Heavy metal spatial distribution, source analysis, and ecological risks in the central hilly area of Hainan Island, China: results from a high-density soil survey.

The presence of heavy metals in soil has gained considerable attention due to their potential risks to ecosystems and human health. In this study, a thorough soil investigation was performed in the hilly region of central Hainan, which was formerly regarded as an area with the highest ecological environmental quality. A total of 7094 soil samples were systematically collected with high density over a large area. Simultaneously, a detailed investigation was conducted on the surrounding environment of each sampling point, including environmental factors such as soil, land use and crop types. The soil samples were analysed for heavy metals, pH, organic matter, and other parameters. The soil heavy metal pollution level, ecological risk and health risk were evaluated using the geo-accumulation index and the potential ecological risk index. The findings showed that the average contents of the heavy metals As, Cd, Cr, Cu, Hg, Ni, Pb and Zn in the soil were 1.68, 0.042, 24.2, 6.49, 0.0319, 7.06, 29.6 and 49.8 mg·kg-1 respectively. Except for Hg, the mean values of the other heavy metals were either lower than or similar to the background values of Hainan. Also, only a few localised areas showed contamination by heavy metals. The primary sources of heavy metals, identified by a positive matrix factorisation model, could be categorised into four types: natural sources related to the soil formation process from acidic intrusive rocks (such as granite); natural sources primarily influenced by atmospheric deposition; anthropogenic sources associated with agricultural activities; and natural sources related to the soil formation process from middle-mafic intrusive rocks and black shales. The correlation analysis and variance analysis findings suggested that the content of heavy metals in the soil was primarily associated with the parent rock. The study area generally had low heavy metal levels and was not significantly polluted. However, agricultural activities still affected the enrichment of heavy metals. Therefore, it is imperative to remain vigilant about the ecological risks linked to soil heavy metals while continuing land development and expanding agricultural activities in the future. These findings indicate that conducting high-density soil surveys can enhance our understanding of regional soil heavy metals and enable reliable recommendations for agricultural planning. Whether in areas with low pollution risk or potential pollution risk, it is recommended that high-density soil surveys be conducted provide scientific guidance for further agricultural development.

Prediction of cross-border spread of the COVID-19 pandemic: A predictive model for imported cases outside China.

The COVID-19 pandemic has been present globally for more than three years, and cross-border transmission has played an important role in its spread. Currently, most predictions of COVID-19 spread are limited to a country (or a region), and models for cross-border transmission risk assessment remain lacking. Information on imported COVID-19 cases reported from March 2020 to June 2022 was collected from the National Health Commission of China, and COVID-19 epidemic data of the countries of origin of the imported cases were collected on data websites such as WHO and Our World in Data. It is proposed to establish a prediction model suitable for the prevention and control of overseas importation of COVID-19. Firstly, the SIR model was used to fit the epidemic infection status of the countries where the cases were exported, and most of the r2 values of the fitted curves obtained were above 0.75, which indicated that the SIR model could well fit different countries and the infection status of the region. After fitting the epidemic infection status data of overseas exporting countries, on this basis, a SIR-multiple linear regression overseas import risk prediction combination model was established, which can predict the risk of overseas case importation, and the established overseas import risk model overall P <0.05, the adjusted R2 = 0.7, indicating that the SIR-multivariate linear regression overseas import risk prediction combination model can obtain better prediction results. Our model effectively estimates the risk of imported cases of COVID-19 from abroad.

Predictive model for identifying mild cognitive impairment in patients with type 2 diabetes mellitus: A CHAID decision tree analysis.

