Antagonistic Strain Bacillus velezensis JZ Mediates the Biocontrol of Bacillus altitudinis m-1, a Cause of Leaf Spot Disease in Strawberry.

Biofertilizers are environmentally friendly compounds that can enhance plant growth and substitute for chemically synthesized products. In this research, a new strain of the bacterium Bacillus velezensis, designated JZ, was isolated from the roots of strawberry plants and exhibited potent antagonistic properties against Bacillus altitudinis m-1, a pathogen responsible for leaf spot disease in strawberry. The fermentation broth of JZ exerted an inhibition rate of 47.43% against this pathogen. Using an optimized acid precipitation method, crude extracts of lipopeptides from the JZ fermentation broth were obtained. The crude extract of B. velezensis JZ fermentation broth did not significantly disrupt the cell permeability of B. altitudinis m-1, whereas it notably reduced the Ca2+-ATPase activity on the cell membrane and markedly elevated the intracellular reactive oxygen species (ROS) concentration. To identify the active compounds within the crude extract, QTOF-MS/MS was employed, revealing four antimicrobial compounds: fengycin, iturin, surfactin, and a polyene antibiotic known as bacillaene. The strain JZ also produced various plant-growth-promoting substances, such as protease, IAA, and siderophore, which assists plants to survive under pathogen infection. These findings suggest that the JZ strain holds significant potential as a biological control agent against B. altitudinis, providing a promising avenue for the management of plant bacterial disease.

UBE2L3 promotes benzene-induced hematotoxicity via autophagy-dependent ferroptosis.

Benzene is a common environmental pollutant and significant health hazard. Low-dose benzene exposure is common in most industrial settings, and some workers exhibit hematotoxicity characterized by impaired hematopoietic function. Consequently, understanding the early hematopoietic damage and biomarkers associated with low-dose benzene exposure is of critical importance for health risk assessment. Using data from a 5-year prospective cohort study on benzene exposure and the National Center for Biotechnology Information's Gene Expression Omnibus database, we detected significant downregulation of the ubiquitin-conjugating enzyme UBE2L3 (E2) in benzene-exposed subjects compared to control subjects. Liquid chromatography tandem mass spectrometry and co-immunoprecipitation experiments illustrated the binding interaction between UBE2L3 and the ubiquitin-protein ligase ZNF598 (E3). We applied deep learning algorithms to predict candidate interacting proteins and then conducted validation via co-immunoprecipitation experiments, which showed that ZNF598 engages in binding with the autophagy protein LAMP-2. Subsequent overexpression and knockdown of UBE2L3 coupled with immunofluorescence experiments and transmission electron microscopy revealed that UBE2L3 disrupts the ubiquitination-degradation of LAMP-2 by ZNF598, reduces GPX4 expression levels, and activates an autophagy-dependent ferroptosis pathway. It also leads to increased lipid peroxidation, thereby promoting ferroptosis and contributing to the hematotoxicity induced by benzene. In summary, our results suggest that UBE2L3 may be involved in early hematopoietic damage by modulating the autophagy-dependent ferroptosis signaling pathway in benzene-induced hematotoxicity.

Prognostic significance of exportin-5 in hepatocellular carcinoma.

BACKGROUND: Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related deaths worldwide. As liver cancer often presents no noticeable symptoms in its early stages, most patients are diagnosed at an advanced stage, complicating treatment. Therefore, the identification of new biomarkers is crucial for the early detection and treatment of HCC. Research on exportin-5 (XPO5) could offer new avenues for early diagnosis and improve treatment strategies. AIM: To explore the role of XPO5 in HCC progression and its potential as a prognostic biomarker. METHODS: This study assessed XPO5 mRNA expression in HCC using The Cancer Genome Atlas, TIMER, and International Cancer Genome Consortium databases, correlating it with clinical profiles and disease progression. We performed in vitro experiments to examine the effect of XPO5 on liver cell growth. Gene Set Enrichment Analysis, Kyoto Encyclopedia of Genes and Genomes, and Gene Ontology were used to elucidate the biological roles and signaling pathways. We also evaluated XPO5's impact on immune cell infiltration and validated its prognostic potential using machine learning. RESULTS: XPO5 was significantly upregulated in HCC tissues, correlating with tumor grade, T-stage, and overall survival, indicating poor prognosis. Enrichment analyses linked high XPO5 expression with tumor immunity, particularly CD4 T cell memory activation and macrophage M0 infiltration. Drug sensitivity tests identified potential therapeutic agents such as MG-132, paclitaxel, and WH-4-023. Overexpression of XPO5 in HCC cells, compared to normal liver cells, was confirmed by western blotting and quantitative real-time polymerase chain reaction. The lentiviral transduction-mediated knockdown of XPO5 significantly reduced cell proliferation and metastasis. Among the various machine learning algorithms, the C5.0 decision tree algorithm achieved accuracy rates of 95.5% in the training set and 92.0% in the validation set. CONCLUSION: Our analysis shows that XPO5 expression is a reliable prognostic indicator for patients with HCC and is significantly associated with immune cell infiltration.

