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This study investigated the enhancing effects of soil treatment with ethylene diamine tetraacetic acid (EDTA) and oxalic acid (OA) on the remediation of Cr(VI) contaminated soil by Datura stramonium L. A greenhouse pot experiment was conducted, where Cr(VI) contaminated soil was treated with 100 mg/kg Cr(VI) and varying concentrations of EDTA (5 and 10 mmol/kg) and OA (5 and 10 mmol/kg). The effects of these soil treatments on biomass, chlorophyll content, antioxidant enzyme activities, and Cr(VI) enrichment and translocation efficiency of D. stramonium were evaluated. The results showed that added OA to soil significantly increased the biomass and chlorophyll content of D. stramonium. The addition of 10 mmol/kg of OA to soil increased the plant biomass by 67.16 % and chlorophyll b content by 40.01 %. In addition, OA soil treatment significantly enhanced the activities of superoxide dismutase (SOD) by 6.36 %, peroxidase (POD) by 163.13 %, catalase (CAT) by 36.92 %, and ascorbate peroxidase (APX) by 32.12 %, which effectively alleviated the oxidative stress induced by Cr(VI). In contrast, soil treatment with a high concentration of EDTA (10 mmol/kg) significantly reduced plant biomass and chlorophyll content, although it increased the biological concentration factor (BCF) of the stem and leaf, as well as the translocation factor (TF). In conclusion, appropriate amounts of EDTA and OA added to soil can enhance the phytoremediation efficiency of D. stramonium grown in Cr(VI) contaminated soil, with OA added to soil being more effective than addition of EDTA. This study revealed the potential mechanisms of chelating agents EDTA and OA in enhancing the phytoremediation of Cr(VI) contaminated soil by D. stramonium, providing a scientific basis for further optimization of phytoremediation techniques.

Abstract Background Circulating tumor DNA (ctDNA) has emerged as a promising biomarker for noninvasive cancer diagnostics, particularly in the context of metastatic non‐small‐cell lung cancer (NSCLC). Detecting targetable variants through ctDNA analysis offers the potential to guide treatment decisions, especially in cases where tissue samples are insufficient or unavailable. Method In this study, we developed and validated a next‐generation sequencing panel targeting 101 cancer‐related genes (101‐test) to detect somatic variants in ctDNA from a large cohort of Chinese patients with metastatic NSCLC. The performance of the 101‐test was assessed by evaluating its limit of detection (LOD), accuracy, and precision in identifying molecular variants. Additionally, the concordance between ctDNA and tissue samples for detecting targetable variants was analyzed in 904 patients. Results The 101‐test demonstrated a LOD of 0.38% for single‐nucleotide variants (SNVs), 0.33% for insertions and deletions (InDels), and 0.33% for fusions. Sensitivity was 98.3% for SNVs, 100% for InDels, and 100% for fusions when compared to digital droplet PCR (ddPCR)/breakpoint PCR reference methods. The by‐variant sensitivity for somatic variants was 97.5%, with a specificity of 99.9% between tumor‐only and tumor‐normal analyses. In a real‐world cohort, the concordance between ctDNA and tissue samples for identifying targetable variants was 72.2% (457/633). Notably, the EGFR S768I variant, recently recommended by clinical guidelines, achieved an 80% concordance rate. Furthermore, 4.3% of patients (27/633) with targetable variants were identified exclusively through ctDNA testing. Conclusion The ctDNA‐based 101‐test is a highly sensitive and specific tool for detecting targetable variants in metastatic NSCLC, particularly in cases with insufficient tissue samples. The findings support the use of ctDNA testing as a reliable and complementary method to traditional tissue‐based molecular analysis, enhancing the precision of treatment strategies for NSCLC patients.

Oil pollution is a common type of soil organic pollution that is harmful to the ecosystem. Bioremediation, particularly microbe-assisted phytoremediation of oil-contaminated soil, has become a research hotspot in recent years. In order to explore more appropriate bioremediation strategies for soil oil contamination and the mechanism of remediation, we compared the remediation effects of three plants when applied in combination with a microbial agent and biochar. The combined remediation approach of Tagetes erecta, microbial agent, and biochar exhibited the best plant growth and the highest total petroleum hydrocarbons degradation efficiency (76.60%). In addition, all of the remediation methods provided varying degrees of restoration of carbon and nitrogen contents of soils. High-throughput sequencing found that microbial community diversity and richness were enhanced in most restored soils. Some soil microorganisms associated with oil degradation and plant growth promotion such as Cavicella, C1_B045, Sphingomonas, MND1, Bacillus and Ramlibacter were identified in this study, among which Bacillus was the major component in the microbial agent. Bacillus was positively correlated with all soil remediation indicators tested and was substantially enriched in the rhizosphere of T. erecta. Functional gene prediction of the soil bacterial community based on the KEGG database revealed that pathways of carbohydrate metabolism and amino acid metabolism were up-regulated during remediation of oil-contaminated soils. This study provides a potential method for efficient remediation of oil-contaminated soils and thoroughly examines the biochar–bacteria–plant combined remediation mechanisms of oil-contaminated soil, as well as the combined effects from the perspective of soil bacterial communities.

