Fluid boluses are administered to septic shock patients with the purpose of increasing cardiac output as a means to restore tissue perfusion. Unfortunately, fluid therapy has a narrow therapeutic index, and therefore, several approaches to increase safety have been proposed. Fluid responsiveness (FR) assessment might predict which patients will effectively increase cardiac output after a fluid bolus (FR+), thus preventing potentially harmful fluid administration in non-fluid responsive (FR−) patients. However, there are scarce data on the impact of assessing FR on major outcomes.The recent ANDROMEDA-SHOCK trial included systematic per-protocol assessment of FR. We performed a post hoc analysis of the study dataset with the aim of exploring the relationship between FR status at baseline, attainment of specific targets, and clinically relevant outcomes.ANDROMEDA-SHOCK compared the effect of peripheral perfusion- vs. lactate-targeted resuscitation on 28-day mortality. FR was assessed before each fluid bolus and periodically thereafter. FR+ and FR− subgroups, independent of the original randomization, were compared for fluid administration, achievement of resuscitation targets, vasoactive agents use, and major outcomes such as organ dysfunction and support, length of stay, and 28-day mortality.p 0.0001), exhibited less positive fluid balances, but received more vasopressor testing. No difference in clinically relevant outcomes between FR+ and FR− patients was found, including 24-h SOFA score , need for MV , need for RRT , ICU-LOS , and 28-day mortality . Only thirteen patients remained fluid responsive along the intervention period.FR could be determined in 348 patients at baseline. Two hundred and forty-two patients (70%) were categorized as fluid responders. Both groups achieved comparable successful resuscitation targets, although non-fluid responders received less resuscitation fluids (0 [0–500] vs. 1500 [1000–2500] mL; Systematic assessment allowed determination of fluid responsiveness status in more than 80% of patients with early septic shock. Fluid boluses could be stopped in non-fluid responsive patients without any negative impact on clinical relevant outcomes. Our results suggest that fluid resuscitation might be safely guided by FR assessment in septic shock patients.NCT03078712. Registered retrospectively on March 13, 2017.ClinicalTrials.gov identifier,  Fluid administration is the first line therapy to reverse sepsis-induced tissue hypoperfusion , 2. For Fluid responsiveness (FR) is a physiologic cardiovascular condition where an increase in preload induced by a fluid bolus leads to an increase in cardiac output (CO) by more than 10–15% –11. In nMultiple tests have been described to assess FR at the bedside –15. TheyANDROMEDA-SHOCK is the first major study that incorporated systematic per-protocol assessment of FR  and thusThe complete protocol, statistical analysis, and main results of the ANDROMEDA-SHOCK trial have been previously published –32. InstThe ANDROMEDA-SHOCK trial was a prospective, multicenter, parallel-group randomized control trial conducted in 5 Latin-American countries from March 2017 to March 2018, including a total of 424 patients with septic shock. Its main objective was to evaluate the impact on 28-day mortality of a peripheral perfusion (PPTR)- vs. lactate level-targeted resuscitation (LTR) over an 8-h intervention period. Eligible patients were included within a time frame of 4 h after the diagnosis  or percentage (%) accordingly. Mann-Whitney p = 0.8) in FR− patients. Baseline demographic and severity characteristics of groups are shown in Table Fluid responsiveness was unavailable in 76 patients at baseline, and this increased to 104 at 8 h. The group of patients categorized as with unavailable FR status over time (0 to 8 h) was the sum of early deaths and patients in whom it could be not determined mainly because of technical reasons. FR could be determined in 348 of 424 patients (82%) at randomization time. Of this sample, 242 (70%) patients were categorized as fluid responders. These FR+ patients had received a pre-protocol fluid loading of 26.7 [17–40] vs. 26.8 [20–38] mL/kg (p = 0.4), need for MV , MV days , need for RRT , ICU-LOS , hospital LOS , and 28-day mortality .Evolution of perfusion-related parameters during the intervention period for each group is presented in Additional file p = 0.02) was also significantly more positive at 24 h in the FR+ group.Achievement of resuscitation targets was comparable between FR+ and FR− subgroups at 2 and 8 h, but the trend was significantly higher for FR+ Fig. . Use of Three hundred and twenty-eight patients were mechanically ventilated at the start of the protocol (77%). Different tests were used for the assessment of FR as depicted in Table Most patients evolved into a fluid-unresponsive state during the 8-h intervention period Fig. . Of noteOur main findings can be summarized as follows: (a) fluid responsiveness status could be determined in 82% of early septic shock patients by using diverse tests depending on the clinical context; (b) 30% of patients were already non-fluid responsive before starting ICU-based resuscitation; and (c) despite receiving less fluids, non-fluid responders at baseline resolved hypoperfusion in a similar proportion than FR+ patients by following other steps of the protocol with no difference in clinically relevant outcomes. Our results do not support a clear benefit of FR assessment on major outcomes but suggest that withholding fluid boluses in FR− patients appears to be safe.Fluid overload has been associated with increased mortality and morbidity in patients with septic shock. Therefore, several strategies to deliver fluid resuscitation in a more physiologic, rational, and restrictive way are being tested in ongoing trials , 37. AmoAlthough there is an extensive literature on the fluid responsiveness concept and background physiology, as well as on assessment techniques, the use of FR tests is not considered as a standard of care, nor has been included in recent septic shock trials. There are many possible explanations for this fact. First, some of these techniques are time-consuming, operator-dependent, not universally applicable, and with many inherent limitations. Second, there is a gray zone around the published cutoff values that somehow turns decision-making on further fluid resuscitation uncertain and complex . Third, The behavior of FR status during the 8-h intervention period in our cohort is intriguing. The effect of fluid boluses on CO and fluid responsiveness is thought to be transient based on previous pharmacodynamic studies . This haThis study presents several limitations. First, it has the inherent limitations of a post hoc analysis, so conclusions should be considered only as hypothesis-generating. Second, the use of some techniques might be criticized. PPV was used in one third of the patients, which is far more than expected considering the numerous limitations of this method . On the Systematic assessment allowed determination of fluid responsiveness status in more than 80% of patients with early septic shock. Fluid boluses could be stopped in non-fluid responsive patients without any negative impact on clinical relevant outcomes. Our results suggest that fluid resuscitation might be safely guided by FR assessment in septic shock patients.Additional file 1: Fluid responsiveness assessment algorithm. PPV: Pulse pressure variation; PLR-VTI: Passive leg raising assessed using velocity time integral; IVCV: Inferior vena cava variation; SVCV: Superior vena cava variation; EEOT: end-expiratory occlusion test; SVV: stroke volume variation; ARDS: Acute respiratory distress syndrome; CO: Cardiac output; CI: Cardiac index; VTI: Velocity time integral; Vt: Tidal volume; PBW: Predicted body weight.Additional file 2: Technical details of different fluid responsiveness assessment techniques.Additional file 3: Baseline characteristics including the subgroup of patients in whom FR could not be assessed.Additional file 4: Evolution of perfusion parameters during the first 24 h.Additional file 5: Distribution of fluid responsiveness at baseline according to the test used.