BACKGROUND: As the population ages, mild cognitive impairment (MCI) and type 2 diabetes mellitus (T2DM) become common conditions that often coexist. Evidence has shown that MCI could lead to reduced treatment compliance, medication management, and self-care ability in T2DM patients. Therefore, early identification of those with increased risk of MCI is crucial from a preventive perspective. Given the growing utilization of decision trees in prediction of health-related outcomes, this study aimed to identify MCI in T2DM patients using the decision tree approach. METHODS: This hospital-based case-control study was performed in the Endocrinology Department of Xiangya Hospital affiliated to Central South University between March 2021 and December 2022. MCI was defined based on the Petersen criteria. Demographic characteristics, lifestyle factors, and T2DM-related information were collected. The study sample was randomly divided into the training and validation sets in a 7:3 ratio. Univariate and multivariate analyses were performed, and a decision tree model was established using the chi-square automatic interaction detection (CHAID) algorithm to identify key predictor variables associated with MCI. The area under the curve (AUC) value was used to evaluate the performance of the established decision tree model, and the performance of multivariate regression model was also evaluated for comparison. RESULTS: A total of 1001 participants (705 in the training set and 296 in the validation set) were included in this study. The mean age of participants in the training and validation sets was 60.2  ±  10.3 and 60.4  ±  9.5 years, respectively. There were no significant differences in the characteristics between the training and validation sets (p > .05). The CHAID decision tree analysis identified six key predictor variables associated with MCI, including age, educational level, household income, regular physical activity, diabetic nephropathy, and diabetic retinopathy. The established decision tree model had 15 nodes composed of 4 layers, and age is the most significant predictor variable. It performed well (AUC = .75 [95% confidence interval (CI): .71-.78] and .67 [95% CI: .61-.74] in the training and validation sets, respectively), was internally validated, and had comparable predictive value compared to the multivariate logistic regression model (AUC = .76 [95% CI: .72-.80] and .69 [95% CI: .62-.75] in the training and validation sets, respectively). CONCLUSION: The established decision tree model based on age, educational level, household income, regular physical activity, diabetic nephropathy, and diabetic retinopathy performed well with comparable predictive value compared to the multivariate logistic regression model and was internally validated. Due to its superior classification accuracy and simple presentation as well as interpretation of collected data, the decision tree model is more recommended for the prediction of MCI in T2DM patients in clinical practice.

Willingness to receive the second booster of COVID-19 vaccine among older adults with cancer: a stratified analysis in four provinces of China.

Background: Despite the elevated COVID-19 risk for older adults with cancer, vaccine hesitancy poses a significant barrier to their immunization. Intriguingly, there is limited research on the prevalence of willingness to receive the second booster dose and associated determinants in older adults with cancer. Objective: Our objective was to ascertain the level of awareness about COVID-19 vaccines and to uncover the factors influencing the willingness to receive the second booster among Chinese cancer patients aged 65 years and over. Methods: To achieve our objective, we conducted a multicenter cross-sectional study in four tertiary hospitals from four provinces of China. This involved using a Health Belief Model (HBM) based self-administered questionnaire and medical records. Subsequently, we employed multivariable logistic regression to identify factors influencing the second COVID-19 booster vaccine willingness. Results: Our results showed that among 893 eligible participants, 279 (31.24%) were aged 65 years and over, and 614 (68.76%) were younger. Interestingly, the willingness to receive the second COVID-19 booster vaccine was 34.1% (95/279) (OR: 1.043, 95% CI: 0.858, 1.267) in participants aged 65 years and over, which was similar to participants aged under 65 years (34.1% vs. 35.5%, p = 0.673). Furthermore, our findings revealed that a positive attitude toward the booster and recommendations from healthcare providers and family members were positively associated with vaccine willingness. Conversely, perceptions of negative impacts on cancer control and vaccine accessibility regarding the second COVID-19 booster were inversely related to the outcome event (all p < 0.05). Conclusion: Our study concludes with the finding of a low willingness toward the second COVID-19 booster in Chinese cancer patients, particularly in the older adults, a fact which warrants attention. This reluctance raises their risk of infection and potential for severe outcomes. Consequently, we recommend using media and community outreach to dispel misconceptions, promote the booster's benefits, and encourage vaccine discussions with healthcare providers and family members.

Low uptake of COVID-19 booster doses among elderly cancer patients in China: A multicentre cross-sectional study.

