Objective To analyze the occupational health literacy (OHL) level and its influencing factors of workers in non-metallic mineral product industry in Yunfu City. Methods A total of 947 frontline workers from 24 non-metallic mineral products enterprises in Yunfu City were selected as the research subjects using the stratified random sampling method. The OHL level of the workers were assessed using the Occupational Health Literacy Questionnaire of National Key Populations. Results The overall OHL level of the research subjects was 58.3% (552/947). The OHL levels across four dimensions, from highest to lowest, were basic knowledge of occupational health protection (94.7%), healthy work practices and behaviors (81.8%), legal knowledge of occupational health (65.5%), and basic skills of occupational health protection (25.9%). The results of binary logistic regression analysis showed that workers with 2.0-<10.0 years and ≥10 years of work experience had higher OHL levels than those with <2.0 years of work experience (all P<0.01). Workers with a high school education or above had higher OHL levels than those with a junior high school education or below (all P<0.01). Workers in large- and medium-sized enterprises had higher OHL levels than those in small and micro-sized enterprises (both P<0.01). Conclusion The OHL levels of workers in Yunfu City's non-metallic mineral products industry can be further improved, particularly the occupational health protection skills and related legal knowledge. Workers with short seniority, low educational level, and in small and micro enterprises should be the key groups for improving OHL levels.

Purpose: The genomic characteristics of uterine sarcomas have not been fully elucidated. This study aimed to explore the genomic landscape of the uterine sarcomas (USs). Materials and Methods: Comprehensive genomic analysis through RNA-sequencing was conducted. Gene fusion, differentially expressed genes (DEGs), signaling pathway enrichment, immune cell infiltration, and prognosis were analyzed. A deep learning model was constructed to predict the survival of US patients. Results: A total of 71 US samples were examined, including 47 endometrial stromal sarcomas (ESS), 18 uterine leiomyosarcomas (uLMS), three adenosarcomas, two carcinosarcomas, and one uterine tumor resembling an ovarian sex-cord tumor. ESS (including high-grade ESS [HGESS] and low-grade ESS [LGESS]) and uLMS showed distinct gene fusion signatures; a novel gene fusion site, MRPS18A–PDC-AS1 could be a potential diagnostic marker for the pathology differential diagnosis of uLMS and ESS; 797 and 477 uterine sarcoma DEGs (uDEGs) were identified in the ESS vs. uLMS and HGESS vs. LGESS groups, respectively. The uDEGs were enriched in multiple pathways. Fifteen genes including LAMB4 were confirmed with prognostic value in USs; immune infiltration analysis revealed the prognositic value of myeloid dendritic cells, plasmacytoid dendritic cells, natural killer cells, macrophage M1, monocytes and hematopoietic stem cells in USs; the deep learning model named Max-Mean Non-Local multi-instance learning (MMN-MIL) showed satisfactory performance in predicting the survival of US patients, with the area under the receiver operating curve curve reached 0.909 and accuracy achieved 0.804. Conclusion USs harbored distinct gene fusion characteristics and gene expression features between HGESS, LGESS, and uLMS. The MMN-MIL model could effectively predict the survival of US patients.

Purpose: The genomic characteristics of uterine sarcomas have not been fully elucidated. This study aimed to explore the genomic landscape of the uterine sarcomas (USs). Materials and Methods: Comprehensive genomic analysis through RNA-sequencing was conducted. Gene fusion, differentially expressed genes (DEGs), signaling pathway enrichment, immune cell infiltration, and prognosis were analyzed. A deep learning model was constructed to predict the survival of US patients. Results: A total of 71 US samples were examined, including 47 endometrial stromal sarcomas (ESS), 18 uterine leiomyosarcomas (uLMS), three adenosarcomas, two carcinosarcomas, and one uterine tumor resembling an ovarian sex-cord tumor. ESS (including high-grade ESS [HGESS] and low-grade ESS [LGESS]) and uLMS showed distinct gene fusion signatures; a novel gene fusion site, MRPS18A–PDC-AS1 could be a potential diagnostic marker for the pathology differential diagnosis of uLMS and ESS; 797 and 477 uterine sarcoma DEGs (uDEGs) were identified in the ESS vs. uLMS and HGESS vs. LGESS groups, respectively. The uDEGs were enriched in multiple pathways. Fifteen genes including LAMB4 were confirmed with prognostic value in USs; immune infiltration analysis revealed the prognositic value of myeloid dendritic cells, plasmacytoid dendritic cells, natural killer cells, macrophage M1, monocytes and hematopoietic stem cells in USs; the deep learning model named Max-Mean Non-Local multi-instance learning (MMN-MIL) showed satisfactory performance in predicting the survival of US patients, with the area under the receiver operating curve curve reached 0.909 and accuracy achieved 0.804. Conclusion USs harbored distinct gene fusion characteristics and gene expression features between HGESS, LGESS, and uLMS. The MMN-MIL model could effectively predict the survival of US patients.