There is little published data investigating non-invasive cardiac output monitoring in the emergency department (ED). We assessed six non-invasive fluid responsiveness monitoring methods which measure cardiac output directly or indirectly for their feasibility and repeatability of measurements in the ED: (1) left ventricular outflow tract echocardiography derived velocity time integral, (2) common carotid artery blood flow, (3) suprasternal aortic Doppler, (4) bioreactance, (5) plethysmography with digital vascular unloading method, and (6) inferior vena cava collapsibility index.This is a prospective observational study of non-invasive methods of assessing fluid responsiveness in the ED. Participants were non-ventilated ED adult patients requiring intravenous fluid resuscitation. Feasibility of each method was determined by the proportion of clinically interpretable measurements from the number of measurement attempts. Repeatability was determined by comparing the mean difference of two paired measurements in a fluid steady state (after participants received an intravenous fluid bolus).76 patients were recruited in the study. A total of 207 fluid responsiveness measurement sets were analysed. Feasibility rates were 97.6% for bioreactance, 91.3% for vascular unloading method with plethysmography, 87.4% for common carotid artery blood flow, 84.1% for inferior vena cava collapsibility index, 78.7% for LVOT VTI, and 76.8% for suprasternal aortic Doppler. The feasibility rates difference between bioreactance and all other methods was statistically significant.Our study shows that non-invasive fluid responsiveness monitoring in the emergency department may be feasible with selected methods. Higher repeatability of measurements were observed in non-ultrasound methods. These findings have implications for further studies specifically assessing the accuracy of such non-invasive cardiac output methods and their effect on patient outcome in the ED in fluid depleted states such as sepsis.The online version of this article (10.1186/s13049-019-0586-6) contains supplementary material, which is available to authorized users. Intravenous fluid therapy aims to improve cardiac output and therefore restore oxygen delivery to hypoperfused organs. However, iatrogenic tissue oedema may occur when too much intravenous fluid is given. This is associated with increased mortality, renal failure and respiratory compromise –3. FluidFluid responsiveness is commonly defined as a stroke volume increase of at least 10% following a fluid bolus of 200-500mls 10–15 min . TargetiNon-invasive cardiac output monitoring methods which can rapidly identify fluid responsiveness and guide fluid therapy are emerging in the ED and in the pre-hospital environment , 13. TheFeasibility defined as the proportion of clinically interpretable results yielded from attempted measurements, andRepeatability (or test-retest reliability) defined as the mean difference occurring with repeated measurements of the same target as a marker of internal validity.The range of methods available can be confusing to ED practitioners given the absence of systematic comparison. Assessing the clinical value of a diagnostic test is a multi-phase process which includes assessing its feasibility, repeatability, accuracy, impact on patient outcomes, and cost. The objectives of this study were to systematically compare methods for a broad range of ED patients according to two key diagnostic criteria:The study was a prospective observational study. A predefined point score system was used for each technique to sort clinically interpretable measurements from uninterpretable measurements. Feasibility was determined by dividing the number of clinically interpretable measurements by the total number of attempted measurements for each method. Repeatability was determined by comparing the mean difference of two paired measurements in a fluid steady (replete) state for each method . Participants were placed in a semi-recumbent position at 30 degrees on a trolley. The stroke volume was simultaneously measured by LVOT VTI, CCABF, bioreactance, and PVUT (measurement round 1 - M1). IVCCI was also measured. A 250–500 mls of crystalloid fluid bolus was then delivered over 15 min. A post-fluid measurement round with all six methods was then conducted (M2), immediately followed by a third measurement round (M3) to assess repeatability , invasive or non-invasive mechanical ventilation, and presentations requiring immediate intervention . All patients attending the ED during the study period during the hours of 09:00 to 20:00 Monday to Friday between August to October 2015 were eligible for recruitment. Patients having received fluids in a pre-hospital setting were not excluded from the study.LVOT VTI was measured with a uSmart 3300 ultrasound system . Carotid Doppler traces were assessed by a Sonosite EDGE . Suprasternal aortic Doppler traces were obtained using the USCOM-1A . PVUT was assessed with a LiDCO continuous non-invasive arterial pressure device . Bioreactance was assessed with a Cheetah Medical device .A review of the literature showed that without prior experience performing LVOT VTI at a competent level takes 40–50 practice studies , CCABF rP < 0.05 (two-tailed) was considered statistically significant. 38 participants with a minimum of 2 rounds of measurements each was the required sample size using a previously described power calculation [Measurement data were collected onto a REDCap database  and analysed with SPSS v24 . culation . Descripculation . One-samThis study was approved by the East of England Research Ethics Committee . Informed consent by each subject was required for participation in this study.76 participants were recruited in the study including 68 who completed the study protocol  Fig. . ParticiFeasibility for the six methods in decreasing order were bioreactance with a completion proportion of 97.6% , PVUT with 91.3% , CCABF with 87.4% , IVCCI with 84.1% , LVOT VTI with 78.7%  and SSAD with 76.8%  . In other words, after participants received fluids, differences in measurements for M2 and M3 did not significantly vary from zero for each method. Linear regression analysis showed the absence of proportional bias  showed higher feasibility rates over ultrasound methods. This can be explained by the former requiring less cooperation on part of at times agitated and confused patients and therefore more successful in obtaining measurements. The high feasibility of bioreactance has been reported in trauma patients , 40. PVUThe results also illustrate the impact that patient anatomy, care setting and operator skill may have on feasibility for ultrasound methods. For IVCCI, previous studies have reported feasibility rates of 85 to 93% stating notably that adipose tissue and bowel gas can interfere with IVC visualisation . SimilarThe absence of standardised measurement technique for IVCCI and CCABF is a concern. A review of IVCCI shows that eight different measurement techniques were used in ten separate diagnostic studies , 46–52. With regards to obtaining a first reading (M1), all ultrasound methods had a median measurement time of under 2 min 30 s Fig. . By contIf feasibility is an essential aspect of clinical value, repeatability informs on the internal validity of the method and therefore the actual quality of the data obtained. This study found no statistically significant differences between the post-fluid paired measurements (M2 and M3) for all methods; each method was therefore able to consistently provide comparable repeat readings in the 95% limits of agreement. To verify if the fluid responsiveness threshold was crossed on paired measurements in a presumably fluid-steady state we applied limits of agreement of +/− 10% to the paired measurement mean. We found that CCABF and IVCCI showed least agreement with most paired readings falling outside these limits. This has important implications. It is likely that operators may require more than 50 scans of baseline experience on CCABF and IVCCI to achieve intra-operator variability of less than 10% to correctly identify fluid responsive states with those methods. It also possible that these methods may also have lower intrinsic repeatability.bis to M2bis). This was the preferred approach in view of the time available for measurements. Thirdly, not all participants may have reached a fluid steady state after receiving a fluid bolus of 250-500mls. However, such volume was felt suitable to reverse at least partially fluid depletion in eligible patients.Firstly, it is likely that the absolute feasibility of each technique would be higher if it had been assessed on its own. The higher rates of feasibility generally observed in ICU studies and the longer measurement times seem to support this. This should not affect the relative feasibility between methods. Secondly, repeatability of each method was assessed by paired readings of a fluid steady state (M2 versus M3) rather than paired readings of a fluid dynamic state . Figure illustrating the percentage feasibility for non-invasive cardiac output monitoring methods by participant. (DOCX 132 kb)Additional file 2:Table S2. Proportional bias analysis using linear regression analysis. Table detailing linear regression analysis. (DOCX 13 kb)


Early non‐invasive tumour therapy response assessment requires methods sensitive to biological and physiological tumour characteristics.The aim of this study was to find and evaluate magnetic resonance imaging (MRI) derived tumour tissue parameters that correlate with histological parameters and that reflect effects of radionuclide therapy.i.v. injected with 177Lu‐octreotate. MRI was performed (7 T Bruker Biospec) on different post‐therapy intervals (1 and 13 days) using T2‐weighted imaging, mapping of T2* and T1 relaxation time constants, as well as diffusion and dynamic contrast enhancement (DCE‐MRI) techniques. After MRI, animals were killed and tumours excised. Four differently stained histological sections of the most central imaged tumour plane were digitized, and segmentation techniques were used to produce maps reflecting fibrotic and vascular density, apoptosis, and proliferation. Histological maps were aligned with MRI‐derived parametric maps using landmark‐based registration. Correlations and predictive power were evaluated using linear mixed‐effects models and cross‐validation, respectively.Mice bearing a subcutaneous human small‐intestine neuroendocrine tumour were Several MR parameters showed statistically significant correlations with histological parameters. In particular, three DCE‐MRI‐derived parameters reflecting capillary function additionally showed high predictive power regarding apoptosis (2/3) and proliferation (1/3). T1 could be used to predict vascular density, and perfusion fraction derived from diffusion MRI could predict fibrotic density, although with lower predictive power.This work demonstrates the potential to use multiparametric MRI to retrieve important information on the tumour microenvironment after radiotherapy. The non‐invasiveness of the method also allows longitudinal tumour tissue characterization. Further investigation is warranted to evaluate the parameters highlighted in this study longitudinally, in larger studies, and with additional histological methods. ADCapparent diffusion coefficientATarrival timeBEbrevity of enhancementDdiffusion coefficientD*Pseudo diffusion coefficientDCEdynamic contrast‐enhancedDWIdiffusion weighted MRIfperfusion fractionFDFibrotic densityIFPInterstitial fluid pressureISinitial slopeIVIMintravoxel incoherent motionMVDmicrovascular densityROIregion‐of‐interestSEmaxrelative maximum signal enhancementT1, T2, T2*magnetic relaxation time constantsTICMR signal time‐intensity curveTOPtime of peak intensityTTPtime to peakWIwash inWOwash out1177Lu‐octreotate). This compound binds to receptors expressed on tumour cells. Thereby the radiation dose to primary and metastatic tumours is maximized, while exposure of healthy tissue to radiation is limited. 