Efficient Extraction and Analysis of Nanoplastics by Ionic Liquid-Assisted Cloud-Point Extraction Coupled with Electromagnetic Heating Pyrolysis Mass Spectrometry.

Nanoplastics have attracted much attention due to their potential hazards. However, analysis of nanoplastics remains challenging. In this study, ionic liquid-assisted cloud-point extraction (IL-assisted CPE) was developed to enrich nanoplastics in the aqueous environment and further coupled with electromagnetic heating pyrolysis mass spectrometry. The use of trace ILs improves the extraction efficiency of CPE for nanoplastics. The effects of ILs (types, contents), nanoplastic properties (type, size), and environmental factors (aging time, humic acid content) were systematically investigated to evaluate the applicability. The limits of detection of poly(methyl methacrylate) (PMMA) and polystyrene (PS) were determined to be 1.78 and 2.67 µg/L, respectively. Real environmental samples including lake water, rainwater, and influent and effluent from wastewater treatment plant were analyzed with good accuracy (79.58-116.87%) and satisfactory precision (RSD ≤ 11.99%). A possible mechanism for ILs being absorbed into the ordered surfactant micellar and generating larger micelles to synergically enclose hydrophobic nanoplastics was proposed. This work provides a simple and efficient approach to the extraction and analysis of nanoplastics in aqueous environments.

Comprehensive transcriptomic analysis and machine learning reveal unique gene expression profiles in patients with immune-mediated necrotizing myopathy.

BACKGROUND: Immune-mediated necrotizing myopathy (IMNM) is an autoimmune myopathy characterized by severe proximal weakness and muscle fiber necrosis, yet its pathogenesis remains unclear. So far, there are few bioinformatics studies on underlying pathogenic genes and infiltrating immune cell profiles of IMNM. Therefore, we aimed to characterize differentially expressed genes (DEGs) and infiltrating cells in IMNM muscle biopsy specimens, which may be useful for elucidating the pathogenesis of IMNM. METHODS: Three datasets (GSE39454, GSE48280 and GSE128470) of gene expression profiling related to IMNM were obtained from the Gene Expression Omnibus database. Data were normalized, and DEG analysis was performed using the limma package. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of DEGs were performed using clusterProfiler. The CIBERSORT algorithm was performed to identify infiltrating cells. Machine learning algorithm and gene set enrichment analysis (GSEA) were performed to find distinctive gene signatures and the underlying signaling pathways of IMNM. RESULTS: DEG analysis identified upregulated and downregulated in IMNM muscle compared to the gene expression levels of other groups. GO and KEGG analysis showed that the pathogenesis of IMNM was notable for the under-representation of pathways that were important in dermatomyositis and inclusion body myositis. Three immune cells (M2 macrophages, resting dendritic cells and resting natural killer cells) with differential infiltration and five key genes (NDUFAF7, POLR2J, CD99, ARF5 and SKAP2) in patients with IMNM were identified through the CIBERSORT and machine learning algorithm. The GSEA results revealed that the key genes were remarkably enriched in diverse immunological and muscle metabolism-related pathways. CONCLUSIONS: We comprehensively explored immunological landscape of IMNM, which is indicative for the research of IMNM pathogenesis.

The nematode effector calreticulin competes with the high mobility group protein OsHMGB1 for binding to the rice calmodulin-like protein OsCML31 to enhance rice susceptibility to Meloidogyne graminicola.

The root-knot nematode Meloidogyne graminicola secretes effectors into rice tissues to modulate host immunity. Here, we characterised MgCRT1, a calreticulin protein of M. graminicola, and identified its target in the plant. In situ hybridisation showed MgCRT1 mRNA accumulating in the subventral oesophageal gland in J2 nematodes. Immunolocalization indicated MgCRT1 localises in the giant cells during parasitism. Host-induced gene silencing of MgCRT1 reduced the infection ability of M. graminicola, while over-expressing MgCRT1 enhanced rice susceptibility to M. graminicola. A yeast two-hybrid approach identified the calmodulin-like protein OsCML31 as an interactor of MgCRT1. OsCML31 interacts with the high mobility group protein OsHMGB1 which is a conserved DNA binding protein. Knockout of OsCML31 or overexpression of OsHMGB1 in rice results in enhanced susceptibility to M. graminicola. In contrast, overexpression of OsCML31 or knockout of OsHMGB1 in rice decreases susceptibility to M. graminicola. The GST-pulldown and luciferase complementation imaging assay showed that MgCRT1 decreases the interaction of OsCML31 and OsHMGB1 in a competitive manner. In conclusion, when M. graminicola infects rice and secretes MgCRT1 into rice, MgCRT1 interacts with OsCML31 and decreases the association of OsCML31 with OsHMGB1, resulting in the release of OsHMGB1 to enhance rice susceptibility.