Background: Vaccination is a crucial measure to control the spread of coronavirus disease 2019 (COVID-19) pandemic. The elderly and cancer populations both are more susceptible to SARS-CoV-2 and have higher mortality. However, the uptake of COVID-19 vaccine booster doses among elderly cancer patients remains unclear. This study aimed to investigate the prevalence and associates of COVID-19 vaccine booster doses uptake in elderly cancer patients. Methods: A multi-center cross-sectional survey was conducted in four general populations of China province from April to June 2022. Demographic and clinical characteristics, as well as COVID-19 vaccination status and reasons for not uptake booster doses, were collected through face-to-face interviews and medical records. Multivariable logistic regression models were performed to explore the associates of the first COVID-19 booster dose vaccination uptake of cancer patients. Results: A total of 893 cancer patients were eventually included in this study, of which 279 (31.24%) were aged 65 or older and 614 (68.76%) were under 65 years old. The proportion of the first COVID-19 vaccine booster dose among cancer patients aged 65 and above was lower than among adults aged 65 (23.66 vs. 31.92%). Factors affecting individual-level variables among the aged 65 and above cancer patients group whether to uptake the first COVID-19 booster dose were negative attitudes toward COVID-19 vaccine booster dose, perceived subjective norm, perceived behavioural control, and other types of chronic disease. There is no significant difference in the incidence of related adverse reactions between the two age groups (P = 0.19). Conclusions: Low uptake of COVID-19 vaccine booster doses among elderly cancer patients is a significant concern and implies high susceptibility and high fatality when facing the emergence of SARS Cov-2 outbreak. Efforts to improve vaccine education and accessibility, particularly in rural areas, may help increase uptake and reduce the spread of SARS-Cov-2.

Sleep quality and its associated factors among patients with type 2 diabetes mellitus in Hunan, China: a cross-sectional study.

OBJECTIVES: Type 2 diabetes mellitus (T2DM) is a serious public health issue. Compared with the general population, patients with T2DM have a higher risk of poor sleep quality, which could ultimately result in poor prognosis. Therefore, this study aimed to evaluate sleep quality and its associated factors among patients with T2DM in Hunan, China. DESIGN: This was a cross-sectional study. SETTING: A tertiary hospital in Hunan, China. PARTICIPANTS: Patients with T2DM hospitalised at the Endocrinology Department were consecutively enrolled between March 2021 and December 2022. Sociodemographic characteristics, lifestyle factors and T2DM-related information were collected retrospectively. PRIMARY AND SECONDARY OUTCOME MEASURES: Sleep quality was evaluated using the Pittsburgh Sleep Quality Index, with a cut-off value of >7 suggesting poor sleep quality. Multivariate logistic regression analysis was used to determine factors associated with poor sleep quality. RESULTS: Of the 1039 participants included, 1001 provided complete data. The mean age of the study sample was 60.24±10.09 years, and 40.5% (95% CI 37.5% to 43.5%) of patients had poor sleep quality. Multivariate logistic regression analysis showed that female sex (adjusted OR (aOR) 1.70, 95% CI 1.25 to 2.29), unmarried status (aOR 1.72, 95% CI 1.05 to 2.83), diabetic retinopathy (aOR 1.38, 95% CI 1.04 to 1.83), diabetic foot (aOR 1.80, 95% CI 1.11 to 2.93) and a per capita monthly household income of >5000 RMB (aOR 0.66, 95% CI 0.47 to 0.93) were associated with poor sleep quality. CONCLUSIONS: Nearly two-fifths of patients with T2DM reported poor sleep quality in Hunan, China. Sex, marital status, diabetic retinopathy, diabetic foot and household income were independently associated with sleep quality among patients with T2DM in Hunan, China.

Appraising the value of CircRNAs for the diagnosis and prognosis of esophageal squamous cell cancer: An updated meta-analysis.

OBJECTIVE: The purpose of this study was to thoroughly assess the relevance of circular RNAs (circRNAs) in the diagnosis and prognosis of esophageal squamous cell carcinoma (ESCC)， and design a systematic review and meta-analysis. METHODS: Using Stata 14.0 software, a meta-analysis was carried out by looking for pertinent studies up to February 20, 2023, in the online databases PubMed, Embase, Web of Science, and CNKI. The clinicopathologic and prognostic data were evaluated using the combined advantage ratio (OR) and combined hazard ratio (HR), respectively. The threshold effects and publication bias were quantified using Spearman's correlation and the Deeks funnel plot asymmetry tests, respectively. RESULTS: A total of 36 pertinent studies with a literature quality score of 7 or above were included in this study. Of them, 22 papers dealt with clinicopathological characterization, 15 dealt with prognostic analysis, and 13 dealt with diagnostic analysis. The findings demonstrated that high expression of upregulated circRNAs was associated with worse clinicopathological features (tumor size: OR=3.61, 95% CI:1.45-5.78; TNM stage: OR=2.12, 95% CI:1.41-2.83; lymph node metastasis: OR=2.87, 95% CI:1.67-4.07) and worse OS (HR=1.49, 95% CI:1.26-1.77). High downregulated circRNAs expression was linked to improved clinicopathologic characteristics (TNM staging: OR=0.35, 95% CI:0.13- 0.95) and longer survival (HR=0.48, 95% CI:0.27-0.84); combined sensitivity was 0.77 (95% CI: 0.71-0.82), specificity was 0.80 (95% CI:0.74-0.86), and area under the subject operating characteristic curve (AUC) was 0.86 (95% CI:0.82- 0.88). CONCLUSION: CircRNAs are useful for ESCC patient diagnosis and prognosis, and they are anticipated to be unique potential biomarkers for ESCC clinical diagnosis.