177Lu‐octreotate shows great potential for treatment of NETs, but optimization of therapy protocols is required. To facilitate this, non‐invasive methods for prediction and assessment of therapeutic effects are needed.Neuroendocrine tumours (NETs) are slow‐growing malignancies and metastases are often found at the time of diagnosis. Currently available treatment options have limited effect on patient survival. New therapeutic options are being developed, such as radionuclide therapy (RNT) using the radiolabelled somatostatin analogue octreotate  or magnetic resonance (MR) imaging, but is becoming obsolete for evaluating modern, targeted therapies. Methods that are sensitive to the targeted biological processes, such as angiogenesis, proliferation and apoptosis are required instead.Histological methods provide unambiguous characterization of biological features of tumours, but examining an entire tumour precludes longitudinal assessment, while biopsies only provide information on the focally sampled tissue regions. Erroneous sampling and problematic anatomical locations of tumours are further limiting factors.e.g., spatial distribution of vascularity, vessel structure and function, oxygenation, perfusion, and blood volume.f).T1, T2, and T2*) can reveal altered molecular organization, presence of macromolecules after microvasculature restructuring or leakage, or changes in tissue deoxyhaemoglobin levels.Several functional MR imaging methods are currently being investigated for their ability to indirectly measure tumour characteristics. Promising results regarding early assessment of tumour response to RNT have been found.Furthermore, the interest in combining several MR techniques in a multiparametric MR (mpMR) approach is increasing, since it would enable a non‐invasive, spatiotemporally resolved, comprehensive evaluation of several dynamic and functional biological mechanisms that are associated with tumour response.e.g. water diffusion, vascular parameters, perfusion and hypoxia) have been correlated with biological measures. However, the results sometimes show weak, diverging or even contradictory results, most likely due to the diversity of tumour types, therapeutic modalities, experimental conditions and methodologies described in the literature.In previous studies, individual MR‐derived tissue parameters for tumour response assessment  that were fed with a standard diet and water ad libitum. Each animal grew one tumour in the neck region, which will be referred to as tumour 1 for animal 1, tumour 2 for animal 2, etc. As tumours reached 10–20 mm in diameter, 15 MBq 177Lu‐octreotate  was injected intravenously, resulting in an absorbed dose to the tumour of approximately 4 Gy  and also included data from different time points after treatment.To correlate MRI data with a broad range of biological response effects, rather than with effects from therapeutic doses, and thereby to increase the biological variability of the investigated parameters, we used a non‐curative amount of Imaging experiments including the methods mentioned in Table The 5/21 tumours included in this study fulfilled the following requirements: 1) they were successfully imaged in the MR experiment preceding tumour extraction, 2) they were adequately located, shaped and sized for the resection method used, 3) they were successfully stained at histology, and 4) anatomical landmarks were recognizable on both histological sections and MR images. These inclusion criteria resulted in 4/5 included tumours  being harvested on day 13 and 1/5 included tumours (no. 2) being harvested on day 1.During imaging, animals were anaesthetized using air and isoflurane , body temperature was maintained using a circulating warm water system and a heating pad, and a pressure sensitive pad was used to monitor respiration . For contrast‐material administration during image acquisition, a peripheral venous catheter was fixed to the tail vein and connected to a 1 ml syringe outside the magnet bore via a 70 cm infusion line.Immediately after the MRI experiment preceding the tumour extraction, animals received a lethal intraperitoneal injection of sodium pentobarbitone  followed by heart incision, tumour extraction and histological preparation.3.3MRI examinations were performed using a 20‐cm inner‐diameter horizontal bore 7 T MR system with 400 mT/m gradients .A 4‐channel array rat brain receiver coil  and a 72‐mm volume coil were used to image tumours 1 and 3, 4, and 5, and a 50‐mm quadrature transmit/receive volume coil was used to image tumour 2 .T2* and T1 quantification, dynamic contrast‐enhanced MRI  and T2‐weighted MRI.Field‐map based shimming (MAPSHIM) was used to improve the field homogeneity within the tumour. Transversal images of the longitudinal position of the animal with greatest tumour area were acquired using a field of view (FOV) that covered the tumour extent, including the following methods: Multi b‐value diffusion‐weighted imaging for intravoxel incoherent motion modelling (IVIM‐DWI), Imaging experiments were performed on the same day the animal was killed for tumour tissue harvesting, except for the DCE‐MRI experiments on animals 1, 3, 4, and 5, which were conducted on day 8 instead of day 13 for logistical reasons involving the other studies. Total scan time was approximately 90 min. Detailed acquisition parameters are given in Table 3.4Post processing was performed on a voxel‐by‐voxel basis using MATLAB  with standard and in‐house developed functions, scripts and graphical user interfaces. A 2 × 2 median filter was applied to all images for noise reduction before further post‐processing. The 19 parameters derived from MRI examinations are defined in Table 3.5in situ by placing a section adjacent and parallel to the imaged tumour plane. The same plane was colour coded with tissue ink on the left, right, and dorsal tumour borders to keep track of anatomical orientation during histological processing. At paraffin embedding , the flat tumour surface made by the scalpel was positioned to meet the sweep plane of the microtome knife. When the tissue ink was reached by the microtome, i.e. when the imaged tumour plane was reached, serially produced 3‐μm sections were collected for histological and immunohistochemical staining. Digital images of the stained sections were produced using a Leica SCN400 Slide Scanner , resulting in images of 0.25 × 0.25 μm2 resolution. Image post‐processing was performed in MATLAB, where the tissue ink was located on the magnified images, allowing proper alignment of the digitized histological images and the MR images. Figure Just after the animal had been killed, a scalpel was used to divide the tumour Four stains were applied to adjacent tumour sections: haematoxylin‐eosin , Masson Trichrome , and antibodies to Ki67  and CD31 . The latter two were pre‐treated with EnVision™ FLEX Target Retrieval Solution  followed by incubations using Envision Flex . The staining was performed in an Autostainer Link  following the manufacturer's instructions. Positive and negative controls were included in each run.3.6fitgeotrans function, which fits a geometric transformation to pairs of landmark points defined in the images to be aligned, was then used to infer the transformation matrices linking all images. Tumour regions affected by obvious histological tears/folds or MR‐related artefacts, or where landmarks could not be identified were avoided by performing the registration and data sampling sequentially on tumour sub‐regions. Figure Landmark‐based manual image registration using the MATLAB control point selection tool was performed to intermutually align the histological images and to register them to the T2‐weighted MRI. The MR parameter maps were manually registered to the T2‐weighted image by manual adjustment of rotation, translation and scaling. The MATLAB 3.72, i.e. dimensions similar to the in‐plane resolution of the MR experiments. The HE segmentation was based on previously presented principles.c.f. Kroemer et al. 2005Histological indices were calculated for each sampling position in area units of 250 × 250 μmKi67 positive tumour cells were identified using the method described for HEcount except normalization, which was unnecessary due to the higher contrast between cells and background tissue. The colour threshold was set to detect only cells with brown‐stained Ki67 positive chromatin. Proliferating tumour cell count (Ki67count) was thus defined as the number of Ki67 positive cells per area unit.Micro‐vessel density (MVD) and fibrotic density (FD) were determined by colour thresholding only, and defined as the fraction of CD31 and MT positive area per area unit, respectively.The segmentation and indexing performance was validated by a board‐certified pathologist (ON). Examples are shown in Figure 3.8All data analyses and statistical tests were performed in MATLAB.boxcox function).Non‐normally distributed MR parameters or histological indices were transformed to approximately normal distributions using the Box‐Cox power transformation (MATLAB −4 (0.05 adjusted for multiple comparisons) for the fixed effects variable was considered statistically significant.A linear mixed‐effects model including intercept and slope for both fixed and random effects was used to investigate the pairwise correlations between each MR parameter and histological index, where tumour number (1‐5) accounted for random effects, such as differences in tissue preparation, staining concentration, colour thresholding, and coil configuration. Observations containing missing values in the pairwise comparisons were omitted. A p‐value <5.0 × 10A statistically significant correlation was further evaluated on a per‐animal basis. Five‐fold cross‐validation of a simple linear regression model between the MR parameter and the histological index was calculated, and the mean squared error of the cross validation, normalized to the squared mean of the histological index, was used to indicate the predictive power of the correlation. The normalization enabled comparison between histological indices.To study similarities and differences between histological indices and MR parameters, agglomerative hierarchical clustering was performed on the normalized parameters, omitting observations containing missing values. The single linkage distance measure was evaluated due to its invariance to transforms.44.1D*, which was mostly assigned either of the parameter limit values in the fitting procedure  is shown for tumour 2 in Figure The image quality of the MR technique most sensitive to image distortions . The HEcount index was excluded completely from the samples of tumour 2 due to a digitization artefact visible on magnification, and the DCE‐MRI and T1 parameters were excluded from the tumour 3 samples since no adequate landmarks for registration could be identified. Some MR parameters were excluded from analysis since they were undefined according to Table e.g., if significant contrast enhancement was not reached). A complete overview of the data finally included in the analyses is presented as In total, in vivo using the MR parameters included in this study.The predictive power of simple linear regression models of the pairwise combinations of MR parameters and histological indices that were significantly correlated on the total sample population level are shown in Figure TOP, n = 573 samples, regression coefficient (r) = −0.16, p‐value (p) = 1e−4); time to peak ; brevity of enhancement . MR parameters correlating with Ki67count were: initial slope ; T1 time . MR parameters correlating with MVD were: maximum signal enhancement ; T1 time . The MR parameter correlating with FD was the perfusion fraction . The corresponding sample statistics for non‐significant correlations can be found in the The results from the linear mixed‐effects regression analysis of the total sample population are summarized in Figure CER) and SEmax, as well as D and ADC, contain highly similar information, as expected from similarities in the parameter definitions. Viewed from a perspective of differences , negative slope (NS), T1 and T2*), one group with the IVIM perfusion parameters , one group with the diffusion parameters (D and ADC) and one group with the remaining DCE‐MRI parameters.No obvious clustering was found between the histological indices in the cluster analysis, except for a slightly earlier joining of MVD and FD at approximately 75% of the height of the clade joining all indices  were, to a larger extent than contrast amount‐related parameters (e.g. AUCn and SEmax), negatively associated with HEcount. These findings match those of other studiesTTP, TOP, and BE imply adequate vascular function and perfusion, and thereby delivery of 177Lu‐octreotate, increased apoptotic activity in the affected regions can be inferred. It should be noted that TTP, TOP, and BE were measured a week prior to the tumour extraction for HEcount calculation, but it seems plausible that apoptosis was induced in tumour regions that previously were well perfused, and thereby adequately reached by the therapeutic agent. Our study also showed a positive correlation between IS, i.e. the initial slope of the TIC, and Ki67count. In tumours, the vascular permeability is often high, and IS is therefore considered a measure of perfusion.IS may also reflect tissue oxygen status. In a rat model of prostate cancer, rapid rise of the TIC (similar to high IS) was found in tumour regions that were less hypoxic on histology and vice versa.IS a highly interesting, non‐invasive biomarker for tumour response assessment.DCE‐MRI parameters reflecting exchange rate and perfusion  predicted the histological indices with relatively high power  decreased in a mouse model of ovarian adenocarcinoma receiving anti‐angiogenic therapy.Tissue T1 highly interesting as a non‐invasive imaging biomarker that should be further studied. Furthermore, T1 had relatively high predictive power for Ki67count and, unlike IS, T1 would not require contrast injection for Ki67count assessment. Nevertheless, T1 and IS contain complementary information as shown by the cluster analysis  due to temporal variations in tissue oxygenation, a phenomenon often observed in solid tumours.T2*, is a poor choice for assessing regional perfusion properties, and thereby oxygen status in tumours.Tissue D and ADC) and the evaluated histological indices in this study. Diffusion is affected by the size and viscosity of the EES, membrane structures in cellular debris, and other factors. Although viscosity may not be reflected by any of the evaluated indices, it seems plausible that the level of apoptosis, proliferative activity, or fibrosis should be reflected in Ki67count, HEcount, or FD. Due to the lack of landmarks for registration in central parts of the tumours, however, a majority of the data is based on more peripheral parts of the tumours . Reduced variation of the evaluated parameters may thus have obscured correlations. More likely, however, only day 1 and 13 after therapy were evaluated, so transient or increased effects affecting diffusion may have been missed.No correlations were found between diffusion parameters (f) showed a significant correlation with the fractional tissue area of fibrosis (FD). Tissue response to irradiation may result in fibrotic tissue formation, and the GOT1 tumour model has demonstrated fibrosis in response to 177Lu‐ocreotate therapy, as seen on MT staining.f with fibrosis in tumours. Residual lesions demonstrated significantly higher f in post‐chemoradiotherapy fibrosis compared with untreated tumours in nasopharyngeal carcinoma patients,f in tumour characterization thus remains unclear, and further efforts to optimize acquisition and post‐processing methods for IVIM‐DWI are probably required before f, as well as D*, can be properly understood and evaluated.The perfusion fraction  The analyses were based on data from five tumours only. This was partly due to the index calculations requiring lengthy data processing due to the immense amount of data associated with digitized histological images of microscopic resolution. Furthermore, our own tumour inclusion criteria, namely high image quality, minimal artefacts on histological sections, and adequate landmarks for proper image registration, restricted the number of tumours for evaluation. However, the variation of indices within tumours seems greater than the variation between tumours with some exceptions  Some correlations may have been obscured by the temporal discordance between DCE‐MRI experiments (day 8) and tissue extraction day 13) in 4/5 tumours. It is likely that the tumour biological characteristics evolved between day 8 and 13, which may have led to false negative results,  in 4/5 t6In this work, MR parameters are correlated with spatially registered histological parameters of the imaged tumour tissue. Registration problems due to MR artefacts or morphologically distorted histological sections were minimized by sequential registration of tumour sub‐regions, and objective evaluation on a substantial amount of data samples by statistical regression methods was enabled by the supervised automatic histological indexing method.TOP, TTP, and BE); Ki67count could be predicted by one DCE‐MRI parameter  and one relaxation parameter related to macromolecular content (T1); MVD could also be predicted by T1, and by a DCE‐MRI parameter reflecting the amount of contrast reaching the tissue (SEmax); and FD could be predicted by one IVIM‐DWI parameter reflecting fractional volume of actively perfused tissue (f). In total, seven of the evaluated MR parameters were capable of predicting apoptosis, proliferation, vessel density or fibrosis in the studied tumour type. Apart from the similarly defined TOP and TTP, the data provided by these MR parameters seem, based on cluster analysis, to contain unique information.Predictive power and statistical significance were high for several of the correlations found between MR parameters and histological indices. Models predicting HEcount and Ki67count had the strongest predictive power, while models describing MVD and FD had slightly lower predictive power. Altogether, HEcount could be predicted by three DCE‐MRI parameters related mainly to perfusion .Data S1. Supplemental 1. Total amount of samples (n) underlying the correlations from the mixed‐effects regression analysis, and the distribution of samples over tumours (y‐axis). Sample numbers for the tumour extracted day 1 (tumour 2) is shown in blue in the left column, separated from the other tumours, which are stacked in the right column. Note that HEcount could not be extracted from tumour 2Click here for additional data file.


Acute respiratory distress syndrome (ARDS) is characterized by acute, diffuse, inflammatory lung injury leading to increased pulmonary vascular permeability, pulmonary oedema and loss of aerated tissue. Previous literature showed that restrictive fluid therapy in ARDS shortens time on mechanical ventilation and length of ICU-stay. However, the effect of intravenous fluid use on mortality remains uncertain. We investigated the relationship between cumulative fluid balance (FB), time on mechanical ventilation and mortality in ARDS patients.Retrospective observational study. Patients were divided in four cohorts based on cumulative FB on day 7 of ICU-admission: ≤0 L (Group I); 0–3.5 L (Group II); 3.5–8 L (Group III) and ≥8 L (Group IV). In addition, we used cumulative FB on day 7 as continuum as a predictor of mortality. Primary outcomes were 28-day mortality and ventilator-free days. Secondary outcomes were 90-day mortality and ICU length of stay.Six hundred ARDS patients were included, of whom 156 (26%) died within 28 days. Patients with a higher cumulative FB on day 7 had a longer length of ICU-stay and fewer ventilator-free days on day 28. Furthermore, after adjusting for severity of illness, a higher cumulative FB was associated with 28-day mortality  2.1 [1.0–4.6], p = 0.045; Group III, aOR 3.3 [1.7–7.2], p = 0.001; Group IV, aOR 7.9 [4.0–16.8], p<0.001). Using restricted cubic splines, a non-linear dose-response relationship between cumulative FB and probability of death at day 28 was found; where a more positive FB predicted mortality and a negative FB showed a trend towards survival.A higher cumulative fluid balance is independently associated with increased risk of death, longer time on mechanical ventilation and longer length of ICU-stay in patients with ARDS. This underlines the importance of implementing restrictive fluid therapy in ARDS patients. Acute respiratory distress syndrome (ARDS) occurs in around 10% of patients admitted to the intensive care unit (ICU) and is still heavily associated with mortality. ARDS is characterized by acute, diffuse, inflammatory lung injury leading to increased pulmonary vascular permeability, pulmonary oedema and loss of aerated tissue . MoreoveUnderlying causes of ARDS include sepsis, pneumonia, direct trauma, pancreatitis, blood transfusion and burns . Fluid rWe aimed to investigate the association between cumulative fluid balance, mortality and ventilator-free days in a large cohort of patients with ARDS admitted to the ICU. We hypothesized that there is a negative association between increased cumulative fluid balance and adverse outcome in patients with ARDS.This cohort study was a retrospective observational study performed on data collected for a previously published study . No sepa2/FiO2 ≤ 300 mmHg, bilateral opacities consistent with pulmonary oedema on radiographic imaging, requirement for positive pressure ventilation via endotracheal tube and not fully explained by cardiac failure or fluid overload , p = 0.017; Group III OR 3.7 [1.8–7.8], p<0.001; Group IV OR 9.3 [4.8–19.5], p<0.001). This finding persisted after adjusting for severity of illness by APACHE II score (Group II adjusted Odds Ratio (aOR) 2.1 [1.0–4.6], p = 0.045; Group III, aOR 3.3 [1.7–7.2], p = 0.001; Group IV aOR 7.9 [4.0–16.8], p<0.001). While exploring for confounders using our pre-determined covariates, no other confounders were identified. Similar results were found for 90-day mortality .Using restricted cubic splines, a non-linear dose-response relationship was found between cumulative fluid balance on day 7 and 28-day mortality. A more positive fluid balance predicted 28-day mortality, whereas there was a trend for negative fluid balance to predict survival. These findings persisted after adjusting for severity of illness (APACHE II). See Figs We investigated the association between cumulative fluid balance, mortality and ventilator-free days in a large cohort of ICU patients with ARDS. A more positive cumulative fluid balance on day 7 was associated with an increased risk of death, even after adjusting for severity of illness (APACHE II score). Moreover, a higher positive cumulative fluid balance was associated with fewer ventilator-free days and an increased length of ICU-stay. We found a non-linear relationship between 28-day mortality and cumulative fluid balance on day 7. A more positive cumulative fluid balance predicted death, whereas a negative fluid balance showed a trend towards survival. Found association could be described as a dose-response relationship, as patients with higher cumulative fluid balances had worse outcome.CVVH was associated with 28-day mortality and higher cumulative fluid balances. CVVH is an indicator of acute kidney injury (AKI), which contributes to