Silica-magnesium coupling in lignin-based biochar: A promising remediation for composite heavy metal pollution in environment.

Lignin hydrothermal silica-carbon material served as a backbone for MgCl2 activation to prepare lignin-based silicon/magnesia biochar (ALB/Si-Mg) for Cd2+, Pb2+, Cu2+, and Zn2+ removal from water and soil environment. Characterization studies revealed a 1017.71-fold increase in the specific surface area of ALB/Si-Mg compared to the original lignin biochar (ALB), producing abundant oxygen functional groups (OC-O, Si-O, Mg-O), and mineral matter (Mg2SiO4 and MgO). Crucially, batch adsorption experiments demonstrated that the adsorption capacity of ALB/Si-Mg for Cd2+, Pb2+, Cu2+, and Zn2+ was 848.17, 665.07, 151.84, and 245.78 mg/g, which were 29.09-140.45 times of the ALB. Soil remediation experiments showed that applying ALB/Si-Mg increased soil effective silicon (109.04%-450.2%) and soil exchangeable magnesium (276.41%-878.66%), enhanced plant photosynthesis, and notably reduced the bioavailability of heavy metals in soil as well as the content of heavy metals in Pakchoi, thereby promoting Pakchoi growth and development. The presence of oxygen-containing functional groups on ALB/Si-Mg, along with Mg2SiO4 and MgO nanoparticles, enhanced the adsorption capacity for heavy metals through the promotion of heavy metal precipitation, ion exchange, and complexation mechanisms. This study establishes the groundwork for the coupling of silica and magnesium elements in biochar and the remediation of composite heavy metal environmental pollution.

Screening of Cross-Reactive Aptamers for the Detection of 24 Quinolones by Using a Liebig's Law-Guided Parallel-Series Strategy.

Quinolones, a widely used class of antibiotics, present significant environmental and health concerns if they excessively remain in the environment and in food. Aptamers specific to quinolones can be applied as bioreceptors for the detection of quinolone residues in the environment and food. The quinolone family contains dozens of different individuals that share the same core structure coupled with various substituents at six different positions. The diversity and complexity of the substitution sites make it a challenge to choose a set of representative molecules that encompass all the desired sites and preserve the core molecular framework for the screening of quinolone-specific aptamers via systematic evolution of ligands by exponential enrichment (SELEX). To address this challenge, we introduce a novel parallel-series strategy guided by Liebig's law for isolating quinolone-specific cross-reactive aptamers by using the library-immobilized SELEX method. Through this approach, we successfully identified 5 aptamers (Apt.AQ01-Apt.AQ05) with high binding affinity and excellent specificity to 24 different quinolone individuals. Among them, Apt.AQ03 showcased optimal performance with affinities ranging from 0.14 to 1.07 µM across the comprehensive set of 24 quinolones, exhibiting excellent specificity against nontarget interferents. The binding performance of Apt.AQ03 was further characterized with microscale thermophoresis, circular dichroism spectra, and an exonuclease digestion assay. By using Apt.AQ03 as a bioreceptor, a fluorescence resonance energy transfer (FRET) aptasensor was developed for the detection of 24 quinolones in milk, achieving a remarkable detection limit of 14.5-21.8 ng/mL. This work not only establishes a robust and effective strategy for selecting cross-reactive aptamers applicable to other small-molecule families but also provides high-quality aptamers for developing various high-throughput and reliable methods for the detection of multiple quinolone residues in food.

Conditional knockdown of OsMLH1 to improve plant prime editing systems without disturbing fertility in rice.