Cost-effectiveness analysis of artificial intelligence-based diabetic retinopathy screening in rural China based on the Markov model.

This study assessed the cost-effectiveness of different diabetic retinopathy (DR) screening strategies in rural regions in China by using a Markov model to make health economic evaluations. In this study, we determined the structure of a Markov model according to the research objectives, which required parameters collected through field investigation and literature retrieval. After perfecting the model with parameters and assumptions, we developed a Markov decision analytic model according to the natural history of DR in TreeAge Pro 2011. For this model, we performed Markov cohort and cost-effectiveness analyses to simulate the probabilistic distributions of different developments in DR and the cumulative cost-effectiveness of artificial intelligence (AI)-based screening and ophthalmologist screening for DR in the rural population with diabetes mellitus (DM) in China. Additionally, a model-based health economic evaluation was performed by using quality-adjusted life years (QALYs) and incremental cost-effectiveness ratios. Last, one-way and probabilistic sensitivity analyses were performed to assess the stability of the results. From the perspective of the health system, compared with no screening, AI-based screening cost more (the incremental cost was 37,257.76 RMB (approximately 5,211.31 US dollars)), but the effect was better (the incremental utility was 0.33). Compared with AI-based screening, the cost of ophthalmologist screening was higher (the incremental cost was 14,886.76 RMB (approximately 2,070.19 US dollars)), and the effect was worse (the incremental utility was -0.31). Compared with no screening, the incremental cost-effectiveness ratio (ICER) of AI-based DR screening was 112,146.99 RMB (15,595.47 US dollars)/QALY, which was less than the threshold for the ICER (< 3 times the per capita gross domestic product (GDP), 217,341.00 RMB (30,224.03 US dollars)). Therefore, AI-based screening was cost-effective, which meant that the increased cost for each additional quality-adjusted life year was merited. Compared with no screening and ophthalmologist screening for DR, AI-based screening was the most cost-effective, which not only saved costs but also improved the quality of life of diabetes patients. Popularizing AI-based DR screening strategies in rural areas would be economically effective and feasible and can provide a scientific basis for the further formulation of early screening programs for diabetic retinopathy.

Effect of chronic diseases on willingness to receive the second COVID-19 vaccine booster dose among cancer patients: A multicenter cross-sectional survey in China.

BACKGROUND: Cancer patients and those with chronic diseases face severe outcomes from SARS-CoV-2 infection. However, their willingness to receive a second booster dose remains low. This study identified factors affecting the willingness of cancer patients with chronic diseases (CPCD) and cancer patients without chronic diseases (non-CPCD) to receive the second COVID-19 booster vaccine dose. METHODS: A multicenter cross-sectional study was conducted across 4 tertiary care hospitals in China. Based on the Health Belief Model, a questionnaire assessed respondents' perceptions of the second booster dose. Multivariable logistic regression analyzed factors influencing the willingness to receive a second vaccine dose. RESULTS: Out of 171 CPCD and 722 non-CPCD, CPCD showed a higher willingness to receive the second booster dose than non-CPCD (46.8% vs 32.3%, P < .001). Factors influencing CPCD's willingness included the belief that vaccination was detrimental to cancer treatment and the perceived higher infection risk compared to healthy individuals. Fear of vaccination's negative impact on cancer treatment was the main factor affecting non-CPCD's willingness (each P < .05). CONCLUSIONS: Different factors influenced the willingness of the 2 groups. Health education interventions should be implemented alongside vaccination, involving patients' relatives and medical staff, for both CPCD and non-CPCD populations. Additionally, health management service interventions should emphasize the benefits of vaccination for CPCD to improve their second dose coverage.

Differentiating environmental scenarios to establish geochemical baseline values for heavy metals in soil: A case study of Hainan Island, China.