Context-dependent T-BOX transcription factor family: from biology to targeted therapy.

T-BOX factors belong to an evolutionarily conserved family of transcription factors. T-BOX factors not only play key roles in growth and development but are also involved in immunity, cancer initiation, and progression. Moreover, the same T-BOX molecule exhibits different or even opposite effects in various developmental processes and tumor microenvironments. Understanding the multiple roles of context-dependent T-BOX factors in malignancies is vital for uncovering the potential of T-BOX-targeted cancer therapy. We summarize the physiological roles of T-BOX factors in different developmental processes and their pathological roles observed when their expression is dysregulated. We also discuss their regulatory roles in tumor immune microenvironment (TIME) and the newly arising questions that remain unresolved. This review will help in systematically and comprehensively understanding the vital role of the T-BOX transcription factor family in tumor physiology, pathology, and immunity. The intention is to provide valuable information to support the development of T-BOX-targeted therapy.

Specific enterotype of gut microbiota predicted clinical effect of methotrexate in patients with rheumatoid arthritis.

OBJECTIVE: The most used drug for the treatment of rheumatoid arthritis (RA) remains methotrexate (MTX). Unfortunately, up to 50% of patients do not achieve a clinically adequate outcome. Here we study whether the gut microbiota patterns can aid in the prediction of MTX efficacy for RA. METHOD: To dissect gut microbiome profiles of RA patients (n = 145), 16S rRNA gene sequencing was performed. Dirichlet multinomial mixture (DMM) clustering was used to identify enterotypes at genus level. The relationships between enterotypes and clinical measures (such as lymphocyte subsets and cytokines detected by flow cytometry) were explored. Then, enterotype stability was evaluated by the stratification of the RA patient cohort (n = 66) in Shanghai, China, using the same method. Finally, the enterotype-based gut microbial human index classifier was applied to another independent RA patient cohort (n = 27) to identify the factors associated with MTX clinical response. RESULTS: Our analysis revealed that the RA patients always displayed two different dysbiotic microbiota patterns: RA E1 comprised predominantly Prevotella and RA E2 comprised predominantly Bacteroides. Among all of the lymphocyte subsets and cytokines, only the number of CD8+ T cells showed a significant difference between RA E1 and RA E2. These results were validated in the RA patient cohort in Shanghai, China. Significant associations of RA E1 with clinical response to subsequent MTX treatment were confirmed by another independent RA patient cohort. CONCLUSION: Together, the enterotype-based gut microbial human index (EGMI) classifier was useful to precisely and effectively identify enterotypes of individual RA patients, which could effectively evaluate MTX clinical responses.

Risk factors analysis and risk assessment model construction of systemic lupus erythematosus patients with infection.

OBJECTIVE: To analyze the characteristics of peripheral blood lymphocyte subsets in systemic lupus erythematosus (SLE) patients with infection and non-infection group. Explore the risk factors of infection in SLE patients and establish a risk matrix model to predict the occurrence of co-infection. METHODS: total of 333 SLE patients without infection, 163 patients suffering from infection, and 132 healthy controls (HCs) were recruited. General clinical data and disease activity indicators were collected. The levels of total T, B, CD4+T, CD8+T, NK, Th1, Th2, Th17, and Treg cells in peripheral blood of HCs, SLE patients (including infected and non-infected group) were analyzed by flow cytometry. The risk assessment model was constructed, and the receiver operating characteristic curve was drawn. 39 SLE patients with infection and 20 patients without infection were randomly selected to evaluate the predictive power of the regression model. RESULTS: The levels of T, B, CD4+T, CD8+T, and NK cells in the infected patients were significantly decreased when compared with that of both non-infected patients and HCs (p < .05). The non-infected patients had a higher level of Th17 than that of HCs (p < . 05), but the absolute numbers of Th17 in infected patients was the lowest among the three groups (p < .001). The number of Treg cells in SLE patients was significantly lower than that of HCs (p < .01), and the infected patients had the fewest Treg cells among all these groups (p < . 05). A risk assessment model for SLE with infection was established, p = 1/(1-e-y), Y = 1.763-0.004 × Absolute number of CD4 + T cells-0.005 × Absolute number of NK cells -0.005 × Platelet count(×1012/L) + 1.033 × Absolute number of lymphocytes (×109/L) + 0.023 × C-reactive protein (mg/dL), whose predictive sensitivity is 77.5%, and specificity is 78.3%. CONCLUSION: The new risk assessment model exhibits good predictive ability to assess co-infection risk in SLE patients. T cells, NK cells, and CD4 + T cells along with other parameters help in differentiating Lupus with infection from Lupus alone.

