INTRODUCTION: During the coronavirus disease 2019 (COVID-19) pandemic, contact tracers were under immense pressure to deliver effective and timely contact tracing, raising concerns of higher susceptibility to burnout. Our study aimed to determine burnout prevalence among hospital-based contact tracers and associated risk factors, so that interventions to reduce burnout risk could be formulated. METHODS: One hundred and ninety-six active contact tracers across three hospitals within a healthcare cluster were invited to complete an anonymous online survey. To identify burntout, data such as demographics, work-related variables and contact tracing-related variables were collected using the Copenhagen Burnout Inventory. Associated factors were identified using multivariate statistics. Open-ended questions were included to understand the challenges and potential improvements through qualitative analysis. RESULTS: A total of 126 participants completed the survey, giving a completion rate of 64%, and almost half of these participants (42.9%) reported burnout. Protective factors included being on work-from-home arrangements (adjusted odds ratio [OR] 0.22, 95% confidence interval [CI] 0.08-0.56), perception of being well supported by their institution (adjusted OR 0.25, 95% CI 0.08-0.80) and being married (adjusted OR 0.28, 95% CI 0.12-0.64). Risk factors included having an administrative role pre-COVID-19 (adjusted OR 3.62, 95% CI 1.33-9.83). Work-related burnout was related to being activated for more than 1 day in the preceding week (unadjusted OR 3.25, 95% CI 1.33-7.94) and multiple activations in a day (unadjusted OR 3.54, 95% CI 1.44-4.41). Biggest challenges identified by participants were language barrier (62.7%), followed by workflow-related issues (42.1%). CONCLUSION Our study demonstrated burnout and other challenges faced by a team of mostly hospital-based administrative staff redeployed on a part-time basis to ensure timely contact tracing. To mitigate burnout, we recommend choosing staff on work-from-home arrangements and ensuring adequate manpower and rostering arrangements.
Humans ; COVID-19/epidemiology* ; Burnout, Professional/epidemiology* ; Singapore/epidemiology* ; Female ; Male ; Cross-Sectional Studies ; Adult ; Middle Aged ; Risk Factors ; Surveys and Questionnaires ; Contact Tracing/methods* ; SARS-CoV-2 ; Prevalence ; Pandemics

OBJECTIVE: The aim of this study is to investigate the independent risk factors of benign prostatic hyperplasia (BPH) complicated with bladder stones, and construct a nomogram prediction model for clinical progression of bladder stones in patients with BPH. METHODS: The clinical data of 368 BPH patients who underwent transurethral resection of the prostate in the Second Affiliated Hospital of Zhengzhou University from January 2018 to January 2021 were retrospectively analyzed. Patients with BPH were divided into group 1 (with bladder stones, n=94) and group 2 (without bladder stones, n=274). Univariate and multivariate logistic regression analyses were performed to determine the independent risk factors of bladder stones in patients with BPH. A nomogram model was developed, and the areas under the ROC curve and calibration curve were calculated to assess the accuracy of clinical application. RESULTS: Logistic analysis showed that age (HR:1.075,95%CI:1.032 to 1.120), hypertension (HR:2.801,95%CI:1.520 to 5.161), blood uric acid (HR:1.006,95%CI:1.002 to 1.010), intravesical prostatic protrusion (HR:1.189,95%CI1.119 to 1.264）, prostatic urethral angel（HR:1.127,95%CI:1.078to 1.178）were independent risk factors for bladder stones in patients with BPH. The discrimination of the nomogram model based on independent risk factors to predict the occurrence of bladder stones in patients with BPH was 0.874. CONCLUSION The nomogram model can predict the risk of bladder stones in BPH patients with good differentiation and calibration, which is a good guide for clinical work on BPH patients with high risk of bladder stones.
Humans ; Male ; Prostatic Hyperplasia/complications* ; Nomograms ; Urinary Bladder Calculi/etiology* ; Retrospective Studies ; Risk Factors ; Aged ; Logistic Models ; Middle Aged ; ROC Curve ; Transurethral Resection of Prostate