Soil heavy metal distributions exhibit regional heterogeneity due to the complex characteristics of parent materials and soil formation processes, emphasizing the need for appropriate regional standards prior to assessing soil risks. This study focuses on Hainan Island and employs the Multi-purpose Regional Geochemical Survey dataset to establish heavy metal geochemical baseline and background values for soil using an iterative method. Geographical detector analysis reveals that parent materials are the primary factor influencing heavy metal distribution, followed by soil types and land use. Heavy metal geochemical baseline values are established for the island's three environments and administrative regions. Notably, a universal geochemical baseline value cannot adequately represent regional variations in heavy metal distribution, with parent materials playing a crucial role in various scenarios. Locally applicable values based on parent material are the most representative for Hainan Island. This study provides a reference framework for developing region-specific environmental baseline values for soil heavy metal assessments.

Prevalence of Anxiety and Associated Factors Among Inpatients with Type 2 Diabetes Mellitus in China: A Cross-Sectional Study.

This study aimed to investigate the prevalence of anxiety and its associated factors among inpatients with type 2 diabetes mellitus (T2DM) in China. This study was a cross-sectional study. Inpatients with T2DM admitted to the Endocrinology Department of Xiangya Hospital, Central South University in Hunan Province of China from March 2021 to December 2021 were consecutively included in this study. Participants were interviewed to obtain the data on socio-demographic characteristics, lifestyle characteristics, T2DM-related information, and social support. Anxiety was measured using the Hospital Anxiety and Depression Scale-anxiety subscale by experienced physicians. Multivariable logistic regression analysis was used to estimate the independent contribution of each independent variable to anxiety. A total of 496 inpatients with T2DM were included in this study. The prevalence of anxiety was 21.8% (95% confidence interval [CI]: 18.1%-25.4%). The results of multivariable logistic regression analysis indicated that age of at least 60 (adjusted odd ratio [aOR] = 1.79, 95% CI: 1.04-3.08), and having diabetes specific complications (aOR = 4.78, 95% CI: 1.02-22.44) were risk factors for anxiety, and an educational level of high school or above (aOR = 0.55, 95% CI: 0.31-0.99), regular physical activity (aOR = 0.36, 95% CI: 0.22-0.58), and high social support (aOR = 0.30, 95% CI: 0.17-0.53) were protective factors for anxiety. A predictive model based on these five variables showed good performance (area under the curve = 0.80). Almost one in five inpatients with T2DM suffered from anxiety in China. Age, educational level, regular physical activity, diabetes specific complications, and social support were independently associated with anxiety.

Concerns related to the interactions between COVID-19 vaccination and cancer/cancer treatment were barriers to complete primary vaccination series among Chinese cancer patients: A multicentre cross-sectional survey.

COVID-19 vaccination is effective for cancer patients without safety concerns. However, COVID-19 vaccination hesitancy is common among cancer patients. This study investigated factors affecting primary COVID-19 vaccination series completion rate among cancer patients in China. A multicentre cross-sectional study was conducted in four Chinese cities in different geographic regions between May and June 2022. A total of 893 cancer inpatients provided written informed consent and completed the study. Logistic regression models were fitted. Among the participants, 58.8% completed the primary COVID-19 vaccination series. After adjusting for background characteristics, concerns about interactions between COVID-19 vaccination and cancers/cancer treatment (adjusted odds ratios [AOR]: 0.97, 95%CI: 0.94, 0.99) were associated with lower completion of primary vaccination series. In addition, perceived higher risk of COVID-19 infection comparing to people without cancers (AOR: 0.46, 95%CI: 0.24, 0.88), perceived a high chance of having severe consequences of COVID-19 infection (AOR: 0.68, 95%CI: 0.51, 0.91) were also associated with lower completion rate. Being suggested by significant others (AOR: 1.32, 95%CI: 1.23, 1.41) and perceived higher self-efficacy to receive COVID-19 vaccination (AOR: 1.48, 95%CI: 1.31, 1.67) were positively associated with the dependent variable. Completion rate of primary COVID-19 vaccination series was low among Chinese cancer patients. Given the large population size and their vulnerability, this group urgently needs to increase COVID-19 vaccination coverage. Removing concerns about interactions between COVID-19 vaccination and cancers, using fear appeal approach, involving significant others, and facilitating patients to make a plan to receive COVID-19 vaccination might be useful strategies.

