In December 2019, a new type viral pneumonia cases occurred in Wuhan, Hubei Province; and then named "2019 novel coronavirus (2019-nCoV)" by the World Health Organization (WHO) on 12 January 2020. For it is a never been experienced respiratory disease before and with infection ability widely and quickly, it attracted the world's attention but without treatment and control manual. For the request from frontline clinicians and public health professionals of 2019-nCoV infected pneumonia management, an evidence-based guideline urgently needs to be developed. Therefore, we drafted this guideline according to the rapid advice guidelines methodology and general rules of WHO guideline development; we also added the first-hand management data of Zhongnan Hospital of Wuhan University. This guideline includes the guideline methodology, epidemiological characteristics, disease screening and population prevention, diagnosis, treatment and control (including traditional Chinese Medicine), nosocomial infection prevention and control, and disease nursing of the 2019-nCoV. Moreover, we also provide a whole process of a successful treatment case of the severe 2019-nCoV infected pneumonia and experience and lessons of hospital rescue for 2019-nCoV infections. This rapid advice guideline is suitable for the first frontline doctors and nurses, managers of hospitals and healthcare sections, community residents, public health persons, relevant researchers, and all person who are interested in the 2019-nCoV.

Methodological quality (risk of bias) assessment is an important step before study initiation usage. Therefore, accurately judging study type is the first priority, and the choosing proper tool is also important. In this review, we introduced methodological quality assessment tools for randomized controlled trial (including individual and cluster), animal study, non-randomized interventional studies (including follow-up study, controlled before-and-after study, before-after/ pre-post study, uncontrolled longitudinal study, interrupted time series study), cohort study, case-control study, cross-sectional study (including analytical and descriptive), observational case series and case reports, comparative effectiveness research, diagnostic study, health economic evaluation, prediction study (including predictor finding study, prediction model impact study, prognostic prediction model study), qualitative study, outcome measurement instruments (including patient -reported outcome measure development, content validity, structural validity, internal consistency, cross-cultural validity/ measurement invariance, reliability, measurement error, criterion validity, hypotheses testing for construct validity, and responsiveness), systematic review and meta-analysis, and clinical practice guideline. The readers of our review can distinguish the types of medical studies and choose appropriate tools. In one word, comprehensively mastering relevant knowledge and implementing more practices are basic requirements for correctly assessing the methodological quality.

When reporting the results of clinical studies, some researchers may choose the five‐number summary (including the sample median, the first and third quartiles, and the minimum and maximum values) rather than the sample mean and standard deviation (SD), particularly for skewed data. For these studies, when included in a meta‐analysis, it is often desired to convert the five‐number summary back to the sample mean and SD. For this purpose, several methods have been proposed in the recent literature and they are increasingly used nowadays. In this article, we propose to further advance the literature by developing a smoothly weighted estimator for the sample SD that fully utilizes the sample size information. For ease of implementation, we also derive an approximation formula for the optimal weight, as well as a shortcut formula for the sample SD. Numerical results show that our new estimator provides a more accurate estimate for normal data and also performs favorably for non‐normal data. Together with the optimal sample mean estimator in Luo et al., our new methods have dramatically improved the existing methods for data transformation, and they are capable to serve as “rules of thumb” in meta‐analysis for studies reported with the five‐number summary. Finally for practical use, an Excel spreadsheet and an online calculator are also provided for implementing our optimal estimators.

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Objective: To identify candidate biomarkers correlated with clinical prognosis of patients with bladder cancer (BC).Methods: Weighted gene co-expression network analysis was applied to build a co-expression network to identify hub genes correlated with tumor node metastasis (TNM) staging of BC patients. Functional enrichment analysis was conducted to functionally annotate the hub genes. Protein-protein interaction network analysis of hub genes was performed to identify the interactions among the hub genes. Survival analyses were conducted to characterize the role of hub genes on the survival of BC patients. Gene set enrichment analyses were conducted to find the potential mechanisms involved in the tumor proliferation promoted by hub genes.Results: Based on the results of topological overlap measure based clustering and the inclusion criteria, top 50 hub genes were identified. Hub genes were enriched in cell proliferation associated gene ontology terms (mitotic sister chromatid segregation, mitotic cell cycle and, cell cycle, etc.) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways (cell cycle, Oocyte meiosis, etc.). 17 hub genes were found to interact with ≥5 of the hub genes. Survival analysis of hub genes suggested that lower expression of MMP11, COL5A2, CDC25B, TOP2A, CENPF, CDCA3, TK1, TPX2, CDCA8, AEBP1, and FOXM1were associated with better overall survival of BC patients. BC samples with higher expression of hub genes were enriched in gene sets associated with P53 pathway, apical junction, mitotic spindle, G2M checkpoint, and myogenesis, etc.Conclusions: We identified several candidate biomarkers correlated with the TNM staging and overall survival of BC patients. Accordingly, they might be used as potential diagnostic biomarkers and therapeutic targets with clinical utility.

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Background and Objective: Ventilator-associated pneumonia (VAP) is still an important cause of morbidity and mortality in mechanically ventilated patients. The efficacy of the probiotics for preventing VAP is still controversial. Present study was conducted to comprehensively evaluate the effect of probiotics on VAP prevention in mechanically ventilated patients.Methods: PubMed, Embase, and CENTRAL were searched up to September 2016. Eligible trials designed with randomized controlled trials (RCTs) comparing probiotics with control in mechanically ventilated patients were included. Risk ratios (RRs) and mean differences (MDs) with 95% confidence intervals (CIs) were estimated with fixed or random effects models. Trial sequential analysis (TSA) was performed using TSA 0.9beta software.Results: Thirteen RCTs (N = 1969) were included. Overall, probiotics were associated with reduced incidence of VAP (RR = 0.73, 95% CI = 0.60–0.89; P = 0.002), which was confirmed by TSA (TSA adjusted 95% CI = 0.55–0.96). However, no significant difference was observed in 90-day mortality (RR = 1.00, 95% CI = 0.72–1.37; P = 0.99), overall mortality (RR = 0.84, 95% CI = 0.70–1.02; P = 0.09), 28-day mortality (RR = 1.06, 95% CI = 0.72–1.57; P = 0.99), intensive care unit (ICU) mortality (RR = 0.97, 95% CI = 0.74–1.27; P = 0.82), hospital mortality (RR = 0.81, 95% CI = 0.65–1.02; P = 0.07), diarrhea (RR = 0.99, 95% CI = 0.83–1.19; P = 0.92), length of ICU stay (MD = −2.40 days, 95% CI = −6.75 to 1.95; P = 0.28), length of hospital stay (MD = −1.34 days, 95% CI = −6.21 to 3.54; P = 0.59), and duration of mechanical ventilation (MD = −3.32 days, 95% CI = −6.74 to 0.09; P = 0.06).Conclusions: In this meta-analysis, we found that probiotics could reduce the incidence of VAP in mechanically ventilated patients. It seems likely that probiotics provide clinical benefits for mechanically ventilated patients.

Based on current evidence, the IL-4 -33 C/T and 70-bp polymorphisms were not associated with an increased risk of periodontitis. However, the IL-4 -590 T allele and TT genotype were associated with increased risk of periodontitis in whites.