Prevalence and Risk Factors of Burnout Among Chinese Pediatric Orthopedic Surgeons.

BACKGROUND: Burnout is an occupational hazard among Chinese pediatric orthopedists, characterized by extreme physical and emotional exhaustion, and reduced professional efficacy; however, it has yet to be studied among this group of professionals in China. Our study aimed to assess the levels of burnout in Chinese pediatric orthopedists, and to identify the potential risk factors for burnout. METHODS: A 32-question, anonymous, cross-sectional survey was conducted from August to September 2019. Overall, 1392 Chinese pediatric orthopedists participated in the survey. RESULTS: Seven hundred valid questionnaires (50.3% response rate) were retrieved from 387 (55.3%) and 313 (44.7%) full-time and part-time pediatric orthopedists, respectively. Overall, 73.7% of the participants experienced burnout, of which 64.7% and 9.0% had some and severe burnout symptoms, respectively. The burnout levels significantly differed based on age (P=0.005), years in service (P=0.006), professional rank (P=0.03), weekly working hours (P<0.001), and monthly income (P=0.03). A binary logistic regression model showed that longer weekly working hours (adjusted odds ratio=1.29, 95% confidence interval: 1.09-1.52, P=0.004) was a risk factor for burnout, while higher monthly income (adjusted odds ratio=0.78, 95% confidence interval: 0.64 to 0.95, P=0.02) was protective against burnout, suggesting that younger pediatric orthopedists were more susceptible. No significant difference between full-time and part-time pediatric orthopedists or between sexes was detected in the adjusted analysis. CONCLUSIONS: Chinese pediatric orthopedists have a relatively high rate of burnout. Younger pediatric orthopedists have a greater chance of experiencing burnout. These results highlight the need for further policies, especially focused on younger pediatric orthopedists, to assist in better developing Chinese pediatric orthopedics. LEVEL OF EVIDENCE: Level: IV.

High-glucose conditions attenuate the response of macrophages to Legionella pneumophila infection by inhibiting NOD1 and MAPK signaling.

BACKGROUND: Patients with diabetes are particularly susceptible to Legionella pneumophila (LP) infection, but the exact pathogenesis of LP infection in diabetic patients is still not fully understood. Herein, we investigated the effect of diabetes on immune function during LP infection in vitro and in vivo. METHODS: The time course of LP infection in macrophages under normal and high-glucose (HG) conditions was examined in vitro. Western blot was used to determine nucleotide-binding oligomerization domain 1 (NOD1), kinase 1/2 (ERK1/2), mitogen-activated protein kinase p38 (MAPK p38), and c-Jun N-terminal kinases (JNK). Enzyme-linked immunosorbent assay (ELISA) was used to assess the secretion of tumor necrosis factor-α (TNF-α) and interleukin-6 (IL-6). Cell Counting Kit-8 (CCK8) assay assessed U937 cell viability after treating cells with different concentrations of high sugar medium and ML130 (NOD1 inhibitor). For the in vivo study, normal and streptozocin-induced diabetic guinea pigs were infected with LP for 6, 24, and 72 h, after which NOD1, MAPK-related signals, TNF-α, and IL-6 expression in lung tissues were assessed using immunohistochemistry, western blot, and RT-PCR. RESULTS: HG attenuated the upregulation of NOD1 expression and reduced TNF-α and IL-6 secretion caused by LP compared with LP-infected cells exposed to normal glucose levels (all p < 0.05). In diabetic guinea pigs, HG inhibited the upregulation of NOD1 expression in lung tissues and the activation of p38, ERK1/2, and cJNK caused by LP infection compared to control pigs (all p < 0.05). CONCLUSION: HG attenuates the response of macrophages to LP infection by inhibiting NOD1 upregulation and the activation of MAPK signaling.

An open auscultation dataset for machine learning-based respiratory diagnosis studies.

Machine learning enabled auscultating diagnosis can provide promising solutions especially for prescreening purposes. The bottleneck for its potential success is that high-quality datasets for training are still scarce. An open auscultation dataset that consists of samples and annotations from patients and healthy individuals is established in this work for the respiratory diagnosis studies with machine learning, which is of both scientific importance and practical potential. A machine learning approach is examined to showcase the use of this new dataset for lung sound classifications with different diseases. The open dataset is available to the public online.

Compatibility and Interaction Mechanism between EPDM Rubber and a SF6 Alternative Gas-C4F7N/CO2/O2.

Among the numerous novel eco-friendly insulating gases, C4F7N has attracted much attention due to its excellent electrical performance. However, except for the electrical perfomance, the compatibility between the gas medium and the sealing materials is equally important for gas-insulated equipment. At present, studies about the compatibility between C4F7N and EPDM, a widely used sealing material in power systems, are available in some previous works, but few focused on the compatibility comparison between C4F7N gas mixtures and EPDM with different third monomers. In this paper, we carried out the thermal aging test on ENB-EPDM, DCPD-EPDM, and C4F7N gas mixture to perfect the compatibility mechanism between EPDM and C4F7N. It was found that both of the EPDM reacted with the gas mixture and led to the property changes in the solid samples and the decomposition of C4F7N. On the other hand, by coating silicone grease, the contact between gas and rubber was effectively blocked and the concentration of the decomposition product was significantly reduced. The performance comparison indicates that ENB-EPDM is more suitable for sealing the C4F7N gas mixture, which is due to the superior thermal stability of ENB.

Risk prediction of in-hospital mortality among patients with type 2 diabetes mellitus and concomitant community-acquired pneumonia.

BACKGROUND: The aim of the present study was to investigate the risk factors for in-hospital mortality among patients with type 2 diabetes mellitus (T2DM) and concomitant community-acquired pneumonia (CAP) and establish a risk prediction score. METHODS: Data from 1,360 adult patients with T2DM and concomitant CAP hospitalized in two grade 3A hospitals between 2009 and 2019 were collected through electronic medical records. Data obtained included the status of diabetes mellitus, comorbidities, laboratory and imaging findings, and treatment outcomes. Statistical analysis was conducted to investigate the risk factors affecting prognosis, and a clinical risk prediction score was designed. RESULTS: Based on the patients' treatment outcomes (deceased, improved and cured), the cohort was divided into two groups: deceased and improved; 16 parameters were significant after segmentation. However, the following nine parameters were independent predictors of mortality: neutrophil-lymphocyte ratio (NLR) ≥4, pulse rate ≥125 bpm, change in state of consciousness, arterial blood pH ≤7.35, age ≥65 years, serum sodium ≤130 mmol/L, initial fasting blood glucose ≥9 mmol/L, multilobar involvement, and diabetic nephropathy. Based on these findings, a risk prediction score was established, and bootstrap validation was performed. The risk prediction score was significantly superior to CURB-65 [confusion, urea >7 mmol/L, respiratory rate >30/min, low blood pressure (systolic <90 mmHg or diastolic <60 mmHg), age >65 years] and slightly superior than the pneumonia severity index (PSI). CONCLUSIONS: The influencing factors for in-hospital mortality among patients with T2DM and concomitant CAP included advanced age, change in state of consciousness, increased pulse rate, acidosis, high NLR, high platelet-lymphocyte ratio, hyponatremia, hyperglycemia, and diabetic nephropathy. These parameters should be recognized in clinical practice, with active interventions to improve the treatment success rate. The risk prediction score effectively differentiated the mortality risk of inpatients, thereby providing guidance on clinical decision-making.