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/.

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.

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.

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.

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.

[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.

Single nucleotide polymorphisms in JNK1 are associated with susceptibility to noise-induced hearing loss in a Chinese population.

PURPOSE: This study intended to explore the effect of C-Jun N-terminal kinases 1 (JNK1) polymorphisms on the sensitivity of individual hearing loss. METHODS: A total of 1333 subjects, including 683 NIHL workers and 650 normal-hearing workers from east China, were included in this cross-sectional study. Genotyping of three JNK1 single nucleotide polymorphisms (rs9284, rs8428, and rs11598320) was performed. The relationship between different genotypes and noise-induced hearing loss was analyzed. RESULTS: Results show that rs11598320 TT genotype was associated with a higher risk of NIHL (OR 1.57, 95% CI 0.91-2.70). Stratified analysis indicated that the rs11598320 AT + AA genotype was associated with a decreased risk of hearing loss in subjects exposed to noise ≤ 16 years or a noise level > 92 dB (OR 0.68, 95% CI 0.50-0.93 and OR 0.64, 95% CI 0.42-0.96, respectively). The rs8428 TT genotype was associated with an increased risk of noise-induced hearing loss when the noise level was > 92 dB (OR 1.73, 95% CI 1.11-2.70). Haplotype TCT (rs9284-rs8424-rs11598320) was associated with an increased risk of noise-induced hearing loss (OR 1.30, 95% CI 1.00-1.68). CONCLUSION: Single nucleotide polymorphisms (rs11598320 and rs8424) in JNK1 can be used as new biomarkers of susceptibility for noise-induced hearing loss in Chinese workers.

Self-poisoning with pesticides in Jiangsu Province, China: a cross-sectional study on 24,602 subjects.

BACKGROUND: With an estimated > 800,000 suicide-related deaths and potentially several attempts for each death in the world. The purpose of this study was to determine the epidemiological characteristics of self-poisoning with pesticides within the Jiangsu province in China. METHODS: Suicide rate was calculated the Routine Surveillance System by Jiangsu Provincial Center for Disease Control and Prevention, stratified by gender, age and region, combined with socioeconomic and agriculture-related factors to investigate trends in suicide over the study period. A logistic regression model was used to investigate the associations between pesticide types and pesticide-related deaths. RESULTS: In recent years, Jiangsu Province has witnessed a decrease in pesticide self-poisoning cases and consequent deaths. Among all suicides by deliberate ingestion of pesticides, the proportion of cases were mainly in the age 40, accounting for 3.43% of all cases with pesticide suicide. The proportion of suicide due to pesticide poisoning in females was markedly higher than that in males (p < 0.001). Suicide using organophosphate and carbamate insecticides was most common, with 10,303 reported cases accounting for 42.02% of all suicides. CONCLUSIONS: For national responses to be effective, the characteristics of pesticide suicides should be comprehensively investigated for the formulation of corresponding prevention strategies. At present, more pesticide suicide prevention policies for the elderly people and women should be implemented, and stronger pesticide management policies should be implemented for rural areas.

Occupational benzene exposure and the risk of genetic damage: a systematic review and meta-analysis.