BACKGROUND: High-efficiency prime editing (PE) is desirable for precise genome manipulation. The activity of mammalian PE systems can be largely improved by inhibiting DNA mismatch repair by coexpressing a dominant-negative variant of MLH1. However, this strategy has not been widely used for PE optimization in plants, possibly because of its less conspicuous effects and inconsistent performance at different sites. RESULTS: We show that direct RNAi knockdown of OsMLH1 in an ePE5c system increases the efficiency of our most recently updated PE tool by 1.30- to 2.11-fold in stably transformed rice cells, resulting in as many as 85.42% homozygous mutants in the T0 generation. The high specificity of ePE5c is revealed by whole-genome sequencing. To overcome the partial sterility induced by OsMLH1 knockdown of ePE5c, a conditional excision system is introduced to remove the RNAi module by Cre-mediated site-specific recombination. Using a simple approach of enriching excision events, we generate 100% RNAi module-free plants in the T0 generation. The increase in efficiency due to OsMLH1 knockdown is maintained in the excised plants, whose fertility is not impaired. CONCLUSIONS: This study provides a safe and reliable plant PE optimization strategy for improving editing efficiency without disturbing plant development via transient MMR inhibition with an excisable RNAi module of MLH1.

Construction of CDKN2A-related competitive endogenous RNA network and identification of GAS5 as a prognostic indicator for hepatocellular carcinoma.

BACKGROUND: Competitive endogenous RNA (ceRNA) is an innovative way of gene expression modulation, which plays a crucial part in neoplasia. However, the intricacy and behavioral characteristics of the ceRNA network in hepatocellular carcinoma (HCC) remain dismal. AIM: To establish a cyclin dependent kinase inhibitor 2A (CDKN2A)-related ceRNA network and recognize potential prognostic indicators for HCC. METHODS: The mutation landscape of CDKN2A in HCC was first explored using the cBioPortal database. Differential expression analysis was implemented between CDKN2Ahigh and CDKN2Alow expression HCC samples. The targeted microRNAs were predicted by lncBasev3.0, and the targeted mRNAs were predicted by miRDB, and Targetscan database. The univariate and multivariate analysis were utilized to identify independent prognostic indicators. RESULTS: CDKN2A was frequently mutated and deleted in HCC. The single-cell RNA-sequencing analysis revealed that CDKN2A participated in cell cycle pathways. The CDKN2A-related ceRNA network-growth arrest specific 5 (GAS5)/miR-25-3p/SRY-box transcription factor 11 (SOX11) was successfully established. GAS5 was recognized as an independent prognostic biomarker, whose overexpression was correlated with a poor prognosis in HCC patients. The association between GAS5 expression and methylation, immune infiltration was explored. Besides, traditional Chinese medicine effective components targeting GAS5 were obtained. CONCLUSION: This CDKN2A-related ceRNA network provides innovative insights into the molecular mechanism of HCC formation and progression. Moreover, GAS5 might be a significant prognostic biomarker and therapeutic target in HCC.

Three-Component Reaction for the Synthesis of Spiro-Heterocycles from Isatins, Substituted Ureas, and Cyclic Ketones.

We reported an efficient three-component reaction to access new spiro heterocycles through the annulation reactions of isatins, substituted ureas, and cyclic ketones under normal laboratory conditions, which is another example of isatins being used to build spiro compounds by the ring-opening and recyclization processes. The wide range of substrates, simple operation, normal experimental conditions, and high yields make the approach of high practical value.

Estimation of potential wildfire behavior characteristics to assess wildfire danger in southwest China using deep learning schemes.