OBJECTIVE: To explore the neuroprotective effects of Xuefu Zhuyu Decoction (XFZYD) based on in vivo and metabolomics experiments. METHODS: Traumatic brain injury (TBI) was induced via a controlled cortical impact (CCI) method. Thirty rats were randomly divided into 3 groups (10 for each): sham, CCI and XFZYD groups (9 g/kg). The administration was performed by intragastric administration for 3 days. Neurological functions tests, histology staining, coagulation and haemorheology assays, and Western blot were examined. Untargeted metabolomics was employed to identify metabolites. The key metabolite was validated by enzyme-linked immunosorbent assay and immunofluorescence. RESULTS: XFZYD significantly alleviated neurological dysfunction in CCI model rats (P<0.01) but had no impact on coagulation function. As evidenced by Evans blue and IgG staining, XFZYD effectively prevented blood-brain barrier (BBB) disruption (P<0.05, P<0.01). Moreover, XFZYD not only increased the expression of collagen IV, occludin and zona occludens 1 but also decreased matrix metalloproteinase-9 (MMP-9) and cyclooxygenase-2 (COX-2), which protected BBB integrity (all P<0.05). Nine potential metabolites were identified, and all of them were reversed by XFZYD. Adenosine was the most significantly altered metabolite related to BBB repair. XFZYD significantly reduced the level of equilibrative nucleoside transporter 2 (ENT2) and increased adenosine (P<0.01), which may improve BBB integrity. CONCLUSIONS XFZYD ameliorates BBB disruption after TBI by decreasing the levels of MMP-9 and COX-2. Through further exploration via metabolomics, we found that XFZYD may exert a protective effect on BBB by regulating adenosine metabolism via ENT2.
Animals ; Drugs, Chinese Herbal/therapeutic use* ; Blood-Brain Barrier/metabolism* ; Brain Injuries, Traumatic/metabolism* ; Adenosine/metabolism* ; Male ; Rats, Sprague-Dawley ; Rats

Objective This study investigated the impact of occupational mercury(Hg)exposure on human gene transcription and expression,and its potential biological mechanisms. Methods Differentially expressed genes related to Hg exposure were identified and validated using gene expression microarray analysis and extended validation.Hg-exposed cell models and PTEN low-expression models were established in vitro using 293T cells.PTEN gene expression was assessed using qRT-PCR,and Western blotting was used to measure PTEN,AKT,and PI3K protein levels.IL-6 expression was determined by ELISA. Results Combined findings from gene expression microarray analysis,bioinformatics,and population expansion validation indicated significant downregulation of the PTEN gene in the high-concentration Hg exposure group.In the Hg-exposed cell model(25 and 10 μmol/L),a significant decrease in PTEN expression was observed,accompanied by a significant increase in PI3K,AKT,and IL-6 expression.Similarly,a low-expression cell model demonstrated that PTEN gene knockdown led to a significant decrease in PTEN protein expression and a substantial increase in PI3K,AKT,and IL-6 levels. Conclusion This is the first study to report that Hg exposure downregulates the PTEN gene,activates the PI3K/AKT regulatory pathway,and increases the expression of inflammatory factors,ultimately resulting in kidney inflammation.

Objective To explore the efficiency of artificial intelligence and radiomics-assisted X-ray in diagnosis of lumbar osteoporotic vertebral compression fractures(OVCF).Methods The clinical data of 455 patients diagnosed as lumbar OVCF by MRI in our hospital were selected.The patients were divided into the training group(n=364)and the validation group(n=91),X-ray films were extracted,the image delineation,feature extraction and data analysis were carried out,and the artificial intelligence radiomics deep learning was applied to establish a diagnostic model for OVCF.After verifying the effectiveness of the model by receiver operating characteristic(ROC)curve,area under the curve(AUC),calibration curve,and decision curve analysis(DCA),the efficiencies of manual reading,model reading,and model-assisted manual reading of X-ray in the early diagnosis of OVCF were compared.Results The ROC curve,AUC and calibration curve proved that the model had good discrimination and calibration,and excellent diagnostic performance.DCA demonstrated that the model had a higher clinical net benefit.The diagnostic efficiency of the manual reading group:the accuracy rate was 0.89,the recall rate was 0.62.The diagnostic efficiency of the model reading group:the accuracy rate was 0.93,the recall rate was 0.86,the model diagnosis showed good predictive performance,which was significantly better than the manual reading group.The diagnostic efficiency of the model-assisted manual reading group:the accuracy rate was 0.92,the recall rate was 0.72,and the recall rate of the model-assisted manual reading group was higher than that of the manual reading group,but lower than that of the model reading group,indicating the superiority of the model diagnosis.Conclusion The diagnostic model established based on artificial intelligence and radiomics in this study has reached an ideal level of efficacy,with better diagnostic efficacy compared with manual reading,and can be used to assist X-ray in the early diagnosis of OVCF.

