One possible reason for the lack of clear advantages may be that apart from increasing the average BOLD sensitivity at the single-subject level, the advanced weighted averaging methods also inflate the inter-subject variance. As the echo combination methods provide very similar results, the recommendation is to choose between them depending on the availability of time for collecting additional resting-state data or whether subject-level or group-level analyses are planned.Multi-echo fMRI data acquisition has been widely investigated and suggested to optimize sensitivity for detecting the BOLD signal. Several methods have also been proposed for the combination of data with different echo times. The aim of the present study was to investigate whether these advanced echo combination methods provide advantages over the simple averaging of echoes when state-of-the-art group-level random-effect analyses are performed. Both resting-state and task-based dual-echo fMRI data were collected from 27 healthy adult individuals  using standard echo-planar acquisition methods at 3T. Both resting-state and task-based data were subjected to a standard image pre-processing pipeline. Subsequently the two echoes were combined as a weighted average, using four different strategies for calculating the weights: (1) simple arithmetic averaging, (2) BOLD sensitivity weighting, (3) temporal-signal-to-noise ratio weighting and (4) temporal BOLD sensitivity weighting. Our results clearly show that the simple averaging of data with the different echoes is sufficient. Advanced echo combination methods may provide advantages on a single-subject level but when considering random-effects group level statistics they provide no benefit regarding sensitivity (i.e., group-level  Increases in blood oxygenation level dependent (BOLD) signal in functional magnetic resonance imaging (fMRI) are only transient and thus it is advantageous to acquire fMRI data with high temporal resolution. Most often echo planar imaging (EPI)  after each excitation  of Zurich approved involvement of the human volunteers and each participant signed a written informed consent before the experiment.Thirty  right-handed, non-smoking, medication-free volunteers participated in the study. Each had normal eyesight and no history of neurological disorders. The MRI or the physiological data from three participants were compromised due to computer disk error or physiological sensor failure. Because the physiological data were used for correcting time-series artifacts, henceforth this paper pertains to the remaining 27 participants .2, in-plane resolution 2.5 × 2.5 mm2, slice gap 0.6 mm. The total EPI readout duration for each echo was 24.792 ms. For each subject the resting-state data set contained 116 image volumes while the task-based fMRI data were acquired twice, each set containing 282 image volumes. Cardiac and respiratory signals were concurrently acquired via electrocardiogram and a breathing belt, respectively, to allow for removal of fluctuations caused by cardiac pulsation and breathing from the fMRI time-series as part of the image pre-processing pipeline.All MRI data were acquired on a 3T Philips Achieva scanner  equipped with an 8-channel receive-only head coil and single-channel body transmit coil. For both the resting-state and the task-based fMRI data sets 36 axial slices were collected at 2.6 mm thickness using single-shot double-echo GE-EPI with twofold SENSE acceleration  and orbitofrontal cortex. We direct the interested reader toward Neurosynth , and Matlab  were used.http://www.translationalneuromodeling.org/tapas) that uses Fourier expansions of various orders for the phases of cardiac pulsation (3rd order), respiration (4th order), and cardio-respiratory interaction (1st order) .First, physiological time-series were transformed following the RETROICOR procedure  segment of the brain a binary mask was created by segmenting  and S2(t) are the signal amplitude of a given voxel in the same spatial and temporal position acquired with TE1 and TE2, respectively, while w1 and w2 are the corresponding weights and type is one of “AVE,” “BS,” “tSNR,” or “tBS” representing the four different methods of echo combinations for calculating the weights (see below). Note, the weights, w1 and w2, are calculated separately for each time point in Echo combination method #2 below. For the other three echo combination methods they are identical for all time points in the time series.where AVE)Echo combination #1 .BS)Echo combination #2  asS1(t) and S2(t) are defined as in Equation (1) above. This is commonly considered BOLD sensitivity (BS) weighting Echo combination #3  as weights calculated separately for the two TEs by dividing the temporal mean of a voxel time-series with its temporal standard deviationtSNR1 and tSNR2 are the voxel-wise temporal signal-to-noise values for short (TE1) and long (TE2) echo data respectively.where tBS)Echo combination #4 r et al.  and is ttBS1 and tBS2 are the temporal-BOLD-contrast-to-noise ratio values, while tSNR1 and tSNR2 are as in Echo combination #3 above.where AVE_data), a pseudo measure of the temporal BOLD sensitivity (ptBS) was calculated asIn each of the above echo combination methods the result is a single time-series of resting-state fMRI data. BOLD sensitivity is often defined as in Equation (2) above  and long (TE2), respectively, and both the mean and the standard deviation are calculated across time. Note that in the case BS-based echo combination method the ptBS measure will depend on the square of the voxel signal and that in this case each time point will have a unique weight.where AVE_ptBS map was co-registered to the corresponding T1-weigthed anatomical image for each subject. Given that each of the four types of ptBS maps are calculated from the same data these maps are perfectly aligned. Therefore, the coregistration parameters from AVE_ptBS were used for the other three ptBS maps. Subsequently the T1-weighted anatomical image was normalized in SPM12 to the Montreal Neurological Institute (MNI) template and the normalization parameters were written onto each ptBS map. No spatial smoothing was applied to the normalized images.In order to create group-averaged results the TEs were also combined as a weighted sum using Equation (1). Echo combinations #1 and #2 were performed by using the task-based data for calculating weights, w1 and w2, as well as extracting S1 and S2.Similarly to the resting-state data, realigned and de-trended task-based time-series with two Echo combinations #3 and #4 participant-specific resting-state data were used to calculate the weights, w1 and w2, but the task-based data were used for extracting S1 and S2 for Equation (1). This is because Echo combinations #3 and #4 rely on tSNR  for calculating the weights. Hence to avoid including the BOLD response of the task-based time-series into the calculation of the temporal standard deviation, and in turn the weights, the resting-state data were used. More specifically, the tSNR1 map of TE1 derived from the resting-state data was realigned with the temporal mean of the realigned task-based data with TE1. These movement parameters were then applied to the tSNR2 map to bring it in line with the corresponding task-based data with TE2.In The four combined task-based datasets were corrected for the different timing of the slice acquisition by temporal interpolation relative to the acquisition time of the slice in the center of the volume using the standard slice time correction method in SPM12  template detailed as follows. The time-mean of the echo averaging combination was co-registered to the TptBS maps. Voxel-wise ratio of the resulting mean and SD maps were calculated for all combination methods. AVE_ptBS was considered the reference to which the other 3 echo combinations were compared. To extract group-level quantitative results, each of the four ptBS maps was multiplied by the group GM mask and the mean and standard deviation were calculated from voxels within the maskTo quantitatively assess the possible advantages of the four different echo combination methods, voxel-wise mean and voxel-wise standard deviation were computed across the subjects for each of the four normalized http://www.neurosynth.org  of each subject. This GLM included, 8 regressors and 24 covariates (physiology and movement) per session. Analysis was performed for one contrast defined as the sum of the two predictors representing reward in the two runs contrasted to baseline.Single subject general linear model (GLM) analysis was performed separately on each of four pre-processed combined time-series datasets . Both the contrast maps and the variance maps were multiplied by the GM mask before histogram calculations.where To estimate and compare the relative contributions of inter-subject vs. intra-subject variance we followed the procedure outlined in Kirilina et al. , which iAVE. First, first-level analysis was performed for each subject by treating each the four echo-weighting methods as four different sessions. To remove any overall scaling and/or change in the variance of the resulting parameter maps between the combinations, the parameters were voxel-wise divided by their inter-subject standard deviation, separately for each echo combination. Next, from the rescaled first-level parameters three contrast maps were calculated for each subject by taking the difference of the parameters representing reward in one of the advanced echo combination method vs. that of AVE. Finally, standard group-level random-effects analysis was performed on these contrast maps separately.Thus far, comparisons of group-level analysis results of statistical analyses were presented. We also performed three separate statistical analyses to compare the advanced echo combination methods with type_ptBS measure (Equation 5) for the advanced echo combination methods BS and tBS but not for tSNR. Out of the four examined echo-weighting strategies the BOLD sensitivity (BS) weighting of Echo combination #2 produces the time-series with the highest sensitivity when averaged across the entire group. The tBS method of echo combination also produces the expected improvement, which confirms the findings of Poser et al. . The actual group standard deviation values within the GM mask for the four ptBS maps are listed in Table Note however that the center two rows of Figure t-value map of the group-level random effects-analysis on the time-series data in which the echoes were simply averaged . The group-level results were highly similar when using the time-series data resulting from the other three echo-weighting strategies (data not shown). In Figure t-value. Within the GM mask the actual percentages were 14.5% for AVE, 24.0% for BS, 23.3% for tSNR and 38.2% tBS. Thus, neither of the four echo combination methods comes out as the major winner, not even in the orbitofrontal cortex, where multi-echo data has been shown to provide increased sensitivity  is plotted along the horizontal axis against that resulting from the statistical analysis of each of the other three echo combination methods  plotted along the vertical axis. Because all points fall in the vicinity of the identity line, neither of the echo combination methods seems superior to simple averaging. It must be noted that the spread around the identity line is broader for the statistical analysis based on BS_data.The scatter plots in Figure t-value differences of the random-effects group-level analyses within the GM segment are centered on zero , which is their expected value if the echo combination methods provide equivalent statistical results. However, the spreads of the histograms are not the same, showing that the spatial variance of t-values is dependent on the echo combination method, with tBS weighting showing the smallest variation . It is worth mentioning that the distributions of histogram differences are not symmetric. Skewness also depends on the echo combination strategy with BS weighting resulting in the highest asymmetry. The tails of these histograms represent the voxels where the group-level statistical t-values differ between the echo combination strategies. The anatomical pattern of the histograms tails is presented in the bottom of Figure In Figure t-values are defined by both the mean contrast  and the parameter variance. Further scrutiny into the t-value histograms from above indicates that, unlike the t-values themselves, the contrast is actually dependent on the method used for echo combination . Figure tBS weighting results in increased contrast while tSNR weighting and BS weighting have decreased mean contrast compared to echo averaging.The group-level t-values across the different echo combination methods can be concluded from the total variance differences in histograms . Based on these it can be argued that the increased spatial variance of t-values of BS weighting strategy originates from its parameter variance, as its histogram shows an asymmetric “tail” in positive direction, according to the similar “tail” on t-value differences histogram in negative direction in Figure The reason for comparable s Figure   are related to those derived from the other three echo combination methods  where the intra-subject . All of them are centered near zero with their mean inside the  interval, which is the result expected if the combination methods are equivalent at group level  there are less than 30 significant voxels scattered around all the brain, in all combination pairs (advanced vs. AVE), for both positive and negative t-contrasts.The group-level paired AVE) performs at least as well as any of the other more advanced methods. Although, the proposal for these advanced echo combination methods seems well founded theoretically and BS weighting and tBS weighting even result in an improved BOLD sensitivity  and to calculate the tSNR for Echo combinations #3 and #4—but no other statistical analyses were performed.Task-based fMRI data were put through a standard preprocessing pipeline that included spatial smoothing of the images before commencing the statistical analysis, while resting-state data were left unsmoothed. This distinction is due to the fact that the resting-state data were only used for investigating the effects of echo combination methods on the BOLD sensitivity , the weights are calculated separately for each time point based on the voxel signal at that time point. This can lead to an unwanted amplification of the variance in the final, combined dataset because the variance from each signal component will propagate as the square. Perhaps it is for this reason that this weighting method provides the lowest It must be mentioned that apart from simply combining the echoes, as in this paper, multi-echo fMRI data can be used in additional ways to improve the sensitivity of the measurement to effects of interest. One example is employing independent component analysis to rid the data from unwanted slow drifts while maintaining slow BOLD activity patterns . It has been shown that the reliability of functional connectivity measures improves significantly with longer acquisition (Birn et al., One limitation of this study may be the number of echoes acquired. Multi-echo data usually includes more than two echoes and sometimes as many as six (Poser et al., BS or tBS echo averaging method will slightly increase the sensitivity of the experiment.In conclusion, we could not identify clear benefits that would make the advanced echo combination methods preferable for group-level random-effects statistical analyses, because the inter-subject variance component washes out any benefit that more intricate echo combination methods may provide on an individual level. It is important to point out that we chose the simplest echo averaging method as a reference only for convenience and would not like to advocate it in all cases. On the one hand, simple echo combination methods remove the need for collecting additional resting-state data and may be preferable in cases when time is of essence. On the other hand, if the resting-state fMRI data is available and/or subject-level analyses are planned the CH, AK, and ZN designed the study. CH collected the data. All authors contributed to data analysis pipeline. ZN drafted the manuscript. The submitted version is the combined effort of all authors.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The objective of the present prospective observational study is to evaluate the effect of depressive symptoms on 1-year mortality in a population of elderly patients discharged from a rehabilitation unit after orthopaedic surgery of the lower limbs. A total of 222 elderly inpatients were included, and stratified according to 12-months survival. 14 (6.3%) of the patients who were eligible for this study died during the 12-months period after discharge. As expected, patients who died were significantly older, lower cognitive performance, more depressive symptoms, poorer nutritional status and higher comorbidity in comparison to those who survived. Furthermore, they were generally more functionally dependent on admission to the Department, had worse functional recovery and were more disable at discharge, although a longer length of stay comparing to survived patients. In the adjusted logistic regression model, after adjustment for possible confounders and covariates, the presence of severe depressive symptoms significantly predicted a four-fold risk of death at 12 months. The only other factor associated poor 12-months survival was comorbidity, that predicted a 6-fold risk of death. In conclusions this study suggests that severe depressive symptoms on admission predicts 1-year mortality in elderly patients discharged from a post-acute care unit after orthopaedic rehabilitation.


PTEN did not tend to overlap, and hsa-miR-92b and PTEN coordinately regulated the pathway of ‘cell cycle' and so on. Moreover, we discovered a new prognostic signature, hsa-miR-29c, whose downregulation was associated with poor survival of breast cancer patients.MicroRNAs (miRNAs) have key roles in breast cancer progression, and their expression levels are heterogeneous across individual breast cancer patients. Traditional methods aim to identify differentially expressed miRNAs in populations rather than in individuals and are affected by the expression intensities of miRNAs in different experimental batches or platforms. Thus it is urgent to conduct miRNA differential expression analysis at an individual level for further personalized medicine research. We proposed a straightforward method to determine the differential expression of each miRNA in an individual patient by utilizing the reversal expression order of miRNA pairs between two conditions . We applied our method to breast cancer miRNA expression profiles from The Cancer Genome Atlas and two other independent data sets. In total, 292 miRNAs were differentially expressed in individual breast cancer patients. Using the differential expression profile of miRNAs in individual patients, we found that the deregulations of miRNA tend to occur in specific breast cancer subtypes. We investigated the coordination effect between the miRNA and its target, based on the hypothesis that one gene function can be changed by copy number alterations of the corresponding gene or deregulation of the miRNA. We revealed that patients exhibiting an upregulation of hsa-miR-92b and patients with deletions of  The fold change is simple but has the obvious disadvantage that it does not provide an estimation of significance. The T-test and significance analysis of microarrays rely on the assumption that expression values are normally distributed. These intensity-based methods are also affected by experimental batch effects and data normalization processes.2 Some studies have proposed new methods, such as the rank product,3 which use the relative order of gene expression values within each sample, considering that the relative order is more robust against batch effects and insensitive to data normalization.5 However, these new methods are not appropriate for determining differentially expressed miRNAs in individual breast cancer samples. Differentially expressed miRNAs are highly heterogeneous among individuals of the same cancer type.6 Gaire et al.7 discretized miRNAs based on their expression level rankings in each sample and roughly discretized the first 5% of the miRNAs as low expression and the last 5% as high expression, which lacks statistical estimation.MicroRNAs (miRNAs) are short (approximately 22 nt), endogenous non-coding RNAs that regulate gene expression by promoting mRNA degradation or repressing mRNA translation. MiRNAs that function as oncogenes or tumor suppressors are differentially expressed in cancer patients compared with normal samples.RankComp to detect differentially expressed genes (DEGs) in individual cancer samples using the disrupted ordering of gene expression values in individual cancer sample, based on the observation that the relative ordering of gene expression is overall stable in a particular type of normal tissue but widely disturbed in cancer.8RankComp performed well in analyzing mRNA expression profiles, which contain tens of thousands of genes. However, because miRNA expression profiles contain only hundreds of miRNAs, RankComp was sensitive to the rank changes of miRNAs in individual patients and not suitable to be applied to individual-level analysis of miRNAs.To avoid these problems, our previous work proposed a method called 9 For example, the cell cycle process may become oncogenic by attenuating the tumor-suppressor gene PTEN or by elevating the expression of hsa-miR-29b, which can attenuate PTEN activity in cancer. PTEN and hsa-miR-29b regulate the cell cycle process in a mutually exclusive manner, which indicates that deregulated expression of the miRNA and genomic alterations of its targets do not tend to co-occur in the same patient.7 Hence, it is important to detect the deregulation of hsa-miR-29b in individual patients along without alterations of PTEN. In our previous study, we discovered that both the deregulation of miRNAs and alterations of BRCA1/2 could coordinately disrupt the DNA repair process and further affect the overall survival of ovarian cancer patients receiving platinum-based treatment.9 Thus it is necessary to develop a method to determine differentially expressed miRNAs in individual patients. Moreover, deregulated miRNAs are considered as diagnostic and prognostic biomarkers because of their significant roles in proliferation, apoptosis and invasion in breast cancer.11 Some studies use the average or median score or the expression level as cutoffs to distinguish between high- and low-risk patients.14 However, these methods are arbitrary in setting a threshold for prognostic marker detection and are difficult to include in practical application.17 To overcome the limitation of these threshold-based methods, it is essential to develop a new method to detect prognostic biomarkers that can be used to obtain prognostic predictions for individual breast cancer patients.18The same biological process can be disturbed by the deregulation of miRNA expression or an aberration of their corresponding miRNA targets.Breast cancer is a complex disease that is characterized by heterogeneity of genetic and epigenetic alterations. Aiming to detect miRNAs with aberrant expression in individual breast cancer patients, we developed a simple and intuitive procedure to determine whether miRNAs are differentially expressed in an individual patient. Given that the expression of miR-B was approximately constant across the cancer and normal samples, the reversal relationship of miR-A and miR-B may be because of the differential expression of miR-A, which can be used as evidence to determine whether miR-A is differentially expressed in individual cancer compared with normal samples. In our analysis, applications of our method were to detect deregulation of miRNAs in specific breast cancer subtypes and miRNA–target pairs with mutually exclusive alterations in breast cancer. Furthermore, based on the individual differential miRNA profiles in breast cancer, we identified that hsa-miR-29c was a new robust prognostic maker for breast cancer, which did not rely on presetting thresholds for prognostic prediction.We distinguished miRNA pairs with stable rank relationships from all pair-wise miRNAs and detected reversal miRNA pairs using Fisher's exact tests across 81 pair-wise breast cancer and normal samples . In totaRankComp method,8 only 17.5 miRNAs were differentially expressed in each breast cancer patient on average, and the average precision for each patient was 68.18%. Each miRNA was detected with differential expression in 1.01 breast cancer patients on average, and the average precision for each miRNA was 51.88%. Thus our method performed better at identifying differential expression miRNAs in individual breast cancer samples.Using the miRNA reversal pairs derived from the training data set, we determined whether the 292 miRNAs were differentially expressed in individual patients of a testing data set containing 17 pair-wise breast cancer and normal samples . AverageThe paired 81 normal samples in the training data set were used to simulate for disease samples. In the training data set, averagely 50 miRNAs, among which 40 miRNAs were upregulated and 10 miRNAs were downregulated, were detected with differential expression in an individual patient by our method. Averagely, 1 miRNA was differentially expressed in 14 patients. Thus 40 and 10 randomly selected miRNAs in each disease sample were separately set to be differentially upregulated and downregulated by adding or subtracting the maximum absolute value of the corresponding miRNA in the original expression values. Each miRNA was set to be differentially expressed in 14 disease samples during the simulation process. The results showed that average values of sensitivity, specificity and F-score were 93.28, 93.36 and 0.9172, respectively. When increasing the number of samples in which each miRNA was differentially expressed, slight changes in sensitivity, specificity and F-score were observed for each scenario .20 According to the differential expression profile of miRNAs in individual breast cancer patients and the known subtype labels of breast cancer, we tested whether deregulation of miRNAs tended to be in specific subtypes by hypergeometric distribution model. Totally, we found that 26 miRNAs, 79 miRNAs, 55 miRNAs, 105 miRNAs and 3 miRNAs were significantly altered in luminal A, luminal B, HER2-enriched, basal-like and normal-like, respectively , which has been confirmed by Farazi et al.21 The hsa-miR-17 was reported as a basal-like subtype-specific miRNA,22 and our results showed that the breast cancer patients with upregulation of hsa-miR-17 were significantly enriched in patients with basal-like subtype (P=2.22 × 10−16). The Venn diagram was used to show the number of miRNAs shared between or among subtypes . The pathway enrichment results revealed that hsa-miR-92b-related DEGs were significantly enriched in 13 pathways  and that PTEN-related DEGs were significantly enriched in 22 pathways . Four pathways  were overlapped between the two pathway lists, which cannot be expected by random chance . Thus the upregulation of hsa-miR-92b and deletions of PTEN could coordinately regulate the cell cycle pathway and hence to contribute to the progression of breast cancer. Moreover, we found that deregulation of hsa-miR-92b was luminal A subtype specific  were significantly associated with the overall survival of breast cancer patients in the TCGA data set .P=3.60 × 10−4, log-rank test, P=1.15 × 10−2, log-rank test, P=6.35 × 10−3, log-rank test, 23 we divided the breast cancer samples into basal-like, HER2-enriched, Luminal A, Luminal B and normal-like subtypes for GSE22220 and GSE19536 data sets, respectively. We tested whether hsa-miR-29c was an independent prognostic marker for breast cancer using multivariate cox regression analysis. When considering other clinical factors, including subtype information, the results of multivariate cox regression analysis showed that hsa-miR-29c exhibited significant association with patient survival in TCGA (hazard ratio (HR)=4.31, 95% confidence interval (CI)=, P=3.49 × 10−4, P=0.038, P=0.099, P<0.05, T-test to detect DEGs between the hsa-miR-29c downregulated group and others in each data set and performed pathway enrichment analyses in the three data sets. Scrutinizing the top four enriched pathways from the three pathway lists, ‘Cell cycle' and ‘DNA replication' were overlapped, which was not expected by random chance . Thus we took hsa-miR-29c as an example for further analysis. Hsa-miR-29c was differentially downregulated in 148 of the 207 samples in the GSE22220 data set, 9 of the 99 samples in the GSE19536 data set and 55 of the 444 samples in the TCGA data set. For each data set, we further divided the samples into two groups: hsa-miR-29c downregulated group and others. Patients in the ‘hsa-miR-29c differentially downregulated' group displayed significantly shorter median survival time than those in the ‘others' group in the TCGA  of the corresponding gene or modification of the expression of the miRNA that attenuates the target gene.24 Furthermore, hsa-miR-29c was validated as a new robust prognostic biomarker for breast cancer in our study. Patients with hsa-miR-29c downregulation displayed significantly shorter survival than patients without hsa-miR-29c downregulation. Multivariate Cox survival analyses demonstrated that hsa-miR-29c was independent of other clinical factors, including the subtypes, except in the GSE19536 data set, which may be because the majority of samples in this data set (75 of the 99 samples) were censored. Compared with the artificial cutoffs used to identify prognostic biomarkers, our method does not need to accumulate many patients to determine the optimal threshold in practice. In clinical translational application, we only need to compare the expression values of hsa-miR-29c and its reversal miRNA (hsa-miR-30b), which are achieved by our method. Furthermore, we validated that the individual-level differential expression profile of hsa-miR-29c was reliable in the three data sets by the pathway enrichment results. The reproducible pathway enrichment results indicated that downregulation of hsa-miR-29c participated in the pathways of cell cycle and DNA replication by regulating its targets and further affecting breast cancer survival, which deserves further experiments to investigate the detailed mechanism.Moreover, our method can be used to identify prognostic biomarkers for breast cancer. In total, the deregulated expression levels of six miRNAs were associated with overall survival of breast cancers, and hsa-miR-127 has previous evidence for their prognostic effects in breast cancer patients.C-index) is a popular measure to quantify the predictive accuracy of the prognostic marker and could be used in clinical practice.25 A higher C-index value than 0.5 indicates a better overall concordance between the predicted risk classification and the observed survival.26 Thus we calculated the C-index to quantify the predictive accuracy of the miRNAs. The C-index values of hsa-miR-29c were 0.602, 0.566 and 0.584 for TCGA, GSE22220 and GSE19536, respectively. C-index values of the six miRNAs are presented in C-index for breast cancer patients, we need to integrate multiple miRNAs that have potential prognostic value for breast cancers and corresponding targets to construct effective classifier, which warrants our future detailed work.Harrell's concordance index . There were 98 pair-wise breast cancer and normal samples, among which the 81 pair-wise samples from the IlluminaHiSeq platform was set as the training data set and the 17 pair-wise samples from the IlluminaGA platform was used as the testing data set  were retained for following analysis.Level 3 miRNA expression profiles detected by IlluminaHiSeq and IlluminaGA platforms were obtained from the TCGA data portal .30 miRanda,31 PITA,32 PicTar,33 miRBase,34 DIANA-microT,35 miRTarBase,36 miRecords,37 and RNAhybrid.38 Only miRNA–target interactions appearing in at least two databases were retained.9In this study, the miRNA–target interaction data set was obtained from the following nine databases: TargetScan,The expression values of miRNAs within each sample were ranked in descending order . Each miStep 1: Identification of stable miRNA pairs in normal samples.Step 2: Identification of reversal miRNA pairs in cancer samples.Step 3: Two criteria were used to filter the reversal miRNA pairs that could determine whether a miRNA was differentially expressed in an individual patient. We used miR-A as an example. First, only the miRNAs that had the same deregulation directions as miR-A in the miR-A reversal pairs were retained. Here the deregulation directions indicated upregulation or downregulation in the cancer group compared with the normal group. Second, we calculated the variable coefficients in all samples for the miRNAs in the miR-A reversal pairs and ranked these miRNAs by the variable coefficients in increasing order. If there were <3 reversal pairs for miR-A, all were retained; otherwise, only the top 3 reversal pairs were retained for determining whether miR-A was differentially expressed in individual samples. In the main text, the results of the top three miRNA pairs are presented, and similar results of top five and seven miRNA pairs are listed in Step 4: The selected reversal miRNA pairs  were used to determine whether miR-A was differentially expressed in an individual patient in the testing data set. If the pattern of miR-A>miR-B occurred in an individual patient, the rectangle was marked with blue color, otherwise it was marked with gray color. If more than half of reversal miRNA pairs were detected in a patient, we concluded that miR-A was differentially expressed in this individual, which was marked with red color . Notably39We used the breast cancer samples with matched normal samples to evaluate the performance of our method. If the expression of miR-A in cancer samples was greater than that of matched normal samples, the gold standard deregulation direction of miR-A was upregulated (and vice versa). True positive (TP) represented miRNAs whose deregulation directions were the same as the gold standard, whereas false positive (FP) represented miRNAs whose deregulation directions were judged as the opposite of gold standard. Precision was calculated as positive predictive value: TP/(TP+FP).Moreover, a simulation was performed to evaluate the performance of our method. To keep the intrinsic structure of real miRNA data, the simulations were conducted based on the real miRNA data set. Here the sensitivity for each miRNA was defined as the ratio of correctly identified samples with differential expression of the miRNA to all samples with differential expression of the miRNA. The specificity for each miRNA was defined as the ratio of correctly identified samples without differential expression of the miRNA to all samples without differential expression of the miRNA. The F-score, a harmonic mean of sensitivity and specificity, was calculated as follows:After performing the same simulation 1000 times, the average sensitivity, specificity and F-score values were used to evaluate our method.7 which was tested using the hypergeometric distribution (P<0.05). Then we used the following criteria to filter the mutually exclusive pairs and find coordinated effects of miRNA expression changes and gene copy number alterations in breast cancer. First, we only retained the mutually exclusive pairs that showed upregulation of miRNA and deletion of its targets or downregulation of miRNA and amplification of its targets. Second, as the miRNA–target pairs have been associated with loss or gain functions of the targets, it should be possible to observe the respective changes in the gene expression as well. Thus we only retained the mutually exclusive pairs that showed consistent expression changes of the targets, which means that the target is significantly downregulated or upregulated in both the samples only with differential expression of the miRNA deregulated samples and the samples only with the copy number alterations of the target when comparing with the normal cohort . Finally, for each mutually exclusive miRNA–target pair, based on the hypothesis that differential expression of the miRNA and copy number alterations of its target would affect the same pathway during the progression of breast cancer, we detected DEGs (miRNA-related DEGs) between samples only with differential expression of the miRNA and normal samples and DEGs (target-related DEGs) between samples only with copy number of alterations of the target and normal samples using T-test. The genes with FDR<0.05 were defined as DEGs. Then we used the hypergeometric distribution model to test whether the two DEG lists were significantly enriched with pathways derived from the KEGG database. We identified two lists of pathways that were significantly enriched at P<0.05. And we used the hypergeometric distribution model to test whether the overlap between the two pathway lists was significant more than expected by chance. Only those mutually exclusively pairs that passed the pathway test were retained.For each miRNA and its target, a mutually exclusive pair was defined as the differential expression of that miRNA and copy number alterations of its target did not tend to co-occur in the same patient,40 and survival curves were plotted using the Kaplan–Meier method.41 Cox proportional hazard models were used for univariate and multivariate survival analyses.42 Benjamini–Hochberg multiple testing correction was used to estimate the FDR when multiple testing correction was applied.43 All statistical analyses were performed using R 3.0.0 (www.bioconductor.org).The survival differences between different groups of patients were estimated using the log-rank test,


The availability of reliable laboratory methods for determining recent HIV infection is vital for accurate estimation of population-based incidence. The mean duration of recent infection (MDRI) and false recent rate (FRR) are critical parameters for HIV incidence assays, as they impact HIV incidence estimates and provide a measure of assay performance. The HIV-1 Multiplex assay is an in-house developed, magnetic bead-based assay that measures virus-specific antibody levels and avidity to multiple analytes. To ensure quality control and to facilitate transfer of the assay to external laboratories or testing facilities, the in-house assay has been adapted and produced in kit form. Here, we describe the performance characteristics of the multiplex kit and demonstrate the stability of the kit components over a one-year period. Two statistical methods were employed to estimate the MDRI of the individual analytes and five different algorithms, combining multiple analyte values. The MDRI estimates for the individual analytes and five algorithms were all between 200 and 300 days post-seroconversion, with no notable difference between the two statistical approaches. All five algorithms exhibited a 0% FRR with specimens from long-term, subtype B HIV-1-infected individuals. The assay parameters described in this study provide the necessary tools to implement the HIV-1 multiplex assay and improves the utility of the assay for field use. Estimation of HIV incidence, the rate of new infections in a population, is a vital public health tool for evaluating the efficacy of intervention measures, monitoring recent transmission, and identifying high-risk populations. Laboratory methods for determining recent HIV infection have become an integral component in obtaining incidence estimates, since they are less costly than follow-up studies, can be applied to cross-sectional collections of specimens, and the assays are relatively easy to perform. Since the pioneer study describing the use of a laboratory assay for determining recent infection , severalHIV-1 incidence in the United States has been estimated by testing cross-sectional sero-surveillance specimens with the BED-CEIA and LAg assays –21. SuchAccording to the target product profile (TPP) for HIV incidence assays, the desired FRR for any incidence assay is less than 2% . Given tFor defining cutoff values and subsequent estimation of MDRIs for the HIV-1 Multiplex Assay based upon selected cutoffs, 608 specimens from 95 ART-naïve, subtype B HIV-1-infected subjects were evaluated. Longitudinal specimens from recent HIV-1 seroconverters were obtained commercially or from previous studies, described in detail elsewhere , 27, 31.For estimation of FRR, 296 specimens from long-term HIV-1-infected subjects from a historic prospective study were evaluated. The archived plasma specimens were obtained from a study involving subtype B HIV-1-infected men who have sex with men (MSM), enrolled between 1982 and 1983 in Atlanta, Georgia –38. The The in-house, CDC-developed HIV-1 Multiplex assay was adapted for kit production by Radix BioSolutions, Ltd. , as described previously . SpecifiThe analyte gp41-n is not considered for determining recent infection, as antibodies to gp41 develop rapidly, providing poor discrimination between recent and long-term infection . All kitStability of the magnetic beads and kit assay reagents was assessed over a twelve month period. Upon receipt of the kits from the manufacturer, high and low positive controls were tested (t = 0). The controls were tested with kits from same production lot every month for up to 12 months. The high/low controls were obtained from a proficiency testing panel derived from HIV-1-positive serum . The higThe cutoff values used to distinguish recent from long-term infection for individual analytes and data reduction scores were defined as a level below the observed plateau following increasing values as graphically displayed in Figs Methods for estimation of the MDRI were described in detail previously . BrieflyHighly correlated analytes assayed as part of the Bio-Plex system are challenging to consider individually in studies planned for estimation of recent infection. We therefore sought methods to better characterize the individual analyte values as either a multi-analyte algorithm based on a combination of analytes or as a reduced number of component scores, with both approaches resulting in a new value or score that may subsequently be used to test for recent HIV infection. Because performance of multi-analyte algorithms was assessed previously , we inclWi are analyte PCA weights, Xi are observed analyte values, Si are analyte standard deviations. The first principal component explained 81.2% of the variance in the correlated analytes; the variance explained by other components was trivial. Therefore, only the predominant first component was retained for further analyses. The principle component accounted for a greater percentage of analyte variation when all five analytes were included in the formula (data not shown). Similar to PCA, standardization with mean zero and standard deviation of one but with equal weighting per analyte, was implemented so that all five resulting scaled variables could be averaged to create a new score for testing recency. In other words, each analyte contributes equally when calculating the standardized score, as opposed to the PCA score, which assigns a different weight to each analyte. The standardized score = Xij are observed values for analyte i and person j, i are analyte standard deviations. In addition to PCA and standardized scores, we also considered the mean value for the three avidity measures  and the mean value for the two normalized MFI measures (gp120-n and gp160-n) for classifying recent HIV infection.Given differences in magnitude of avidity and normalized MFI values that do not necessarily reflect biological importance in characterizing recent infection, we evaluated methods for transformation of the 5 highly correlated analytes (Pearson correlations range 0.65–0.88) with a goal to characterize a reduced number of components that represent key construct(s) in the data. Principal component analysis (PCA), a statistical procedure that transforms a set of observations from correlated variables into a set of values of linearly uncorrelated components, was implemented , 41. TheThe FRR is the percent probability that a person infected for longer than 2 years will be misclassified as recently infected by having a measurement value below the selected cutoff value for a given analyte. FRR was calculated from n = 334 specimens collected from persons with known long-term infection. Exact binomial confidence limits (95%) were calculated. To adjust for proportional representation by stage of HIV disease, we also calculated an average FRR across stratum defined by CD4 T-lymphocyte counts. Specimens were classified into four CD4 categories: 0–199 (n = 39), 200–499 (n = 138), 500–799 (n = 93), 800+ (n = 64), with the 38 subjects from our seroconverter panel dataset who were missing CD4 data assigned into the 800+ stratum.The MFI-n and AI values for the high/ low positive controls over a 12-month period are displayed in HIV-1 antibody reactivity for each individual analyte over days since estimated seroconversion is shown in The MDRI estimates for each individual analyte and the analyte combinations/algorithms are summarized in The FRR for 334 specimens with long-term HIV infection (>2 years) corresponding to each analyte or algorithm is shown in The MDRI and FRR are critical parameters for all HIV incidence assays, as they impact the estimation of population-based HIV-1 incidence. Previously, we demonstrated the performance characteristics of an in-house developed HIV-1 multiplex assay, based on the Bio-Plex platform, which allows for the use of multi-analyte algorithms for improved estimation of HIV incidence . To faciThe statistical methods for estimating MDRI have garnered much debate and significant efforts have been made to improve upon these estimates. Recent studies have addressed potential differences in approaches to estimate MDRI by including multiple statistical methods for comparison , 30. AltThe MDRI estimates for the individual analytes and all five algorithms were >200 days post-seroconversion, some approaching 300 days. According to the TPP composed by the Incidence Assay Critical Path Working Group, the acceptable MDRI for HIV incidence assay is between 4 to 12 months . ConcurrEstimates of assay metrics for the algorithms and scores described in this study will require validation for accurate estimation of HIV incidence. However, the use of multiple analytes that are relatively predictive of early HIV infection, transformed into one principal component that accounts for more than 80% of the variability inherent to these key analytes, is a novel approach for estimation of HIV incidence. The inherent variability observed for certain individual analytes is lower for the principal component and standardized scores, which is visually demonstrated in One of the limitations of this study is the number of recent seroconverters with 1–2 years of follow-up at regular intervals available for estimation of the MDRI. Accurate and precise estimates of MDRI are paramount to valid estimation of HIV incidence. Furthermore, given that the FRR may increase with greater cohort/subtype diversity and the study population consisted predominately of subtype B infections, the FRRs described here may not be representative of all populations. Potential differences in the MDRI between subtypes will be addressed in a follow-up study. Although, all five algorithms demonstrated acceptable MDRIs and FRRs, according to the TPP for HIV incidence assays, it is difficult to ascertain which algorithm will be the most useful for field studies and whether there is an advantage of one algorithm over another. All five algorithms exhibited 0% FRR, however, it is not recommended to use an algorithm based solely on titer-based or MFI-n values. Studies have shown that antibody titer is more susceptible to variation in the presence of ART or natural virus suppression . It is lIn summary, the HIV-1 Multiplex assay may offer added levels of accuracy and precision due to the analytical sensitivity and specificity of the multiplex assay format, the high reproducibility of the kit, and ease in ability to implement intra-assay algorithms in defining of key parameters used in estimation of HIV incidence. The CDC-developed, in-house assay has been produced in kit form to ensure quality control and to facilitate transfer to other laboratories or testing sites. The assay parameters described here, along with availability of a kit, makes the HIV-1 Multiplex assay accessible to external laboratories or testing sites.


The WHO yaws eradication strategy consists of one round of total community treatment (TCT) of single-dose azithromycin with coverage of > 90%.The efficacy of the strategy to reduce the levels on infection has been demonstrated previously in isolated island communities in the Pacific region. We aimed to determine the efficacy of a single round of TCT with azithromycin to achieve a decrease in yaws prevalence in communities that are endemic for yaws and surrounded by other yaws-endemic areas.Surveys for yaws seroprevalence and prevalence of skin lesions were conducted among schoolchildren aged 5–15 years before and one year after the TCT intervention in the Abamkrom sub-district of Ghana. We used a cluster design with the schools as the primary sampling unit. Among 20 eligible primary schools in the sub district, 10 were assigned to the baseline survey and 10 to the post-TCT survey. The field teams conducted a physical examination for skin lesions and a dual point-of-care immunoassay for non-treponemal and treponemal antibodies of all children present at the time of the visit. We also undertook surveys with non-probabilistic sampling to collect lesion swabs for etiology and macrolide resistance assessment.Treponema pallidum subsp. pertenue was seen.At baseline 14,548 (89%) of 16,287 population in the sub-district received treatment during TCT. Following one round of TCT, the prevalence of dual seropositivity among all children decreased from 10.9% (103/943) pre-TCT to 2.2% (27/1211) post-TCT . The prevalence of serologically confirmed skin lesions consistent with active yaws was reduced from 5.7% (54/943) pre-TCT to 0.6% (7/1211) post-TCT . No evidence of resistance to macrolides against A single round of high coverage TCT with azithromycin in a yaws affected sub-district adjoining other endemic areas is effective in reducing the prevalence of seropositive children and the prevalence of early skin lesions consistent with yaws one year following the intervention. These results suggest that national yaws eradication programmes may plan the gradual expansion of mass treatment interventions without high short-term risk of reintroduction of infection from contiguous untreated endemic areas. In this study, we provided a single round of total community treatment (TCT) with azithromycin to the population of a sub-district in Ghana  that is endemic for yaws and surrounded by other yaws-endemic communities to determine whether a sustained decrease in yaws prevalence could be achieved up to one year after the intervention. The efficacy of TCT was assessed by performing a clinical evaluation and serological testing of any yaws-like lesions found as well as serological screening of asymptomatic schoolchildren aged 5–15 years pre-TCT and at one year post-TCT. The results indicate that after a single round of high coverage TCT (89%) with azithromycin, the prevalence of active and latent yaws was significantly reduced. We also found that the use of a dual point-of-care immunoassay to detect non-treponemal and treponemal antibodies among school-going children is a practical alternative to laboratory-based serological testing to evaluate the effectiveness of yaws interventions in resource-poor settings. Treponema pallidum spp. pertenue, a spirochaete closely related to that which causes syphilis . ThPrior to the implementation of the study, the health workers and village volunteers were trained on the objectives of the study, recognition of yaws-like lesions, implementation and data collection tools. The TCT programme was conducted by 13 teams of two trained volunteers drawn from affected communities who were supervised by local health-care workers or members of the national yaws eradication programme. During a five-day period in November–December 2013, the teams offered azithromycin tablets  at a single oral dose of 30 mg/kg per body weight (maximum 2 g) to all members of the 36 targeted communities aged 6 months and above at no cost to the participants. The tablets (500g strength) were administered to the eligible population according to age as described in the WHO Morges Strategy document . For chiWe used cluster sampling with individual schools as the cluster unit. Eligibility for inclusion was met by 20 primary schools with a population larger than 100 children among 24 schools located in the sub-district. The schools were randomly assigned to either the pre- or post- TCT evaluation surveys. Therefore, the sets of schools for pre- and post- TCT surveys were mutually exclusive and schools could not be chosen repeatedly for more than one survey. Every child present at the time of our visit to the schools selected was invited to participate in the study.sero-positivity, defined as dually non-treponemal and treponemal antibody positivity using a point-of-care test among symptomatic or asymptomatic children. Secondary outcomes, were the prevalence of suspected yaws, defined as a child with a history of residence in an endemic area who presented with clinically active (visible) yaws-like lesions, and the prevalence of serologically-confirmed yaws, defined as a suspected case with a dually-positive serological test result for non-treponemal and treponemal antibodies.The primary outcome was prevalence of In October 2013, before the mass treatment campaign (November–December 2013), we conducted a baseline assessment survey in 10 randomly selected primary schools and every child present, 943 children in total, was enrolled. One year after the intervention (November–December 2014), every child from a further group of 10 randomly selected schools, 1211 children in total, were selected for an identical post-intervention assessment of impact survey. During both assessments, all children were examined clinically for skin lesions consistent with early infectious yaws  and a specimen of capillary blood was collected from all, symptomatic and asymptomatic, participants to perform a point-of-care immunoassay for antibody to yaws infection. All children with active yaws-like lesions detected during either the initial or post-TCT assessments or those who were asymptomatic but had a dually non-treponemal and treponemal antibody-positive result on initial field screening were treated with a single dose of azithromycin (30 mg/kg) and followed up for adverse events.A point-of-care immunoassay, developed for the serological diagnosis of syphilis, that can simultaneously detect both non-treponemal and treponemal antibodies  –23 was uT. pallidum PCR positive cases detected in Abamkrom sub-district before the intervention, we decided to extend the baseline study to the entire West Akim district . We had 8 teams, one in each sub-district, that examined the skin of schoolchildren aged less than 15 years of age. The sampling for this study was non-probabilistic; teams visited enough schools and enrolled all consecutive eligible children to achieve 150 symptomatic children sampled. If they identified any skin lesion, the child was invited for diagnosis, swab sample collection and treatment after their parents or guardians provided written informed consent. Similar procedures were used for post-TCT surveys that were conducted only in the Abamkrom sub-district.We conducted clinical surveys to collect lesion swabs for PCR testing before and one year after TCT. Due to the low number of PCR-confirmed yaws was defined as a suspected case with a positive PCR detection of DNA of the polymerase I gene and/or the T. pertenue-specific 23S rRNA gene sequence on material collected from suspected lesions  years) compared with that of the post-TCT children (9.4 [3.6] years) was not significantly different.The prevalence rate of dual seropositivity in the DPP test decreased significantly, from 103/943 (10.9% 95% CI 6.5–17.5) among children in the pre-TCT survey to 27/1211  in the post-TCT survey . In addiT. pertenue-specific DNA sequence (none of which contained mutations associated with azithromycin resistance) and 45/158 were H. ducreyi positive including 7/158 lesions that were dually T. pertenue and H. ducreyi-PCR positive. M. ulcerans-specific DNA sequences were not detected in any specimen obtained from lesions, either pre- or post-TCT. In the Abamkrom sub-district 3/53 (5.7%) sampled cases pre-TCT were T. pertenue-PCR positive  which can be explained because serology remains detectable in serofast status.Of the 158 blood samples collected pre-TCT from children with active lesions that underwent serological testing at the CDC laboratory , 57  recorded in the routine reporting system (DHIMS2) of the Ministry of Health. Although the sample size was extremely small, we were unable to detect T. pertenue-PCR positive lesions  which could be related to the higher coverage rate achieved in Ghana or to the initial lower burden of infection in the present study or both when compared to PNG.The population coverage that was achieved in the Ghanaian population reported here (89%) was slightly higher than that achieved in the PNG study (84%) . The impH. ducreyi as an important cause of skin ulcers in yaws endemic communities. Isolates of H. ducreyi obtained from skin lesions from children in Ghana are fully sensitive to azithromycin in vitro and the antibiotic is frequently used to treat chancroid, a sexually transmitted infection also caused by H. ducreyi. In our study, the overall prevalence of lesions caused by H. ducreyi was greatly reduced after TCT. However, unlike yaws, the relative proportion of H. ducreyi-positive ulcers remained essentially unchanged following TCT. It seems logical to speculate that community mass treatment with azithromycin may have less impact on lesions caused by this bacterium than those caused by T. pallidum spp. pertenue. We raise two possible explanations: Firstly, H. ducreyi strains that cause skin lesions in children may be more infectious than T. pallidum spp. pertenue. Secondly, in common with sexually transmitted H. ducreyi infections, non-sexual transmission of H. ducreyi does not appear to engender protective immunity to subsequent re-infection, unlike the transient immunity that occurs following treponemal infection [Recent studies on yaws conducted in PNG , Ghana , Vanuatunfection , 37.T. pertenue. Finally, to determine the sample size for our surveys, we used a design effect of 2 based on previous experience in the Solomon Islands and Fiji [Our study has some limitations. First, the use of school-going children as a sampling methodology may introduce bias because the poorest children, who are at most risk of the disease, may not attend school. However, school-sampling is generally considered a good and convenient sub-population sample for other NTDs and routine surveillance data from DHIMS2 confirmed the overall decrease of yaws-like cases seen in the sub-district one year after the mass treatment. Further studies to compare the impact of interventions on school versus community-based populations are clearly indicated. Second, we selected two different groups of schools for pre- and post-TCT assessments, rather than returning to the original schools to determine the impact of TCT on the children who were seen initially. We considered that the additional survey for clinical and serological screening, and treatment of positive cases, that these schools would receive was effectively an additional public health intervention that is not a normal part of the larger interventions that the study aimed to evaluate. We therefore randomly selected a different group of schools for the post-TCT evaluation one year later to avoid measuring the potential impact of a double treatment. Third, the DPP point-of-care test that was used in this study lacks some sensitivity at low titres compared with conventional laboratory-based testing . Howeverand Fiji ,35, but and Fiji . In our H. ducreyi or other unknown pathogens may continue to persist after a single-round of mass treatment giving the erroneous impression to both the affected population and health authorities alike that yaws has not been eliminated from a previously endemic community. This situation is compounded by the finding that H. ducreyi-positive lesions may be associated with dually positive non-treponemal and treponemal serological results in children with latent yaws or serofast status. Azithromycin is one of the antibiotics recommended for the treatment of sexually transmitted H. ducreyi infections [H. ducreyi [H. ducreyi lesions following the successful elimination of yaws through TCT. Further studies on the etiology of these yaws-like skin lesions in various yaws-endemic regions around the world are required.It seems clear, from the results of this intervention study, that yaws-like lesions caused by fections ,40, and  ducreyi . In the T. pertenue strains and to warrant the treatment of such cases with benzathine benzylpenicillin.In conclusion, our findings provide additional evidence that one round of TCT with azithromycin with high coverage ~ 90%, as part of the WHO Morges Strategy, is highly effective in providing a sustained and significant decrease in the prevalence of yaws 12 months after mass treatment from endemic communities, even if they adjoin other untreated endemic areas. Because 6-monthly resurveys using field staff may be costly, perhaps, if the initial coverage is >90%, in some places, a practical approach is to use trained village volunteers for ongoing active community surveillance and health promotion activities for yaws, especially, in the post-TCT phase similar to the experience of the guinea worm eradication programme . AlthougS1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.S5 Table(XLSX)Click here for additional data file.


Stroke is a very time-sensitive pathology, and many new solutions target the optimization of prehospital stroke care to improve the stroke management process. In-ambulance telemedicine, defined by live bidirectional audio-video between a patient and a neurologist in a moving ambulance and the automated transfer of vital parameters, is a promising new approach to speed up and improve the quality of acute stroke care. Currently, no evidence exists on the cost effectiveness of in-ambulance telemedicine.We aim to develop a first cost effectiveness model for in-ambulance telemedicine and use this model to estimate the time savings needed before in-ambulance telemedicine becomes cost effective.Current standard stroke care is compared with current standard stroke care supplemented with in-ambulance telemedicine using a cost-utility model measuring costs and quality-adjusted life-years (QALYs) from a health care perspective. We combine a decision tree with a Markov model. Data from the UZ Brussel Stroke Registry (2282 stroke patients) and linked hospital claims data at individual level are combined with literature data to populate the model. A 2-way sensitivity analysis varying both implementation costs and time gain is performed to map the different cost-effective combinations and identify the time gain needed for cost effectiveness and dominance. For several modeled time gains, the cost-effectiveness acceptability curve is calculated and mapped in 1 figure.Under the base-case scenario  and taking a lifetime horizon into account, in-ambulance telemedicine is a cost-effective strategy compared to standard stroke care alone starting from a time gain of 6 minutes. After 12 minutes, in-ambulance telemedicine becomes dominant, and this results in a mean decrease of costs by US –$30 (95% CI –$32 to –$29) per patient with 0.00456 (95% CI 0.00448 to 0.00463) QALYs on average gained per patient. In over 82% of all probabilistic simulations, in-ambulance telemedicine remains under the cost-effectiveness threshold of US $47,747.Our model suggests that in-ambulance telemedicine can be cost effective starting from a time gain of 6 minutes and becomes a dominant strategy after approximately 15 minutes. This indicates that in-ambulance telemedicine has the potential to become a cost-effective intervention assuming time gains in clinical implementations are realized in the future. Stroke is a very time-sensitive pathology, and many new solutions target the optimization of prehospital stroke care to improve the stroke management process . One appCurrently, no evidence exists on the cost effectiveness of in-ambulance telemedicine. We aim to develop a model which predicts the potential costs and benefits associated with this new in-ambulance telemedicine approach. Consequently, this model allows identification of the minimum time gain that is needed before in-ambulance telemedicine becomes cost effective.Current standard stroke care is compared with current standard stroke care supplemented with in-ambulance telemedicine using a cost-utility model measuring costs and quality adjusted life-years (QALYs) from a health care perspective. We combine a decision tree model (3 months) with a Markov model using a lifetime horizon . One posAll patients with suspicion of acute stroke are included in the model and are divided into 4 main categories: (1) stroke mimic, (2) transient ischemic attack (TIA), (3) ischemic stroke, and (4) hemorrhagic stroke. Ischemic strokes are divided into 3 treatment groups: (1) intravenous administration of IVT alone, (2) intravenous administration of IVT in combination with endovascular treatment (EVT), or (3) conservative care. Patients in the in-ambulance telemedicine model can either receive in-ambulance telemedicine on top of standard stroke care or standard stroke care alone. This additional arm accounts for the missed opportunities related to the accuracy of dispatchers to recognize a stroke and the proportion of patients not transported by an ambulance equipped with telemedicine technology.To effectively model the time-sensitive nature of IVT with and without EVT, the treatment effect per time interval is modeled. For IVT alone, we assume that patients are treated up to 4.5 hours after stroke onset . In combThe comparator of standard stroke care is based on the performance indicators of Universitair Ziekenhuis Brussel (UZ Brussel), the university hospital of the Free University of Brussels (VUB), between 2011 and 2014. In this center, in-hospital prealerting of potential stroke patients and streamlined in-hospital workflows are part of standard medical practice [For each end node in the decision tree, the modified Rankin Scale (mRS) at month 3 is used to classify patients into 3 categories: (1) favorable outcome (mRS 0-2), (2) unfavorable outcome (mRS 3-5), and (3) death (mRS 6). Data from the UZ Brussel Stroke Registry (2282 suspected stroke patients admitted to the UZ Brussel Stroke Unit between February 2009 and February 2014) are combined with literature data to populate the model for standard care . Each mRFor the costs per treatment arm, we link hospital data and emergency claims data  at the individual patient level  and 3. CThe impact of in-ambulance telemedicine on top of standard care is modeled by assuming an average time gain ranging from 5 to 60 minutes . This inBased on previous in-ambulance telemedicine pilot studies ,5, we asCosts and QALYs are used to calculate the incremental cost-effectiveness ratio (ICER) after 3 months (decision tree only) and after a lifetime horizon (decision tree plus Markov model). Time gain after in-ambulance telemedicine is varied between 0 and 60 minutes, and cost for the implementation of in-ambulance telemedicine is varied between 50% and 400% of baseline cost in a 2-way sensitivity analysis, mapping the ICER for all combinations of both variables. Based on this analysis, we select the time gain for which in-ambulance telemedicine becomes cost effective. For this time gain, we perform a 1-way sensitivity analysis, varying all input parameters between 70% and 130% of their deterministic value and ranking the parameters according to the highest interval between calculated outcome parameters . A probabilistic sensitivity analysis is applied using Monte Carlo simulations with 5000 bootstraps to account for the uncertainty around the input parameters and assess the robustness of the model. The cost-effectiveness acceptability curve is constructed for 5, 10, 15, and 30 minutes of time gain (1000 bootstraps). The health economic model was built and runs in Excel , and the UZ Brussel Stroke Registry was analyzed using Stata MP 13.0 for Windows (StataCorp LLC). The study was approved by the UZ Brussel ethical committee, and the model was validated by MF.Under the base-case scenario  and taking a lifetime horizon into account, in-ambulance telemedicine is a cost-effective strategy compared to standard stroke care alone, starting from a time gain of 6 minutes .After 12 minutes, in-ambulance telemedicine becomes a dominant strategy over standard best medical practice . In a coNot taking into account these long-term savings, in-ambulance telemedicine yields an ICER of US $201,557/QALY  after 3 months. This incremental cost per saved QALY is explained by the cumulative cost of the intervention and the costs associated with more IVT and EVT after implementation of in-ambulance telemedicine.One-way sensitivity analysis at 12 minutes time gain reveals that parameters involving outcome of ischemic stroke have the largest impact on calculated costs and QALYs -9. For lUnder 4 times baseline costs , in-ambulance telemedicine becomes cost effective after 19 minutes and is dominant after 39 minutes . For lowThe cost-effectiveness acceptability curves  reveal tWe report on the first ever comprehensive in-ambulance telemedicine cost-utility model combining a decision tree with a Markov model, using detailed cost information per treatment arm. In-ambulance telemedicine is dominant from a health care payer perspective starting from 12 minutes time gain.Two previous publications describe health economic aspects of MSUs for improvement of prehospital stroke care ,27. BothNo impact of in-ambulance telemedicine on mortality was modeled, even though evidence exists that earlier stroke treatment reduces in-hospital mortality . We chosThe impact of in-ambulance telemedicine can vary greatly from 1 hospital or region to another depending on the standard quality and speed of care. Streamlining in-hospital workflows will always be a crucial part of a successful in-ambulance telemedicine implementation and can influence costs and potential benefit. For example, realized time gains could be more modest if prehospital notification through mobile phones or tablets is already part of current practice.The major limitation of the presented model lies in the absence of information from RCTs evaluating the effects of in-ambulance telemedicine on costs and patient outcome. This drawback was addressed by implementing only solid criteria originating from RCTs for the outcome parameters. However, combining data from multiple trials is not without risk, and the results of this model should be interpreted carefully.An advantage of in-ambulance telemedicine is the limited amount of additional resources needed from the hospital. If state-of-the art stroke care is available, no additional staff may be needed and existing ambulances can readily be equipped with the technology. However, depending on the catchment area and the number of stroke patients supported via in-ambulance telemedicine, additional teleconsultants on call may be needed. Although this could increase the organizational cost of in-ambulance telemedicine, more patients would be treated with in-ambulance telemedicine, which would further decrease the cost per patient and improve outcome. Training is required but was included in the cost of the telemedicine implementation.Total implementation costs will decrease when in-ambulance telemedicine technology becomes more widely available. Lower cost alternatives such as tablet-based approaches are currently being investigated for remote stroke severity assessment in driving ambulances ,7.Widespread implementation of in-ambulance telemedicine will not only depend on its (cost-)effectiveness but also on the creation of the required legal framework. In Belgium, currently consultations are only officially recognized if face-to-face contact between the patient and treating physician occurs. Other issues include clear regulations for reimbursement and liability.Our analysis only takes benefits of the expected time gain into account. Other benefits of in-ambulance telemedicine include a lower risk of stroke misdiagnosis and consequently missed opportunities for treatment with IVT or EVT and triage of patients to inadequate facilities . EstimatWe excluded the implementation of rapid blood pressure lowering for hemorrhagic stroke patients in our model, even though indications exist on the benefit of this approach . In-ambuFurther, expert prehospital care may help avoid secondary brain damage, as the teleconsultant can support the ambulance personnel in obtaining and maintaining homeostasis during ambulance transportation through optimal application of the standard operating procedures for airway protection, blood oxygen saturation, arterial blood pressure, heart rate, cardiac arrhythmia, decreased level of consciousness, dysglycemia, and other supportive measures .The combination of in-ambulance telemedicine with MSUs is another interesting approach that could further decrease costs of the MSU as it would avoid sending highly trained physicians into the field for each individual patient. This approach is feasible, and preliminary analysis has shown that median time savings of 23 minutes between alarm-to-CT times can be attained when compared to standard care .Recent clinical trials showing impressive benefits from EVT -34 heralIn-ambulance telemedicine has the potential to improve the organization of care for other medical emergencies, further strengthening the cost-effectiveness potential of this technology. The use of tablet computers as a support system for general emergency medical services and better patient triage have shown a decrease in transportation times by ambulances, showing the possibilities for further innovation in emergency care organization and delivery using telemedicine and mobile health solutions .We believe that this positive health economic evaluation can inspire decision makers in hospitals and governments to actively pursue the implementation of and further research on in-ambulance telemedicine.In-ambulance telestroke is highly cost effective from a health care perspective, resulting in more QALYs and less costs starting from a realized time gain of 12 minutes. The model is not directly based on results from RCTs on the effects of in-ambulance telemedicine, and trials to further crystalize these effects in various care models are needed. Support from governments and hospitals to facilitate implementation in clinical practice is indispensable and can be justified by the dominant cost effectiveness of in-ambulance telemedicine under several scenarios both in terms of implementation costs and time gain.