil NO emissions. More field measurements over multiyears are highly needed to minimize the estimate uncertainties and mitigate soil NO emissions, particularly in forests and vegetable croplands.

Methane and Nitrous Oxide Emissions Reduced Following Conversion of Rice Paddies to Inland Crab-Fish Aquaculture in Southeast China.

Aquaculture is an important source of atmospheric methane (CH4) and nitrous oxide (N2O), while few direct flux measurements are available for their regional and global source strength estimates. A parallel field experiment was performed to measure annual CH4 and N2O fluxes from rice paddies and rice paddy-converted inland crab-fish aquaculture wetlands in southeast China. Besides N2O fluxes dependent on water/sediment mineral N and CH4 fluxes related to water chemical oxygen demand, both CH4 and N2O fluxes from aquaculture were related to water/sediment temperature, sediment dissolved organic carbon, and water dissolved oxygen concentration. Annual CH4 and N2O fluxes from inland aquaculture averaged 0.37 mg m(-2) h(-1) and 48.1 µg m(-2) h(-1), yielding 32.57 kg ha(-1) and 2.69 kg N2O-N ha(-1), respectively. The conversion of rice paddies to aquaculture significantly reduced CH4 and N2O emissions by 48% and 56%, respectively. The emission factor for N2O was estimated to be 0.66% of total N input in the feed or 1.64 g N2O-N kg(-1) aquaculture production in aquaculture. The conversion of rice paddies to inland aquaculture would benefit for reconciling greenhouse gas mitigation and agricultural income increase as far as global warming potentials and net ecosystem economic profits are of concomitant concern. Some agricultural practices such as better aeration and feeding, and fallow season dredging would help to lower CH4 and N2O emissions from inland aquaculture. More field measurements from inland aquaculture are highly needed to gain an insight into national and global accounting of CH4 and N2O emissions.

Engineering a sensitive visual-tracking reporter system for real-time monitoring phosphorus deficiency in tobacco.

Plant phosphorus (P) diagnosis is widely used for monitoring P status and guiding P fertilizer application in field conditions. The common methods for predicting plant response to P are time- and labour-consuming chemical measurements of the extractable soil P and plant P concentrations. In this study, we successfully generated a visual reporter system in tobacco (Nicotiana tabacum L.) to monitor plant P status by expressing of a Purple gene (Pr) isolated from cauliflower (Brassica oleracea var botrytis) driven by the promoter (Pro) of OsPT6, a P-starvation-induced rice gene. The leaves of OsPT6pro::Pr (PT6pro::Pr) transgenic tobacco continuously turned into dark purple with the increase of duration and severity of P deficiency, and recovered rapidly to basal green colour upon resupply of P. The expression of several anthocyanin biosynthesis involving genes was strongly activated in the transgenic tobacco in comparison to wild type under P-deficient condition. Such additive purple colour was not detected by deficiencies of other major- and micronutrients or stresses of salt, drought and cold. There was an extremely high correlation between P concentration and anthocyanin accumulation in the transgenic tobacco leaves. Using a hyperspectral sensing technology, P concentration in the leaves of transgenic plants could be predicted by the reflectance spectra at 554 nm wavelength with approximately 0.16 as the threshold value of the P deficiency. Taken together, the colour-based visual reporter system could be specifically and readily used for monitoring the plant P status by naked eyes and accurately assessed by spectral reflectance.

Inferring land use and land cover impact on stream water quality using a Bayesian hierarchical modeling approach in the Xitiaoxi River Watershed, China.