Accurate estimation of potential wildfire behavior characteristics (PWBC) can improve wildfire danger assessment. However, wildfire behavior has been estimated by most fire spread models with immeasurable uncertainties and difficulties in large-scale applications. In this study, a PWBC estimation model (named PWBC-QR-BiLSTM) was proposed by coupling the Bi-directional Long Short-Term Memory (BiLSTM) and quantile regression (QR) methods. Multi-source data, including fuel, weather, topography, infrastructure, and landscape variables, were input into the PWBC-QR-BiLSTM model to estimate the potential rate of spread (ROS) and fire radiative power (FRP) over western Sichuan of China, and then to estimate the probability density of ROS and FRP. Daily ROS and FRP were extracted from the Global Fire Atlas and the MOD14A1/MYD14A1 product. The optimal PWBC-QR-BiLSTM model was determined using the Non-dominated Sorting Genetic Algorithm Ⅱ (NAGA-Ⅱ). Results showed that the PWBC-QR-BiLSTM performed well in estimating potential ROS and FRP with high accuracy (ROS: R2 > 0.7 and MAPE<30%, FRP: R2 > 0.8 and MAPE<25%). The modal PWBC values extracted from the estimated probability density were closer to the observed values, which can be regarded as a good indicator for wildfire danger assessment. The variable importance analysis also verified that fuel and infrastructure variables played an important role in driving wildfire behavior. This study suggests the potential of utilizing artificial intelligence to estimate PWBC and its probability density to improve the guidance on wildfire management.

Safety Stressors and Coal Miners' Safety Performance: The Mediating Role of Resilience and Coping Styles.

Purpose: This study aimed to investigate the effects of three different safety stressors (safety role ambiguity, safety role conflicts, and safety interpersonal conflicts) on safety performance of coal miners under the mediating role of resilience and coping styles. Patients and Methods: The study is cross-sectional. To collect data to analyze the hypothesized relationships in the present study, a total of 450 questionnaires were distributed to coal miners in Shannxi Province of China. Regression analysis was employed as the main statistical technique in analyzing the data using SPSS 22.0 and Process 4.1. Results: The results of regression analysis indicate that the three kind of safety stressors have a negative predictive effect on coal miners' safety performance. Resilience and coping styles both were the mediating variables between the safety stressors (safety role ambiguity, safety role conflicts, and safety interpersonal conflicts) and coal miners' safety performance, and resilience and coping styles play a chain mediating role between the safety stressors (safety role ambiguity, safety role conflicts, and safety interpersonal conflicts) and safety performance of coal miners. Conclusion: This study further explores the mechanism between safety stressors and safety performance, providing theoretical guidance for improving the safety performance of coal mines. It emphasizes the importance of coal miner's resilience intervention, positive coping styles promotion, and negative coping styles prevention in coal mine safety production.

Gas explosion early warning method in coal mines by intelligent mining system and multivariate data analysis.

In order to predict gas explosion disasters rapidly and accurately, this study utilizes real-time data collected from the intelligent mining system, including mine safety monitoring, personnel positioning, and video surveillance. Firstly, the coal mine disaster system is decomposed into sub-systems of disaster-causing factors, disaster-prone environments, and vulnerable bodies, establishing an early warning index system for gas explosion disasters. Then, a training set is randomly selected from known coal mine samples, and the training sample set is processed and analyzed using Matlab software. Subsequently, a training model based on the random forest classification algorithm is constructed, and the model is optimized using two parameters, Mtry and Ntree. Finally, the constructed random forest-based gas explosion early warning model is compared with a classification model based on the support vector machine (SVM) algorithm. Specific coal mine case studies are conducted to verify the applicability of the optimized random forest algorithm. The experimental results demonstrate that: The optimized random forest model has achieved 100% accuracy in predicting gas explosion disaster of coal mines, while the accuracy of SVM model is only 75%. The optimized model also shows lower model error and relative error, which proves its high performance in early warning of coal mine gas explosion. This study innovatively combines intelligent mining system with multidimensional data analysis, which provides a new method for coal mine safety management.

Construction of a Novel Cuproptosis-Related ceRNA Network-SNHG3/miR-1306-5p/PDHA1 and Identification of SNHG3 as a Prognostic Biomarker in Hepatocellular Carcinoma.

