f available, recommend adjusting for ICU location in the multivariable analyses. It is a likely confounder. Choiceof sedative is often provider or ICU specific, and outcomes are different based on ICU.

lease clarify if you consider the RAS a function of the drug or of the definedtarget by the clinician

please clarify which gender was independent risk factor for in­hospital death. Pleasestate the OR and 95%CI and p value. Please state if this effect was similar in all three groups. Please clarify thevalue of including these data in your stud

please state the numerical value for each of the secondary outcomes in each of thegroups, and state the p value

lease state the mortality rates for dex and propofol and p value

please add the words 'for propofol' at the end of the sentence

In the Methods section, P5L50­53, please clarify if the SAPS II was present in the MIMIC­III database or wascalculated by the authors based on MIMIC­III data

In the Introduction section, P4L45, please correct the typo 'effort' into 'effect(s)'.

Please report p values as < 0.001 when 10e­16, etc

Please state the city/state/country from which Beth Israel Deaconness Medical Center is located. What ICUswere included within this database (ie: surgical, medical, burn

Please give the time frame of the records pulled from the database in the abstrac

n the discussion, it appears that the argument for the decreased mortality in the dexmedetomidine group isrelated to lack of oversedation, sleep quality, and sleep patterns There was a lack of RASS scores or supportingevidence for this in this manuscript, which needs to be identified as a limitation. Further, additional reasons couldbe identified including avoidance of deep sedation episodes and decreased rates of delirium.11. Delirium is associated with increased length of stay and mechanical ventilation. It may have been whydexmedetomidine was administered. This needs to added to your discussion on page 11. Also that delirium wasnot adjusted for in the analyses needs to be mentioned as a limitatio

It was found that midazolam was used in more patients with high SAPS II scores (Quarters 3 and 4). HigherSAPS II scores were independently associated with increased mortality in your study. It is unclear if this wasadjusted for to identify midazolam as an independent risk within these groups. Therefore, the sentence stating "wesuggest avoiding midazolam infusion in cases that SAPS II is more than 47" may not be supported

A flow chart is needed in a supplement to demonstrate how arrived at 3,983 patients from the 40,000 beganwith

Under outcomes analysis you write, "for analysis of above outcomes, the data without dead patients wereutilized" (page 6, lines 32­34) and "death records were excluded in order to avoid bias caused by early death"(page 10, lines 56/57). This is misleading. Please instead state that the analyses examining duration wereperformed only in survivors (N=?) to discharge (if this is indeed a correct statement). Furthermore, anystatements throughout the manuscript about it being associated with increased length of stay needs to be qualifiedby adding "in survivors." A better method overall, however, would be to analyze ICU free days, hospital free days,and mechanical ventilation free days between sedative groups which would allow use of the entire cohort andaccount for bias due to death.

nclear which analyses were used for the outcomes reported in the abstract (univariate, multivariate,propensity matching

Not sure of the benefit of analyzing RASS scores between propofol and midazolam since these are typicallytitrated to a RASS target by a nursing staff, causing dose adjustments as opposed to the choice of drug changingthe RASS. Further, they both work through GABA and can achieve the same range of sedation. This is not arandomized controlled trial with set RASS targets. Would recommend removing this topic from the manuscript

Please clarify further how groups were designated and identified in the database. It is rare for a patient to onlyget dexmedetomidine, for instance, without having exposure to other sedatives. If a patient received only onesedative for 48 hours and then only another sedative for an additional 48 hours, would this patient be excluded?The identification of groups has to be crystal clear because it is central to your hypothesis testing and theunderstanding of your manuscript.

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both cor.geneModuleMembership > 0.8 and cor.geneTraitSignificance > 0.2) (

Figure 10

And 10 real hub genes (CCNA2, CCNB1, CENPF, DLGAP5, KIF14, KIF23, NEK2, RACGAP1, TPX2 andUBE2C) among 36 common network genes were highlighted with p value significant less than 0.05(Figure6-9). Theremaining 26 genes wereshown in FigureS1-4.

dataset GSE62452 and TCGA data. The genes with pvalue significantly less than 0.05 were identified as real hub genes.