Lake eutrophication has become a very serious environmental problem in China. If water pollution is to be controlled and ultimately eliminated, it is essential to understand how human activities affect surface water quality. A recently developed technique using the Bayesian hierarchical linear regression model revealed the effects of land use and land cover (LULC) on stream water quality at a watershed scale. Six LULC categories combined with watershed characteristics, including size, slope, and permeability were the variables that were studied. The pollutants of concern were nutrient concentrations of total nitrogen (TN) and total phosphorus (TP), common pollutants found in eutrophication. The monthly monitoring data at 41 sites in the Xitiaoxi Watershed, China during 2009-2010 were used for model demonstration. The results showed that the relationships between LULC and stream water quality are so complicated that the effects are varied over large areas. The models suggested that urban and agricultural land are important sources of TN and TP concentrations, while rural residential land is one of the major sources of TN. Certain agricultural practices (excessive fertilizer application) result in greater concentrations of nutrients in paddy fields, artificial grasslands, and artificial woodlands. This study suggests that Bayesian hierarchical modeling is a powerful tool for examining the complicated relationships between land use and water quality on different scales, and for developing land use and water management policies.

Yiqi Yangxue formula inhibits cartilage degeneration in knee osteoarthritis by regulating LncRNA-UFC1/miR-34a/MMP-13 axis.

ETHNOPHARMACOLOGICAL RELEVANCE: Knee osteoarthritis (KOA) is a prevalent and disabling clinical condition affecting joint structures worldwide. The Yiqi Yangxue formula (YQYXF) is frequently prescribed in clinical settings for the treatment of KOA. Existing research has primarily focused on alterations in drug metabolism, with limited investigation into the epigenetic effects of YQYXF, particularly in relation to non-coding RNA. AIM OF THE STUDY: Exploring the effects of YQYXF on critical factors of long chain non-coding RNA UFC1/miR-34a/matrix metalloproteinase-13 (MMP-13) axis and their interrelationships. METHODS: UHPLC-QE-MS technology was used to identify the YQYXF ingredients in rat serum. KEGG and GO analysis were performed on the targets of blood components acting on KOA using a database. Simultaneously, a protein interaction network was constructed using target proteins and metabolites to identify the core components and key pathways of YQYXF. The KOA rat model was established using an improved Hulth method. SPF SD rats were randomly divided into normal group, sham surgery group, model group, celecoxib capsules group (18 mg/kg), YQYXF low, medium and high dose groups (4.6 g/kg, 9.2 g/kg, 18.4 g/kg). Observe the synovial and cartilage tissues of rats using pathological methods. RT-PCR was used to detect the levels of UFC1, miR-34a, and MMP-13 in cartilage. Immunohistochemistry was used to detect the levels of MMP-13 and ADAMTS-5 in cartilage. ELISA method was used to detect the levels of MMP-13 and ADAMTS-5 in serum. In addition, we further validated the regulation of crucial factor expression levels of UFC1/miR-34a/MMP-13 axis in rat chondrocytes and degenerative chondrocytes of KOA patients by YQYXF, providing a basis for its treatment of KOA. RESULTS: The compounds that YQYXF enters the bloodstream mainly contain flavonoids and phenylpropanoid compounds. The core components that act on OA include quercetin, fisetin, and demethylweldelolactone. The main target pathways are the IL-17 signaling pathway, lipid and atherosclerosis, cellular sensitivity, inflammatory mediator regulation of TRP channels, TNF signaling pathway, relaxin signaling pathway and C-type lectin receptor signaling pathway. YQYXF inhibited the expression of miR-34a and MMP-13 mRNA, and reduced the protein levels of MMP-13 and ADAMTS-5. In vitro studies have confirmed that 20% YQYXF serum promoted UFC1 and reduce miR-34a levels. In addition, miR-34a in sh-UFC1+10% YQYXF serum and sh-UFC1+20% YQYXF serum groups significantly decreased compared to the sh-UFC1 group. CONCLUSION: The anti-KOA cartilage degeneration effect of YQYXF might be related to inhibiting cell apoptosis and promoting cell proliferation, which regulated the lncRNA-UFC1/miR-34a/MMP-13 axis.

Novel and potential future therapeutic options in Sjögren's syndrome.