Rapid Estimation of Soil Pb Concentration Based on Spectral Feature Screening and Multi-Strategy Spectral Fusion.

Traditional methods for obtaining soil heavy metal content are expensive, inefficient, and limited in monitoring range. In order to meet the needs of soil environmental quality evaluation and health status assessment, visible near-infrared spectroscopy and XRF spectroscopy for monitoring heavy metal content in soil have attracted much attention, because of their rapid, nondestructive, economical, and environmentally friendly features. The use of either of these spectra alone cannot meet the accuracy requirements of traditional measurements, while the synergistic use of the two spectra can further improve the accuracy of monitoring heavy metal lead content in soil. Therefore, this study applied various spectral transformations and preprocessing to vis-NIR and XRF spectra; used the whale optimization algorithm (WOA) and competitive adaptive re-weighted sampling (CARS) algorithms to identify feature spectra; designed a combination variable model (CVM) based on multi-layer spectral data fusion, which improved the spectral preprocessing and spectral feature screening process to increase the efficiency of spectral fusion; and established a quantitative model for soil Pb concentration using partial least squares regression (PLSR). The estimation performance of three spectral fusion strategies, CVM, outer-product analysis (OPA), and Granger-Ramanathan averaging (GRA), was discussed. The results showed that the accuracy and efficiency of the CARS algorithm in the fused spectra estimation model were superior to those of the WOA algorithm, with an average coefficient of determination (R2) value of 0.9226 and an average root mean square error (RMSE) of 0.1984. The accuracy of the estimation models established, based on the different spectral types, to predict the Pb content of the soil was ranked as follows: the CVM model > the XRF spectral model > the vis-NIR spectral model. Within the CVM fusion strategy, the estimation model based on CARS and PLSR (CARS_D1+D2) performed the best, with R2 and RMSE values of 0.9546 and 0.2035, respectively. Among the three spectral fusion strategies, CVM had the highest accuracy, OPA had the smallest errors, and GRA showed a more balanced performance. This study provides technical means for on-site rapid estimation of Pb content based on multi-source spectral fusion and lays the foundation for subsequent research on dynamic, real-time, and large-scale quantitative monitoring of soil heavy metal pollution using high-spectral remote sensing images.

Diagnosis of COVID-19 via acoustic analysis and artificial intelligence by monitoring breath sounds on smartphones.

Scientific evidence shows that acoustic analysis could be an indicator for diagnosing COVID-19. From analyzing recorded breath sounds on smartphones, it is discovered that patients with COVID-19 have different patterns in both the time domain and frequency domain. These patterns are used in this paper to diagnose the infection of COVID-19. Statistics of the sound signals, analysis in the frequency domain, and Mel-Frequency Cepstral Coefficients (MFCCs) are then calculated and applied in two classifiers, k-Nearest Neighbors (kNN) and Convolutional Neural Network (CNN), to diagnose whether a user is contracted with COVID-19 or not. Test results show that, amazingly, an accuracy of over 97% could be achieved with a CNN classifier and more than 85% on kNN with optimized features. Optimization methods for selecting the best features and using various metrics to evaluate the performance are also demonstrated in this paper. Owing to the high accuracy of the CNN model, the CNN model was implemented in an Android app to diagnose COVID-19 with a probability to indicate the confidence level. The initial medical test shows a similar test result between the method proposed in this paper and the lateral flow method, which indicates that the proposed method is feasible and effective. Because of the use of breath sound and tested on the smartphone, this method could be used by everybody regardless of the availability of other medical resources, which could be a powerful tool for society to diagnose COVID-19.

Isopentenylated Bibenzyls and Phenolic Compounds from Dendrobium chrysotoxum Lindl.