The crucial role of competitive endogenous RNA (ceRNA) in the malignant biological behavior of tumors has been certificated. Nevertheless, the detailed function and molecular mechanism of ceRNA associated with cuproptosis in hepatocellular carcinoma (HCC) remains dismal. In this study, we first constructed a protein-protein interaction network and identified the module with the highest degree of aggregation degree. DLAT and PDHA1 were screened out of the module after differential expression and survival analysis. Next, we reverse-predicted the upstream miRNA and lncRNA from mRNA (DLAT, PDHA1) and successfully established the ceRNA network-SNHG3/miR-1306-5p/PDHA1. SNHG3 was identified to be an independent prognostic biomarker based on the outcome of univariate and multivariate Cox analyses. Subsequently, we implemented methylation, immune infiltration, and drug sensitivity analysis to investigate the potential biological functions of SNHG3 in HCC. In addition, SNHG3 expression was upregulated in liver cancer cell lines. In vitro functional assay revealed that SNHG3 knockdown significantly attenuated proliferation, migration, and invasion of liver cancer cells. In summary, SNHG3 exhibited oncogenic characterization via sponging miR-1306-5p to regulate PDHA1, which might function as a promising prognostic indicator and a potential therapeutic target for HCC and shed new light on the molecular mechanism of HCC progression.

The biological functions and metabolic pathways of valine in swine.

Valine is an essential amino acid and a type of branched-chain amino acid. Due to the involvement of branched-chain amino acids in various metabolic pathways, there has been a surge of interests in valine nutrition and its role in animal physiology. In pigs, the interactions between valine and other branched-chain amino acids or aromatic amino acids are complex. In this review, we delve into the interaction mechanism, metabolic pathways, and biological functions of valine. Appropriate valine supplementation not only enhances growth and reproductive performances, but also modulates gut microbiota and immune functions. Based on past observations and interpretations, we provide recommended feed levels of valine for weaned piglets, growing pigs, gilts, lactating sows, barrows and entire males. The summarized valine nutrient requirements for pigs at different stages offer valuable insights for future research and practical applications in animal husbandry.

RDD-YOLOv5: Road Defect Detection Algorithm with Self-Attention Based on Unmanned Aerial Vehicle Inspection.

Road defect detection is a crucial aspect of road maintenance projects, but traditional manual methods are time-consuming, labor-intensive, and lack accuracy. Leveraging deep learning frameworks for object detection offers a promising solution to these challenges. However, the complexity of backgrounds, low resolution, and similarity of cracks make detecting road cracks with high accuracy challenging. To address these issues, a novel road crack detection algorithm, termed Road Defect Detection YOLOv5 (RDD-YOLOv5), was proposed. Firstly, a model was proposed to integrate the transformer structure and explicit vision center to capture the long-distance dependency and aggregate key characteristics. Additionally, the Sigmoid-weighted linear activations in YOLOv5 were replaced with the Gaussian Error Linear Units to enhance the model's nonlinear fitting capability. To evaluate the algorithm's performance, a UAV flight platform was constructed, and experimental freebies were provided to boost inspection efficiency. The experimental results demonstrate the effectiveness of RDD-YOLOv5, achieving a mean average precision of 91.48%, surpassing the original YOLOv5 by 2.5%. The proposed model proves its ability to accurately identify road cracks, even under challenging and complex traffic backgrounds. This advancement in road crack detection technology has significant implications for improving road maintenance and safety.

Co-delivery of azithromycin and ibuprofen by ROS-responsive polymer nanoparticles synergistically attenuates the acute lung injury.