Metabolomics of Benzene Exposure and Development of Biomarkers for Exposure Hazard Assessment.

Benzene, a common industrial solvent, poses significant health risks including poisoning and hematopoietic diseases. However, its precise toxicity mechanisms remain unclear. To assess the health impact of prolonged benzene exposure through metabolomic analyses of exposed workers and benzene-poisoned mice, aiming to identify biomarkers and minimize occupational hazards. This study compared 18 benzene-exposed workers with 18 non-exposed workers, matching for age, lifestyle, and BMI. The metabolites in the workers' samples were analyzed using ultra-high-performance liquid chromatography and mass spectrometry. A larger study included 118 exposed and 158 non-exposed workers, incorporating surveys and routine blood and urine tests with differential metabolites targeted via an enzyme-linked immunosorbent assay. The animal studies consisted of two 15- and 60-day benzene staining and control experiments on 28 C57BL/6J mice, followed by sample collection and organ analysis. The data analysis employed eXtensible Computational Mass Spectrometry (XCMS), Python, MetaboAnalyst 6.0, and SPSS24.0. The exposed workers exhibited altered metabolites indicating external benzene exposure, lower glucose levels, and changes in white blood cell counts and urinary ketone bodies. The plasma metabolomics revealed disturbances in energy and lipid metabolism. The benzene-exposed mice displayed reduced weight gain, behavioral changes, and organ damage. Oxidative stress and abnormal purine and lipid metabolism were observed in both the long-term benzene-exposed workers and benzene-exposed mice. Metabolic markers for the early detection of benzene exposure hazards were identified, underscoring the need to mitigate occupational risks.

CASTpFold: Computed Atlas of Surface Topography of the universe of protein Folds.

Geometric and topological properties of protein structures, including surface pockets, interior cavities and cross channels, are of fundamental importance for proteins to carry out their functions. Computed Atlas of Surface Topography of proteins (CASTp) is a widely used web server for locating, delineating, and measuring these geometric and topological properties of protein structures. Recent developments in AI-based protein structure prediction such as AlphaFold2 (AF2) have significantly expanded our knowledge on protein structures. Here we present CASTpFold, a continuation of CASTp that provides accurate and comprehensive identifications and quantifications of protein topography. It now provides (i) results on an expanded database of proteins, including the Protein Data Bank (PDB) and non-singleton representative structures of AlphaFold2 structures, covering 183 million AF2 structures; (ii) functional pockets prediction with corresponding Gene Ontology (GO) terms or Enzyme Commission (EC) numbers for AF2-predicted structures and (iii) pocket similarity search function for surface and protein-protein interface pockets. The CASTpFold web server is freely accessible at https://cfold.bme.uic.edu/castpfold/.

CASTpFold: Computed Atlas of Surface Topography of the universe of protein Folds.

Geometric and topological properties of protein structures, including surface pockets, interior cavities, and cross channels, are of fundamental importance for proteins to carry out their functions. Computed Atlas of Surface Topography of proteins (CASTp) is a widely used web server for locating, delineating, and measuring these geometric and topological properties of protein structures. Recent developments in AI-based protein structure prediction such as AlphaFold2 (AF2) have significantly expanded our knowledge on protein structures. Here we present CASTpFold, a continuation of CASTp that provides accurate and comprehensive identifications and quantifications of protein topography. It now provides (i) results on an expanded database of proteins, including the Protein Data Bank (PDB) and non-singleton representative structures of AlphaFold2 structures, covering 183 million AF2 structures; (ii) functional pockets prediction with corresponding Gene Ontology (GO) terms or Enzyme Commission (EC) numbers for AF2-predicted structures; and (iii) pocket similarity search function for surface and protein-protein interface pockets. The CASTpFold web server is freely accessible at https://cfold.bme.uic.edu/castpfold/.

