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.

Integrated mRNA and microRNA profiling in lung tissue and blood from human silicosis.

BACKGROUND: The overwhelming majority of subjects in the current silicosis mRNA and microRNA (miRNA) expression profile are of human blood, lung cells or a rat model, which puts limits on the understanding of silicosis pathogenesis and therapy. To address the limitations, our investigation was focused on differentially expressed mRNA and miRNA profiles in lung tissue from silicosis patients to explore potential biomarker for early detection of silicosis. METHODS: A transcriptome study was conducted based on lung tissue from 15 silicosis patients and eight normal people, and blood samples from 404 silicosis patients and 177 normal people. Three early stage silicosis, five advanced silicosis and four normal lung tissues were randomly selected for microarray processing and analyze. The differentially expressed mRNAs were further used to conduct Gene Ontology and pathway analyses. Series test of cluster was performed to explore possible changes in differentially expressed mRNA and miRNA expression patterns during the process of silicosis. The blood samples and remaining lung tissues were used in a quantitative real-time PCR (RT-qPCR) (RT-qPCR). RESULTS: In total, 1417 and 241 differentially expressed mRNAs and miRNAs were identified between lung tissue from silicosis patients and normal people (p < 0.05). However, there was no significant difference in most mRNA or miRNA expression between early stage and advanced stage silicosis lung tissues. RT-qPCR validation results in lung tissues showed expression of four mRNAs (HIF1A, SOCS3, GNAI3 and PTEN) and seven miRNAs was significantly down-regulated compared to those of control group. Nevertheless, PTEN and GNAI3 expression was significantly up-regulated (p < 0.001) in blood samples. The bisulfite sequencing PCR demonstrated that PTEN had significantly decreased the methylation rate in blood samples of silicosis patients. CONCLUSIONS: PTEN might be a potential biomarker for silicosis as a result of low methylation in the blood.

The prevalence and risk factors of work related musculoskeletal disorders among electronics manufacturing workers: a cross-sectional analytical study in China.

OBJECTIVES: To describe the prevalence of self-reported musculoskeletal disorders among workers in the electronics manufacturing industry and to investigate the relations between work-related musculoskeletal disorders (WMSDs) and work-related variables. METHODS: An interview-based questionnaire survey was carried out in thirty electronics manufacturing factories in China in 2018. The prevalence of WMSDs was estimated using the modified Nordic Musculoskeletal Questionnaire (NMQ). A multivariate logistic regression model was applied to evaluate the effects of risk factors on WMSDs on multiple body parts. RESULTS: The 12-month prevalence of WMSDs among participants was 40.6%, and the common body sites affected were the neck (26.8%), shoulder (22.8%), upper back (14.9%), and lower back (14.8%). The results of logistic regression showed that female adults, > 5 job tenure and work-related factors (including awkward posture, lifting or carrying weights, excessive repetition, prolonged sitting, monotonous work and working under conditions of cold or temperature variations) led to a higher risk of WMSDs on most body parts. Upper back, wrist/hand and elbow pain levels were significantly higher for workers with vibration. However, more frequently, physical exercise was a protective factor against WMSDs on most body parts except the upper back, leg and knee. CONCLUSIONS: The study indicates a high prevalence of musculoskeletal pain among the electronics manufacturing industry in China. Different personal and work factors are related to the occurrence of WMSD on different body parts. Preventive measures should be implemented based on the characteristics of WMSD in the electronic manufacturing industry. Furthermore, the training and intervention guidance of ergonomic hazards in the workplace need to be strengthened by understanding the impact of bad posture, avoiding long-term sitting posture and increasing physical activities.

Lead acetate induces cartilage defects and bone loss in zebrafish embryos by disrupting the GH/IGF-1 axis.