Bacterial infection causes lung inflammation and recruitment of several inflammatory factors that may result in acute lung injury (ALI). During bacterial infection, reactive oxygen species (ROS) and other signaling pathways are activated, which intensify inflammation and increase ALI-related mortality and morbidity. To improve the ALI therapy outcome, it is imperative clinically to manage bacterial infection and excessive inflammation simultaneously. Herein, a synergistic nanoplatform (AZI+IBF@NPs) constituted of ROS-responsive polymers (PFTU), and antibiotic (azithromycin, AZI) and anti-inflammatory drug (ibuprofen, IBF) was developed to enable an antioxidative effect, eliminate bacteria, and modulate the inflammatory milieu in ALI. The ROS-responsive NPs (PFTU NPs) loaded with dual-drugs (AZI and IBF) scavenged excessive ROS efficiently both in vitro and in vivo. The AZI+IBF@NPs eradicated Pseudomonas aeruginosa (PA) bacterial strain successfully. To imitate the entry of bacterial-derived compounds in body, a lipopolysaccharide (LPS) model was adopted. The administration of AZI+IBF@NPs via the tail veins dramatically reduced the number of neutrophils, significantly reduced cell apoptosis and total protein concentration in vivo. Furthermore, nucleotide oligomerization domain-like receptor thermal protein domain associated protein 3 (NLRP3) and Interleukin-1 beta (IL-1ß) expressions were most effectively inhibited by the AZI+IBF@NPs. These findings present a novel nanoplatform for the effective treatment of ALI.

Diagnostic value of mesenteric CTA combined with D-dimer level and inflammatory factor changes in severity of mesenteric artery embolism.

Objective: To investigate the value of mesenteric CTA combined with D-dimer (DD) level and inflammatory factor changes in evaluating the severity of mesenteric artery embolism. Methods: This is a retrospective study. The imaging data of mesenteric CTA and the levels of plasma DD and inflammatory factors in 120 patients with mesenteric artery embolism confirmed by DSA or surgery in Baoding No.1 Central Hospital were analyzed retrospectively from January 2021 to December 2022. The coincidence rate of CTA alone and CTA combined with DD and inflammatory factors with the results of surgery or DSA was compared and analyzed. The specificity, sensitivity and accuracy of CTA alone and CTA combined with DD and inflammatory factors in diagnosing superior mesenteric artery embolism were compared. The correlations of different severity of mesenteric artery embolism with DD and inflammatory factor levels were compared and analyzed. Results: There was a significant difference in the coincidence rate between CTA diagnosis and CTA combined with DD and inflammatory factors diagnosis (p= 0.01). And the sensitivity and accuracy of the latter were significantly higher than those of the former (sensitivity, p= 0.01; accuracy, p= 0.00). The levels of plasma DD, TNF-a, CRP and IL-6 in the intestinal wall thinning group were significantly higher than those in the thickening group (p= 0.00). The above indexes increased significantly in the decreased intestinal wall enhancement group compared with the increased intestinal wall enhancement group (p= 0.00). DD, TNF-ɑ, CRP and IL-6 levels increased with the increase in stenosis severity. Conclusion: Mesenteric CTA combined with plasma DD and inflammatory factor levels can effectively determine the severity of mesenteric arterial embolism, and provide a scientific basis for early clinical diagnosis and treatment.

The role of KPNA2 as a monotonically changing differentially expressed gene in the diagnosis, risk stratification, and chemotherapy sensitivity of chronic hepatitis B-liver cirrhosis-hepatocellular carcinoma.

PURPOSE: Chronic hepatitis B-liver cirrhosis-hepatocellular carcinoma (CLH), commonly called the "liver cancer trilogy", is a crucial evolutionary phase in the emergence of hepatocellular carcinoma (HCC) in China. Previous studies on early diagnostic biomarkers of HCC were limited to the end-stage of HCC and did not focus on the evolutionary process of CLH. METHODS: 11 monotonically changing differentially expressed genes (MCDEGs) highly correlated with CLH were screened through bioinformatic analysis and KPNA2 was identified for further research. The serum KPNA2 expression in different CLH states was detected by Enzyme linked immunosorbent assay (ELISA). A nomogram model was constructed using univariate and multivariate Cox regression methods. RESULTS: The single-cell RNA-seq and bulk RNA-seq revealed that KPNA2 related to immune infiltration in HCC and may participate in cell cycle pathways in HCC. The serum KPNA2 expression was monotonically upregulated in CLH and was valuable for diagnosing different CLH states. Besides, chronic hepatitis B(CHB) patients, liver cirrhosis (LC) patients, and HCC patients were classified into subgroups with distinct serum KPNA2 expressions. Accordingly, patients with different serum KPNA2 expressions displayed various clinicopathological features. The AUC value of the nomogram model was 0.959 in predicting the likelihood of developing HCC in CHB patients or LC patients. Finally, we found that KPNA2 expression was negatively correlated with the IC50 of four chemotherapeutic drugs in HCC. CONCLUSION: KPNA2 was a novel serum biomarker for diagnosing different CLH states, monitoring the dynamic evolution of CLH, and a new therapeutic target for intervening in the progression of CLH.