BACKGROUND: Benzene, an important component of organic solvents, is commonly used in industry. Meanwhile, benzene is a human carcinogen leading to leukemia. Although the links between benzene and various types of genetic damage indicators have been evaluated in several studies, but their results remain inconsistent. So we conducted a meta-analysis, and to explore the influence of low concentration benzene exposure on workers' genetic damage indicators using 3.25 mg/m3 as the boundary value, in order to provide a basis for improved prevention and control of the harm from benzene exposure to the occupational population. METHODS: We conducted a search of five databases, including Pub Med, Web of Science, China National Knowledge Infrastructure (CNKI), Wan Fang Data and Chongqing VIP, to identify relevant articles up to December 25, 2018. Two researchers independently extracted and evaluated the data according to the inclusion and exclusion criteria of the literature. The imported articles were managed by Endnote X7, and the data were extracted and sorted by Excel 2013. We utilized Stata 12.0 software to perform the meta-analysis in the present study. RESULTS: A total of 68 eligible articles were finally included for the synthetic analyses. The meta-analysis results showed that occupational benzene exposure led to significantly increased Micronucleus (MN) frequency, Sister chromatid exchange (SCE) frequency, Chromosome aberration (CA) frequency, Olive Tail moment (OTM), Tail moment (TM), Tail length (TL), and Tail DNA% (T DNA%) compared to the control group (P < 0.05), and the pooled effect value estimates were 1.36, 0.98, 0.76, 1.06, 0.96, 1.78, and 1.42, respectively. Subsequent analysis of the effect of low concentration benzene exposure on genetic damage found significantly increased MN frequency increased compared with the control group (P < 0.05). CONCLUSIONS: Occupational benzene exposure can affect multiple genetic damage indicators. Even at an exposure concentration lower than 3.25 mg/m3, benzene exposure has genotoxicity. These data provide an important scientific basis for the further revision of occupational disease prevention strategies. At the same time, increased attention should be focused on the health monitoring of the occupational population exposed to benzene, and health management should be strengthened to improve the health of the occupational population.

Folding-Degradation Relationship of a Membrane Protein Mediated by the Universally Conserved ATP-Dependent Protease FtsH.

ATP-dependent protein degradation mediated by AAA+ proteases is one of the major cellular pathways for protein quality control and regulation of functional networks. While a majority of studies of protein degradation have focused on water-soluble proteins, it is not well understood how membrane proteins with abnormal conformation are selectively degraded. The knowledge gap stems from the lack of an in vitro system in which detailed molecular mechanisms can be studied as well as difficulties in studying membrane protein folding in lipid bilayers. To quantitatively define the folding-degradation relationship of membrane proteins, we reconstituted the degradation using the conserved membrane-integrated AAA+ protease FtsH as a model degradation machine and the stable helical-bundle membrane protein GlpG as a model substrate in the lipid bilayer environment. We demonstrate that FtsH possesses a substantial ability to actively unfold GlpG, and the degradation significantly depends on the stability and hydrophobicity near the degradation marker. We find that FtsH hydrolyzes 380-550 ATP molecules to degrade one copy of GlpG. Remarkably, FtsH overcomes the dual-energetic burden of substrate unfolding and membrane dislocation with the ATP cost comparable to that for water-soluble substrates by robust ClpAP/XP proteases. The physical principles elucidated in this study provide general insights into membrane protein degradation mediated by ATP-dependent proteolytic systems.

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.

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.

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.

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.

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.

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.

Research development and trends of benzene-induced leukemia from 1990 to 2019-A bibliometric analysis.

Benzene is an occupational and environmental toxicant, causing hematopoietic damage. Our study is aimed to extract the trend of benzene-induced leukemia (BIL) and qualitatively and quantitatively estimate research on it. Publications on BIL were identified from the Web of Science Core Collection (WoSCC). Microsoft Excel 2019 (Redmond, WA) and The CiteSpace 5.6.R5 software (Drexel University, Philadelphia, PA) were used to analyze the publication outcomes, countries, institutions, authors, keywords, and research frontiers. The overall 1152 publications were collected from 1990 to 2019 until November 6, 2020. Environ Health Persp had the highest number of articles published. The USA were the top country in terms of BIL. The Smith MT, Yin SN, Lan Q, and Hayes RB are both listed in the top 10 of co-cited authors, high contribution authors, and the authors of co-cited references. High IF articles account for a considerable proportion, among all the publications. Chinese institutions engaged in BIL and contributed a large part of articles. Exposure population, exposure dose, and exposure risk are the research hotspots in this field. The risk of benzene exposure on childhood leukemia is at issue, and the studies on attributable risk of benzene-induced leukemia are few. More early, sensitive, and specific epigenetic biomarkers of benzolism may be the leading research fields of benzene-induced leukemia in the next few years.

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.