Optimization of benzene exposure risk assessment: An integrated approach utilizing internal and external concentrations with a focus on biomarkers S-PMA & t, t-MA.

In the majority of occupational settings within China, the concentrations of benzene are observed to fall markedly below the demarcated detection thresholds. Employing traditional risk assessment models, the presence of exceptionally low airborne benzene exposure concentrations may infuse heightened degrees of uncertainty. Consequently, the necessity arises to investigate risk assessment methodologies more apt for the prevalent exposure environment among employees. In the present study, a pharmacokinetic model premised on urinary benzene metabolites (S-PMA and t, t-MA) was employed to ascertain a more precise daily airborne benzene exposure concentration per individual. This value was integrated into the linear multistage model as the 'internal exposure concentration'. In conjunction with the U.S National Environmental Protection Agency's (EPA) inhalation risk assessment model predicated on the external exposure concentration, the Singapore Ministry of Manpower's (MOM) model, and the linear multistage (LMS) model, the carcinogenic and non-carcinogenic effects of benzene were evaluated for 1781 benzene-exposed employees across 76 enterprises in Jiangsu Province. Findings suggest that in the linear multilevel model assessment, the cancer risk levels based on t, t-MA and S-PMA were higher in the printing and recording media reproduction industry, automobile manufacturing industry, general equipment manufacturing industry and the furniture manufacturing industry (median 2.842 × 10-4, 2.819 × 10-4, 2.809 × 10-4, and 2.678 × 10-4), which align more consistently with the actual benzene exposure circumstances of each industry's study participants, with overall risk levels calculated by the linear multistage model exceeding those of the EPA inhalation risk assessment model and the MOM model. This implies that the linear multistage model of internal exposure, based on the reciprocal of benzene biomarkers S-PMA and t, t-MA for airborne benzene exposure, presents enhanced sensitivity and suitability for the current occupational health risk assessment of workers. Without doubt, biomarker-based benzene exposure risk assessment emerges as the optimal choice.

G6PD and machine learning algorithms as prognostic and diagnostic indicators of liver hepatocellular carcinoma.

BACKGROUND: Liver Hepatocellular carcinoma (LIHC) exhibits a high incidence of liver cancer with escalating mortality rates over time. Despite this, the underlying pathogenic mechanism of LIHC remains poorly understood. MATERIALS & METHODS: To address this gap, we conducted a comprehensive investigation into the role of G6PD in LIHC using a combination of bioinformatics analysis with database data and rigorous cell experiments. LIHC samples were obtained from TCGA, ICGC and GEO databases, and the differences in G6PD expression in different tissues were investigated by differential expression analysis, followed by the establishment of Nomogram to determine the percentage of G6PD in causing LIHC by examining the relationship between G6PD and clinical features, and the subsequent validation of the effect of G6PD on the activity, migration, and invasive ability of hepatocellular carcinoma cells by using the low expression of LI-7 and SNU-449. Additionally, we employed machine learning to validate and compare the predictive capacity of four algorithms for LIHC patient prognosis. RESULTS: Our findings revealed significantly elevated G6PD expression levels in liver cancer tissues as compared to normal tissues. Meanwhile, Nomogram and Adaboost, Catboost, and Gbdt Regression analyses showed that G6PD accounted for 46%, 31%, and 49% of the multiple factors leading to LIHC. Furthermore, we observed that G6PD knockdown in hepatocellular carcinoma cells led to reduced proliferation, migration, and invasion abilities. Remarkably, the Decision Tree C5.0 decision tree algorithm demonstrated superior discriminatory performance among the machine learning methods assessed. CONCLUSION: The potential diagnostic utility of G6PD and Decision Tree C5.0 for LIHC opens up a novel avenue for early detection and improved treatment strategies for hepatocellular carcinoma.

Predicting Pathology of Missense Mutations through Protein-Specific Evolutionary Pattern.