Advances in MicroRNA Therapy for Heart Failure: Clinical Trials, Preclinical Studies, and Controversies.

Heart failure (HF) is a rapidly growing public health issue with more than 37.7 million patients worldwide and an annual healthcare cost of $108 billion. However, HF-related drugs have not changed significantly for decades, and it is essential to find biological drugs to provide better treatment for HF patients. MicroRNAs (miRNAs) are non-coding RNAs (ncRNAs) with a length of approximately 21 nucleotides and play an important role in the onset and progression of cardiovascular diseases. Increasing studies have shown that miRNAs are widely involved in the pathophysiology of HF, and the regulation of miRNAs has promising therapeutic effects. Among them, there is great interest in miRNA-132, since the encouraging success of anti-miRNA-132 therapy in a phase 1b clinical trial in 2020. However, it is worth noting that the multi-target effect of miRNA may produce side effects such as thrombocytopenia, revascularization dysfunction, severe immune response, and even death. Advances in drug delivery modalities, delivery vehicles, chemical modifications, and plant-derived miRNAs are expected to address safety concerns and further improve miRNA therapy. Here, we reviewed the preclinical studies and clinical trials of HF-related miRNAs (especially miRNA-132) in the past 5 years and summarized the controversies of miRNA therapy.

Research progress on anti-stress nutrition strategies in swine.

In swine production, stress is a common encounter that leads to serious bacterial infection and adverse effects on growth performance. Though antibiotics have been frequently used to control pathogen spread, sustained negative impacts from antibiotics have been found to affect intestinal integrity and the immune system. Multiple nutritional strategies have shown potential to counteract stress and replace antibiotics, including functional amino acids, low protein diet, plant extracts, organic acids, prebiotics, probiotics, minerals and vitamins. These additives relieve the stress response in swine via different mechanisms and signal transduction pathways. Based on the overview of signaling pathways and stress models, this review highlights the potential of nutritional strategies in swine for preventing or treating stress-related health problems. For wider application in the pig industry, the dose ranges measured require for further validation in different physiological contexts and formulations. In the future, microfluid devices and novel stress models are expected to enhance the efficiency of screening for new anti-stress candidates.

Biosynthetic Gene Clusters from Swine Gut Microbiome.

The abuse of antibiotics has become a serious health challenge in the veterinary field. It creates environmental selection pressure on bacteria and facilitates the rapid spread of antibiotic resistance genes. The speed of discovery and application of cost-effective alternatives to antibiotics is slow in pig production. Natural products from biosynthetic gene clusters (BGCs) represent promising therapeutic agents for animal and human health and have attracted extraordinary passion from researchers due to their ability to participate in biofilm inhibition, stress resistance, and the killing of competitors. In this study, we detected the presence of diverse secondary metabolite genes in porcine intestines through sequence alignment in the antiSMASH database. After comparing variations in microbial BGCs' composition between the ileum and the colon, it was found that the abundance of the resorcinol gene cluster was elevated in the ileal microbiome, whereas the gene cluster of arylpolyene was enriched in the colonic microbiome. The investigation of BGCs' diversity and composition differences between the ileal and colonic microbiomes provided novel insights into further utilizing BGCs in livestock. The importance of BGCs in gut microbiota deserves more attention for promoting healthy swine production.