Sjögren's syndrome (SS) is a chronic autoimmune disease affecting the exocrine glands and can lead to various systemic symptoms impacting multiple organs. Despite its common occurrence, treatment options for SS have been largely limited, primarily focusing on alleviating symptoms rather than addressing the underlying autoimmune causes. A shift towards personalized medicine leads to the development of new therapeutic strategies aimed at targeting specific molecular pathways implicated in SS. Innovations in biologics are paving the way for inhibiting particular cytokines or cell surface molecules directly involved in the autoimmune mechanism. Furthermore, advancements in regenerative medicine, including the promising field of stem cell therapy, offer the potential for restoring or replacing the impaired salivary and lacrimal glands, providing hope for a more permanent resolution to this condition. This review encompasses cutting-edge treatment strategies for SS, spanning clinical and preclinical drugs to the latest treatment technology. Such advancements promise to drive targeted therapy development and inspire innovative ideas for treatment paradigms in SS.

A comprehensive review of Sjögren's syndrome: Classification criteria, risk factors, and signaling pathways.

Sjögren's syndrome (SS) is a chronic autoimmune disease that affects the exocrine glands and may lead to a range of systemic symptoms that impact various organs. Both innate and adaptive immune pathways might trigger the disease. Studying the signaling pathways underlying SS is crucial for enhancing diagnostic and therapeutic effectiveness. SS poses an ongoing challenge for medical professionals owing to the limited therapeutic options available. This review offers a comprehensive understanding of the intricate nature of SS, encompassing disease classification criteria, risk factors, and signaling pathways in immunity and inflammation. The advancements summarized herein have the potential to spark new avenues of research into SS.

Corrigendum: Study on the effects of intestinal flora on gouty arthritis.

[This corrects the article DOI: 10.3389/fcimb.2024.1341953.].

Study on the effects of intestinal flora on gouty arthritis.

Gouty arthritis (GA), a metabolic and immunologic disease, primarily affects joints. Dysbiosis of intestinal flora is an important cause of GA. The metabolic disorders of intestinal flora leading to GA and immune disorders might play an important role in patients with hyperuricemia and established GA. However, the exact mechanisms, through which the dysbiosis of intestinal flora causes the development of GA, are not fully understood yet. Moreover, several therapies commonly used to treat GA might alter the intestinal flora, suggesting that modulation of the intestinal flora might help prevent or treat GA. Therefore, a better understanding of the changes in the intestinal flora of GA patients might facilitate the discovery of new diagnostic and therapeutic approaches. The current review article discusses the effects of intestinal flora dysbiosis on the pathogenesis of GA and the cross-regulatory effects between gut flora and drugs for treating GA. This article also highlights the modulatory effects of gut flora by traditional Chinese medicine (TCM) to lower uric acid levels and relieve joint pain as well as provides a summary and outlook, which might help guide future research efforts.

Synchronous monitoring agricultural water qualities and greenhouse gas emissions based on low-cost Internet of Things and intelligent algorithms.

This study addressed the challenges of cost and portability in synchronous monitoring water quality and greenhouse gas emissions in paddy-dominated regions by developing a novel Internet of Things (IoT)-based monitoring system (WG-IoT-MS). The system, equipped with low-cost sensors and integrated intelligent algorithms, enabled real-time monitoring of dissolved N2O concentrations. Combined with an air-water gas exchange model, the system achieved efficient monitoring and simulation of CO2 and N2O emissions from agricultural water bodies while reducing monitoring costs by approximately 60 %. The proposed method was validated in paddy-dominated regions in Danyang, China. Results indicated the excellence of the dissolved N2O concentration model based on support vector regression, demonstrating accurate predictions within a concentration range of 2.003 to 13.247 µg/L. Notably, the model maintained acceptable predictive accuracy (R2 > 0.70) even when some variables were partially absent (with the number of missing variables < 2 and the missing proportion (MP) ≤ 50 %), making up for the data loss caused by sensor malfunctions. Furthermore, the model performed well (R2 > 0.80) when testing data sourced from paddy fields and lakes. Finally, CO2 and N2O emissions were successfully monitored, with the results validated using a floating chamber method (R2 > 0.70). The method provides crucial technical support for quantitative assessment of water quality and greenhouse gas emissions in paddy-dominated regions, laying a foundation for formulating effective emission reduction strategies.