Purpose: The genomic characteristics of uterine sarcomas have not been fully elucidated. This study aimed to explore the genomic landscape of the uterine sarcomas (USs). Materials and Methods: Comprehensive genomic analysis through RNA-sequencing was conducted. Gene fusion, differentially expressed genes (DEGs), signaling pathway enrichment, immune cell infiltration, and prognosis were analyzed. A deep learning model was constructed to predict the survival of US patients. Results: A total of 71 US samples were examined, including 47 endometrial stromal sarcomas (ESS), 18 uterine leiomyosarcomas (uLMS), three adenosarcomas, two carcinosarcomas, and one uterine tumor resembling an ovarian sex-cord tumor. ESS (including high-grade ESS [HGESS] and low-grade ESS [LGESS]) and uLMS showed distinct gene fusion signatures; a novel gene fusion site, MRPS18A–PDC-AS1 could be a potential diagnostic marker for the pathology differential diagnosis of uLMS and ESS; 797 and 477 uterine sarcoma DEGs (uDEGs) were identified in the ESS vs. uLMS and HGESS vs. LGESS groups, respectively. The uDEGs were enriched in multiple pathways. Fifteen genes including LAMB4 were confirmed with prognostic value in USs; immune infiltration analysis revealed the prognositic value of myeloid dendritic cells, plasmacytoid dendritic cells, natural killer cells, macrophage M1, monocytes and hematopoietic stem cells in USs; the deep learning model named Max-Mean Non-Local multi-instance learning (MMN-MIL) showed satisfactory performance in predicting the survival of US patients, with the area under the receiver operating curve curve reached 0.909 and accuracy achieved 0.804. Conclusion USs harbored distinct gene fusion characteristics and gene expression features between HGESS, LGESS, and uLMS. The MMN-MIL model could effectively predict the survival of US patients.

Objective To analyze the factors influencing the occupational health literacy (OHL) level among workers in key industries from the perspectives of both individual workers and enterprises. Methods A total of 32 336 front-line workers from 12 key industries in the secondary industry in Guangdong Province were selected as the research subjects by a stratified cluster random sampling method. Their OHL level was investigated using Occupational Health Literacy Questionnaire of National Key Populations, and the influencing factors were analyzed. Results The OHL level of the research subjects was 48.5%. The OHL level of the research subjects in four dimensions from high to low was basic knowledge of occupational health protection, occupational health practice and behavior, legal knowledge of occupational health, and basic skills of occupational health protection (80.7%, 61.2%, 48.3% and 29.5%, respectively). The multivariable logistic regression analysis showed that the OHL level of female workers was lower than that of males (P<0.05). Lower OHL was also associated with older age, lower education level, lower personal monthly income of workers (all P<0.01). The workers with length of service < 3 years and ≥ 20 years had lower OHL level than those with length of service 3-<10 years and 10-<20 years, respectively (all P<0.05). Workers in larger enterprises had higher OHL levels (all P<0.01). The OHL level of workers in the sixth category of industries with occupational injuries had higher occupational injury risks than those in the third and fourth categories (all P<0.05). The OHL levels of workers in state-owned enterprises, private enterprises, foreign-funded enterprises, and other enterprises were higher than that of workers in public institutions (all P<0.05). Conclusion The influencing factors of workers′ OHL in key industries of the secondary industry include individual factors (gender, age, education level, personal monthly income, length of service) and enterprise factors (enterprise size, enterprise nature and industry injury risk category). Female, older workers, those with lower education or income, and those with short length of service represent priority groups for OHL interventions, while small and micro enterprises are priority units for future workplace health promotion intervention.

Objective To analyze the effect of night-shift work, anxiety and their interaction on work-related musculoskeletal disorders (WMSDs) among electronics manufacturing employees. Methods A total of 2 676 employees from 58 electronic manufacturing enterprises in the Pearl River Delta region of Guangdong Province were selected as the research subjects using the judgment sampling method. The Basic Situation Survey Scale, Generalized Anxiety Disorder 7-item Scale and Questionnaire of Musculoskeletal Disorders were used to assess night-shift work, anxiety and the prevalence of WMSDs in employees. The multivariate logistic regression model was used to analyze the effects of night-shift work, anxiety and their combined effects on the risk of WMSDs. Results The proportion of night-shift work was 30.3%, and the detection rates of anxiety and WMSDs were 26.8% and 41.3%, respectively. The results of multivariate logistic regression analysis showed that night-shift work and anxiety were independent risk factors of WMSDs in the research subjects, after excluding the influence of confounding factors such as age, marital status, enterprise size and length of service [odds ratio (OR) and 95% confidence interval (CI) were 1.307 (1.092-1.564) and 3.282 (2.739-3.934), respectively, both P<0.01]. Compared with those without night-shift work or anxiety, the risk of WMSDs was higher in individuals with only night-shift work, only anxiety, or both night-shift work and anxiety [OR and 95%CI were 1.347 (1.091-1.663), 3.395 (2.727-4.227) and 4.117 (3.072-5.519), respectively, all P<0.01]. Conclusion Both night-shift work and anxiety can increase the risk of WMSDs among electronic manufacturing employees, and these two factors exhibit a synergistic effect in increasing the risk of WMSDs.