Missense mutations, which are single base pair genetic alternation resulting in a different amino acid, are among the most common occurring variants in exon regions of the human genome and may lead to diseases. Thus to assess the effects of missense mutations, it is essential to investigate the evolutionary history of the protein under selection pressures. In this study, we employ a continuous-time Markov model to investigate the evolutionary patterns in protein sequences and a Bayesian Markov chain Monte Carlo method to estimate the substitution rates for protein of interest, from which we obtain scoring matrices. Specifically, we examined the evolutionary patterns of protein sequences containing missense mutations using a species tree to define the phylogeny of the protein of interest. We thoroughly studied the evolutionary pattern of human muscle glycogen phosphorylase containing 127 known missense mutations, and identified characteristic evolutionary patterns in 63 proteins with 2,238 missense mutations, including both deleterious and neutral effects. Our results show that the estimated protein-specific evolutionary pattern-based scoring matrices (PSM) lead to higher sensitivity in detecting the pathological effects of missense mutations, compared to the general evolutionary pattern-based scoring matrix of Blosum62 (BL62) matrix. By incorporating PSM, the performance of a recently released structure-based model SPRI for evaluating missense mutations is further improved.

A novel, effective machine learning-based RNA editing profile for predicting the prognosis of lower-grade gliomas.

Patients with low-grade glioma (LGG) may survive for long time periods, but their tumors often progress to higher-grade lesions. Currently, no cure for LGG is available. A-to-I RNA editing accounts for nearly 90% of all RNA editing events in humans and plays a role in tumorigenesis in various cancers. However, little is known regarding its prognostic role in LGG. On the basis of The Cancer Genome Atlas (TCGA) data, we used LASSO and univariate Cox regression to construct an RNA editing site signature. The results derived from the TCGA dataset were further validated with Gene Expression Omnibus (GEO) and Chinese Glioma Genome Atlas (CGGA) datasets. Five machine learning algorithms (Decision Trees C5.0, XGboost, GBDT, Lightgbm, and Catboost) were used to confirm the prognosis associated with the RNA editing site signature. Finally, we explored immune function, immunotherapy, and potential therapeutic agents in the high- and low-risk groups by using multiple biological prediction websites. A total of 22,739 RNA editing sites were identified, and a signature model consisting of four RNA editing sites (PRKCSH|chr19:11561032, DSEL|chr18:65174489, UGGT1|chr2:128952084, and SOD2|chr6:160101723) was established. Cox regression analysis indicated that the RNA editing signature was an independent prognostic factor, according to the ROC curve (AUC = 0.823), and the nomogram model had good predictive power (C-index = 0.824). In addition, the predictive ability of the RNA editing signature was confirmed with the machine learning model. The sensitivity of PCI-34051 and Elephantin was significantly higher in the high-risk group than the low-risk group, thus potentially providing a marker to predict the effects of lung cancer drug treatment. RNA editing may serve as a novel survival prediction tool, thus offering hope for developing editing-based therapeutic strategies to combat LGG progression. In addition, this tool may help optimize survival risk assessment and individualized care for patients with low-grade gliomas.

Structure-based pathogenicity relationship identifier for predicting effects of single missense variants and discovery of higher-order cancer susceptibility clusters of mutations.

We report the structure-based pathogenicity relationship identifier (SPRI), a novel computational tool for accurate evaluation of pathological effects of missense single mutations and prediction of higher-order spatially organized units of mutational clusters. SPRI can effectively extract properties determining pathogenicity encoded in protein structures, and can identify deleterious missense mutations of germ line origin associated with Mendelian diseases, as well as mutations of somatic origin associated with cancer drivers. It compares favorably to other methods in predicting deleterious mutations. Furthermore, SPRI can discover spatially organized pathogenic higher-order spatial clusters (patHOS) of deleterious mutations, including those of low recurrence, and can be used for discovery of candidate cancer driver genes and driver mutations. We further demonstrate that SPRI can take advantage of AlphaFold2 predicted structures and can be deployed for saturation mutation analysis of the whole human proteome.

A bibliometric and visualization study of Meniere's disease: Current status and global hotspots and emerging trends.