Hesitancy to Receive the Booster Doses of COVID-19 Vaccine Among Cancer Patients in China: A Multicenter Cross-Sectional Survey - Four PLADs, China, 2022.

What is already known about this topic?: Cancer patients are more vulnerable and have higher mortality rates from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) than the general population; however, coverage for booster doses of the coronavirus disease 2019 (COVID-19) vaccine was low among cancer patients in China. What is added by this report?: Overall, 32.0% and 56.4% of cancer patients from four Provincial Level Administrative Divisions (PLADs) expressed hesitancy toward the first and second booster doses, respectively. Factors negatively associated with hesitancy to receive booster doses included positive attitudes, perceived support, and higher exposure to COVID-19 vaccination information. Conversely, postvaccination fatigue was positively associated with vaccine hesitancy. What are the implications for public health practice?: Improved COVID-19 vaccination coverage is needed to promote health for cancer patients.

The use of nomogram for detecting mild cognitive impairment in patients with type 2 diabetes mellitus.

BACKGROUND: Type 2 diabetes mellitus (T2DM) is highly prevalent worldwide and may lead to a higher rate of cognitive dysfunction. This study aimed to develop and validate a nomogram-based model to detect mild cognitive impairment (MCI) in T2DM patients. METHODS: Inpatients with T2DM in the endocrinology department of Xiangya Hospital were consecutively enrolled between March and December 2021. Well-qualified investigators conducted face-to-face interviews with participants to retrospectively collect sociodemographic characteristics, lifestyle factors, T2DM-related information, and history of depression and anxiety. Cognitive function was assessed using the Mini-Mental State Examination scale. A nomogram was developed to detect MCI based on the results of the multivariable logistic regression analysis. Calibration, discrimination, and clinical utility of the nomogram were subsequently evaluated by calibration plot, receiver operating characteristic curve, and decision curve analysis, respectively. RESULTS: A total of 496 patients were included in this study. The prevalence of MCI in T2DM patients was 34.1% (95% confidence interval [CI]: 29.9%-38.3%). Age, marital status, household income, diabetes duration, diabetic retinopathy, anxiety, and depression were independently associated with MCI. Nomogram based on these factors had an area under the curve of 0.849 (95% CI: 0.815-0.883), and the threshold probability ranged from 35.0% to 85.0%. CONCLUSIONS: Almost one in three T2DM patients suffered from MCI. The nomogram, based on age, marital status, household income, duration of diabetes, diabetic retinopathy, anxiety, and depression, achieved an optimal diagnosis of MCI. Therefore, it could provide a clinical basis for detecting MCI in T2DM patients.

Prevalence of comorbid depression and associated factors among hospitalized patients with type 2 diabetes mellitus in Hunan, China.

BACKGROUND: Depression and diabetes are major health challenges, with heavy economic social burden, and comorbid depression in diabetes could lead to a wide range of poor health outcomes. Although many descriptive studies have highlighted the prevalence of comorbid depression and its associated factors, the situation in Hunan, China, remains unclear. Therefore, this study aimed to identify the prevalence of comorbid depression and associated factors among hospitalized type 2 diabetes mellitus (T2DM) patients in Hunan, China. METHODS: This cross-sectional study involved 496 patients with T2DM who were referred to the endocrinology inpatient department of Xiangya Hospital affiliated to Central South University, Hunan. Participants' data on socio-demographic status, lifestyle factors, T2DM-related characteristics, and social support were collected. Depression was evaluated using the Hospital Anxiety and Depression Scale-depression subscale. All statistical analyses were conducted using the R software version 4.2.1. RESULTS: The prevalence of comorbid depression among hospitalized T2DM patients in Hunan was 27.22% (95% Confidence Interval [CI]: 23.3-31.1%). Individuals with depression differed significantly from those without depression in age, educational level, per capita monthly household income, current work status, current smoking status, current drinking status, regular physical activity, duration of diabetes, hypertension, chronic kidney disease, stroke, fatty liver, diabetic nephropathy, diabetic retinopathy, insulin use, HbA1c, and social support. A multivariable logistic regression model showed that insulin users (adjusted OR = 1.86, 95% CI: 1.02-3.42) had a higher risk of depression, while those with regular physical activity (adjusted OR = 0.48, 95% CI: 0.30-0.77) or greater social support (adjusted OR = 0.20, 95% CI: 0.11-0.34) had a lower risk of depression. The area under the curve of the receiver operator characteristic based on this model was 0.741 with a sensitivity of 0.785 and specificity of 0.615. CONCLUSIONS: Depression was moderately prevalent among hospitalized T2DM patients in Hunan, China. Insulin treatment strategies, regular physical activity, and social support were significantly independently associated with depression, and the multivariable model based on these three factors demonstrated good predictivity, which could be applied in clinical practice.

