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SummaryBackgroundTo investigate the impact of goggles on their health and clinical practice during management of patients with COVID-19.Methods231 nurse practitioners were enrolled who worked in isolation region in designated hospitals to admit patients with COVID-19 in China. Demographic data, goggle-associated symptoms and underlying reasons, incidence of medical errors or exposures, the effects of fog in goggles on practice were all collected. Data were stratified and analyzed by age or working experience. Risk factors of goggle-associated medical errors were analyzed by multivariable logistical regression analysis.FindingsGoggle-associated symptoms and foggy goggles widely presented in nurses. The most common symptoms were headache, skin pressure injury and dizziness. Headache, vomit and nausea were significantly fewer reported in nurses with longer working experience while rash occurred higher in this group. The underlying reasons included tightness of goggles, unsuitable design and uncomfortable materials. The working status of nurses with more working experience was less impacted by goggles. 11.3% nurses occurred medical exposures in clinical practice while 19.5% nurses made medical errors on patients. The risk factors for medical errors were time interval before adapting to goggle-associated discomforts, adjusting goggles and headache.InterpretationGoggle-associated symptoms and fog can highly impact the working status and contribute to medical errors during management of COVID-19. Increased the experience with working in PPE through adequate training and psychological education may benefit for relieving some symptoms and improving working status. Improvement of goggle design during productive process was strongly suggested to reduce incidence of discomforts and medical errors.

Background The severity and outcome of COVID-19 cases has been associated with percentage of circulating lymphocytes (LYM%), levels of C-reactive protein (CRP), interleukin 6 (IL-6), procalcitonin (PCT), lactic acid (LA) and viral load (ORF1ab Ct). However, the predictive power of each of these indicators in disease classification and prognosis remains largely unclear. Methods we retrospectively collected information on the above parameters in 142 patients with COVID-19, stratifying them by survival or disease severity. Findings CRP, PCT, IL-6, LYM% and ORF1ab Ct were significantly altered between survivors and non-survivors. LYM%, CRP and IL-6 were the most sensitive and reliable factors in distinguishing between survivors and non-survivors. These indicators were significantly different between critically ill and severe/moderate patients. Only LYM% levels were significantly different between severe and moderate types. Among all the investigated indicators, LYM% was the most sensitive and reliable in discriminating between critically ill, severe and moderate types, and between survivors and non-survivors. Conclusions CRP, PCT, IL-6, LYM% and ORF1ab Ct, but not LA, could predict prognosis and guide classification of COVID-19 patients. LYM% was the most sensitive and reliable predictor for disease typing and prognosis. We recommend that LYM% be further investigated in the management of COVID-19., Graphical Abstract, Highlights: (1) Inflammatory cytokines, lymphocytes and viral load predict prognosis in COVID-19 patients. (2) Inflammatory cytokines, lymphocytes and viral load indicate disease severity. (3) Lymphocytes and C-reactive protein distinguish between severe and moderate types. (4) Lymphocyte count is the most effective indicator of disease severity and prognosis.

Background Coronavirus disease 2019 (COVID-19) has spread rapidly worldwide from Wuhan. An easy-to-use index capable of the early identification of inpatients who are at risk of becoming critically ill is urgently needed in clinical practice. Hence, the aim of this study was to explore an easy-to-use nomogram and a model to triage patients into risk categories to determine the likelihood of developing a critical illness. Methods A retrospective cohort study was conducted. We extracted data from 84 patients with laboratory-confirmed COVID-19 from one designated hospital. The primary endpoint was the development of severe/critical illness within 7 days after admission. Predictive factors of this endpoint were selected by LASSO Cox regression model. A nomogram was developed based on selected variables. The predictive performance of the derived nomogram was evaluated by calibration curves and decision curves. Additionally, the predictive performances of individual and combined variables under study were evaluated by receiver operating characteristic curves. The developed model was also tested in a separate validation set with 71 laboratory-confirmed COVID-19 patients. Results None of the 84 inpatients were lost to follow-up in this retrospective study. The primary endpoint occurred in 23 inpatients (27.4%). The neutrophil-to-lymphocyte ratio (NLR) and C-reactive protein (CRP) were selected as the final prognostic factors. A nomogram was developed based on the NLR and CRP. The calibration curve and decision curve indicated that the constructed nomogram model was clinically useful. The AUCs for the NLR, CRP and Combined Index in both training set and validation sets were 0.685 (95% CI: 0.574-0.783), 0.764 (95% CI: 0.659-0.850), 0.804 (95% CI: 0.702-0.883), and 0.881 (95% CI: 0.782-0.946), respectively. Conclusions Our results demonstrated that the nomogram and Combined Index calculated from the NLR and CRP are potential and reliable predictors of COVID-19 prognosis and can triage patients at the time of admission.