BACKGROUND: Meniere's disease (MD) is a clinical condition characterized by endolymphatic hydrops. Persistent symptoms negatively affect patients mood, and the underlying etiology remains unclear. It is necessary to comprehensively understand the relevant publications, review the history and current status of research, and analyze hotspots and frontiers of research on MD. METHODS: We retrieved literature on Meniere's disease from 2003 to 2022 from the Web of Science database and extracted the data. Data visualization and analysis was conducted using Cite Space, VOS viewer, an online web tool, and Microsoft Office Power Point 2019. RESULTS: In total, 2847 publications were analyzed. The number of annual publications was relatively stable, with an accelerated upward trend over the past 5 years. The country with the most publications was USA (751, 26.38%), while the University of Munich contributed more publications than any other institution (117, 4.11%). The article titled "Diagnostic criteria for Meniere's disease" by Lopez-Escamez J et al in 2015 was the most cited and co-cited publication, and also had the top co-cited references with the strongest citation bursts. Naganawa S was the author with the most publications (85, 2.99%). The top 3 journals and co-cited journals were Otology Neurotology, Acta Oto Laryngologica, and Laryngoscope. Recently, the key theme words were "sensorineural hearing loss," "therapy," "intratympanic injection method," "vestibular-evoked myogenic potentials," "vestibular migraine," "magnetic resonance imaging," and "meniere's disease." CONCLUSIONS: The US has the largest number of publications and research institutions, many European countries have high-quality journals, and Japan has the highest number of scholars. The international opinion on Meniere's disease is relatively uniform. The stepped-therapy for MD is scientific and clear. Intratympanic injection of steroids and intratympanic injection of gentamicin are commonly used, but steroids are considered safer. Saccular dysfunction may be more common in patients with MD than in those with utricular dysfunctions. It is worth paying attention to study the relationship between MD and vestibular migraine through headache. Progress in magnetic resonance imaging technology is still required for the imaging diagnosis of MD.

Machine learning assessment of white blood cell counts in workers exposed to benzene: a historical cohort study.

To explore the fitting effect of the ARIMA, GM(1,1), and RANSAC model in the changes of white blood cells (WBC) in benzene-exposed workers, and select the optimal model to predict the WBC count of workers. Among 350 employees in an aerospace process manufacturing enterprise in Nanjing, workers with 10 years of benzene exposure were selected, and used Excel software to organize the WBC data, and the ARIMA model and RANSAC model were established by R software, and the GM(1, 1) model was established by DPS software, and the magnitude of the mean absolute percentage error (MAPE) of fitting three models to WBC counts was compared. The MAPE based on the ARIMA(2,1,2) model is 6.78%, the MAPE based on the GM(1,1) model is 5.19%, and the MAPE based on the RANSAC model is 6.37%, so the GM( 1,1) model was more suitable for fitting the trend of WBC counts in benzene exposed workers in this study. The GM(1,1) model is suitable for fitting WBC counts in a small sample size and can provide a short-term prediction of WBC counts in benzene-exposed workers and provide basic information for occupational health risk assessment of workers.

Let-7e-5p, a promising novel biomarker for benzene toxicity, is involved in benzene-induced hematopoietic toxicity through targeting caspase-3 and p21.

Benzene is a common industrial chemical and environmental pollutant. However, the mechanism of hematotoxicity caused by exposure to low doses of benzene is unknown. Let-7e-5p pathway regulatory networks were constructed by bioinformatics analysis using a benzene-induced aplastic anemia (BIAA) mouse model. The MTT assay, EdU staining, flow cytometric analysis, dual luciferase reporter gene assay, and RIP assay were utilized to evaluate the effects of benzoquinone (1,4-BQ) on let-7e-5p pathway. This study consisted of 159 workers with a history of low-level benzene exposure and 159 workers with no history of benzene exposure. After the confounding factors were identified, the associations between let-7e-5p expression and hematotoxicity were assessed by multiple linear regression. Furthermore, we used four machine learning algorithms (decision trees, neural network, Bayesian network, and support vector machines) to construct a predictive model for detecting benzene-causing hematotoxicity in workers. In this study, compared with respective controls, let-7e-5p expression was decreased in BIAA mice and benzene-exposed workers. After 1,4-BQ exposure, let-7e-5p overexpression negatively regulated caspase-3 and p21 expression, protected cells from apoptosis, and facilitated cell proliferation. RIP assays, and dual luciferase reporter gene assays confirmed that let-7e-5p could target p21 and caspase-3 and regulate the cell cycle and apoptosis. The support vector machines classifier achieved the best prediction of benzene-induced hematotoxicity (prediction accuracy = 88.27, AUC = 0.83) by statistically characterizing the internal dose of benzene exposure and the oxidative stress index, as well as the expression levels of let-7e-5p pathway-related genes in benzene-exposed workers. Let-7e-5p may be a potential therapeutic target of benzene-induced hematotoxicity, provide a basis for evaluating the health hazards of long-term and low-dose benzene exposure in workers, and supply a reference for revising occupational health standards.