Uptake of Heterologous or Homologous COVID-19 Booster Dose and Related Adverse Events Among Diabetic Patients: A Multicenter Cross-Sectional Study - China, 2022.

What is already known about this topic?: Although a third coronavirus disease 2019 (COVID-19) vaccination (booster) dose is highly recommended for diabetic patients, the vaccination behaviors and related adverse events are unclear among diabetic patients with a COVID-19 booster dose. What is added by this report?: Diabetic patients with higher postprandial blood glucose, worrying about the safety of the booster dose were less likely to get the vaccine. While having positive attitudes towards COVID-19 booster vaccination, trusting the health professionals' advice on vaccination, diabetic patients were more likely to get the booster vaccine. Furthermore, the prevalence of adverse events was not significantly different between the homologous and heterologous boosting groups. What are the implications for public health practice?: Effective measures should be taken to promote the COVID-19 booster dose uptake among diabetic patients. Health professionals should educate Chinese diabetic patients about the safety and efficacy of booster doses and continue to increase the COVID-19 booster dose vaccination coverage.

Spatial distribution and ecological risk assessment of soil heavy metals in a typical volcanic area: Influence of parent materials.

To understand the distribution characteristics and potential ecological risks of heavy metals in soils in the typical volcanic area, 2,592 soil samples were collected from the surface layer (0-20 cm) and 269 samples were collected from the middle (80-100 cm) and deep layers (180-200 cm) in northeast of Hainan province, China. Accordingly, eight heavy metals (Cu, Pb, Zn, Cr, Ni, Cd, As, and Hg) were analyzed and determined. The effects of different parent materials and land use types on the accumulation of heavy metals in soils were compared, and the primary heavy metal sources were analyzed. The pollution level and ecological risk of heavy metals in soils in the study area were evaluated using the geo-accumulation index (Igeo) and potential ecological risk indices (E i & RI). The results showed that, except that of Pb, the median concentrations of the analyzed heavy metals in the surface soils were higher than the background concentrations in the Hainan Island soils, indicating varying degrees of accumulation. The influence of land use type on the accumulation of heavy metals in surface soils varied from that of the parent materials. Anthropogenic activities highly influenced As, Cd, Hg, and Pb concentrations, whereas geological conditions primarily influenced Cr, Cu, Ni, and Zn concentrations. The Igeo results showed that the mean value of the eight metal elements were greater than zero, except for Pb. In the surface soils, the Igeo values of As, Cd, Hg, and Zn mostly fell into the light to moderate pollution class, and those of Cr, Cu, and Ni fell into the medium and heavy pollution class. The RI of the study area showed a high to significantly high ecological risk because of the Cd, Hg, and Ni concentrations. The results give a new insight in the parent material's geochemical control on the heavy metal elements in soils, and it can serve as a reference for the background value of local soil heavy metals and provide a scientific basis for controlling the potential ecological risk of heavy metals and reasonable land use plans.

Vertical distribution and major influencing factors of soil selenium in tropical climate: A case study of Chengmai County, Hainan Island.

Soil selenium is of great significance to human health. Soil-forming parent rocks are the most critical factor that influences soil Se levels. Chengmai County, Hainan Island, has a tropical climate and diverse types of parent rocks, in which soil Se content is high. This study investigated the vertical distribution of soil Se from various parent rock substrates under tropical climatic conditions, and the factors that influence these soil Se contents, with 69 vertical soil profiles covering Chengmai County. The vertical distribution of soil Se and correlations with CIA (chemical alteration index), Al2O3, TFe2O3 (total iron oxide expressed as Fe2O3), total iodine, SOC (soil organic carbon), and pH were analysed. As per the results, the mean ± standard error of Se content in the A, B, and C horizons was 0.88 ± 0.13 mg/kg, 0.77 ± 0.08 mg/kg and 0.45 ± 0.05 mg/kg, respectively. The parent rock strictly controlled the horizon distribution of Se in the A-horizon. Soil Se showed A-B-horizons-enrichment in the vertical profile, especially in soil profiles overlying granite and basalt. It is hypothesised that the Se enriched in soils developed from the Tuolie Formation due to the release of Se from the weathering process of Se-rich rocks. Meanwhile, Se in soils developed from granite and basalt is more closely associated with exogenous input. Another crucial factor for the high level of Se in Chengmai County is the tropical climate, which has led the rocks to generally undergo intense chemical weathering. This results in soils rich in clay minerals and Fe/Al oxyhydroxides, which easily absorb and retain Se. Furthermore, the Se content of the B-horizon was generally higher than that of the A-horizon due to leaching. These results provide further knowledge and understanding of the geochemical behaviour of soil Se and guide the evaluation of Se-rich land resources under tropical climatic conditions.