the accumulation of fluids. Nonetheless, fluid accumulation itself can also result in AKI . UnfortuThe 2006 Fluids and Catheters Treatment Trial (FACTT) investigated the effects of fluid restriction on outcome in patients with acute lung injury (ALI) . In thisThe mechanisms involved with the negative impact of fluid balance on the outcome of ARDS patients are still not fully elucidated. One explanation is that in patients with ARDS, pulmonary oedema may increase when receiving too much fluid. An increase in pulmonary oedema causes a reduction of pulmonary compliance and an increase in respiratory work. Alveolar oedema may cause intrapulmonary shunting resulting in hypoxemia. Pulmonary oedema is one of the key determinants of pulmonary arterial hypertension, not only by causing hypoxemia but also due to pulmonary vascular compression . Hereby,Our study has its limitations. The retrospective and observational study design limits the possibility to determine a cause-effect relationship between fluid balance and outcome. Even though we have a large patient cohort in which we used multivariate analysis to correct for potential confounding, residual confounding may still exist. Moreover, previous observational studies already showed that a positive fluid balance is associated with mortality in patients with ARDS due to a variety of causes, secondary to septic shock or concomitant with acute kidney injury –10, 20. Our study underlines the importance of implementing conservative fluid balance approaches in patients with ARDS. Nevertheless, positive fluid balances may not always be avoidable in patients with ARDS, as some conditions (e.g. septic shock) are dependent on initial aggressive fluid therapy for resuscitation. A recent retrospective study in adults requiring invasive mechanical ventilation showed that a negative fluid balance achieved with de-resuscitation is associated with improved patient outcome . WhetherWe found that a higher cumulative fluid balance is independently associated with increased risk of death, longer time on mechanical ventilation and a longer length of ICU-stay in patients with ARDS. This underlines the importance of implementing restrictive fluid therapy protocols in ARDS patients.S1 DatasetPrivacy sensitive patient data were excluded.(XLSX)Click here for additional data file. 7 Oct 2019PONE-D-19-22932Cumulative fluid balance predicts mortality and increases time on mechanical ventilation in ARDS patients: an observational cohort studyPLOS ONEDear Dr Vlaar,Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. 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Please do not edit.]Reviewers' comments:Reviewer's Responses to QuestionsComments to the Author1. Is the manuscript technically sound, and do the data support the conclusions?The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: YesReviewer #2: Yes**********2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: YesReviewer #2: Yes**********3. Have the authors made all data underlying the findings in their manuscript fully available?PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). 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You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. Reviewer #1: This is a good well-conceived and well-written paper on a hot topic where prospective controlled studies are scarce. Conclusions are reasonable regarding results. Although previous papers have published data consistent with the results observed in the present paper, only the Wiedemann paper is a prospective randomized study while all other papers are retrospective studies carried on a relatively small amount of patients.I have one major and two minor pointMajor: The authors state that "all relevant data are within the manuscript". Considering that individual data are needed, they are not available in the manuscript. So, the authors must indicate how to have access to the full data.Minor: Data are relatively old, more than 15 years for the oldest patients. Presumably, standard of care has moved today towards a more conservative approach about fluids. This could change the overall mortality. This point should be briefly discussed and mentioned in the limitations of the paper.In table 2, how is defined protective ventilation?Reviewer #2: A retrospective analysis of a big but old database showing that FB is related to the mortality. The main problem is that FB is related to the severity of disease or to the physicians, in other words in septic patients high vasopressor is related to the outcome. But we did not discourage the use of vasopress. So the FB is the egg or the chicken ?Thus a correct FB, a correct circulating volemia shoul be appliedIntroductionOne of the main problem is how FB was calculatedHow was the accuracy of FB ??Could you comment the FB and the severity of ARDS and of the underlying diseaseCould you divide the patients according to the severity of ARDSHow was the protocols for fluid managementDid the protocol changed through the years ?MethodsYour computation of FB does not take into account the gastrointestinal losses, sweat …..How did you manage the patients who died before day 7Why did you not assess the ICU outcomeCould you please insert more data on MV settings at admission and during the ICU stayDid you have any data on acid base statusIn tables you present only P value, but the statistical difference among the groups?Were ECMO patients ??Any difference on the type of fluidThus I suggest the authors to provide more results and not only the FBDiscussionInsert and comments the etiology of deathMain problem is not positive or negative FB, is a correct circulating volume and the “rules” for fluid managementPlease comment the role of albumin and colloids**********what does this mean?). If published, this will include your full peer review and any attached files.6. PLOS authors have the option to publish the peer review history of their article  digital diagnostic tool,  14 Oct 2019Response to ReviewersWe thank the editor and reviewers for their valuable time, insights and the possibility to improve our manuscript. We hope that we have now sufficiently improved the manuscript. Below you will find our responses. Reviewer 1Comment 1“Major: The authors state that "all relevant data are within the manuscript". Considering that individual data are needed, they are not available in the manuscript. So, the authors must indicate how to have access to the full data.”We thank the reviewer for pointing this out. Unfortunately, we were not aware individual patient data were needed. Censored raw patient data are now available in S1 Dataset. Comment 2“Minor: Data are relatively old, more than 15 years for the oldest patients. Presumably, standard of care has moved today towards a more conservative approach about fluids. This could change the overall mortality. This point should be briefly discussed and mentioned in the limitations of the paper.”We agree on this. The first patient was entered in our database in 2004 and the last patient was entered in 2007. In this period several potential changes in clinical practice could have occurred impacting fluid balance. However, we studied the influence of time of entry in the database and fluid balance and did not find any significant trend over time that would indicate this. We added this to the limitations of the paper, please see page 13, lines 185-191 of the revised manuscript Comment 3“In table 2, how is defined protective ventilation?”Protective ventilation was defined as 6 mL/kg predicted body weight following the ARDSNet ARMA trial. We added the definition and reference to the table legends. Reviewer 2Comment 1“One of the main problem is how FB was calculated. How was the accuracy of FB ??”We agree with the reviewer that the accuracy of measuring is important. Fluid balance was calculated based on total fluid input  and total fluid output . It was not feasible to correct for insensible water losses like transepidermal diffusion or evaporative water loss from the respiratory tract, as we do not routinely measure this. This reflects current medical practice. We added a comment to the revised manuscript that we did not take into account insensible fluid losses. Please see page 4, lines 65-67.Comment 2“Could you comment the FB and the severity of ARDS and of the underlying disease”We thank the reviewer for this suggestion. We agree that it would be interesting to look at the severity of ARDS specifically and fluid balance as well. We added the severity of ARDS based on PaO2/FiO2 ratio into the tables . We chose to define severity of illness based on APACHE II score, as was already displayed in the tables.Comment 3“Could you divide the patients according to the severity of ARDS”Please see Comment 2.Comment 4“How was the protocols for fluid management. Did the protocol changed through the years ?”We investigated changes in fluid management by analysing differences in fluid balance before or after the FACTT trial , which we did not find. This suggests that, even after Wiedemann’s study, restrictive fluid therapy in ARDS was not yet fully implemented in our ICU. Whilst we agree with the reviewer that it is interesting to look into the fluid management protocols, we believe that by adding fluid management protocols for all different morbidities the manuscript would become a tedious read. Also and maybe even more important, based on clinical parameters, fluid management may have changed for individual patients during the often unpredictable course of critical illness and may have been subject to the clinician’s own views and preferences. Comment 5“Your computation of FB does not take into account the gastrointestinal losses, sweat …..”We thank the reviewer for pointing this out. We addressed this above (Comment 1).Comment 6“How did you manage the patients who died before day 7”We agree that it is important to take into account. Patients that died or were discharged before day 7 were addressed in the methods section : “In patients who died or were discharged prior to day 7, cumulative fluid balance at the time of disposition was carried forward to day 7.”Comment 7“Why did you not assess the ICU outcome”We thank the reviewer for this suggestion. Assuming with outcome ICU mortality was meant, we added this to the manuscript. Comment 8“Could you please insert more data on MV settings at admission and during the ICU stay”As we do not wish to publish data twice, we would like to refer to Vlaar et al  for data on ventilatory settings.Comment 9“Did you have any data on acid base status”Unfortunately, we do not have any data on acid-base status.Comment 10“In tables you present only P value, but the statistical difference among the groups?”We agree with the reviewer that it is important to show statistical difference between the different groups separately as well. That is why significantly differing groups were displayed in superscript within the tables. Comment 11“Were ECMO patients ??”There were no patients on ECMO in the study. Comment 12“Any difference on the type of fluid”Indeed, it would be interesting to look into the different types of fluids used and outcome. However, as stated in the methods section of the original manuscript : “Data on type of fluids used was not available.”