Occupational health risk assessment of the benzene exposure industries: a comprehensive scoring method through 4 health risk assessment models.

Benzene is one of the most common occupational hazards in the working environment which was in the list of group 1 carcinogens. This study applied four occupational health risk assessment models: EPA model; MOM model of Singapore; the International Council on Mining and Metals (ICMM) model, and the Technical guide WS/T 777-2021 of China. The models assessed both non-carcinogenic and carcinogenic effects of benzene for 1629 employees in 50 factories in Jiangsu Province (China) who were exposed to benzene in the working environment and analysis the risk between industries by principal component analysis (PCA) method. The highest occupational health hazard of benzene among the five industries is petroleum processing industry, then followed by chemical products manufacturing industry, special equipment manufacturing industry, wood processing and products industry, and at last the pharmaceutical manufacturing industry. The population of abnormal routine blood parameters in the subjects was mostly in the "wood products industry" group, and the concentration of benzene in "wood products industry" group is the lowest in 5 groups. The industries with low exposure concentration have higher blood abnormality rates; this may be caused by the fact that blood damage is more secretive under low occupational health risk.

[Clinical efficacy of combined therapy in children with stage 4 neuroblastoma]. / 儿童4期神经母细胞瘤联合治疗的临床疗效观察.

OBJECTIVES: To study the early clinical efficacy of combined therapy of stage 4 neuroblastoma. METHODS: A retrospective analysis was performed on the medical data and follow-up data of 14 children with stage 4 neuroblastoma who were diagnosed in Hong Kong University-Shenzhen Hospital from January 2016 to June 2021. RESULTS: The median age of onset was 3 years and 7.5 months in these 14 children. Among these children, 9 had positive results of bone marrow biopsy, 4 had N-Myc gene amplification, 13 had an increase in neuron-specific enolase, and 7 had an increase in vanilmandelic acid in urine. Based on the results of pathological examination, differentiated type was observed in 6 children, undifferentiated type in one child, mixed type, in one child and poorly differentiated type in 6 children. Of all the children, 10 received chemotherapy with the N7 regimen (including 2 children receiving arsenic trioxide in addition) and 4 received chemotherapy with the Rapid COJEC regimen. Thirteen children underwent surgery, 14 received hematopoietic stem cell transplantation, and 10 received radiotherapy. A total of 8 children received Ch14.18/CHO immunotherapy, among whom 1 child discontinued due to anaphylactic shock during immunotherapy, and the other 7 children completed Ch14.18/CHO treatment without serious adverse events, among whom 1 child was treated with Lu177 Dotatate 3 times after recurrence and is still undergoing chemotherapy at present. The median follow-up time was 45 months for all the 14 children. Four children experienced recurrence within 2 years, and the 2-year overall survival rate was 100%; 4 children experienced recurrence within 3 years, and 7 achieved disease-free survival within 3 years. CONCLUSIONS: Multidisciplinary combined therapy is recommended for children with stage 4 neuroblastoma and can help them achieve better survival and prognosis.

Factors Influencing Trace Element Levels in the Blood of Tin Smelting Workers.

BACKGROUND: This study is to assess the correlation between blood concentration ranges of eight elements of tin smelting workers from Guangxi Liuzhou and their job type, working years, age, and sex. METHODS: We collected blood samples of 218 tin smelting workers from a Chinese tin smelting factory and determined the levels of elements by inductively coupled plasma mass spectrometry. RESULTS: Within the blood concentrations of eight metal elements of the objects, the blood concentration of copper and zinc is affected by the job type of comprehensive work; that of arsenic and mercury is affected by refining; and that of chromium, cadmium, and lead is affected by primary smelting. CONCLUSIONS: We present the remarkable influence of four job types on the blood concentration of seven trace elements.

Associations of genetic variation in E3 SUMO-protein ligase CBX4 with noise-induced hearing loss.