Skeletal system toxicity due to lead exposure has attracted extensive attention in recent years, but few studies focus on the skeletal toxicity of lead in the early life stages of zebrafish. The endocrine system, especially the GH/IGF-1 axis, plays an important role in bone development and bone health of zebrafish in the early life. In the present study, we investigated whether lead acetate (PbAc) affected the GH/IGF-1 axis, thereby causing skeletal toxicity in zebrafish embryos. Zebrafish embryos were exposed to lead PbAc between 2 and 120 h post fertilization (hpf). At 120 hpf, we measured developmental indices, such as survival, deformity, heart rate, and body length, and assessed skeletal development by Alcian Blue and Alizarin Red staining and the expression levels of bone-related genes. The levels of GH and IGF-1 and the expression levels of GH/IGF-1 axis-related genes were also detected. Our data showed that the LC50 of PbAc for 120 h was 41 mg/L. Compared with the control group (0 mg/L PbAc), after PbAc exposure, the deformity rate increased, the heart rate decreased, and the body length was shortened at various time periods, in the 20-mg/L group at 120 hpf, the deformity rate increased by 50 fold, the heart rate decreased by 34%, and the body length shortened by 17%. PbAc altered cartilage structures and exacerbated bone loss in zebrafish embryos; in addition, PbAc exposure down-regulated the expression of chondrocyte (sox9a, sox9b), osteoblast (bmp2, runx2) and bone mineralization-related genes (sparc, bglap), and up-regulated the expression of osteoclast marker genes (rankl, mcsf). The GH level increased and the IGF-1 level declined significantly. The GH/IGF-1 axis related genes (ghra, ghrb, igf1ra, igf1rb, igf2r, igfbp2a, igfbp3, igfbp5b) were all decreased. These results suggested that PbAc inhibited the differentiation and maturation of osteoblasts and cartilage matrix, promoted the formation of osteoclasts, and ultimately induced cartilage defects and bone loss by disrupting the GH/IGF-1 axis.

Prevalence and risk factors analysis for low back pain among occupational groups in key industries of China.

BACKGROUND: With the acceleration of industrialization and population aging, low back pain (LBP) has become the leading cause of life loss years caused by disability. Thus, it places a huge economic burden on society and is a global public health problem that needs urgent solution. This study aimed to conduct an epidemiological investigation and research on a large sample of workers in key industries in different regions of China, determine the incidence and distribution characteristics of LBP, explore the epidemic law, and provide a reference basis for alleviating global public health problems caused by LBP. METHODS: We adopted a modified epidemiological cross-sectional survey method and a stratified cluster sampling method. All on-duty workers who fulfill the inclusion criteria are taken as the research participants from the representative enterprises in key industries across seven regions: north, east, central, south, southwest, northwest, and northeast China. The Chinese version of the musculoskeletal disease questionnaire, modified by a standardized Nordic questionnaire, was used to collect information, and 57,501 valid questionnaires were received. Descriptive statistics were used, and multivariate logistic regression analysis (p < 0.05) was performed to explore the association between musculoskeletal disorders and potential risk factors. RESULTS: LBP annual incidence among workers in China's key industries is 16.4%. There was a significant difference in LBP incidence among occupational groups across different industries (p < 0.05). The multivariate regression model showed the following as risk factors for LBP: frequent repetitive movements with the trunk, working in the same positions at a high pace, trunk position, frequently turning around with your trunk, often working overtime, lifting heavy loads (i.e., more than 20 kg), education level, staff shortage, working age (years), cigarette smoking, use of vibration tools at work, body mass index, lifting heavy loads (i.e., more than 5 kg), and age (years). Physical exercise, often standing at work, and absolute resting time were protective factors. CONCLUSION: LBP incidence among key industries and workers in China is high. Thus, it is urgent to take relevant measures according to the individual, occupational, and psychosocial factors of LBP to reduce the adverse impact of LBP on workers' health.

Shoulder work-related musculoskeletal disorders and related factors of workers in 15 industries of China: a cross-sectional study.