.Comment 13“Insert and comments the etiology of death”No data was available on cause-specific mortality.Comment 14“Main problem is not positive or negative FB, is a correct circulating volume and the “rules” for fluid management”We absolutely agree with the reviewer that there is more to fluid management than just fluid balance. Our study showed that fluid balance is an (independent) predictor of outcome in patients with ARDS. Fluid balance can and probably should be used as an aid in the clinician’s arsenal for fluid management. Comment 15“Please comment the role of albumin and colloids”As stated above, we agree with the reviewer that this is an interesting point. We added a short section on crystalloids and colloids in ARDS in the discussion. Please see page 13, lines 191-194 of the revised manuscript 17 Oct 2019Cumulative fluid balance predicts mortality and increases time on mechanical ventilation in ARDS patients: an observational cohort studyPONE-D-19-22932R1Dear Dr. Vlaar,We are pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it complies with all outstanding technical requirements.Within one week, you will receive an e-mail containing information on the amendments required prior to publication. When all required modifications have been addressed, you will receive a formal acceptance letter and your manuscript will proceed to our production department and be scheduled for publication.https://www.editorialmanager.com/pone/, click the "Update My Information" link at the top of the page, and update your user information. 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For more information, please contact With kind regards,Chiara LazzeriAcademic EditorPLOS ONEAdditional Editor Comments :Reviewers' comments: 21 Oct 2019PONE-D-19-22932R1 Cumulative fluid balance predicts mortality and increases time on mechanical ventilation in ARDS patients: an observational cohort study Dear Dr. Vlaar:I am pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department. onepress@plos.org.If your institution or institutions have a press office, please notify them about your upcoming paper at this point, to enable them to help maximize its impact. If they will be preparing press materials for this manuscript, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact plosone@plos.org. 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To find new potentially therapeutic drugs against clear cell Renal Cell Carcinoma (ccRCC), within drugs currently prescribed for other diseases (drug repositioning), we previously searched for drugs which are expected to bring the gene expression of 500 + ccRCC samples from The Cancer Genome Atlas closer to that of healthy kidney tissue samples. An inherent limitation of this bulk RNA-seq data is that tumour samples consist of a varying mixture of cancerous and non-cancerous cells, which influences differential gene expression analyses. Here, we investigate whether the drug repositioning candidates are expected to target the genes dysregulated in ccRCC cells by studying the association with tumour purity. When all ccRCC samples are analysed together, the drug repositioning potential of identified drugs start decreasing above 80% estimated tumour purity. Because ccRCC is a highly vascular tumour, attributed to frequent loss of VHL function and subsequent activation of Hypoxia-Inducible Factor (HIF), we stratified the samples by observed activation of the HIF-pathway. After stratification, the association between estimated tumour purity and drug repositioning potential disappears for HIF-activated samples. This result suggests that the identified drug repositioning candidates specifically target the genes expressed by HIF-activated ccRCC tumour cells, instead of genes expressed by other cell types part of the tumour micro-environment. To discover new potentially therapeutic drugs against ccRCC within drugs already prescribed for diseases (drug repositioning), we previously developed an individualised drug repositioning approach based on the gene expression profiles of over 500 ccRCC tumours generated using bulk RNA-Seq by The Cancer Genome Atlas (TCGA)2.Tumours of metastatic clear cell renal carcinoma (ccRCC) patients typically become resistant to available treatments within 1.5 years3. The presence of non-cancerous cells might be an especially big concern for ccRCC samples, as ccRCC estimated tumour purity was ranked the third most impure tumour type out of the total 21 solid tumours analysed, despite the lower mutational burden typical for other highly impure cancers4. The relatively low content of cancerous cells in ccRCC tumours is likely due to the high degree of vascularity often observed in ccRCC tumours6. This hypervascularity is attributed to the frequent inactivation of the Von Hippel-Lindau gene, which leads to activation of the Hypoxia Inducible Factor (HIF) pathway and the subsequent release of vascularizing growth factors: Vascular Endothelial Growth Factor (VEGF), platelet-derived growth factor beta (PDGFβ), and transforming growth factor alpha (TGFα)7.With bulk RNA-seq the gene expression of all cell types present in the sample is measured simultaneouslyet al. reported in their systematic pan-cancer analysis of TCGA tumour sample purity that variation in estimated tumour purity can significantly influence the results of differential gene expression analyses4. After adjusting for estimated tumour purity, on average 14% of differentially expressed genes lost statistical significance and 11% of expressed genes were now shown to be statistically differentially expressed when they were not before adjustment4. Depending on whether tumour purity is seen as a factor that needs to be corrected, such as in the case when only tumour cells are of interest, the differential gene expression profile can therefore change drastically.Aran Computational drug repositioning methods which rely on transcriptomic data commonly use this data type without accounting for the potential influence of tumour sample composition. The drug repositioning method we used, gene expression signature reversal, functions by searching for drugs which can normalize the genes which are differentially expressed in the tumour tissue . Specifically, drugs which can get tumour gene expression closer to that to normal tissue are considered potentially therapeutic drugs for this tumour. However, if genes are incorrectly classified as differentially expressed due to the confounding presence of non-cancerous cells present in the sample, it naturally follows that this can reduce the predictive validity of the procedure if the intention is to target the tumour cells with the drug. Excluding these drugs early on would therefore save vital time and money spent on laboratory validation experiments to determine whether the drug is likely to be safe and effective at clinically tolerated dosing regimens. Furthermore, while we considered all differentially expressed genes to be of interest in our initial drug repositioning analysis, the same issue comes up when only genes of a specific biological pathway are of interest if these genes are sufficiently expressed in the surrounding non-cancerous cells.The goal of the current study is to investigate whether the previously identified drug repositioning candidates are indeed expected to target the dysregulated genes expressed by ccRCC tumour cells and are not systematically influenced by variation in tumour purity between the ccRCC samples.8.Tumour purity estimations and connectivity scores  were available for 529 tumour sample gene expression profiles, of which 521 also passed the independent Toil quality control−13 for all 8 drugs, Supplementary Figs The distribution of tumour purity estimations of all ccRCC samples (with estimated tumour purity and gene expression results available) has a mode of 70% and an interquartile range of 57–75% Fig. . This find line treatments (the mTOR inhibitor temsirolimus). Therefore, we investigated whether the same increase in connectivity scores occurred for inhibitors of the HIF-pathway, as activation of the HIF-pathway within the cancerous cells is a frequently observed hallmark of ccRCC7 .It is striking that all eight drugs Fig.  show exaCC7 Fig. . Indeed,9), and the 3 most frequent chromosomal alterations observed in ccRCC: loss of chromosome 3p , gain of chromosome 5q  and loss of chromosome 14q . Furthermore, the total number of chromosomal alterations (deletions of negative regulators and gains of positive regulators) in the pathway of genes that regulate p53 and the G1/S cell cycle transition were tested as potential predictors, as genes in this pathway are frequently chromosomally altered in ccRCC12. Batch ID, plate ID, ship date and tissue source site were included as random variables as these were considered potential sources of batch effects. The distribution of all included variables is listed in Table To determine which variables predict HIF-activation and/or the variation in estimated tumour purity, two separate linear regression analyses combined with backward elimination were performed. The input for these analyses were: VHL mutation/methylation status, tumour stage, mRNA subtype  and missing VHL mutation/methylation data (N = 196) occurred frequently, missingness was used as a separate category. Otherwise, a complete case analysis was performed . The variable selection and estimation therefore took place on 510 out of the total 521 remaining ccRCC samples.−9) and tumour stage is associated with estimated tumour purity . As 4 out of the 7 variables are univariately associated with both outcomes, we performed stepwise linear regression using backward elimination, starting with all 7 variables including all possible sources of batch effects in both models . Gain of chromosome 5q further decreases the mean connectivity score by an average of 0.05 points (P = 3 × 10−4). Samples with a missing mRNA subtype had on average a 0.1 points higher connectivity score (P = 4 × 10−6). In addition, each chromosomal alteration in p53 associated genes increases the mean connectivity score by an average of 0.013 points per alteration (P = 1.8 × 10−9). This is likely caused by the fact that the number of chromosomal alterations in p53 associated genes is strongly positively associated with the number of genes in the tumour sample signature . The 2nd strongest effect size with regard to predicting estimated tumour purity was loss of chromosome 3p, estimated to decrease the estimated tumour purity by an average of 4.2% (P = 0.02). Loss of chromosome 14q  and loss of chromosome 14q  further contribute to decreases in estimated tumour purity.With respect to tumour purity, the by far the strongest predictor of estimated tumour purity was mRNA subtype, with samples belonging to the m3 subtype being estimated to have an average 12.5% decrease compared to the m1 subtype  are associated with both HIF activation (as evidenced by on average more negative connectivity scores with HIF inhibitors) and decreased estimated tumour purity regardless of analysis method .et al.13 reported that the methylome of 25 ccRCC samples in the TCGA dataset clustered closer to papillary and chromophobe RCC samples than typical ccRCC samples. 