ABSTRACTPreeclampsia (PE), a pregnancy-specific syndrome, has been associated with the gut bacteriome. Here, to investigate the impact of the gut virome on the development of PE, we identified over 8,000 nonredundant viruses from the fecal metagenomes of 40 early-onset PE and 37 healthy pregnant women and profiled their abundances. Comparison and correlation analysis showed that PE-enriched viruses frequently connected to Blautia species enriched in PE. By contrast, bacteria linked to PE-depleted viruses were often the Bacteroidaceae members such as Bacteroides spp., Phocaeicola spp., Parabacteroides spp., and Alistipes shahii. In terms of viral function, PE-depleted viruses had auxiliary metabolic genes that participated in the metabolism of simple and complex polysaccharides, sulfur metabolism, lipopolysaccharide biosynthesis, and peptidoglycan biosynthesis, while PE-enriched viruses had a gene encoding cyclic pyranopterin monophosphate synthase, which seemed to be special, that participates in the biosynthesis of the molybdenum cofactor. Furthermore, the classification model based on gut viral signatures was developed to discriminate PE patients from healthy controls and showed an area under the receiver operating characteristic curve of 0.922 that was better than that of the bacterium-based model. This study opens up new avenues for further research, providing valuable insights into the PE gut virome and offering potential directions for future mechanistic and therapeutic investigations, with the ultimate goal of improving the diagnosis and management of PE.IMPORTANCEThe importance of this study lies in its exploration of the previously overlooked but potentially critical role of the gut virome in preeclampsia (PE). While the association between PE and the gut bacteriome has been recognized, this research takes a pioneering step into understanding how the gut virome, represented by over 8,000 nonredundant viruses, contributes to this condition. The findings reveal intriguing connections between PE-enriched viruses and specific gut bacteria, such as the prevalence of Blautia species in individuals with PE, contrasting with bacteria linked to PE-depleted viruses, including members of the Bacteroidaceae family. These viral interactions and associations provide a deeper understanding of the complex dynamics at play in PE.

Background: In thyroid cancers, a reduction in the expression of the sodium/iodide symporter (NIS) is observed concomitant with a diminution in cancer cell differentiation. The β-catenin/LEF-1 pathway emerges as a crucial regulatory pathway influencing the functional expression of NIS in human thyroid cancer cells. Further research is required to comprehensively elucidate the role of NIS overexpression in impeding the progression of thyroid cancer cells. Methods: Human papillary thyroid carcinoma (PTC) cell lines, specifically PTC-1 and KTC-1, were subjected to Scratch and Transwell assays, colony formation, and tumor sphere formation tests to investigate invasion and migration, focusing on the impact of NIS overexpression. The assessment involved the use of western blot to analyze the expression levels of β-catenin, NIS, CD133, SRY-related HMG box2 (Sox2), lymphoid enhancer-binding factor 1 (LEF-1), NANOG, octamer-binding transcription factor 4 (Oct4), aldehyde dehydrogenase 1 family, member A1 (ALDH1A1), and epithelial cellular adhesion molecule (EpCAM). Statistical analysis was conducted using SPSS version 20.0, and the graphs were developed using GraphPad Prism 7 (GraphPad Software, Inc.). Results: Our observations revealed that Nthy-ori-3-1 cell lines exhibited notably higher average expression levels of NIS, yet significantly lower levels of LEF-1 and β-catenin compared to PTC-1 and KTC-1 cell lines. Furthermore, the overexpression of β-catenin resulted in reduced binding of LEF-1 to NIF promotion but concurrently increased the expression of NIS. The downregulation of NIS markedly enhanced the expression of ALDH1A1, CD133, OCT4, Nanog, SOX2, and EpCam—all of which are targets within the Wnt/β-catenin signaling pathway. Conversely, the upregulation of NIS suppressed the expression of these proteins. Moreover, cells treated with β-catenin activators demonstrated an increased capability to form more spheroids and displayed heightened aggressiveness. Conversely, the NIS overexpression (OE) group exhibited suppressed abilities in invasion and colony formation. Conclusion: Thyroid cancer cells exhibit diminished expression of NIS, and the invasion and maintenance of stem cells in thyroid cancer cells were hindered by NIS OE through the inhibition of the β-catenin/LEF-1 pathway. Further research is warranted to comprehensively assess this outcome, which holds promise as a potential targeted treatment for thyroid cancer.

