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We aim to quantitatively synthesise available epidemiological evidence on the prevalence rates of workplace violence (WPV) by patients and visitors against healthcare workers. We systematically searched PubMed, Embase and Web of Science from their inception to October 2018, as well as the reference lists of all included studies. Two authors independently assessed studies for inclusion. Data were double-extracted and discrepancies were resolved by discussion. The overall percentage of healthcare worker encounters resulting in the experience of WPV was estimated using random-effects meta-analysis. The heterogeneity was assessed using the I 2 statistic. Differences by study-level characteristics were estimated using subgroup analysis and meta-regression. We included 253 eligible studies (with a total of 331 544 participants). Of these participants, 61.9% (95% CI 56.1% to 67.6%) reported exposure to any form of WPV, 42.5% (95% CI 38.9% to 46.0%) reported exposure to non-physical violence, and 24.4% (95% CI 22.4% to 26.4%) reported experiencing physical violence in the past year. Verbal abuse (57.6%; 95% CI 51.8% to 63.4%) was the most common form of non-physical violence, followed by threats (33.2%; 95% CI 27.5% to 38.9%) and sexual harassment (12.4%; 95% CI 10.6% to 14.2%). The proportion of WPV exposure differed greatly across countries, study location, practice settings, work schedules and occupation. In this systematic review, the prevalence of WPV against healthcare workers is high, especially in Asian and North American countries, psychiatric and emergency department settings, and among nurses and physicians. There is a need for governments, policymakers and health institutions to take actions to address WPV towards healthcare professionals globally.

A photovoltaic array fault diagnosis method based on an improved honey badger optimization algorithm is proposed to improve the accuracy of photovoltaic array fault diagnosis. Firstly, analyze the current and power output characteristic curves of the photovoltaic array under different states, and construct a preliminary set of 10 dimensional fault feature vectors. Secondly, the feature vector set is ranked in importance using the random forest algorithm, and then input into support vector machines, long short-term memory, and bidirectional long short-term memory neural networks to obtain the optimal combination of the base model and the number of features. Then, the honey badger optimization algorithm was improved by combining Tent chaotic mapping column measurement, improved control factors, and pinhole imaging strategy, and compared with other optimization algorithms to demonstrate its effectiveness in optimization ability, stability, and convergence speed. Finally, by combining the improved honey badger optimization algorithm with the optimal base model and number of features, the problem of hyperparameter setting in the base model was effectively solved. The experimental results show that the fault diagnosis accuracy of the proposed photovoltaic array fault diagnosis model is 97.1014%, which is superior to other models and verifies the effectiveness of the proposed method.

Background High occupational burnout among general practitioners (GPs) is an important challenge to China’s efforts to strengthen its primary healthcare delivery; however, data to help understand this issue are unavailable. This study aimed to investigate the prevalence of burnout and associated factors among GPs. Methods A cross-sectional design was used to collect data from December 12, 2014, to March 10, 2015, with a self-administered structured questionnaire from 1015 GPs (response rate, 85.6%) in Hubei Province, Central China. Burnout was measured using a 22-item Maslach Burnout Inventory-Human Services Survey (MBI-HSS). MBI-HSS scores and frequency were analyzed by the three dimensions of emotional exhaustion (EE), depersonalization (DP), and personal accomplishment (PA). Factors associated with burnout among GPs were estimated using a multiple linear regression model. Results Of the respondents, 2.46% had a high level of burnout in all three dimensions, 24.83% reported high levels of EE, 6.21% scored high on DP, and 33.99% were at high risk of PA. GPs who were unmarried, had lower levels of job satisfaction, and had been exposed to workplace violence experienced higher levels of burnout. Intriguingly, no statistically significant associations were found between burnout and the duration of GP practice, age, sex, income, practice setting, and professional level. Conclusion This is the first study of occupational burnout in Chinese general practice. Burnout is prevalent among GPs in Hubei, China. Interventions aimed at increasing job satisfaction, improving doctor-patient relationships and providing safer workplace environments may be promising strategies to reduce burnout among GPs in Hubei, China.