24 of these samples  and far more samples  show no loss of chromosome 3p compared to samples with a typical ccRCC methylome , which is highly statistically significant . Lastly, the 12 samples which upon histopathological re-evaluation were still considered ccRCC or unconventional ccRCC had lower median connectivity scores with PX-12 and CAY-10585 than the 12 samples deemed non-ccRCC .In an earlier paper published in 2016, Winter et al.4, which automatically eliminates genes which are more strongly differentially expressed at lower estimated tumour purity, we would have incorrectly excluded genes representing a real biological signal.We found that HIF-activation likely acts as a confounder between estimated tumour purity and estimated drug repositioning potential of the previously reported top 8 drugs for ccRCC Fig. . If inst5014.To classify samples by probability of HIF-activation, we used the same assumptions underlying the computational drug repositioning method we used in the original analysis, namely that of gene expression signature reversal. This method predicts that drugs which downregulate genes upregulated by HIF-activation, and vice versa, are expected to produce negative connectivity scores with HIF-activated samples. This worked well for 2 out of 3 HIF-inhibitors (PX-12 and CAY-10585): the connectivity scores of tumour samples with these drugs produced concordant negative connectivity scores for the majority (58%) of samples. The third HIF-inhibitor (YC-1) did not correlate well with the other two or even produced more negative connectivity scores than expected by chance. This discrepancy is as of yet unexplained: YC-1 was tested in 13/14 of the same cell lines as CAY-10585 and in concentrations above the ICBy studying predictors of the probability of HIF-activation in samples (with the mean connectivity score of PX-12 and CAY-10585 as the dependent variable), we learned that it can be attributed to two main underlying causes:7.Firstly, observing HIF-activation depends on inactivation of the VHL gene due to loss of chromosome 3p Table  or, in s15. When genes which are not associated with HIF expression are added to the tumour sample signature, on average the genes rank closer to the middle when they are compared to the genes ordered by changes in expression after administration of HIF-inhibitors. Statistical procedures which only look at the overlap of genes differentially expressed between the tumour sample signature and drug signature may be less sensitive to this effect16. Missing mRNA subtype was also found to be an important negative predictor of HIF-activation, for which we have not found a good explanation yet.Secondly, observing HIF-activation can be interfered with by other factors, the most biologically plausible factor being deletions of negative regulators and gains of positive regulators in the genes that regulate p53 and the G1/S cell cycle transition. These copy number alterations result in many more genes differentially expressed in the tumour samples Fig. , which s9) produced the largest contribution. Compared to the m1 subtype, samples belonging to the m3 subtype have on average a 12.5% lower estimated tumour purity .When we explored possible predictors of estimated tumour purity, we found that mRNA subtype  estimated tumour purity of the scale. However, as the 12 samples which were independently histopathologically reclassified as conventional ccRCC (N = 2) or unconventional ccRCC (N = 10) had lower median connectivity scores with the HIF-inhibitors than the 12 samples reclassified as chromophobe or papillary RCC, observed HIF-activation (as a correlate of ccRCC-likeness) does seem to correlate better with independent histopathological review than methylation clustering alone.When we compared our gene expression-based results to the methylome clustering results produced by Winter 9.For both models presented in Tables It is biologically plausible that the cancer cells behave differently (and thus gene expression within tumour cells changes) if the tumour microenvironment becomes purer or less pure (e.g. as a reaction to the invasion of immune cells or infiltration of blood vessels). However, the bulk RNA-seq data from TCGA do not allow us to distinguish directly which mRNA is produced by cancer cells and which is produced by non-cancerous cells. We therefore heavily relied on previously established knowledge to interpret our findings . To conclusively answer this question and explore new hypotheses, it would require a large single cell RNA-seq dataset of the ccRCC tumour micro-environment from many different patients, so that the impact of variation in cancer cell genetic identity  can be distinguished from variation in cell type composition of the micro-environment. However, because of the expense and newness of single cell RNA-seq no such dataset exists yet (to our knowledge), and only the TCGA dataset contains enough samples  to really quantify the impact of inter-sample variation in gene expression and relate this to other genomic determinants.In conclusion, we demonstrated using the example of HIF-activation that confounding between tumour purity and gene expression can occur. Therefore, we recommend correcting for tumour purity in bulk RNA-seq differential expression analyses only after any other possible biological causes are included in the model.All data was processed with R version 3.4.0 and R/Bioconductor packages. All tests produced two-sided P-values and were considered statistically significant at P < 0.05.2. Briefly, the tumour sample signatures were generated by comparing 538 individual tumour gene expression profiles to 72 normal kidney tissue gene expression profiles, both generated by TCGA9. After filtering out genes with low expression (counts per million <0.5) and applying the Benjamini-Hochberg procedure to calculate the False Discovery Rate (FDR), genes with an FDR <1% were included in the tumour sample signatures. As an additional quality control filter, only samples which passed the independent quality control check of the Toil recompute effort  were included8. Drug signatures, based on data generated by The Library of Integrated Network-Based Cellular Signatures (LINCS) Program17, were calculated with the goal of approximating the effect in the average tumour cell line. This was accomplished through the use of a linear model with drug concentration as a linear parameter, and cell line, exposure duration, and batch as categorical variables.The tumour sample and drug signatures were calculated as described previously4. In summary, CPE is based on 3 different data types and algorithms: copy number data (ABSOLUTE), methylation data (LUMP) and transcriptomic data (ESTIMATE). For simplicity and clarity’s sake, CPE is hereafter simply referred to as “Estimated tumour purity”. TCGA batch information  were downloaded using the TCGA Batch Effects Tool18 (version 2.0).To estimate tumour sample purity, we used the previously published consensus measurements of tumour purity estimations (CPE)16 (version 1.8.3). A negative connectivity score implies that the drug is expected to  reverse the tumour signature. The correlation between the connectivity scores and estimated tumour purity was tested non-parametrically using the Spearman rank correlation test. A positive association (i.e. more positive connectivity scores at higher tumour purity %) suggests that the gene expression reversal occurs mainly in non-cancerous cells. Conversely, a negative association implies the opposite, namely that as the % of tumour material in a sample increases so does the drug repositioning potential, which suggests that the drug is targeting the tumour cells.Connectivity scores (the method used to compare the drug and tumour signatures) were calculated using the PharmacoGx packageGenes were classified as more differentially expressed with increasing tumour purity if upregulated and downregulated genes showed a statistically significant (P < 0.05) positive, respectively negative Spearman rank correlation with increasing tumour purity. If it showed the opposite relationship, the gene was classified as becoming less differentially expressed with increasing tumour purity. Lastly, if the Spearman rank correlation with estimated tumour purity was statistically insignificant (P > 0.05) regardless of direction, the gene was classified as insensitive to increasing tumour purity.19. Only mutations and methylation of the VHL gene were considered because this gene is considered the key trigger in starting the angiogenesis cascade. VHL was considered methylated if the beta value for probe cg15267345 exceeded 0.2 . The copy number data processed by GISTIC  was downloaded using the RTCGAToolbox package (version 2.8.0)23. For tumour samples analysed in duplicate, the average value of the variable was used in the analysis.Mutation and methylation data of tumour samples were downloaded using the TCGAbiolinks package (version 2.6.12)The following variables were investigated for their association with observed HIF activation (for which we used negative connectivity scores with the HIF inhibitors PX-12 and CAY-10585 as a proxy) and estimated tumour purity: Estimated tumour purity, VHL mutation/methylation status, tumour stage, mRNA subtype, loss of chromosome 3p, gain of chromosome 5q, loss of chromosome 14q and number of chromosomal alterations in the p53 and the G1/S cell cycle transition pathway. Selected variables were univariately associated with HIF-activation and estimated tumour purity using non-parametric statistical tests: the Wilcoxon Rank-sum test for categorical variables with 2 groups (e.g. no 3p loss versus 3p loss), the Kruskal-Wallis test for variables with more than 2 groups (e.g. mRNA subtype) and Spearman’s rank correlation coefficient for continuous variables . To explain inter-sample variability in HIF-activation and estimated tumour purity, linear mixed models with all variables included were fitted. Batch ID, plate ID, tissue source site and ship date were parameterized as random effects. Estimated tumour purity, plate ID and ship date were only included as a variable to predict inter-sample variability in HIF-activation because estimated tumour purity can’t be used to predict itself and plate ID + ship date are specific only to the RNA-seq experiments. Variables were sequentially dropped from the model until only variables with P values lower than 0.05 were left. This process was repeated on 1,000 bootstrapped datasets (sampled with replacement). Variables which remained in the model after backward selection at least 50% of the time were kept in the final 2 models presented in the main article. The bootstrapped P values of the final models were determined by sampling the original dataset with replacement 10,000 times. Lastly, for each final model, repeated  10-fold cross-validation was performed to detect possible overfitting.et al. analysed 319 TCGA samples for which Illumina HumanMethylation 450 BeadChip results were available and concluded that based on the methylation patterns, 15 of the ccRCC samples showed closer similarity to chromophobe RCC and 10 to papillary RCC13. Additionally, they asked 2 independent pathologists with expertise in renal tumour pathology to re-evaluate the ccRCC samples based on publicly available TCGA diagnostic and histological slide images. The discordant samples and re-evaluation results were extracted from Supplementary Table In a previously published methylation analysis, Winter Supplementary material