Summary: Orexin is a neuropeptide released from hypothalamus regulating feeding, sleeping, arousal, and cardiovascular activity. Past research has demonstrated that orexin receptor 2 (OX2R) agonist infusion in the brain results in sympathoexcitatory responses. Here, we found that epicardial administration of OX2R agonism leads to opposite responses. We proved that OX2R is expressed mainly in DRG neurons and transported to sensory nerve endings innervating the heart. In a capsaicin-induced cardiac sympathetic afferent reflex (CSAR) model, we recorded the calcium influx in DRG neurons, measured heart rate variability, and examined the PVN c-Fos activity to prove that epicardial OX2R agonism administration could attenuate capsaicin-induced CSAR. We further showed that OX2R agonism could partially rescue acute myocardial infarction by reducing sympathetic overactivation. Our data indicate that epicardial application of OX2R agonist exerts a cardioprotective effect by attenuating CSAR. This OX2R-mediated heart-brain axis may provide therapeutic targets for acute cardiovascular diseases.

Inferring small molecule-miRNA associations (MMAs) is crucial for revealing the intricacies of biological processes and disease mechanisms. Deep learning, renowned for its exceptional speed and accuracy, is extensively used for predicting MMAs. However, given their heavy reliance on data, inaccuracies during data collection can make these methods susceptible to noise interference. To address this challenge, we introduce the joint masking and self-supervised (JMSS)-MMA model. This model synergizes graph autoencoders with a probability distribution-based masking strategy, effectively countering the impact of noisy data and enabling precise predictions of unknown MMAs. Operating in a self-supervised manner, it deeply encodes the relationship data of small molecules and miRNA through the graph autoencoder, delving into its latent information. Our masking strategy has successfully reduced data noise, enhancing prediction accuracy. To our knowledge, this is the pioneering integration of a masking strategy with graph autoencoders for MMA prediction. Furthermore, the JMSS-MMA model incorporates a node-degree–based decoder, deepening the understanding of the network’s structure. Experiments on two mainstream datasets confirm the model’s efficiency and precision, and ablation studies further attest to its robustness. We firmly believe that this model will revolutionize drug development, personalized medicine, and biomedical research.

Accurate monitoring of crop drought thresholds at different growth periods is crucial for drought monitoring. In this study, the canopy temperature (Tc) of winter wheat (‘Weilong 169’ variety) during the three main growth periods was extracted from high-resolution thermal and multispectral images taken by a complete unmanned aerial vehicle (UAV) system. Canopy-air temperature difference (ΔT) and statistic Crop Water Stress Index (CWSIsi) indicators were constructed based on Tc. Combined experiment data from the field and drought thresholds for the ΔT and CWSIsi indicators for different drought levels at three main growth periods were monitored. The results showed a strong correlation between the Tc extracted using the NDVI-OTSU method and ground-truth temperature, with an R2 value of 0.94. The CWSIsi was more stable than the ΔT index in monitoring the drought level affecting winter wheat. The threshold ranges of the CWSIsi for different drought levels of winter wheat at three main growth periods were as follows: the jointing–heading period, where the threshold ranges for normal, mild drought, moderate drought, and severe drought are 0.48, respectively; the heading–filling period, where the threshold ranges for normal, and mild, moderate, and severe drought are 0.53, respectively; and the filling–maturation period, where the threshold ranges for normal, mild drought, moderate drought, and severe drought are 0.59, respectively. The UAV thermal threshold method system can improve the accuracy of crop drought monitoring and has considerable potential in crop drought disaster identification.

Background/Aim: Thyroid carcinoma (THCA) is a cancer of the endocrine system that most commonly affects women. Aging-associated genes play a critical role in various cancers. Therefore, we aimed to gain insight into the molecular subtypes of thyroid cancer and whether senescencerelated genes can predict the overall prognosis of THCA patients. Materials and Methods: Thyroid carcinoma (THCA) transcriptome-related expression profiles were obtained from The Cancer Genome Atlas (TCGA) database. These profiles were randomly divided into training and validation subsets at a ratio of 1:1. Unsupervised clustering algorithms were used to compare differences between the two subtypes; prognosisrelated senescence genes were used to further construct our prognostic models by univariate and multivariate Cox analyses and construct a nomogram to predict the 1-, 3-, and 5-year overall survival probability of THCA patients. In addition, we performed gene set enrichment analysis (GSEA) to predict the immune microenvironment and somatic mutations between the different risk groups. Finally, real-time PCR was used to verify the expression levels of key model genes. Results: The 'ConsensusClusterPlus' R package was used to cluster thyroid cancer into two categories (Cluster1 and Cluster2) on the basis of 46 differentially expressed aging-related genes (DE-ARGs); patients in Cluster1 demonstrated a better prognosis than those in Cluster2. Cox analysis was used to screen six prognosis-related DE-ARGs. Finally, our real-time PCR results confirmed our hypothesis. Conclusion: Differences exist between the two subtypes of thyroid cancer that help guide treatment decisions. The six DE-ARG genes have a high predictive value for risk stratifying THCA patients. [ABSTRACT FROM AUTHOR]