BACKGROUND: Changes in modern industrial production practices can easily lead to shoulder work-related musculoskeletal disorders (WMSD). The current reports on shoulder WMSD are limited to some industries are less well studied, and the sample size is usually small. This study aimed to describe the prevalence and severity of shoulder WMSD in a large sample of Chinese workers from 15 industries, analyze the possible correlations with sociodemographic and work-related variables, and compare the differences between industries. METHODS: A cross-sectional study was conducted among a sample of 55,749 participants from 252 enterprises in 15 industries throughout China. A Chinese version of the musculoskeletal disease questionnaire was used to collect the demographic factors, shoulder symptoms in past 12 months, and work-related factors including posture-related factors, repetition, vibration, work organization, job control, and environmental factors as independent variables. Descriptive statistics were used, and the binary logistic regression analysis was performed to explore the association between shoulder WMSD and potential demographic and work-related factors. RESULTS: Nearly 35.5% of participants reported shoulder pain and discomfort in the previous 12 months. Biopharmaceutical manufacturing (56.2%), medical services (54.4%), and aviation services (50.1%) were the three industries with the highest prevalence of shoulder WMSD. The pain score of aviation services workers was the highest. The related factors for shoulder WMSD varied among the different industries. CONCLUSION: Our study found a relatively high prevalence of shoulder WMSD in China. There were large differences in the prevalence of shoulder WMSD among industries, and the related factors were particular to each industry. Such information is useful to help occupational health practitioners and policymakers conduct preventive programs to reduce shoulder disorders in these working populations.

A Multimodality Machine Learning Approach to Differentiate Severe and Nonsevere COVID-19: Model Development and Validation.

BACKGROUND: Effectively and efficiently diagnosing patients who have COVID-19 with the accurate clinical type of the disease is essential to achieve optimal outcomes for the patients as well as to reduce the risk of overloading the health care system. Currently, severe and nonsevere COVID-19 types are differentiated by only a few features, which do not comprehensively characterize the complicated pathological, physiological, and immunological responses to SARS-CoV-2 infection in the different disease types. In addition, these type-defining features may not be readily testable at the time of diagnosis. OBJECTIVE: In this study, we aimed to use a machine learning approach to understand COVID-19 more comprehensively, accurately differentiate severe and nonsevere COVID-19 clinical types based on multiple medical features, and provide reliable predictions of the clinical type of the disease. METHODS: For this study, we recruited 214 confirmed patients with nonsevere COVID-19 and 148 patients with severe COVID-19. The clinical characteristics (26 features) and laboratory test results (26 features) upon admission were acquired as two input modalities. Exploratory analyses demonstrated that these features differed substantially between two clinical types. Machine learning random forest models based on all the features in each modality as well as on the top 5 features in each modality combined were developed and validated to differentiate COVID-19 clinical types. RESULTS: Using clinical and laboratory results independently as input, the random forest models achieved >90% and >95% predictive accuracy, respectively. The importance scores of the input features were further evaluated, and the top 5 features from each modality were identified (age, hypertension, cardiovascular disease, gender, and diabetes for the clinical features modality, and dimerized plasmin fragment D, high sensitivity troponin I, absolute neutrophil count, interleukin 6, and lactate dehydrogenase for the laboratory testing modality, in descending order). Using these top 10 multimodal features as the only input instead of all 52 features combined, the random forest model was able to achieve 97% predictive accuracy. CONCLUSIONS: Our findings shed light on how the human body reacts to SARS-CoV-2 infection as a unit and provide insights on effectively evaluating the disease severity of patients with COVID-19 based on more common medical features when gold standard features are not available. We suggest that clinical information can be used as an initial screening tool for self-evaluation and triage, while laboratory test results should be applied when accuracy is the priority.

Accurately Differentiating Between Patients With COVID-19, Patients With Other Viral Infections, and Healthy Individuals: Multimodal Late Fusion Learning Approach.

BACKGROUND: Effectively identifying patients with COVID-19 using nonpolymerase chain reaction biomedical data is critical for achieving optimal clinical outcomes. Currently, there is a lack of comprehensive understanding in various biomedical features and appropriate analytical approaches for enabling the early detection and effective diagnosis of patients with COVID-19. OBJECTIVE: We aimed to combine low-dimensional clinical and lab testing data, as well as high-dimensional computed tomography (CT) imaging data, to accurately differentiate between healthy individuals, patients with COVID-19, and patients with non-COVID viral pneumonia, especially at the early stage of infection. METHODS: In this study, we recruited 214 patients with nonsevere COVID-19, 148 patients with severe COVID-19, 198 noninfected healthy participants, and 129 patients with non-COVID viral pneumonia. The participants' clinical information (ie, 23 features), lab testing results (ie, 10 features), and CT scans upon admission were acquired and used as 3 input feature modalities. To enable the late fusion of multimodal features, we constructed a deep learning model to extract a 10-feature high-level representation of CT scans. We then developed 3 machine learning models (ie, k-nearest neighbor, random forest, and support vector machine models) based on the combined 43 features from all 3 modalities to differentiate between the following 4 classes: nonsevere, severe, healthy, and viral pneumonia. RESULTS: Multimodal features provided substantial performance gain from the use of any single feature modality. All 3 machine learning models had high overall prediction accuracy (95.4%-97.7%) and high class-specific prediction accuracy (90.6%-99.9%). CONCLUSIONS: Compared to the existing binary classification benchmarks that are often focused on single-feature modality, this study's hybrid deep learning-machine learning framework provided a novel and effective breakthrough for clinical applications. Our findings, which come from a relatively large sample size, and analytical workflow will supplement and assist with clinical decision support for current COVID-19 diagnostic methods and other clinical applications with high-dimensional multimodal biomedical features.