Two new isopentenyl bibenzyls, denchrysotonols A and B (1-2), along with 26 known phenolic compounds, were isolated from the stems of cultivated Dendrobium chrysotoxum Lindl. Their chemical structures were clearly elucidated by extensive spectroscopic analysis. Biological evaluation of isolated compounds revealed that phenanthrenes (14, 16-17, 20, and 22) and fluorenone 25 exhibited anti-inflammatory activities which inhibited nitric oxide (NO) production in LPS-activated RAW264.7 macrophages with the IC50 values ranging from 9.4 to 32.5 µM. Moreover, bibenzyls (1-2 and 7) showed good anti-proliferative activities against triple-negative breast cancer (TNBC) cells (HCC1806, MDA-MB-231, and MB-MB-468) with the IC50 values ranging from 8.1 to 18.6 µM, of which 1 and 2 seemed preferentially inhibit MDA-MB-231 cells.

Identification of novel hub genes associated with gastric cancer using integrated bioinformatics analysis.

BACKGROUND: Gastric cancer (GC) is one of the most common solid malignant tumors worldwide with a high-recurrence-rate. Identifying the molecular signatures and specific biomarkers of GC might provide novel clues for GC prognosis and targeted therapy. METHODS: Gene expression profiles were obtained from the ArrayExpress and Gene Expression Omnibus database. Differentially expressed genes (DEGs) were picked out by R software. The hub genes were screened by cytohubba plugin. Their prognostic values were assessed by Kaplan-Meier survival analyses and the gene expression profiling interactive analysis (GEPIA). Finally, qRT-PCR in GC tissue samples was established to validate these DEGs. RESULTS: Total of 295 DEGs were identified between GC and their corresponding normal adjacent tissue samples in E-MTAB-1440, GSE79973, GSE19826, GSE13911, GSE27342, GSE33335 and GSE56807 datasets, including 117 up-regulated and 178 down-regulated genes. Among them, 7 vital upregulated genes (HMMR, SPP1, FN1, CCNB1, CXCL8, MAD2L1 and CCNA2) were selected. Most of them had a significantly worse prognosis except SPP1. Using qRT-PCR, we validated that their transcriptions in our GC tumor tissue were upregulated except SPP1 and FN1, which correlated with tumor relapse and predicts poorer prognosis in GC patients. CONCLUSIONS: We have identified 5 upregulated DEGs (HMMR, CCNB1, CXCL8, MAD2L1, and CCNA2) in GC patients with poor prognosis using integrated bioinformatical methods, which could be potential biomarkers and therapeutic targets for GC treatment.

Advances in researches on long coronavirus disease in children: a narrative review.

Background and Objective: In the context of the global pandemic of coronavirus disease 2019 (COVID-19), more than 700 million infections and millions of deaths have occurred in countries around the world. Currently, two main sequelae of this disease are considered to occur in children, namely, multi-system inflammatory syndrome in children and long COVID. Among these two, the incidence of long COVID is higher and its impact on the population is more extensive, which is the focus of us. However, due to the lack of relevant studies and the limitations of most studies, the studies on sequelae of COVID-19 infection lag behind those of adults, but they have begun to attract the attention of some clinicians and researchers. We aim to summarize the current knowledge of long COVID in children, helping pediatricians and researchers to better understand this disease and providing guidance on research and clinical treatment of it. Methods: We reviewed all the studies on "long COVID", pediatric, children, adolescent, post-COVID syndrome in PubMed published after 2019. Key Content and Findings: This review summarizes the latest researches on epidemiology, pathogenesis, clinical manifestations, prevention and treatment of long COVID in children. Based on the existing research data, we summarized and analyzed the characteristics of long COVID in children, discovering the means to decipher the diagnosis of COVID-19 in children and some potential therapeutic treatments. Conclusions: We aim to summarize existing research on long COVID in children and help pediatricians and government agencies quickly understand the disease so that it can be used for clinical diagnosis, treatment and prevention in the population. In addition, providing a research basis for further researches on the cellular and even molecular level to explain the occurrence and development of diseases, and has a guiding role for future research direction.

The switch triggering the invasion process: Lipid metabolism in the metastasis of hepatocellular carcinoma.