Afterwards, the ME in the yellowmodule showed a higher correlation with disease progression than other modules (Figure4C). Based on the two methods

Figure2

When AUC value was greater than 0.5, the hub gene was considered capable of distinguishingrecurrent and non-recurrent PDAC with excellent specificity and sensitivity

GSE62165 performed on Affymetrix Human Gene 1.0 ST Array (Affymetrix, Santa Clara, CA, USA) consisting of 118 PDAC samples and 13 control samples was used as a validation set to perform gene set enrichment analysis (GSEA) in order to identify potential function of PDAC.

GSE62452

In our study, the different expression genes in dataset GSE62452 was used to constructed a co-expression network by WGCNA. Significant gene module was identified to explore key genes and pathways associated with the process of PDAC. And then we useddataset GSE62165 and TCGA database to validate our results. According to survival and regression analysis, we identified the real hub genes related to PDAC. Since the recurrence rate of PDAC was unusually high, we performed the ROC curve and AUC to distinguishing recurrent and non-recurrent PDAC[14]. And finally functional enrichment analysis was performed to explore potential mechanisms for PDAC.

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robust gene

This indicated an interesting result that variabilityin methodology of DEG extraction methodsmay have an effect on number of DEGs identified at greater FC cutoff values.

his is accomplished with the help of genes like UBC which act as a connectionbetween these significant gene sets, at protein-interaction level.

topological analysis of the PPI networkswas performed to identify hub genes with high degree and betweenness centrality (BC) which serve as two fundamental parameters in the network theory [33][34]. Degree, the most basic characteristic of a node in a network measures the number of adjacent links, i.e. the number of interactions that connect one protein to its neighbors. BC measures the fraction of the number of shortest paths that pass through each node, which measures how often nodes occur on the shortest paths between other nodes.

P1served as a superset of all the genes in intersection results

Moreover, at FC=2, no genes were found in the intersection results, but RNA-seq analysis revealed a set of 89 genes (R2)

This method wasapplied to all individual raw microarray datasets to minimize the inconsistency due to normalization. Reasons behind choosing this method fornormalization include good differential change detection, stable variance on log scale and less number of false positives. This was projected in a comparison between different normalization methods, where it was shown that RMA outperformed in terms of specificity and sensitivity when dealing with fold change criteria in the detection of differential expression [19]. The box plots of the RMA normalized intensity were plotted toconfirm that measurements of data become approximately aligned towards a central mean, and thus become comparabl

Many statistical methods are available for DEG identification, but using a single method was not adequate due to the inconsistency of the results of DEG lists, when different methods were applied to microarray data. This was explained by [20]where they point the issue of result inconsistency in context to high level microarray analysis methods. Authors suggested to use a set of few methods and acknowledge DEGs as only those genes which fall within an intersection of sets of DEGs obtained by different methods. Based on the overall method scoring presented in their paper, they recommend at least Limma, Significance Analysis of Microarrays (SAM), and T-test (TT). Based upon these results, we used three different methods for DEG extraction from microarray data, Limma [21], SAM [22]and Fold Change Rank Ordering Statistic (FCROS) [23].Out of all methods available for DEG extraction, Limmaremains to be highly recommended and quite excellent with its power emphasized in [24]. Authors clearly advocate the application of Limma, in a comparison between eight such methods, Welch’s t-test, ANOVA, Wilcoxon’s test, SAM, RVM, Limma, VarMixt and SMVaris [25]. Instead ofsimple t-statistics, it provides the results for moderated t-statistics, moderated F-statistic, and B-statistic (which shows log-odds of differential expression) by applying Empirical Bayes method and shrinking the standard errors towards a common value. Hence, it has the capability to provide a stable and reproducible result even with a small number of arrays. Moreover, it can be conveniently applied on data from both, microarray and RNA-seq platforms

Fold change was the parameter which was calculated by all these methods. However, measurement scales required some transformation which is explained in Table 3.

six microarray data sets and one RNA-seq data

Reference

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