Objective To develop a clinical prediction model for predicting risk factors for prolonged hospital stay after anterior cervical discectomy and fusion(ACDF).Methods The clinical data of 914 patients underwent ACDF treatment for cervical spondylotic myelopathy(CSM)were retrospectively analyzed.According to the screening criteria,800 eligible patients were eventually included,and the patients were divided into the development cohort(n=560)and the validation cohort(n=240).LASSO regression was used to screen variables,and multivariate Logistic regression analysis was used to establish a prediction model.The prediction model was evaluated from three aspects:differentiation,calibration and clinical effectiveness.The performance of the model was evaluated by area under the curve(AUC)and Hosmer-Lemeshow test.Decision curve analysis(DCA)was used to evaluate the clinical effectiveness of the model.Results In this study,the five factors that were significantly associated with prolonged hospital stay were male,abnormal BMI,mild-to-moderate anemia,stage of surgery(morning,afternoon,evening),and alcohol consumption history.The AUC of the development cohort was 0.778(95%CI:0.740 to 0.816),with a cutoff value of 0.337,and that of the validation cohort was 0.748(95%CI:0.687 to 0.809),with a cutoff value of 0.169,indicating that the prediction model had good differentiation.At the same time,the Hosmer-Lemeshow test showed that the model had a good calibration degree,and the DCA proved that it was effective in clinical application.Conclusion The prediction model established in this study has excellent comprehensive performance,which can better predict the risk of prolonged hospital stay,and can guide clinical intervention as soon as possible,so as to minimize the postoperative hospital stay and reduce the cost of hospitalization.

Objective To explore the risk factors for surgical site infection(SSI)after transforaminal lumbar interbody fusion(TLIF)for the treatment of lumbar degenerative diseases.Methods A total of 1 000 patients who underwent TLIF for lumbar degenerative diseases in our hospital were included and divided into the infection group(n=23)and the non-infection group(n=977)according to whether the surgical incision was infected.General data,surgical and laboratory indicators of patients were collected,and potential risk factors of SSI were screened by univariate analysis and multivariate regression analysis,a nomogram model was established,and its predictive efficiency was validated by the receive operating characteristic(ROC)curve.Results The incidence of SSI in patients after TLIF was 2.3％.The results of univariate analysis showed that age,operative time,intraoperative blood loss,preoperative C-reactive protein(CRP),smoking,and diabetes mellitus were the significant risk factors for the occurrence of SSI.Multivariate regression analysis showed that older age,longer operation time,more intraoperative blood loss,smoking and diabetes mellitus were the independent risk factors for postoperative SSI.ROC curve showed that the nomogram model established in this study has good predictive efficiency.Conclusion Older age,longer operation time,more intraoperative blood loss,smoking,and diabetes mellitus were independent risk factors for postoperative SSI.For patients with these high risk factors,corresponding intervention measures should be taken before operation to reduce the incidence of SSI.