ABSTRACT: In humans, the liver is a central metabolic organ with a complex and unique histological microenvironment. Hepatocellular carcinoma (HCC), which is a highly aggressive disease with a poor prognosis, accounts for most cases of primary liver cancer. As an emerging hallmark of cancers, metabolic reprogramming acts as a runaway mechanism that disrupts homeostasis of the affected organs, including the liver. Specifically, rewiring of the liver metabolic microenvironment, including lipid metabolism, is driven by HCC cells, propelling the phenotypes of HCC cells, including dissemination, invasion, and even metastasis in return. The resulting formation of this vicious loop facilitates various malignant behaviors of HCC further. However, few articles have comprehensively summarized lipid reprogramming in HCC metastasis. Here, we have reviewed the general situation of the liver microenvironment and the physiological lipid metabolism in the liver, and highlighted the effects of different aspects of lipid metabolism on HCC metastasis to explore the underlying mechanisms. In addition, we have recapitulated promising therapeutic strategies targeting lipid metabolism and the effects of lipid metabolic reprogramming on the efficacy of HCC systematical therapy, aiming to offer new perspectives for targeted therapy.

Cumulative sum learning curve analysis of tubularized incised plate repair for hypospadias: a study of a single surgeon with a single surgical procedure.

Purpose: To ascertain the quantity of instances by which a single surgeon achieves competency and proficiency in using tubularized incised plate (TIP) technique for the repair of distal and mid-shaft hypospadias using the cumulative sum (CUSUM) analysis. Methods: We retrospectively evaluated patients with distal and mid-shaft hypospadias who were treated by a single surgeon between 2015 and 2021, using a single primary TIP technique with a de-epithelialized Byars flap. Data including type of hypospadias, age at surgery, curvature, operation time (OT), length of the reconstructed urethra, and postoperative outcomes were collected and assessed. CUSUM was used to assess the trends in OT and complication rate (CR) in order to generate the learning curve. The evolution of OT and CR can be divided into three phases: learning, competence, and proficiency. Results: CUSUM identified three phases in the learning curves of all TIP repairs. The median OT decreased from 135 min [interquartile range (IQR) = 125-155] to 92 min (IQR = 80-100) (P < 0.001), CR decreased from 28 (28%) to 8 (5.3%) (P < 0.001), and reoperations decreased from 15 (15.2%) to 4 (2.6%) (P < 0.001). According to the CUSUM learning curve, technical competency plateaued after the 99th case, and both OT and CR entered a significantly declining proficiency phase after the 231st case. Further, when the neourethral length exceeded the total average, total complications, urethrocutaneous fistula, and reoperations increased (P = 0.013, P = 0.006, and P = 0.028, respectively). Conclusions: Our study suggests that surgeons performing TIP repair may reach technical competency and achieve proficiency after operating on 99,231 cases, respectively. Moreover, the longer the neourethral length, the higher is the CR.

Detection of nanoplastics released from consumer plastic food containers by electromagnetic heating pyrolysis mass spectrometry.

Nanoplastics released from consumer plastic food containers are emerging environmental pollutants and directly ingested as part of the diet. However, quantification methods for nanoplastics are still lacking. Herein, a rapid identification and mass quantification approach was developed for nanoplastics analysis by combining electromagnetic heating with pyrolysis mass spectrometry (Eh-Py-MS). The pyrolysis products directly entered into the MS, which omits the gas phase separation process and shortens the detection time. A compact pyrolysis chamber was used and this increased the sample transfer efficiency and lowered power requirement. The operational parameters were systematically examined. The influence of nanoplastic size, additive, humic acid, and aging on detection was investigated, and it was concluded that environmental factors (humic acid, aging) and plastic properties (size, additives) did not influence the detection. The developed chamber showed that the limit of detection of polystyrene (PS) nanoplastics was 15.72 ng. Several typical food packages were demonstrated with satisfactory recovery rates (87.5-110%) and precision (RSD ≤11.36%). These results suggested that the consumer plastic food containers are a significant source of direct exposure to nanoplastics in humans from the environment.

Augmenting regulatory T cells: new therapeutic strategy for rheumatoid arthritis.

Rheumatoid arthritis (RA) is a chronic, systemic autoimmune condition marked by inflammation of the joints, degradation of the articular cartilage, and bone resorption. Recent studies found the absolute and relative decreases in circulating regulatory T cells (Tregs) in RA patients. Tregs are a unique type of cells exhibiting immunosuppressive functions, known for expressing the Foxp3 gene. They are instrumental in maintaining immunological tolerance and preventing autoimmunity. Increasing the absolute number and/or enhancing the function of Tregs are effective strategies for treating RA. This article reviews the studies on the mechanisms and targeted therapies related to Tregs in RA, with a view to provide better ideas for the treatment of RA.

Predictive modeling of co-infection in lupus nephritis using multiple machine learning algorithms.