Noise-induced hearing loss (NIHL) is a multifactorial disease caused by environmental, genetic and epigenetic variables. SUMOylation is a post-translational modification that regulates biological processes. The objective of this study was to determine the link between genetic variation in the chromobox 4 (CBX4) and the risk of NIHL. This study applied a case-control design with 588 cases and 582 controls, and the sample was predominantly male (93.76%). The T allele of CBX4 rs1285250 was found to be significantly linked with NIHL (P = 0.002) and showed strong associations in both the codominant and recessive models (TT versus CC, P = 0.005; TT/TC versus CC, P = 0.009). By constructing a mouse model of hearing loss because of noise exposure, changes in hearing thresholds were observed in noise-exposed mice, along with a decrease in the number of cochlear hair cells. Furthermore, noise promotes cochlear hair cell apoptosis by inducing SP1/CBX4 pathway activation. Further functional studies demonstrated that SP1 has an influence on the promoter activity of the CBX4 rs1285250 intron, with the promoter activity of the T allele being higher than that of the C allele. Knockdown of transcription factor SP1 reduced the expression of CBX4 expression and simultaneously reduced apoptosis in HEI-OC1 cells. Together, our findings have shown that CBX4 genetic polymorphism rs1285250 T-allele was associated with increased risk of NIHL and might be used as biomarkers for male workers exposed to noise. Furthermore, we speculate that the CBX4 of rs1285250 T-allele leads to a stronger potential enhancer activity from a predicted gain of stronger SP1 binding.

Association between UBAC2 gene polymorphism and the risk of noise-induced hearing loss: a cross-sectional study.

The purpose of this article was to investigate the association between the ubiquitin-associated domain-containing protein 2 (UBAC2) gene polymorphism and noise-induced hearing loss (NIHL) and to further explore the role of single-nucleotide polymorphism (SNP) in UBAC2 in NIHL. A case control study involving 660 NIHL cases and 581 controls was conducted in this research. After genotyping by multiplex polymerase chain reaction (PCR) with next-generation sequencing, the correlation between SNPs and NIHL was analyzed using logistic regression analysis. Haplotype analysis was performed by Haploview 4.1 software. Then luciferase reporter assays and siRNA were used to explore the mechanism of SNPs in UBAC2 affecting NIHL susceptibility. The correlation analysis showed that rs3825427 AA genotype, rs9517701 GG genotype, rs7999348 GG genotype, and rs2296860 AA genotype were all associated with increased risk of NIHL (P < 0.05). The haplotype AGGA (rs3825427-rs9517701-rs7999348-rs2296860) also had a higher risk of NIHL (OR = 1.314; 95% CI, 1.098-1.572; P = 0.003). The results of the luciferase reporter assays showed that the fluorescence intensity of CTCF-OE + UBAC2 WT + TK was significantly higher than that of CTCF-NC + UBAC2 WT + TK and CTCF-OE + UBAC2 MT + TK (all P < 0.01). In CTCF knockdown cells, the expression of UBAC2 was also significantly downregulated (P = 0.0038), indicating that the transcription factor CTCF positively regulated the expression of UBAC2 and the rs3825427 C allele acted as an enhancer, which can promote CTCF to bind to the promoter of UBAC2, thereby promoting transcription. UBAC2 gene polymorphism is related to NIHL susceptibility. The UBAC2 rs3825427 regulates the expression level of UBAC2 by affecting the combination of CTCF and DNA, thus affecting the susceptibility of NIHL.

Application of three prediction models in pesticide poisoning.

To establish a reasonable prediction model of pesticide poisoning and predict the future trend of pesticide poisoning in Jiangsu Province, so as to provide the basis for rational allocation of public health resources and formulation of prevention and control strategies, the number of pesticide poisoning in Jiangsu province from 2006 to 2020 was collected. Grey model (GM(1,1)) model, autoregressive integrated moving average model (ARIMA) model and exponential smoothing model were used for prediction and comparative analysis. Finally, the model with the best fitting effect was selected. The average relative errors of ARIMA(0,1,1)(0,1,0)12 model, Holt-Winters multiplicative model and GM(1,1) were 0.096, 0.058 and 0.274 separately. The fitting effect of GM model is the worst, while the fitting effect of ARIMA(0,1,1) (0,1,0)12 model and Holt-Winters multiplication model is relatively good, which can be basically used for prediction. Holt-Winters multiplicative model has the best fitting effect and the highest accuracy in predicting the number of pesticide poisoning. The numbers of pesticide poisonings in the next 3 years are 454, 410 and 368, with a total of 1232, according to the Holt-Winters multiplicative model. Through the prediction of the number of pesticide poisoning in the next 3 years, this paper also provides a basis for the formulation of pesticide-related policies in the future.