Background The high-quality development of manufacturing in China has spurred industrial transformation and upgrading, placing higher demands on the skills of employees in the electronic manufacturing industry. This situation may induce psychological health problems such as job burnout and depressive symptoms in the employees, and also lead to insomnia, which has become a public health problem that urgently needs attention and solution. Objective To investigate the relationship between job burnout, depressive symptoms, and insomnia among employees in the electronic manufacturing industry. Methods A total of 3034 employees from four electronic manufacturing companies in Beijing City, Shanghai City, and Guangdong Province were selected using judgment sampling from September to November 2019. The Maslach Burnout Inventory-General Survey, Patient Health Questionnaire-9, and Self-Administered Sleep Questionnaire were used to collect data. The correlation between job burnout, depressive symptoms, and insomnia was evaluated by hierarchical regression. Results A total of 3034 survey questionnaires were distributed, and 2614 valid questionnaires were collected, with a response effectiveness rate of 86.2%. The reporting rates of job burnout, depressive symptoms, and insomnia among employees in the electronic manufacturing industry were 47.2%, 19.5%, and 29.1%, respectively. The reporting rates of insomnia of employees in the job burnout group and depressive symptoms group were higher than those in the non-job burnout group and non-depressive symptoms group (35.3% vs. 23.6%, 47.2% vs. 24.7%, P＜0.001). The results of hierarchical regression showed that job burnout explained 2.8% of the variation of insomnia (P<0.001), and the inclusion of depressive symptoms explained 1.2% of the variation of insomnia (P<0.001). Conclusion Job burnout and depressive symptoms could both affect insomnia of employees in the electronic manufacturing industry.

ObjectiveTo explore the effect of occupational stress on work-related musculoskeletal disorders (WMSDs) in electronics manufacturing workers. Methods A total of 392 front-line workers in two electronic manufacturing enterprises in Guangdong Province were selected as the research subjects using the judgment sampling method. The prevalence of WMSDs and the level of occupational stress of the research subjects were investigated using the Musculoskeletal Disorders Questionnaire and the Core Occupational Stress Scale. Results The total WMSDs detection rate was 39.5%, and the multi-site WMSDs detection rate was 30.6%. The detection rate of occupational stress was 14.8%. The total WMSDs detection rate and multi-site WMSDs detection rate in the occupational stress group were higher than those in the non-occupational stress group (65.5% vs 35.0%, 56.9% vs 26.0%, both P<0.01). Binary logistic regression analysis result showed that the risk of WMSDs in the occupational stress group was higher than that in the non-occupational stress group after adjusting the effect of confounding factors such as age, gender, job type and work days per week (P<0.01). Conclusion The occupational stress may increase the risk of WMSDs in electronics manufacturing workers. Reducing the level of occupational stress among workers in electronic manufacturing enterprises is beneficial for reducing the risk of WMSDs.

Objective To investigate the value of urodynamic study(UDS)in guiding the treatment of lower urinary tract dysfunction(LUTD)in elderly patients with ischemic stroke(IS)during convalescence,in order to provide reference for clinical treatment.Methods A total of 50 LUTD patients with IS who were admitted to the First Affiliated Hospital of Xinxiang Medical University during Jan.2020 and Jan.2022 were selected.Oral tolterodine was administered to patients with detrusor overactivity(DO),clean intermittent catheterization(CIC)to those with no detrusor reflex and symptomatic increased residual urine,and oral administration of tamsulosin to those with functional obstruction of bladder outlet.The lower urinary tract symptoms(LUTS)relief rate,UDS parameters and quality of life(QoL)scores were compared before treatment and 3 months after treatment.Results The UDS examination results showed that 25 cases(50.0％)had simple DO,9 cases(18.0％)had DO with impaired detrusor muscle contraction function,5 cases(10.0％)had DO with bladder outlet functional obstruction,4 cases(8.0％)had no detrusor reflex,and 7 cases(14.0％)had simple bladder outlet functional obstruction.After 3 months of treatment,the symptoms of LUTS,including frequent urination,urgent urination,incontinence,dysuria and urinary retention were significantly improved(P＜0.05).The maximum urine flow rate and urine output were significantly increased,the residual urine volume was significantly reduced,QoL scores were significantly reduced,with significant differences(P＜0.001).Conclusion UDS is significant in guiding the treatment of LUTD in elderly patients with IS during convalescence.

Furry animal allergens, particularly cat and dog hair and dander, are common allergens in indoor environments, affecting the health of people world widely. Key sensitizing components such as Fel d 1 from cats and Can f 1 from dogs have been extensively studied and identified by the scientific community. Component resolved diagnosis (CRD) technology in modern diagnostic methods provides an accurate way to identify and distinguish these components, which is extremely important for the prevention of furry animal allergies and the formulation of personalized treatment strategies. To enhance the understanding of furry animal component diagnosis and promote the alignment of the Chinese discipline of allergology with international standards, this article interprets and explains the content of the "Molecular Allergology User′s Guide 2.0" recently released by the European Academy of Allergy and Clinical Immunology. It focuses on the epidemiological characteristics of furry animal components, the diversity of allergen protein families, and their clinical diagnosis and management.