With the advent of the era of rapid information expansion, the massive data backlog that exists on the Internet has led to a serious information overload problem, which makes recommendation systems a crucial part of human life. In particular, the Point-Of-Interest (POI) recommendation system has been applied to many real-life scenarios, such as life services and autonomous driving. Specifically, the goal of POI recommendation is to recommend locations that match their personalized preferences to users. In existing POI recommendation methods, people tend to pay more attention to the impact of temporal and spatial factors of POI on users, which will alleviate the problems of data sparsity and cold start in POI recommendation. However, this tends to ignore the differences among individual users, and considering only temporal and spatial attributes does not support fine-grained POI recommendations. To solve this problem, we propose a new Fine-grained POI Recommendation With Multi-Graph Convolutional Network (FP-MGCN). This model focuses on the content representation of POIs, captures users’ personalized preferences using semantic information from user comments, and learns fine-grained representations of users and POIs through the relationships between content–content, content–POI, and POI–user. FP-MGCN employs multiple embedded propagation layers and adopts information propagation mechanisms to model the higher-order connections of different POI-related relations for enhanced representation. Fine-grained POI is finally recommended to users through the three types of propagation we designed: content–content information propagation, content–POI information propagation, and POI–user information propagation. We have conducted detailed experiments on two datasets, and the results show that FP-MGCN has advanced performance and can alleviate the data sparsity problem in POI recommendation tasks.

Sensitivity to platinum-based combination chemotherapy is associated with a favorable prognosis in patients with non-small cell lung cancer (NSCLC). Here, our results obtained from analyses of the Gene Expression Omnibus database of NSCLC patients showed that cartilage acidic protein 1 (CRTAC1) plays a role in the response to platinum-based chemotherapy. Overexpression of CRTAC1 increased sensitivity to cisplatin in vitro, whereas knockdown of CRTAC1 decreased chemosensitivity of NSCLC cells. In vivo mouse experiments showed that CRTAC1 overexpression increased the antitumor effects of cisplatin. CRTAC1 overexpression promoted NFAT transcriptional activation by increasing intracellular Ca 2+ levels, thereby inducing its regulated STUB1 mRNA transcription and protein expression, accelerating Akt1 protein degradation and, in turn, enhancing cisplatin-induced apoptosis. Taken together, the present results indicate that CRTAC1 overexpression increases the chemosensitivity of NSCLC to cisplatin treatment by inducing Ca 2+ -dependent Akt1 degradation and apoptosis, suggesting the potential of CRTAC1 as a biomarker for predicting cisplatin chemosensitivity. Our results further reveal that modulating the expression of CRTAC1 could be a new strategy for increasing the efficacy of cisplatin in chemotherapy of NSCLC patients.

Developing red fluorescence emitters with simple structures via convenient synthetic routes is highly desirable yet challenging. Herein, two novel donor–acceptor-type red emitters, DCFOPV-TPA and SCFOPV-TPA, featuring the intramolecular charge transfer effect were designed by integrating triphenylamine and trifluoromethyl into a CN-substituted oligo(p-phenylene vinylene) backbone. Both chromophores exhibited aggregation-induced enhanced emission and solvatochromic behavior. Moreover, DCFOPV-TPA also displayed reversible mechanofluorochromic properties under external force.

For student entrepreneurs with insufficient business experience, they need to hold an open attitude to maintain their competitive advantages in the market and improve their entrepreneurial performance via recruiting talents and creating sales channels using various network resources and advanced IT development technologies. Although field scholars in the world have conducted a lot of research on the evaluation of college students’ innovativeness and entrepreneurship, the differences in study regions, education modes, education concepts, and measurement tools have resulted in large deviations in the evaluation results of different study regions and schools. For this reason, this paper aims to find out the influence mechanism of the improvement of college students’ network ability on the scale of Entrepreneurial Cooperation Network (ECN). At first, the network ability of college students and their fitness degree to harmonious entrepreneurship were measured and analyzed; then, the influence of network ability improvement on ECN scale was studied empirically, and the model regression estimation results were given, which has verified that the psychological quality of student entrepreneurs has a moderating effect on the relationship between their network ability and the scale of ECN they created.

Background mTOR pathway is known to promote cancer malignancy and influence cancer immunity but is unknown for its role in immune checkpoint inhibitors (ICI) therapy. Methods Using Memorial Sloan-Kettering Cancer Center dataset (MSKCC), we extracted mTOR pathway gene mutations for stepwise Cox regression in 1661 cancer patients received ICI. We associated the mutation of the gene signature resulted from the stepwise Cox regression with the 1661 patients’ survival. Other 553 ICI-treated patients were collected from 6 cohorts for validation. We also performed this survival association in patients without ICI treatment from MSKCC as discovery (n = 2244) and The Cancer Genome Atlas (TCGA) as validation (n = 763). Pathway enrichment analysis were performed using transcriptome profiles from TCGA and IMvigor210 trial to investigate the potential mechanism. Results We identified 8 genes involved in mTOR pathway, including FGFR2 , PIK3C3 , FGFR4 , FGFR1 , FGF3 , AKT1 , mTOR , and RPTOR , resulted from stepwise Cox regression in discovery (n = 1661). In both discovery (n = 1661) and validation (n = 553), the mutation of the 8-gene signature was associated with better survival of the patients treated with ICI, which was independent of tumor mutation burden (TMB) and mainly attributed to the missense mutations. This survival association was not observed in patients without ICI therapy. Intriguingly, the mutation of the 8-gene signature was associated with increased TMB and PD1/PD-L1 expression. Immunologically, pathways involved in anti-tumor immune response were enriched in presence of this mutational signature in mTOR pathway, leading to increased infiltration of immune effector cells (e.g., CD8 + T cells, NK cells, and M1 macrophages), but decreased infiltration of immune inhibitory M2 macrophages. Conclusions These results suggested that mTOR pathway gene mutations were predictive of better survival upon ICI treatment in multiple cancers, likely by its association with enhanced anti-tumor immunity. Larger studies are warranted to validate our findings.