Pathogenesis of gout: Exploring more therapeutic target.

Gout is a chronic metabolic and immune disease, and its specific pathogenesis is still unclear. When the serum uric acid exceeds its saturation in the blood or tissue fluid, it is converted to monosodium urate crystals, which lead to acute arthritis of varying degrees, urinary stones, or irreversible peripheral joint damage, and in severe cases, impairment of vital organ function. Gout flare is a clinically significant state of acute inflammation in gout. The current treatment is mostly anti-inflammatory analgesics, which have numerous side effects with limited treatment methods. Gout pathogenesis involves many aspects. Therefore, exploring gout pathogenesis from multiple perspectives is conducive to identifying more therapeutic targets and providing safer and more effective alternative treatment options for patients with gout flare. Thus, this article is of great significance for further exploring the pathogenesis of gout. The author summarizes the pathogenesis of gout from four aspects: signaling pathways, inflammatory factors, intestinal flora, and programmed cell death, focusing on exploring more new therapeutic targets.

Models of gouty nephropathy: exploring disease mechanisms and identifying potential therapeutic targets.

Gouty nephropathy (GN) is a metabolic disease with persistently elevated blood uric acid levels. The main manifestations of GN are crystalline kidney stones, chronic interstitial nephritis, and renal fibrosis. Understanding the mechanism of the occurrence and development of GN is crucial to the development of new drugs for prevention and treatment of GN. Currently, most studies exploring the pathogenesis of GN are primarily based on animal and cell models. Numerous studies have shown that inflammation, oxidative stress, and programmed cell death mediated by uric acid and sodium urate are involved in the pathogenesis of GN. In this article, we first review the mechanisms underlying the abnormal intrinsic immune activation and programmed cell death in GN and then describe the characteristics and methods used to develop animal and cell models of GN caused by elevated uric acid and deposited sodium urate crystals. Finally, we propose potential animal models for GN caused by abnormally high uric acid levels, thereby provide a reference for further investigating the methods and mechanisms of GN and developing better prevention and treatment strategies.

An integrative analysis to predict the active compounds and explore polypharmacological mechanisms of Orthosiphon stamineus Benth.

BACKGROUND: Orthosiphon stamineus Benth is a dietary supplement and traditional Chinese herb with widespread clinical applications, but a comprehensive understanding of its active compounds and polypharmacological mechanisms is lacking. This study aimed to systematically investigate the natural compounds and molecular mechanisms of O. stamineus via network pharmacology. METHODS: Information on compounds from O. stamineus was collected via literature retrieval, while physicochemical properties and drug-likeness were evaluated using SwissADME. Protein targets were screened using SwissTargetPrediction, while the compound-target networks were constructed and analyzed via Cytoscape with CytoHubba for seed compounds and core targets. Enrichment analysis and disease ontology analysis were then carried out, generating target-function and compound-target-disease networks to intuitively explore potential pharmacological mechanisms. Lastly, the relationship between active compounds and targets was confirmed via molecular docking and dynamics simulation. RESULTS: A total of 22 key active compounds and 65 targets were identified and the main polypharmacological mechanisms of O. stamineus were addressed. The molecular docking results suggested that nearly all core compounds and their targets possess good binding affinity. In addition, the separation of receptor and ligands was not observed in all dynamics simulation processes, whereas complexes of orthosiphol Z-AR and Y-AR performed best in simulations of molecular dynamics. CONCLUSION: This study successfully identified the polypharmacological mechanisms of the main compounds in O. stamineus, and predicted five seed compounds along with 10 core targets. Moreover, orthosiphol Z, orthosiphol Y, and their derivatives can be utilized as lead compounds for further research and development. The findings here provide improved guidance for subsequent experiments, and we identified potential active compounds for drug discovery or health promotion.