This study aimed to analyze peripheral blood lymphocyte subsets in lupus nephritis (LN) patients and use machine learning (ML) methods to establish an effective algorithm for predicting co-infection in LN. This study included 111 non-infected LN patients, 72 infected LN patients, and 206 healthy controls (HCs). Patient information, infection characteristics, medication, and laboratory indexes were recorded. Eight ML methods were compared to establish a model through a training group and verify the results in a test group. We trained the ML models, including Logistic Regression, Decision Tree, K-Nearest Neighbors, Support Vector Machine, Multi-Layer Perceptron, Random Forest, Ada boost, Extreme Gradient Boosting (XGB), and further evaluated potential predictors of infection. Infected LN patients had significantly decreased levels of T, B, helper T, suppressor T, and natural killer cells compared to non-infected LN patients and HCs. The number of regulatory T cells (Tregs) in LN patients was significantly lower than in HCs, with infected patients having the lowest Tregs count. Among the ML algorithms, XGB demonstrated the highest accuracy and precision for predicting LN infections. The innate and adaptive immune systems are disrupted in LN patients, and monitoring lymphocyte subsets can help prevent and treat infections. The XGB algorithm was recommended for predicting co-infection in LN.

Transcription factor BACH1 in cancer: roles, mechanisms, and prospects for targeted therapy.

Transcription factor BTB domain and CNC homology 1 (BACH1) belongs to the Cap 'n' Collar and basic region Leucine Zipper (CNC-bZIP) family. BACH1 is widely expressed in mammalian tissues, where it regulates epigenetic modifications, heme homeostasis, and oxidative stress. Additionally, it is involved in immune system development. More importantly, BACH1 is highly expressed in and plays a key role in numerous malignant tumors, affecting cellular metabolism, tumor invasion and metastasis, proliferation, different cell death pathways, drug resistance, and the tumor microenvironment. However, few articles systematically summarized the roles of BACH1 in cancer. This review aims to highlight the research status of BACH1 in malignant tumor behaviors, and summarize its role in immune regulation in cancer. Moreover, this review focuses on the potential of BACH1 as a novel therapeutic target and prognostic biomarker. Notably, the mechanisms underlying the roles of BACH1 in ferroptosis, oxidative stress and tumor microenvironment remain to be explored. BACH1 has a dual impact on cancer, which affects the accuracy and efficiency of targeted drug delivery. Finally, the promising directions of future BACH1 research are prospected. A systematical and clear understanding of BACH1 would undoubtedly take us one step closer to facilitating its translation from basic research into the clinic.

Vertical migration behavior simulation and prediction of Pb and Cd in co-contaminated soil around Pb-Zn smelting slag site.

Heavy metal migration in soil poses a serious threat to the soil and groundwater. Understanding the migration pattern of heavy metals (HMs) under different factors could provide a more reasonable position for pollution evaluation and targetoriented treatment of soil heavy metal. In this study, the migration behavior of Pb and Cd in co-contaminated soil under different pH and ionic strength (NaCl concentration) was simulated using convective dispersion equation (CDE). We predicted the migration trends of Pb and Cd in soils after 5, 10, and 20 years via PHREEQC. The results showed that the migration time of Cd in the soil column experiment was about 60 days faster than that of Pb, and the migration trend was much steeper. The CDE was proved to describe the migration behavior of Pb and Cd (R2 > 0.75) in soil. The predicted results showed that Cd migrated to 15-20 cm of soil within 7 years and Pb stayed mainly in the top 0-6 cm of soil within 5 years as the duration of irrigation increased. Overall, our study is expected to provide new insight into the migration of heavy metal in soil ecosystems and guidance for reducing risk of heavy metal in the environment.

SOX on tumors, a comfort or a constraint?

The sex-determining region Y (SRY)-related high-mobility group (HMG) box (SOX) family, composed of 20 transcription factors, is a conserved family with a highly homologous HMG domain. Due to their crucial role in determining cell fate, the dysregulation of SOX family members is closely associated with tumorigenesis, including tumor invasion, metastasis, proliferation, apoptosis, epithelial-mesenchymal transition, stemness and drug resistance. Despite considerable research to investigate the mechanisms and functions of the SOX family, confusion remains regarding aspects such as the role of the SOX family in tumor immune microenvironment (TIME) and contradictory impacts the SOX family exerts on tumors. This review summarizes the physiological function of the SOX family and their multiple roles in tumors, with a focus on the relationship between the SOX family and TIME, aiming to propose their potential role in cancer and promising methods for treatment.