Proteomic profiling reveals differentially expressed proteins associated with amylose accumulation during rice grain filling.

BACKGROUND: Amylose accumulation in rice grains is controlled by genetic and environmental factors. Amylose content is a determinant factor of rice quality in terms of cooking and eating. Great variations in amylose content in indica rice cultivars have been observed. The current study was to identify differentially expressed proteins in starch and sucrose metabolism and glycolysis/gluconeogenesis pathways and their relationships to amylose synthesis using two rice cultivars possess contrasting phenotypes in grain amylose content. RESULTS: Synthesis and accumulation of amylose in rice grains significantly affected the variations between rice cultivars in amylose contents. The high amylose content cultivar has three down-regulated differentially expressed proteins, i.e., LOC_Os01g62420.1, LOC_Os02g36600.1, and LOC_Os08g37380.2 in the glycolysis/gluconeogenesis pathway, which limit the glycolytic process and decrease the glucose-1-phosphate consumption. In the starch and sucrose metabolic pathway, an up-regulated protein, i.e., LOC_Os06g04200.1 and two down-regulated proteins, i.e., LOC_Os05g32710.1 and LOC_Os04g43360.1 were identified (Figure 4). Glucose-1-phosphate is one of the first substrates in starch synthesis and glycolysis that are catalyzed to form adenosine diphosphate glucose (ADPG), then the ADPG is catalyzed by granule-bound starch synthase I (GBSS I) to elongate amylose. CONCLUSIONS: The results indicate that decreasing the consumption of glucose-1-phosphate in the glycolytic process is essential for the formation of ADPG and UDPG, which are substrates for amylose synthesis. In theory, amylose content in rice can be regulated by controlling the fate of glucose-1-phosphate.

Salt-Inducible Kinase 3 Haplotypes Associated with Noise-Induced Hearing Loss in Chinese Workers.

INTRODUCTION: Noise-induced hearing loss (NIHL) is a common occupational disease that represents an irreversible hearing damage to the auditory system. It has been identified as a complicated disease involving both environmental and genetic factors. More efforts need to be made to explore the genes associated with susceptibility to NIHL. The main aim of this research is to detect the associations between SIK3 polymorphisms and NIHL susceptibility in Han people in China. METHODS: A case-control study was performed in 586 cases and 639 controls in a textile factory matched for sex, age, smoking, drinking, work time with noise, and intensity of noise exposure. Three single nucleotide polymorphisms (SNPs) (rs493134, rs6589574, and rs7121898) of SIK3 were genotyped in the participants. Then, the main influences of the SNPs on and their interactions with NIHL were assessed. RESULTS: Under the allelic model, distributions of rs493134 T, rs6589574 G, and rs7121898 A in the NIHL group are statistically different from those of the normal group (p = 0.001, p < 0.001, and p = 0.019, respectively). The following haplotype analysis shows that TAA (rs493134-rs6589574-rs7121898) may have a protective effect, while TGA (rs493134-rs6589574-rs7121898) (OR = 1.49, 95% CI = 1.25-1.79) may be a risk factor for NIHL. Multifactor dimensionality reduction analysis shows that the interaction of the 3 selected SNPs is associated with NIHL susceptibility (OR = 1.88, 95% CI = 1.50-2.36). CONCLUSION: The results suggest that 3 SNPs (rs493134, rs6589574, and rs7121898) of SIK3 may be an important part of NIHL susceptibility and can be applied in the prevention, early diagnosis, and treatment of NIHL in noise-exposed Chinese workers.