Statistical learning theory is developed on the basis of machine learning, and it provides the theoretical basis under the condition of small samples of pattern recognition, function fitting and the density estimation. In recent years, the research on statistical learning theory has made more and more important contribution to the research on artificial intelligence. In view of its importance, this paper has sorted out the basic problems of statistical learning theory :(1) The conditions of statistical learning consistency under the rule of minimum empirical risk minimization;(2)The conclusions about the generalization of statistical learning methods in these conditions;(3) Small sample induction and inference criteria established on the basis of these boundaries;(4) At last, the current research status and the further development of statistical learning theory are discussed.

Focusing on the non-Gaussian distribution of the field and the nonlinearity between the production data and the reservoir model of the fluvial channel reservoir, the HIEnKF method with discrete cosine transform was used to study the fluvial channel reservoir model by automatic history matching method. The non-Gaussian permeability field is transformed to nearly Gaussian distributed DCT coefficient by discrete cosine transform. DCT exhibits excellent energy compaction for highly correlated images. The estimated permeability field is smooth if the low frequency DCT coefficients are retained. The non-Gaussian problem of the fluvial channel reservoir can be solved by the discrete cosine transform. The HIEnKF method can solve the nonlinearity of the production data and the reservoir model. The equivalence of the HIEnKF to EnRML is proved theoretically. The results of standard EnKF, standard EnKF coupling with DCT and HIEnKF coupling with DCT were compared. The effects of retained mode and the number of retained DCT coefficients were discussed. The results show that fewer DCT coefficients need to be retained to capture the geological structure when the triangular area mode was used to truncate the coefficients. The estimated results can get the main geological structure of the fluvial channel reservoir when only 2.5% DCT coefficients were retained.

CONSORT flow diagram. (PDF 483Â kb)

CONSORT 2010 Checklist. (DOC 217Â kb)

To analyze the clinical effects of short-segment posterior pedicle screw combined with transpedicle vertebral bone grafting in treating thoracolumbar burst fractures.From March 2008 to March 2013, 62 patients with thoracolumbar burst fractures were treated with short-segment posterior pedicle screw combined with transpedicle vertebral bone grafting. Including 40 males and 22 females, the age from 17 to 65 years old with an average of 38 years. According to AO classification, 34 cases were type A3.1, 7 cases were type A3.2 and 21 cases were type A3.3. Load-sharing scores were from 4 to 6 points with an average of 5.4 points. According to ASIA grade, 2 cases were grade C, 5 cases were grade D and 55 cases were grade E. Preoperative, postoperative at 3 d and final follow-up, the Cobb angle, the relative height of anterior vertebral body and the encroachment rate of spinal canal were measured by X-ray films and computed tomography (CT) scan, meanwhile, the information of bone healing and spinal nerves recovery were observed.All patients were followed up from 11 to 14 months with an average of 12.2 months. The duration of removing internal fixation were from 9 to 13 months (averaged, 11.5 months). One suffered from infection and was cured by debridement. Two cases had mild pain of back. At 6 months after operation, according to ASIA grade to evaluate never function, 1 case was grade C, 3 cases were grade D and 58 cases were grade E. X-ray and CT showed the fractures obtained good union at final follow-up. The Cobb angle, the relative height of anterior vertebral body and the encroachment rate of spinal canal had obviously improved at 3 days after operation (P0.05); but there was no significant differences between postoperative at 3 d and final follow-up (P0.05).Short-segment posterior pedicle screw combined with transpedicle vertebral bone grafting is an effective method to treat thoracolumbar burst fractures. It can reduce the loss of postoperative correction and prevent the internal fixation failure.

A number of studies have been conducted to investigate associations between genetic polymorphisms in interleukin-17 (IL-17) pathway and the risk of gastrointestinal diseases. Results, however, have been inconclusive. We aimed to evaluate these associations through a meta-analysis.We searched electronic databases (PubMed, Embase, Web of Science, CBM and CNKI) to identify papers, published before October 2014, on associations between polymorphisms in the IL-17 pathway genes (rs2275913, rs763780, rs3748067, rs3819025, rs9382084, rs12203582 and rs8193036) and the risk of gastrointestinal diseases. We estimated the strength of candidate associations through calculating pooled odds ratios (ORs) and 95% confidence intervals (CIs). A test of heterogeneity, a false-positive report probability (FPRP) test, sensitivity analyses and examination for bias were further conducted to evaluate cumulative evidence of these associations.We identified 25 eligible case-control studies retrieved from 16 articles involved in a total of 4507 cases and 5733 controls. Of them, IL-17 (-197G/A) polymorphism (rs2275913) was statistically significantly associated with risk of gastrointestinal diseases, especially for gastrointestinal malignancy and gastroduodenal diseases. Based on heterogeneity (P0.05) and FPRP (P0.2) identified in this study, we graded cumulative epidemiological evidence of an association with gastrointestinal diseases for this polymorphism. However, IL-17 (7488T/C) polymorphism (rs763780) did not show significant associations with gastrointestinal diseases either individually or overall (P0.05).Our study provides moderate evidence that IL-17 (-197G/A) polymorphism may be a potential risk factor for gastrointestinal diseases, particularly for malignancy. Further validation of this association in a large homogeneous study is warranted.