Background Estimated life expectancy is one of the most important factors in determining treatment options for prostate cancer (PCa) patients. However, clinicians have few effective prognostic tools to individually assess survival in patients with PCa. Methods We screened 283,252 patients diagnosed with PCa from the Surveillance, Epidemiology, and End Results (SEER) database between 2004 and 2015, and randomly divided them into the training and validation groups. We used univariate and multivariate Cox analyses to identify independent prognostic factors and further established nomograms to predict 1‐, 3‐, 5‐, and 10‐year overall survival (OS) and cancer‐specific survival (CSS) for PCa patients. The prediction performance of nomograms was tested and externally validated by Concordance index (C‐index) and receiver operating characteristic (ROC) curve. Calibration curve and decision curve analysis (DCA) were used for internal validation. We further developed PCa prognostic scoring system based on the impact of available variables on survival. Results The variables age, race, marital status, TNM stage, surgery method, radiotherapy, chemotherapy, PSA value, and Gleason score identified as independent prognostic factors were included in the survival nomograms. The results of training (C‐index: OS = 0.776, CSS = 0.889; AUC value: OS = 0.772–0.802, CSS = 0.892–0.936) and external validation (C‐index: OS = 0.759, CSS = 0.875) indicated our nomograms had good performance in predicting 1‐, 3‐, 5‐, and 10‐year OS and CSS prediction. Internal validation using the calibration curves and DCA curves demonstrated the effectiveness of the prediction models. The prognostic scoring system was more effective than the AJCC staging system in predicting the survival of PCa patients, especially for OS. Conclusion The prognostic nomograms and prognostic scoring system have favorable performance in predicting OS and CSS of PCa patients. These individualized survival prediction tools may contribute to clinical decisions. Based on a large data set of prostate cancer patients, we developed and valdated prognostic nomograms and demonstrated their favourable ability in predicting 1‐, 3‐, 5‐, and 10‐year overall and cancer‐specific survival for prostate cancer patients. We further proposed the prognostic scoring system for the first time to more intuitively present the influence of clinical pathological factors on overall and cancer‐specific survival of prostate cancer. The proposed survival model of prostate cancer can help clinicians select individualized and effective treatment.

Background Glioblastoma (GB) is recognized as one of the most aggressive brain tumors, with a median survival of 14.6 months. However, there are still some patients whose survival time was greater than 3 years, and the biological reasons behind this clinical phenomenon arouse our research interests. By conducting proteomic analysis on tumor tissues obtained from GB patients who survived over 3 years compared to those who survived less than 1 year, we identified a significant upregulation of SelK in patients with shorter survival times. Therefore, we hypothesized that SelK may be an important indicator related to the occurrence and progression of GBM. Methods Proteomics and immunohistochemistry from GB patients were analyzed to investigate the correlation between SelK and clinical prognosis. Cellular phenotypes were evaluated by cell cycle analysis, cell viability assays, and xenograft models. Immunoblots and co-immunoprecipitation were conducted to verify SelK-mediated ubiquitin-dependent degradation of CDK4. Results SelK was found to be significantly upregulated in GB samples from short-term survivors (≤ 1 year) compared to those from long-term survivors (≥ 3 years), and its expression levels were negatively correlated with clinical prognosis. Knocking down of SelK expression reduced GB cell viability, induced G0/G1 phase arrest, and impaired the growth of transplanted glioma cells in nude mice. Down-regulation of SelK-induced ER stress leads to a reduction in the expression of SKP2 and an up-regulation of β-TrCP1 expression. Up-regulation of β-TrCP1, thereby accelerating the ubiquitin-dependent degradation of CDK4 and ultimately inhibiting the malignant proliferation of the GB cells. Conclusion This study discovered a significant increase in SelK expression in GB patients with poor prognosis, revealing a negative correlation between SelK expression and patient outcomes. Further mechanistic investigations revealed that SelK enhances the proliferation of GB cells by targeting the endoplasmic reticulum stress/SKP2/β-TrCP1/CDK4 axis.