Objective To summarize the clinical characteristics of 2 cases of styloid-carotid syndrome(SCS)and review the literature to enhance understanding of the disease.Methods A retrospective analysis was conducted on the clinical manifestations,auxiliary examinations,and diagnosis and treatment of 2 patients with SCS admitted to the Neurology Department of Kaifeng Central Hospital.Additionally,relevant literature was searched through domestic and foreign databases such as PubMed,WOS,Embase,CNKI and VIP.The clinical characteristics of SCS were summarized based on the literature results.Results The 2 cases were diagnosed as transient cerebral ischemia(TIA)combined with SCS through head and neck CT angiography(CTA)and styloid process CT.Apart from the 2 cases treated in our hospital,a total of 11 cases of SCS have been reported in Chinese and English literature up to October 2023.Among the 13 cases,11 cases(84.6%)started with episodic TIA symptoms,and 11 cases(84.6%)had obvious inducing factors related to specific head position changes.Common clinical manifestations included unilateral limb weakness with or without sensory disturbance(10 cases,76.9%),slurred speech(7 cases,53.8%),unilateral limb sensation disorder(4 cases,30.7%),syncope(3 cases,23.1%)and amaurosis(2 cases,15.4%).All 13 cases underwent 64-row head and neck CTA examination,and 6 cases(46.2%)dynamically observed the changes in blood flow velocity through examinations such as transcranial Doppler ultrasound(TCD),cervical vascular ultrasound,and digital subtraction angiography(DSA).All patients were followed up for more than 3 months;and 10 cases(76.9%)achieved clinical cure after treatment,of which 8 cases underwent styloid process shortening surgery;3 cases(23.1%)achieved clinical symptom improvement after treatment.Conclusions For patients with recurrent TIA and/or cerebral infarction,it is necessary to identify whether there are inducing factors related to specific body position changes.For patients highly suspected of SCS,routine examinations such as styloid process CT and 64-row head and neck CTA should be performed,and if necessary,whole brain DSA,dynamic TCD and/or carotid ultrasound should be conducted to guide the diagnosis and treatment.When non-surgical treatment is ineffective,radical styloid process truncation can be considered as a treatment option.

The complex hemodynamic environment within the aortic lumen plays a crucial role in the progression of aortic diseases such as aneurysms and dissections. Traditional imaging modalities often fail to provide comprehensive flow dynamics that are essential for precise risk assessment and timely intervention. The advent of time-resolved, three-dimensional (3D) phase-contrast magnetic resonance imaging (4D flow MRI) has revolutionized the evaluation of aortic diseases by allowing a detailed visualizations of flow patterns and quantification of hemodynamic parameters. This review explores the utility of 4D flow MRI in the assessment of thoracic aortic diseases, highlighting the key hemodynamic parameters, including flow velocity, wall shear stress, oscillatory shear index, relative residence time, vortex, turbulent kinetic energy, flow displacement, pulse wave velocity, aortic distensibility, energy loss, and stasis. We elucidate the significant findings of studies utilizing 4D flow MRI in the context of aortic aneurysms and dissections, highlighting its role in enhancing our understanding of disease mechanisms and improving clinical outcomes. This review underscores the potential of 4D flow MRI to refine risk stratification and guide therapeutic decisions, ultimately contributing to better management of aortic diseases.

The complex hemodynamic environment within the aortic lumen plays a crucial role in the progression of aortic diseases such as aneurysms and dissections. Traditional imaging modalities often fail to provide comprehensive flow dynamics that are essential for precise risk assessment and timely intervention. The advent of time-resolved, three-dimensional (3D) phase-contrast magnetic resonance imaging (4D flow MRI) has revolutionized the evaluation of aortic diseases by allowing a detailed visualizations of flow patterns and quantification of hemodynamic parameters. This review explores the utility of 4D flow MRI in the assessment of thoracic aortic diseases, highlighting the key hemodynamic parameters, including flow velocity, wall shear stress, oscillatory shear index, relative residence time, vortex, turbulent kinetic energy, flow displacement, pulse wave velocity, aortic distensibility, energy loss, and stasis. We elucidate the significant findings of studies utilizing 4D flow MRI in the context of aortic aneurysms and dissections, highlighting its role in enhancing our understanding of disease mechanisms and improving clinical outcomes. This review underscores the potential of 4D flow MRI to refine risk stratification and guide therapeutic decisions, ultimately contributing to better management of aortic diseases.