Clinical evaluation of AI-assisted screening for diabetic retinopathy in rural areas of midwest China.

BACKGROUND: Although numerous studies have described the application of artificial intelligence (AI) in diabetic retinopathy (DR) screening among diabetic populations, studies among populations in rural areas are rare. The purpose of this study was to evaluate the application value of an AI-based diagnostic system for DR screening in rural areas of midwest China. METHODS: In this diagnostic accuracy study, diabetes mellitus (DM) patients in the National Basic Public Health Information Systems of Licheng County and Lucheng County of Changzhi city from July to December 2020 were selected as the target population. A total of 7824 eyes of 3933 DM patients were enrolled in this screening; the patients included 1395 males and 2401 females, with an average age of 19-87 years (63±8.735 years). All fundus photographs were collected by a professional ophthalmologist under natural pupil conditions in a darkroom using the Zhiyuan Huitu fundus image AI analysis software EyeWisdom. The AI-based diagnostic system and ophthalmologists were tasked with diagnosing the photos independently, and the consistency rate, sensitivity and specificity of the two methods in diagnosing DR were calculated and compared. RESULTS: The prevalence rates of DR according to the ophthalmologist and AI diagnoses were 22.7% and 22.5%, respectively; the consistency rate was 81.6%. The sensitivity and specificity of the AI system relative to the ophthalmologists' grades were 81.2% (95% confidence interval [CI]: 80.3% 82.1%) and 94.3% (95% CI: 93.7% 94.8%), respectively. There was no significant difference in diagnostic outcomes between the methods (χ2 = 0.329, P = 0.566, P>0.05), and the AI-based diagnostic system had high consistency with the ophthalmologists' diagnostic results (κ = 0.752). CONCLUSION: Our research demonstrated that DR patients in rural area hospitals can be screened feasibly. Compared with that of the ophthalmologists, however, the accuracy of the AI system must be improved. The results of this study might lend support to the large-scale application of AI in DR screening among different populations.

[Enrichment Factors, Health Risk, and Source Identification of Heavy Metals in Agricultural Soils in Semi-arid Region of Hainan Island].

To understand the enrichment factors and pollution levels of heavy metals in agricultural soils in the semi-arid region of Hainan island, 1818 surface soil samples were collected in Gancheng Town and analyzed for their heavy metal contents and physicochemical composition. Correlation analysis was used to determine the heavy metal enrichment factors. The geo-accumulation index (Igeo), comprehensive ecological risk index (RI), and hazard index (HI), as well as carcinogenic risk (CR), were used to assess the degree of pollution and health risk. Positive matrix factorization (PMF) was used to determine the primary sources of pollution and priority sources. The average values of heavy metal contents in the topsoil were 22.7, 0.128, 33.4, 14.5, 0.032, 9.32, 32.5, and 43.3 mg·kg-1 for As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn, respectively. With the exception of Zn, the concentrations of other heavy metals in the topsoil were higher than the soil background values of Hainan, showing different degrees of heavy metal accumulation effect. The Igeo revealed that the major pollutant element in soils was As, followed by Cd and Cu. The RI showed that the proportion of soil samples that were high-risk level or worse was 29.4% of the total number of samples, among which As was the major source of risk. The health risk assessment results indicated that As, Cr, and Ni exposure presented carcinogenic risk for children with high CR values. Based on PMF, four major sources of heavy metals were identified in the study area. Hg was derived mainly from industrial sources, and As was closely associated with agricultural activities. Ni, Cu, Cr, and Zn were related to soil parent materials. Pb and Cd were associated with agricultural activities and traffic emissions. The PMF models combined with correlation analysis were useful for estimating the source apportionment of heavy metals in soils.