In oncological drug development, animal studies continue to play a central role in which the volume of subcutaneous tumours is monitored to assess the efficacy of new drugs. The tumour volume is estimated by taking the volume to be that of a regular spheroid with the same dimensions. However, this method is subjective, insufficiently traceable, and is subject to error in the accuracy of volume estimates as tumours are frequently irregular.TM, a 3D scanning technique. BioVolume simultaneously captures both stereo RGB  images from different light sources and infrared thermal images of the tumour in under a second. It then detects the tumour region automatically and estimates the tumour volume in under a minute. Furthermore, images can be processed in parallel within the cloud and so the time required to process multiple images is similar to that required for a single image. We present data of a pre-production unit test consisting of 297 scans from over 120 mice collected by four different operators.This paper reviews the standard technique for tumour volume assessment, calliper measurements, by conducting a statistical review of a large dataset consisting of 2,500 tumour volume measurements from 1,600 mice by multiple operators across 6 mouse strains and 20 tumour models. Additionally, we explore the impact of six different tumour morphologies on volume estimation and the detection of treatment effects using a computational tumour growth model. Finally, we propose an alternative method to callipers for estimating volume–BioVolumeThis work demonstrates that it is possible to record tumour measurements in a rapid minimally invasive, morphology-independent way, and with less human-bias compared to callipers, whilst also improving data traceability. Furthermore, the images collected by BioVolume may be useful, for example, as a source of biomarkers for animal welfare and secondary drug toxicity / efficacy. Animal models of human cancers are fundamental to our understanding of tumour biology. Tumour volume is a significant metric for preclinical trials where it provides a surrogate measure of both disease progression and treatment efficacy. Thus, accurate and repeatable estimation of tumour volume is crucial to declare a given trial to be a success or failure with confidence . At presBioVolumeTM which combines 3D stereo photometry (to capture depth) and infrared/thermal imaging (to delineate the poorly vascularised tumour region).In what follows, we explore how manual calliper measurements introduce human bias into pre-clinical trials. Furthermore, we use a cellular automaton model to investigate how calliper measurements influence our understanding of study outcomes for six different tumour morphologies . We propWe analysed two datasets:Dataset 1, Calliper statistical review: Records for 1,608 mice and 2,488 calliper measurements were collected by 29 AstraZeneca operators over a period of 17 months from February 2017 until June 2018. The measurements belong to 43 pre-clinical studies  & Envigo UK (www.envigo.com): SCID, BALB/c, C57BL/6, NSG and Nude. Hairy mice were shaven as usual for the experiments. More information regarding the data can be found in Five different strains of mice were sourced from Charles River UK , which captures both thermal (infrared) and 3D surface images. To acquire a scan, the shaven mouse is held such that the tumour region is exposed to the device aperture see , left. TThe BioVolume unit consists of a stereo system with two RGB cameras, three white light flashes, and an infrared thermal camera. Upon activation, the unit collects 6 photographic (RGB) images and a single thermal frame  for the estimation of tumour volume:- Spheroid formula :- Cylindrical volume (BioVolume):The cellular automaton model consisted of a rule-based model operating on two simulated cell populations growing on a 3D lattice. The rules and parametrisation originate from logical assumptions for tumour growth and treatment. There are four main parameters: vertical bias, cell division rate, magnitude, and length of treatment. These parameters are depicted in mathematical notation as: len} see . The modσ and μ are the standard deviation and mean, respectively (15). To assess the consistency between the linear measurements of both methods, we compared the length and width measurements of BioVolume and callipers using a two-sample one-sided t-test with unequal variance, adjusted for multiple testing [3) [Vc) and treated (VT) growth curves we used Tumour Growth Inhibition (TGI):AUCT,AUCC area-under-the-curve for treated and control  as a measure of precision and intra-class correlation (ICC) as a measure of reliability. The ICC provides a measure of the correlation between two different people acquiring scans from BioVolume on different occasions . The coe testing . For a lting [3) . For thetrol see .A full description of the methods for the analysis of the calliper and BioVolume data can be found in Inter-operator repeatability is a significant challenge when measuring subcutaneous tumours see . It is oVEq = V·ρtum⋅ We find that calliper-derived volume estimates exceeded excised tumour weight in 93.7% of cases  model of tumour growth and its treatment see .We simulated the growth of both control and treated tumours for each morphology described in To determine the impact of simulated calliper measurements on the accuracy of treatment efficacy we calculated and compared two commonly used treatment efficacy metrics using the GT and SC-derived volumes. Specifically, we computed the Tumour Growth Inhibition (TGI) and Area Under the Curve (AUC) indices see .We quantified the consistency between the linear length and width measurements made using BioVolume and callipers by making contemporary measurements of a given tumour using both methods and then, for each tumour, counting the number of scan measurements which fell within +/- 3mm of the calliper measurements made on the same day. These counts are displayed as histograms for both length  and widt3, median: 24 mm3), the smallest bias (m = 0.499) and the best correlation coefficient . Notably, BioVolume displayed a systematic tendency to estimate lower tumour volumes than callipers, which corresponds to the lower values of observed weights relative to calliper-derived volumes observed in There are large discrepancies in the volume–weight correlations. Firstly, the scan ellipsoid formula  shows both the smallest mean discrepancy  and Dataset 2 , see In the calliper statistical review, we demonstrated that callipers are subject to high inter-operator variability, with values reaching 130% in inter-operator CV. Additionally, correlation between calliper-estimated volumes and excised tumour weight was poor . These mpdivi Click here for additional data file.S2 Appendix(DOCX)Click here for additional data file.S1 Dataset(CSV)Click here for additional data file.S2 Dataset(CSV)Click here for additional data file.S3 Dataset(CSV)Click here for additional data file.S1 Scan(7Z)Click here for additional data file.


The compound may find application as a stable trimeric form of the fragile cyclobutane carbaldehyde.The synthesis and structure of the substituted trioxane compound 2,4,6,-tricyclobutyl-1,3,5-trioxane is described. The three cyclobutane rings attached to the 1,3,5-trioxane six-membered ring are all in a  15H24O31, is described. It was formed in 39% yield during the work-up of the Swern oxidation of cyclobutylmethanol and may serve as a stable precursor of the cyclobutane carbaldehyde. The molecule of 1 occupies a special position (3.m) located at the center of its 1,3,5-trioxane ring. The latter is in a chair conformation, with the symmetry-independent O and C atoms deviating by 0.651 (4) Å from the least-squares plane of the other atoms of the trioxane ring. All three cyclobutane substituents, which have a butterfly conformation with an angle between the two planes of 25.7 (3)°, are in the cis conformation relative to the 1,3,5-trioxane ring. Intermolecular C—H⋯O interactions between the 1,3,5-trioxane rings consolidate the crystal structure, forming stacks along the c-axis direction. The crystal studied was refined a as a racemic twin.The synthesis and structure of 2,4,6,-tricyclobutyl-1,3,5-trioxane, C Some of these trimers are commercially-available, stable forms of the corresponding aldehydes, from which monomers can be regenerated by simple heating. Examples are trioxane and paraldehyde. For our research in the area of polymer chemistry, we need regular amounts of cyclobutane carbaldehyde. Besides this, cyclobutane carbaldehyde is a versatile synthetic building block for the incorporation of the cyclobutane ring into organic molecules in medicinal chemistry and drug discovery  in the 1H NMR spectrum and no signals of the aldehyde carbon (ca 180-220 ppm) in the 13C NMR spectrum]. The most downfield signal in the 1H NMR spectrum was a doublet at 4.81 ppm and the most downfield signal in the 13C NMR spectrum was observed at 103.3 ppm. The number of signals and their integral intensities (in the 1H NMR spectrum) demonstrated that, aside from the disappearance of the aldehyde group, no further degradation of the parent molecule had taken place. We made an assumption that the desired trimer had formed. However, dimeric, trimeric, tetrameric and higher cyclic forms or a linear polymer could also form. It should be noticed that the NMR technique cannot distinguish between these forms, since all the integral intensities and coupling constants would be the same, unless a large macrocycle is formed, where conformational effects would make some equivalent signals become non-equivalent. The mass spectrum of 1 solved the question and proved the structure to be trimeric. However, neither NMR nor mass spectrometry can provide information about the assignment of the cyclobutyl groups with respect to the trioxane ring. It is known that paraldehyde – the trimer of acetaldehyde – exists in the form of two structural isomers: the cis isomer with all three methyl groups being in equatorial positions with respect to the trioxane ring and the trans isomer with one methyl group in an axial position and the two methyl groups in equatorial positions . It is a trimeric form of the cyclobutane carbaldehyde. The aldehyde itself is unstable and quickly polymerizes even at room temperature. The described trimer could probably serve as a stable form of the cyclobutane carbaldehyde. The compound is also of potential interest as a solid fuel.The molecular structure of 2,4,6-tricyclobutyl-1,3,5-trioxane, 1  occurring in the center of its 1,3,5-trioxane ring. The latter is in a chair conformation with the symmetry-independent atoms O1 and C1 deviating by 0.651 (4) Å from the least-squares plane of the other ring atoms. All three cyclobutane substituents, which show a butterfly conformation with an angle between the two planes of 25.7 (3)°, are in a cis conformation relative to the 1,3,5-trioxane ring.The molecule of 1 Fig. 2 occupies1 along the c-axis direction consolidate the crystal structure  spectrometer using CDCl3 as solvent. Dichloromethane, triethylamine and DMSO were distilled over calcium hydride. Methanol was distilled over magnesium turnings. Oxalyl chloride was distilled over phosphorus pentoxide. Cyclobutylmethanol is commercially available and was used without further purification.The synthesis of Synthesis of compound 11. 1H NMR : δ 1.65–2.16 , 2.55 , 4.81 . 13C NMR : δ 18.5; 22.4; 38.1; 103.3. Mass spectrum m/z: 252 ([M+]), 169, 85, 67.To a stirred solution of oxalyl chloride  in 110 ml of dry dichloromethane was added dropwise at 195K the solution of dry dimethylsulfoxide  in 30 ml of dry dichloromethane followed by the solution of cyclobutylmethanol  in 110 ml of dry dichloromethane. The reaction mixture was stirred at 195 K for 1 h and then neat triethylamine  was added. The cooling bath was removed and when the reaction mixture had returned to room temperature, 100 ml of water was added and the reaction mixture was further stirred for 10 min. The flask content was then transferred to a separatory funnel, the bottom organic phase was collected and the aqueous phase was extracted with dichloromethane (2 × 100 ml). The combined organic phase was washed with 10% HCl (3 × 100 ml), water (2 × 100 ml), dried with anhydrous magnesium sulfate, filtered and the solvent was removed on the rotary evaporator. The residue was distilled under atmospheric pressure and the fraction boiling in the range 399–393 K was collected. The yield was 3.09 g (39%) and the product solidified after standing at room temperature overnight, quantitatively forming compound 1 suitable for X-ray analysis were obtained by dissolving the crude solid material (0.05 g) in 5 ml of warm methanol and keeping the resulting solution in a vial tightly stoppered with a plug of cotton wool for two days at room temperature.Colourless crystals of 2 groups, respectively) and refined using a riding model with Uiso(H) = 1.2Ueq(C). The crystal studied was refined as a racemic twin with a BASF of 0.146.Crystal data, data collection and structure refinement details are summarized in Table 210.1107/S205698901900896X/kq2025sup1.cifCrystal structure: contains datablock(s) Trioxane. DOI: 1854532CCDC reference:  crystallographic information; 3D view; checkCIF reportAdditional supporting information: